keyword stringclasses 7 values | repo_name stringlengths 8 98 | file_path stringlengths 4 244 | file_extension stringclasses 29 values | file_size int64 0 84.1M | line_count int64 0 1.6M | content stringlengths 1 84.1M ⌀ | language stringclasses 14 values |
|---|---|---|---|---|---|---|---|
2D | hpfem/hermes-examples | 2d-advanced/maxwell/resonator-time-domain-II-rk/definitions.h | .h | 2,813 | 97 | #include "hermes2d.h"
/* Namespaces used */
using namespace Hermes;
using namespace Hermes::Hermes2D;
using namespace Hermes::Hermes2D::Views;
using namespace Hermes::Hermes2D::RefinementSelectors;
/* Initial condition for E */
class CustomInitialConditionWave : public ExactSolutionVector < double >
{
public:
CustomInitialConditionWave(MeshSharedPtr mesh) : ExactSolutionVector<double>(mesh) {};
virtual Scalar2<double> value(double x, double y) const;
virtual void derivatives(double x, double y, Scalar2<double>& dx, Scalar2<double>& dy) const;
virtual Ord ord(double x, double y) const;
virtual MeshFunction<double>* clone() const;
};
/* Weak forms */
class CustomWeakFormWaveRK : public WeakForm < double >
{
public:
CustomWeakFormWaveRK(double c_squared);
private:
class MatrixFormVolWave_0_1 : public MatrixFormVol < double >
{
public:
MatrixFormVolWave_0_1() : MatrixFormVol<double>(0, 1) {};
virtual double value(int n, double *wt, Func<double> *u_ext[], Func<double> *u,
Func<double> *v, GeomVol<double> *e, Func<double>* *ext) const;
virtual Ord ord(int n, double *wt, Func<Ord> *u_ext[], Func<Ord> *u, Func<Ord> *v,
GeomVol<Ord> *e, Func<Ord>* *ext) const;
virtual MatrixFormVol<double>* clone() const;
};
class MatrixFormVolWave_1_0 : public MatrixFormVol < double >
{
public:
MatrixFormVolWave_1_0(double c_squared)
: MatrixFormVol<double>(1, 0), c_squared(c_squared) {};
virtual double value(int n, double *wt, Func<double> *u_ext[], Func<double> *u,
Func<double> *v, GeomVol<double> *e, Func<double>* *ext) const;
virtual Ord ord(int n, double *wt, Func<Ord> *u_ext[], Func<Ord> *u, Func<Ord> *v,
GeomVol<Ord> *e, Func<Ord>* *ext) const;
virtual MatrixFormVol<double>* clone() const;
// Member.
double c_squared;
};
class VectorFormVolWave_0 : public VectorFormVol < double >
{
public:
VectorFormVolWave_0() : VectorFormVol<double>(0) {};
virtual double value(int n, double *wt, Func<double> *u_ext[], Func<double> *v,
GeomVol<double> *e, Func<double>* *ext) const;
virtual Ord ord(int n, double *wt, Func<Ord> *u_ext[], Func<Ord> *v, GeomVol<Ord> *e,
Func<Ord>* *ext) const;
virtual VectorFormVol<double>* clone() const;
};
class VectorFormVolWave_1 : public VectorFormVol < double >
{
public:
VectorFormVolWave_1(double c_squared)
: VectorFormVol<double>(1), c_squared(c_squared) {};
virtual double value(int n, double *wt, Func<double> *u_ext[], Func<double> *v,
GeomVol<double> *e, Func<double>* *ext) const;
virtual Ord ord(int n, double *wt, Func<Ord> *u_ext[], Func<Ord> *v, GeomVol<Ord> *e, Func<Ord>* *ext) const;
virtual VectorFormVol<double>* clone() const;
double c_squared;
};
};
| Unknown |
2D | hpfem/hermes-examples | 2d-advanced/maxwell/resonator-time-domain-II-rk/main.cpp | .cpp | 7,037 | 171 | #include "definitions.h"
// This example solves a time-domain resonator problem for the Maxwell's equation.
// It is very similar to resonator-time-domain-I but B is eliminated from the
// equations, thus converting the first-order system into one second -order
// equation in time. The second-order equation in time is decomposed back into
// a first-order system in time in the standard way (see example wave-1). Time
// discretization is performed using arbitrary Runge-Kutta methods entered via
// their Butcher's tables. For a list of available R-K methods see the file
// hermes_common/tables.h.
//
// The function rk_time_step_newton() needs more optimisation, see a todo list at
// the beginning of file src/runge-kutta.h.
//
// PDE: \frac{1}{SPEED_OF_LIGHT**2}\frac{\partial^2 E}{\partial t^2} + curl curl E = 0,
// converted into
//
// \frac{\partial E}{\partial t} = F,
// \frac{\partial F}{\partial t} = -SPEED_OF_LIGHT**2 * curl curl E.
//
// Domain: Square (-pi/2, pi/2) x (-pi/2, pi/2)... See mesh file domain.mesh->
//
// BC: E \times \nu = 0 on the boundary (perfect conductor),
// F \times \nu = 0 on the boundary (E \times \nu = 0 => \partial E / \partial t \times \nu = 0).
//
// IC: Prescribed wave for E, zero for F.
//
// The following parameters can be changed:
// Initial polynomial degree of mesh elements.
const int P_INIT = 6;
// Number of initial uniform mesh refinements.
const int INIT_REF_NUM = 1;
// Time step.
const double time_step = 0.05;
// Final time.
const double T_FINAL = 35.0;
// Stopping criterion for the Newton's method.
const double NEWTON_TOL = 1e-5;
// Maximum allowed number of Newton iterations.
const int NEWTON_MAX_ITER = 100;
// Choose one of the following time-integration methods, or define your own Butcher's table. The last number
// in the name of each method is its order. The one before last, if present, is the number of stages.
// Explicit methods:
// Explicit_RK_1, Explicit_RK_2, Explicit_RK_3, Explicit_RK_4.
// Implicit methods:
// Implicit_RK_1, Implicit_Crank_Nicolson_2_2, Implicit_SIRK_2_2, Implicit_ESIRK_2_2, Implicit_SDIRK_2_2,
// Implicit_Lobatto_IIIA_2_2, Implicit_Lobatto_IIIB_2_2, Implicit_Lobatto_IIIC_2_2, Implicit_Lobatto_IIIA_3_4,
// Implicit_Lobatto_IIIB_3_4, Implicit_Lobatto_IIIC_3_4, Implicit_Radau_IIA_3_5, Implicit_SDIRK_5_4.
// Embedded explicit methods:
// Explicit_HEUN_EULER_2_12_embedded, Explicit_BOGACKI_SHAMPINE_4_23_embedded, Explicit_FEHLBERG_6_45_embedded,
// Explicit_CASH_KARP_6_45_embedded, Explicit_DORMAND_PRINCE_7_45_embedded.
// Embedded implicit methods:
// Implicit_SDIRK_CASH_3_23_embedded, Implicit_ESDIRK_TRBDF2_3_23_embedded, Implicit_ESDIRK_TRX2_3_23_embedded,
// Implicit_SDIRK_BILLINGTON_3_23_embedded, Implicit_SDIRK_CASH_5_24_embedded, Implicit_SDIRK_CASH_5_34_embedded,
// Implicit_DIRK_ISMAIL_7_45_embedded.
ButcherTableType butcher_table = Implicit_RK_1;
//ButcherTableType butcher_table = Implicit_SDIRK_2_2;
// Problem parameters.
// Square of wave speed.
const double C_SQUARED = 1;
int main(int argc, char* argv[])
{
// Choose a Butcher's table or define your own.
ButcherTable bt(butcher_table);
if (bt.is_explicit()) Hermes::Mixins::Loggable::Static::info("Using a %d-stage explicit R-K method.", bt.get_size());
if (bt.is_diagonally_implicit()) Hermes::Mixins::Loggable::Static::info("Using a %d-stage diagonally implicit R-K method.", bt.get_size());
if (bt.is_fully_implicit()) Hermes::Mixins::Loggable::Static::info("Using a %d-stage fully implicit R-K method.", bt.get_size());
// Load the mesh.
MeshSharedPtr mesh(new Mesh);
MeshReaderH2D mloader;
mloader.load("domain.mesh", mesh);
// Perform initial mesh refinemets.
for (int i = 0; i < INIT_REF_NUM; i++) mesh->refine_all_elements();
// Initialize solutions.
MeshFunctionSharedPtr<double> E_time_prev(new CustomInitialConditionWave(mesh));
MeshFunctionSharedPtr<double> F_time_prev(new ZeroSolutionVector<double>(mesh));
std::vector<MeshFunctionSharedPtr<double> > slns_time_prev({ E_time_prev, F_time_prev });
MeshFunctionSharedPtr<double> E_time_new(new Solution<double>);
MeshFunctionSharedPtr<double> F_time_new(new Solution<double>);
std::vector<MeshFunctionSharedPtr<double> > slns_time_new({ E_time_new, F_time_new });
// Initialize the weak formulation.
WeakFormSharedPtr<double> wf(new CustomWeakFormWaveRK(C_SQUARED));
// Initialize boundary conditions
DefaultEssentialBCConst<double> bc_essential("Perfect conductor", 0.0);
EssentialBCs<double> bcs(&bc_essential);
SpaceSharedPtr<double> E_space(new HcurlSpace<double>(mesh, &bcs, P_INIT));
SpaceSharedPtr<double> F_space(new HcurlSpace<double>(mesh, &bcs, P_INIT));
std::vector<SpaceSharedPtr<double> > spaces({ E_space, F_space });
int ndof = HcurlSpace<double>::get_num_dofs(spaces);
Hermes::Mixins::Loggable::Static::info("ndof = %d.", ndof);
// Initialize views.
ScalarView E1_view("Solution E1", new WinGeom(0, 0, 400, 350));
E1_view.fix_scale_width(50);
ScalarView E2_view("Solution E2", new WinGeom(410, 0, 400, 350));
E2_view.fix_scale_width(50);
ScalarView F1_view("Solution F1", new WinGeom(0, 410, 400, 350));
F1_view.fix_scale_width(50);
ScalarView F2_view("Solution F2", new WinGeom(410, 410, 400, 350));
F2_view.fix_scale_width(50);
// Initialize Runge-Kutta time stepping.
RungeKutta<double> runge_kutta(wf, spaces, &bt);
// Time stepping loop.
double current_time = 0; int ts = 1;
do
{
// Perform one Runge-Kutta time step according to the selected Butcher's table.
Hermes::Mixins::Loggable::Static::info("Runge-Kutta time step (t = %g s, time_step = %g s, stages: %d).",
current_time, time_step, bt.get_size());
try
{
runge_kutta.set_time(current_time);
runge_kutta.set_time_step(time_step);
runge_kutta.set_newton_max_allowed_iterations(NEWTON_MAX_ITER);
runge_kutta.set_newton_tolerance(NEWTON_TOL);
runge_kutta.rk_time_step_newton(slns_time_prev, slns_time_new);
}
catch (Exceptions::Exception& e)
{
e.print_msg();
throw Hermes::Exceptions::Exception("Runge-Kutta time step failed");
}
// Visualize the solutions.
char title[100];
sprintf(title, "E1, t = %g", current_time + time_step);
E1_view.set_title(title);
E1_view.show(E_time_new, H2D_FN_VAL_0);
sprintf(title, "E2, t = %g", current_time + time_step);
E2_view.set_title(title);
E2_view.show(E_time_new, H2D_FN_VAL_1);
sprintf(title, "F1, t = %g", current_time + time_step);
F1_view.set_title(title);
F1_view.show(F_time_new, H2D_FN_VAL_0);
sprintf(title, "F2, t = %g", current_time + time_step);
F2_view.set_title(title);
F2_view.show(F_time_new, H2D_FN_VAL_1);
//View::wait();
// Update solutions.
E_time_prev->copy(E_time_new);
F_time_prev->copy(F_time_new);
// Update time.
current_time += time_step;
} while (current_time < T_FINAL);
// Wait for the view to be closed.
View::wait();
return 0;
} | C++ |
2D | hpfem/hermes-examples | 2d-advanced/maxwell/resonator-time-domain-II-rk/definitions.cpp | .cpp | 4,054 | 124 | #include "definitions.h"
template<typename Real, typename Scalar>
static Scalar int_e_f(int n, double *wt, Func<Real> *u, Func<Real> *v)
{
Scalar result = Scalar(0);
for (int i = 0; i < n; i++)
result += wt[i] * (u->val0[i] * conj(v->val0[i]) + u->val1[i] * conj(v->val1[i]));
return result;
}
template<typename Real, typename Scalar>
static Scalar int_curl_e_curl_f(int n, double *wt, Func<Real> *u, Func<Real> *v)
{
Scalar result = Scalar(0);
for (int i = 0; i < n; i++)
result += wt[i] * (u->curl[i] * conj(v->curl[i]));
return result;
}
Scalar2<double> CustomInitialConditionWave::value(double x, double y) const
{
return Scalar2<double>(std::sin(x) * std::cos(y), -std::cos(x) * std::sin(y));
}
void CustomInitialConditionWave::derivatives(double x, double y, Scalar2<double>& dx, Scalar2<double>& dy) const
{
dx[0] = std::cos(x) * std::cos(y);
dx[1] = std::sin(x) * std::sin(y);
dy[0] = -std::sin(x) * std::sin(y);
dy[1] = -std::cos(x) * std::cos(y);
}
Ord CustomInitialConditionWave::ord(double x, double y) const
{
return Ord(10);
}
MeshFunction<double>* CustomInitialConditionWave::clone() const
{
return new CustomInitialConditionWave(this->mesh);
}
CustomWeakFormWaveRK::CustomWeakFormWaveRK(double c_squared) : WeakForm<double>(2)
{
// Stationary Jacobian.
add_matrix_form(new MatrixFormVolWave_0_1);
add_matrix_form(new MatrixFormVolWave_1_0(c_squared));
// Stationary Residual.
add_vector_form(new VectorFormVolWave_0());
add_vector_form(new VectorFormVolWave_1(c_squared));
}
double CustomWeakFormWaveRK::MatrixFormVolWave_0_1::value(int n, double *wt, Func<double> *u_ext[], Func<double> *u,
Func<double> *v, GeomVol<double> *e, Func<double>* *ext) const
{
return int_e_f<double, double>(n, wt, u, v);
}
Ord CustomWeakFormWaveRK::MatrixFormVolWave_0_1::ord(int n, double *wt, Func<Ord> *u_ext[], Func<Ord> *u, Func<Ord> *v,
GeomVol<Ord> *e, Func<Ord>* *ext) const
{
return int_e_f<Ord, Ord>(n, wt, u, v);
}
MatrixFormVol<double>* CustomWeakFormWaveRK::MatrixFormVolWave_0_1::clone() const
{
return new MatrixFormVolWave_0_1(*this);
}
double CustomWeakFormWaveRK::MatrixFormVolWave_1_0::value(int n, double *wt, Func<double> *u_ext[], Func<double> *u,
Func<double> *v, GeomVol<double> *e, Func<double>* *ext) const
{
return -c_squared * int_curl_e_curl_f<double, double>(n, wt, u, v);
}
Ord CustomWeakFormWaveRK::MatrixFormVolWave_1_0::ord(int n, double *wt, Func<Ord> *u_ext[], Func<Ord> *u, Func<Ord> *v,
GeomVol<Ord> *e, Func<Ord>* *ext) const
{
return -c_squared * int_curl_e_curl_f<Ord, Ord>(n, wt, u, v);
}
MatrixFormVol<double>* CustomWeakFormWaveRK::MatrixFormVolWave_1_0::clone() const
{
return new MatrixFormVolWave_1_0(*this);
}
double CustomWeakFormWaveRK::VectorFormVolWave_0::value(int n, double *wt, Func<double> *u_ext[], Func<double> *v,
GeomVol<double> *e, Func<double>* *ext) const
{
Func<double>* F_prev_newton = u_ext[1];
return int_e_f<double, double>(n, wt, F_prev_newton, v);
}
Ord CustomWeakFormWaveRK::VectorFormVolWave_0::ord(int n, double *wt, Func<Ord> *u_ext[], Func<Ord> *v, GeomVol<Ord> *e,
Func<Ord>* *ext) const
{
Func<Ord>* F_prev_newton = u_ext[1];
return int_e_f<Ord, Ord>(n, wt, F_prev_newton, v);
}
VectorFormVol<double>* CustomWeakFormWaveRK::VectorFormVolWave_0::clone() const
{
return new VectorFormVolWave_0(*this);
}
double CustomWeakFormWaveRK::VectorFormVolWave_1::value(int n, double *wt, Func<double> *u_ext[], Func<double> *v,
GeomVol<double> *e, Func<double>* *ext) const
{
Func<double>* E_prev_newton = u_ext[0];
return -c_squared * int_curl_e_curl_f<double, double>(n, wt, E_prev_newton, v);
}
Ord CustomWeakFormWaveRK::VectorFormVolWave_1::ord(int n, double *wt, Func<Ord> *u_ext[], Func<Ord> *v, GeomVol<Ord> *e, Func<Ord>* *ext) const
{
Func<Ord>* E_prev_newton = u_ext[0];
return -c_squared * int_curl_e_curl_f<Ord, Ord>(n, wt, E_prev_newton, v);
}
VectorFormVol<double>* CustomWeakFormWaveRK::VectorFormVolWave_1::clone() const
{
return new VectorFormVolWave_1(*this);
} | C++ |
2D | hpfem/hermes-examples | 2d-advanced/maxwell/microwave-oven/definitions.h | .h | 5,034 | 162 | #include "hermes2d.h"
/* Namespaces used */
using namespace Hermes;
using namespace Hermes::Hermes2D;
using namespace Hermes::Hermes2D::Views;
using namespace Hermes::Hermes2D::RefinementSelectors;
using namespace Hermes::Hermes2D::WeakFormsHcurl;
typedef std::complex<double> complex;
/* Weak forms */
// Jacobian.
class CustomMatrixForm : public MatrixFormVol < ::complex >
{
public:
CustomMatrixForm(unsigned int i, unsigned int j, double e_0, double mu_0, double mu_r, double kappa, double omega, double J, bool align_mesh)
: MatrixFormVol<::complex>(i, j), e_0(e_0), mu_0(mu_0),
mu_r(mu_r), kappa(kappa), omega(omega), J(J), align_mesh(align_mesh) {
this->setSymFlag(HERMES_SYM);
};
template<typename Real, typename Scalar>
Scalar matrix_form(int n, double *wt, Func<Scalar> *u_ext[], Func<Real> *u,
Func<Real> *v, GeomVol<Real> *e, Func<Scalar>* *ext) const;
virtual std::complex<double> value(int n, double *wt, Func<::complex> *u_ext[], Func<double> *u,
Func<double> *v, GeomVol<double> *e, Func<::complex> **ext) const;
virtual Ord ord(int n, double *wt, Func<Ord> *u_ext[], Func<Ord> *u,
Func<Ord> *v, GeomVol<Ord> *e, Func<Ord>* *ext) const;
// Gamma as a function of x, y.
double gamma(int marker, double x, double y) const;
Ord gamma(int marker, Ord x, Ord y) const;
// Relative permittivity as a function of x, y.
double er(int marker, double x, double y) const;
Ord er(int marker, Ord x, Ord y) const;
virtual MatrixFormVol<::complex>* clone() const;
// Geometry of the load.
bool in_load(double x, double y) const;
private:
double e_0, mu_0, mu_r, kappa, omega, J;
bool align_mesh;
};
// Residual.
class CustomResidualForm : public VectorFormVol < ::complex >
{
public:
CustomResidualForm(int i, double e_0, double mu_0, double mu_r, double kappa, double omega, double J, bool align_mesh)
: VectorFormVol<::complex>(i), e_0(e_0), mu_0(mu_0),
mu_r(mu_r), kappa(kappa), omega(omega), J(J), align_mesh(align_mesh) {};
template<typename Real, typename Scalar>
Scalar vector_form(int n, double *wt, Func<Scalar> *u_ext[], Func<Real> *v,
GeomVol<Real> *e, Func<Scalar>* *ext) const;
virtual std::complex<double> value(int n, double *wt, Func<::complex> *u_ext[], Func<double> *v, GeomVol<double> *e,
Func<::complex> **ext) const;
virtual Ord ord(int n, double *wt, Func<Ord> *u_ext[], Func<Ord> *v, GeomVol<Ord> *e,
Func<Ord>* *ext) const;
virtual VectorFormVol<::complex>* clone() const;
// Gamma as a function of x, y.
double gamma(int marker, double x, double y) const;
Ord gamma(int marker, Ord x, Ord y) const;
// Relative permittivity as a function of x, y.
double er(int marker, double x, double y) const;
Ord er(int marker, Ord x, Ord y) const;
// Geometry of the load.
bool in_load(double x, double y) const;
private:
double e_0, mu_0, mu_r, kappa, omega, J;
bool align_mesh;
};
class CustomVectorFormSurf : public VectorFormSurf < ::complex >
{
public:
CustomVectorFormSurf(double omega, double J, std::string bnd)
: VectorFormSurf<::complex>(0), omega(omega), J(J) { this->set_area(bnd); };
template<typename Scalar, typename Real>
Scalar vector_form_surf(int n, double *wt, Func<Scalar> *u_ext[], Func<Real> *v,
GeomSurf<Real> *e, Func<Scalar>* *ext) const;
virtual std::complex<double> value(int n, double *wt, Func<::complex> *u_ext[],
Func<double> *v, GeomSurf<double> *e, Func<::complex> **ext) const;
virtual Ord ord(int n, double *wt, Func<Ord> *u_ext[], Func<Ord> *v,
GeomSurf<Ord> *e, Func<Ord>* *ext) const;
virtual VectorFormSurf<::complex>* clone() const { return new CustomVectorFormSurf(omega, J, this->areas[0]); }
double omega, J;
};
class CustomWeakForm : public WeakForm < ::complex >
{
public:
CustomWeakForm(double e_0, double mu_0, double mu_r, double kappa, double omega,
double J, bool align_mesh, MeshSharedPtr mesh, std::string current_bdy);
int get_marker();
private:
int marker;
};
// Custom error form.
class CustomErrorForm : public NormFormVol < ::complex >
{
public:
CustomErrorForm(double kappa) : NormFormVol<::complex>(0, 0, SolutionsDifference)
{
kappa_squared = sqr(kappa);
};
template<typename Real, typename Scalar>
Scalar hcurl_form_kappa(int n, double *wt, Func<Scalar> *u, Func<Scalar> *v, GeomVol<Real> *e) const
{
Scalar result = Scalar(0);
for (int i = 0; i < n; i++)
{
result += wt[i] * (u->curl[i] * conj(v->curl[i]));
result += kappa_squared * wt[i] * (u->val0[i] * conj(v->val0[i]) + u->val1[i] * conj(v->val1[i]));
}
return result;
}
virtual std::complex<double> value(int n, double *wt, Func<::complex> *u, Func<::complex> *v, GeomVol<double> *e) const
{
return hcurl_form_kappa<double, std::complex<double> >(n, wt, u, v, e);
}
virtual Ord ord(int n, double *wt, Func<Ord> *u, Func<Ord> *v, GeomVol<Ord> *e) const
{
return hcurl_form_kappa<Ord, Ord>(n, wt, u, v, e);
}
double kappa_squared;
};
| Unknown |
2D | hpfem/hermes-examples | 2d-advanced/maxwell/microwave-oven/plot_graph.py | .py | 628 | 32 | # import libraries
import numpy, pylab
from pylab import *
# plot DOF convergence graph
pylab.title("Error convergence")
pylab.xlabel("Degrees of freedom")
pylab.ylabel("Error [%]")
axis('equal')
data = numpy.loadtxt("conv_dof_est.dat")
x = data[:, 0]
y = data[:, 1]
loglog(x, y, '-s', label="error (est)")
legend()
# initialize new window
pylab.figure()
# plot CPU convergence graph
pylab.title("Error convergence")
pylab.xlabel("CPU time (s)")
pylab.ylabel("Error [%]")
axis('equal')
data = numpy.loadtxt("conv_cpu_est.dat")
x = data[:, 0]
y = data[:, 1]
loglog(x, y, '-s', label="error (est)")
legend()
# finalize
show()
| Python |
2D | hpfem/hermes-examples | 2d-advanced/maxwell/microwave-oven/main.cpp | .cpp | 8,786 | 232 | #include "definitions.h"
// This example solves adaptively the electric field in a simplified microwave oven.
// The waves are generated using a harmonic surface current on the right-most edge.
// (Such small cavity is present in every microwave oven). There is a circular
// load located in the middle of the main cavity, defined through a different
// permittivity -- see function in_load(...). One can either use a mesh that is
// aligned to the load via curvilinear elements (ALIGN_MESH = true), or an unaligned
// mesh (ALIGN_MESH = false). Convergence graphs are saved both wrt. the dof number
// and cpu time.
//
// PDE: time-harmonic Maxwell's equations;
// there is circular load in the middle of the large cavity, whose permittivity
// is different from the rest of the domain.
//
// Domain: square cavity with another small square cavity attached from outside
// on the right.
//
// Meshes: you can either use "oven_load_circle.mesh" containing curved elements
// aligned with the circular load, or "oven_load_square.mesh" which is not
// aligned.
//
// BC: perfect conductor on the boundary except for the right-most edge of the small
// cavity, where a harmonic surface current is prescribed
//
// The following parameters can be changed:
// Number of initial uniform mesh refinements.
const int INIT_REF_NUM = 0;
// Initial polynomial degree. NOTE: The meaning is different from
// standard continuous elements in the space H1. Here, P_INIT refers
// to the maximum poly order of the tangential component, and polynomials
// of degree P_INIT + 1 are present in element interiors. P_INIT = 0
// is for Whitney elements.
const int P_INIT = 1;
// if ALIGN_MESH == true, curvilinear elements aligned with the
// circular load are used, otherwise one uses a non-aligned mesh->
const bool ALIGN_MESH = false;
// This is a quantitative parameter of the adapt(...) function and
// it has different meanings for various adaptive strategies.
const double THRESHOLD = 0.6;
// Predefined list of element refinement candidates. Possible values are
// H2D_P_ISO, H2D_P_ANISO, H2D_H_ISO, H2D_H_ANISO, H2D_HP_ISO,
// H2D_HP_ANISO_H, H2D_HP_ANISO_P, H2D_HP_ANISO.
const CandList CAND_LIST = H2D_HP_ANISO;
// Stopping criterion for adaptivity.
const double ERR_STOP = 3e-2;
// Adaptivity process stops when the number of degrees of freedom grows
// over this limit. This is to prevent h-adaptivity to go on forever.
const int NDOF_STOP = 100000;
// Newton's method.
const double NEWTON_TOL = 1e-4;
const int NEWTON_MAX_ITER = 100;
// Problem parameters.
const double e_0 = 8.8541878176 * 1e-12;
const double mu_0 = 1.256 * 1e-6;
const double e_r = 1.0;
const double mu_r = 1.0;
const double rho = 3820.0;
const double Cp = 7.531000;
const double freq = 1.0*2450000000.0;
const double omega = 2 * M_PI * freq;
const double c = 1 / std::sqrt(e_0 * mu_0);
const double kappa = 2 * M_PI * freq * std::sqrt(e_0 * mu_0);
const double J = 0.0000033333;
// Boundary markers.
const std::string BDY_PERFECT_CONDUCTOR = "b2";
const std::string BDY_CURRENT = "b1";
class CustomErrorCalculator : public ErrorCalculator < ::complex >
{
public:
CustomErrorCalculator(CalculatedErrorType errorType) : ErrorCalculator<::complex>(errorType)
{
this->add_error_form(new CustomErrorForm(kappa));
}
virtual ~CustomErrorCalculator()
{
}
};
int main(int argc, char* argv[])
{
// Load the mesh.
MeshSharedPtr mesh(new Mesh);
MeshReaderH2D mloader;
if (ALIGN_MESH)
mloader.load("oven_load_circle.mesh", mesh);
else
mloader.load("oven_load_square.mesh", mesh);
// Perform initial mesh refinemets.
for (int i = 0; i < INIT_REF_NUM; i++)
mesh->refine_all_elements();
// Initialize boundary conditions
DefaultEssentialBCConst<::complex> bc_essential(BDY_PERFECT_CONDUCTOR, std::complex<double>(0.0, 0.0));
EssentialBCs<::complex> bcs(&bc_essential);
// Create an Hcurl space with default shapeset.
SpaceSharedPtr<::complex> space(new HcurlSpace<::complex>(mesh, &bcs, P_INIT));
int ndof = space->get_num_dofs();
Hermes::Mixins::Loggable::Static::info("ndof = %d", ndof);
// Initialize the weak formulation.
WeakFormSharedPtr<::complex> wf(new CustomWeakForm(e_0, mu_0, mu_r, kappa, omega, J, ALIGN_MESH, mesh, BDY_CURRENT));
// Initialize coarse and reference mesh solution.
MeshFunctionSharedPtr<::complex> sln(new Solution<::complex>), ref_sln(new Solution<::complex>);
// Initialize refinements selector.
HcurlProjBasedSelector<::complex> selector(CAND_LIST);
// Error calculation.
CustomErrorCalculator errorCalculator(RelativeErrorToGlobalNorm);
// Stopping criterion for an adaptivity step.
AdaptStoppingCriterionSingleElement<::complex> stoppingCriterion(THRESHOLD);
// Adaptivity.
Adapt<::complex> adaptivity(space, &errorCalculator, &stoppingCriterion);
// Initialize views.
ScalarView eview("Electric field", new WinGeom(0, 0, 580, 400));
OrderView oview("Polynomial orders", new WinGeom(590, 0, 550, 400));
// DOF and CPU convergence graphs initialization.
SimpleGraph graph_dof, graph_cpu;
// Time measurement.
Hermes::Mixins::TimeMeasurable cpu_time;
cpu_time.tick();
Hermes::Hermes2D::NewtonSolver<::complex> newton(wf, space);
newton.set_max_allowed_iterations(NEWTON_MAX_ITER);
newton.set_tolerance(NEWTON_TOL, Hermes::Solvers::ResidualNormAbsolute);
// Newton's iteration.
// Adaptivity loop:
int as = 1; bool done = false;
do
{
Hermes::Mixins::Loggable::Static::info("---- Adaptivity step %d:", as);
// Construct globally refined reference mesh and setup reference space.
Mesh::ReferenceMeshCreator refMeshCreator(mesh);
MeshSharedPtr ref_mesh = refMeshCreator.create_ref_mesh();
Space<::complex>::ReferenceSpaceCreator refSpaceCreator(space, ref_mesh);
SpaceSharedPtr<::complex> ref_space = refSpaceCreator.create_ref_space();
int ndof_ref = Space<::complex>::get_num_dofs(ref_space);
// Initialize reference problem.
Hermes::Mixins::Loggable::Static::info("Solving on reference mesh.");
// Time measurement.
cpu_time.tick();
newton.set_space(ref_space);
try
{
newton.solve();
}
catch (Hermes::Exceptions::Exception e)
{
e.print_msg();
throw Hermes::Exceptions::Exception("Newton's iteration failed.");
};
// Translate the resulting coefficient vector into the Solution<::complex> sln->
Hermes::Hermes2D::Solution<::complex>::vector_to_solution(newton.get_sln_vector(), ref_space, ref_sln);
// Project the fine mesh solution onto the coarse mesh.
Hermes::Mixins::Loggable::Static::info("Projecting reference solution on coarse mesh.");
OGProjection<::complex> ogProjection; ogProjection.project_global(space, ref_sln, sln);
// View the coarse mesh solution and polynomial orders.
MeshFunctionSharedPtr<double> real(new RealFilter(ref_sln));
MeshFunctionSharedPtr<double> magn(new MagFilter<double>(real));
MeshFunctionSharedPtr<double> limited_magn(new ValFilter(magn, 0.0, 4e3));
char title[100];
sprintf(title, "Electric field, adaptivity step %d", as);
eview.set_title(title);
eview.set_min_max_range(0.0, 4e3);
eview.set_linearizer_criterion(LinearizerCriterionFixed(3));
eview.show(limited_magn);
sprintf(title, "Polynomial orders, adaptivity step %d", as);
oview.set_title(title);
oview.show(space);
// Calculate element errors and total error estimate.
Hermes::Mixins::Loggable::Static::info("Calculating error estimate.");
// Calculate error estimate.
errorCalculator.calculate_errors(sln, ref_sln);
double err_est_rel = errorCalculator.get_total_error_squared() * 100.;
// Report results.
Hermes::Mixins::Loggable::Static::info("ndof_coarse: %d, ndof_fine: %d, err_est_rel: %g%%",
Space<::complex>::get_num_dofs(space),
Space<::complex>::get_num_dofs(ref_space), err_est_rel);
// Time measurement.
cpu_time.tick();
// Add entry to DOF and CPU convergence graphs.
graph_dof.add_values(Space<::complex>::get_num_dofs(space), err_est_rel);
graph_dof.save("conv_dof_est.dat");
graph_cpu.add_values(cpu_time.accumulated(), err_est_rel);
graph_cpu.save("conv_cpu_est.dat");
// If err_est too large, adapt the mesh.
if (err_est_rel < ERR_STOP) done = true;
else
{
Hermes::Mixins::Loggable::Static::info("Adapting coarse mesh.");
done = adaptivity.adapt(&selector);
}
if (space->get_num_dofs() >= NDOF_STOP) done = true;
// Increase counter.
as++;
} while (done == false);
Hermes::Mixins::Loggable::Static::info("Total running time: %g s", cpu_time.accumulated());
// Wait for all views to be closed.
View::wait();
return 0;
} | C++ |
2D | hpfem/hermes-examples | 2d-advanced/maxwell/microwave-oven/definitions.cpp | .cpp | 6,633 | 199 | #include "definitions.h"
template<typename Real, typename Scalar>
Scalar CustomMatrixForm::matrix_form(int n, double *wt, Func<Scalar> *u_ext[], Func<Real> *u,
Func<Real> *v, GeomVol<Real> *e, Func<Scalar>* *ext) const
{
std::complex<double> ikappa = std::complex<double>(0.0, kappa);
Scalar result1 = Scalar(0);
Scalar result2 = Scalar(0);
Scalar result3 = Scalar(0);
for (int i = 0; i < n; i++)
result1 += wt[i] * gamma(e->elem_marker, e->x[i], e->y[i]) * (u->val0[i] * conj(v->val0[i]) + u->val1[i] * conj(v->val1[i]));
for (int i = 0; i < n; i++)
result2 += wt[i] * er(e->elem_marker, e->x[i], e->y[i]) * (u->val0[i] * conj(v->val0[i]) + u->val1[i] * conj(v->val1[i]));
for (int i = 0; i < n; i++)
result3 += wt[i] * (u->curl[i] * conj(v->curl[i]));
return 1.0 / mu_r * result3 - ikappa * Hermes::sqrt(mu_0 / e_0) * result1 - sqr(kappa) * result2;
}
std::complex<double> CustomMatrixForm::value(int n, double *wt, Func<::complex> *u_ext[], Func<double> *u,
Func<double> *v, GeomVol<double> *e, Func<::complex> **ext) const
{
return matrix_form<double, std::complex<double> >(n, wt, u_ext, u, v, e, ext);
}
Ord CustomMatrixForm::ord(int n, double *wt, Func<Ord> *u_ext[], Func<Ord> *u, Func<Ord> *v,
GeomVol<Ord> *e, Func<Ord>* *ext) const
{
return matrix_form<Ord, Ord>(n, wt, u_ext, u, v, e, ext);
}
double CustomMatrixForm::gamma(int marker, double x, double y) const
{
if (in_load(x, y))
{
double cx = -0.152994121; double cy = 0.030598824;
double r = std::sqrt(sqr(cx - x) + sqr(cy - y));
return (0.03 + 1) / 2.0 - (0.03 - 1) * std::atan(10.0*(r - 0.043273273)) / M_PI;
}
return 0.0;
}
Ord CustomMatrixForm::gamma(int marker, Ord x, Ord y) const
{
return Ord(0.0);
}
MatrixFormVol<::complex>* CustomMatrixForm::clone() const
{
CustomMatrixForm* form = new CustomMatrixForm(i, j, e_0, mu_0, mu_r, kappa, omega, J, align_mesh);
form->wf = this->wf;
return form;
}
double CustomMatrixForm::er(int marker, double x, double y) const
{
if (in_load(x, y))
{
double cx = -0.152994121; double cy = 0.030598824;
double r = std::sqrt(sqr(cx - x) + sqr(cy - y));
return (7.5 + 1) / 2.0 - (7.5 - 1) * std::atan(10.0*(r - 0.043273273)) / M_PI;
}
return 1.0;
}
Ord CustomMatrixForm::er(int marker, Ord x, Ord y) const
{
return Ord(1.0);
}
bool CustomMatrixForm::in_load(double x, double y) const
{
double cx = -0.152994121;
double cy = 0.030598824;
double r = 0.043273273;
if (sqr(cx - x) + sqr(cy - y) < sqr(r)) return true;
else return false;
}
template<typename Real, typename Scalar>
Scalar CustomResidualForm::vector_form(int n, double *wt, Func<Scalar> *u_ext[], Func<Real> *v,
GeomVol<Real> *e, Func<Scalar>* *ext) const
{
std::complex<double> ikappa = std::complex<double>(0.0, kappa);
Scalar result1 = Scalar(0);
Scalar result2 = Scalar(0);
Scalar result3 = Scalar(0);
for (int i = 0; i < n; i++)
result1 += wt[i] * gamma(e->elem_marker, e->x[i], e->y[i]) * (u_ext[0]->val0[i] * conj(v->val0[i])
+ u_ext[0]->val1[i] * conj(v->val1[i]));
for (int i = 0; i < n; i++)
result2 += wt[i] * er(e->elem_marker, e->x[i], e->y[i]) * (u_ext[0]->val0[i] * conj(v->val0[i])
+ u_ext[0]->val1[i] * conj(v->val1[i]));
for (int i = 0; i < n; i++)
result3 += wt[i] * (u_ext[0]->curl[i] * conj(v->curl[i]));
return 1.0 / mu_r * result3 - ikappa * Hermes::sqrt(mu_0 / e_0) * result1 - sqr(kappa) * result2;
}
std::complex<double> CustomResidualForm::value(int n, double *wt, Func<::complex> *u_ext[], Func<double> *v,
GeomVol<double> *e, Func<::complex> **ext) const
{
return vector_form<double, std::complex<double> >(n, wt, u_ext, v, e, ext);
}
Ord CustomResidualForm::ord(int n, double *wt, Func<Ord> *u_ext[], Func<Ord> *v,
GeomVol<Ord> *e, Func<Ord>* *ext) const
{
return vector_form<Ord, Ord>(n, wt, u_ext, v, e, ext);
}
double CustomResidualForm::gamma(int marker, double x, double y) const
{
if (in_load(x, y))
{
double cx = -0.152994121; double cy = 0.030598824;
double r = std::sqrt(sqr(cx - x) + sqr(cy - y));
return (0.03 + 1) / 2.0 - (0.03 - 1) * std::atan(10.0*(r - 0.043273273)) / M_PI;
}
return 0.0;
}
Ord CustomResidualForm::gamma(int marker, Ord x, Ord y) const
{
return Ord(0.0);
}
CustomResidualForm::VectorFormVol<::complex>* CustomResidualForm::clone() const
{
CustomResidualForm* form = new CustomResidualForm(i, e_0, mu_0, mu_r, kappa, omega, J, align_mesh);
form->wf = this->wf;
return form;
}
double CustomResidualForm::er(int marker, double x, double y) const
{
if (in_load(x, y))
{
double cx = -0.152994121; double cy = 0.030598824;
double r = std::sqrt(sqr(cx - x) + sqr(cy - y));
return (7.5 + 1) / 2.0 - (7.5 - 1) * std::atan(10.0*(r - 0.043273273)) / M_PI;
}
return 1.0;
}
Ord CustomResidualForm::er(int marker, Ord x, Ord y) const
{
return Ord(1.0);
}
bool CustomResidualForm::in_load(double x, double y) const
{
double cx = -0.152994121;
double cy = 0.030598824;
double r = 0.043273273;
if (sqr(cx - x) + sqr(cy - y) < sqr(r)) return true;
else return false;
}
template<typename Scalar, typename Real>
Scalar CustomVectorFormSurf::vector_form_surf(int n, double *wt, Func<Scalar> *u_ext[], Func<Real> *v,
GeomSurf<Real> *e, Func<Scalar>* *ext) const
{
Scalar result = Scalar(0);
for (int i = 0; i < n; i++)
result += wt[i] * (v->val1[i]);
std::complex<double> ii = std::complex<double>(0.0, 1.0);
return ii * omega * J * result;
}
std::complex<double> CustomVectorFormSurf::value(int n, double *wt, Func<::complex> *u_ext[],
Func<double> *v, GeomSurf<double> *e, Func<::complex> **ext) const
{
return vector_form_surf<std::complex<double>, double>(n, wt, u_ext, v, e, ext);
}
Ord CustomVectorFormSurf::ord(int n, double *wt, Func<Ord> *u_ext[], Func<Ord> *v,
GeomSurf<Ord> *e, Func<Ord>* *ext) const
{
return vector_form_surf<Ord, Ord>(n, wt, u_ext, v, e, ext);
}
CustomWeakForm::CustomWeakForm(double e_0, double mu_0, double mu_r, double kappa, double omega,
double J, bool align_mesh, MeshSharedPtr mesh, std::string current_bdy) : WeakForm<::complex>(1), marker(mesh->get_element_markers_conversion().get_internal_marker("e1").marker)
{
// Jacobian forms - volumetric.
add_matrix_form(new CustomMatrixForm(0, 0, e_0, mu_0, mu_r, kappa, omega, J, align_mesh));
// Residual forms - volumetric.
add_vector_form(new CustomResidualForm(0, e_0, mu_0, mu_r, kappa, omega, J, align_mesh));
// Residual forms - surface.
add_vector_form_surf(new CustomVectorFormSurf(omega, J, current_bdy));
}
int CustomWeakForm::get_marker()
{
return this->marker;
} | C++ |
2D | hpfem/hermes-examples | 2d-advanced/maxwell/magnetostatics-sensor/definitions.h | .h | 1,186 | 34 | #include "hermes2d.h"
/* Namespaces used */
using namespace Hermes;
using namespace Hermes::Hermes2D;
using namespace Hermes::Hermes2D::Views;
using namespace Hermes::Hermes2D::RefinementSelectors;
using namespace Hermes::Hermes2D::WeakFormsH1;
using namespace Hermes::Hermes2D::WeakFormsMaxwell;
/* Weak forms */
class CustomWeakFormMagnetostatics : public WeakForm < double >
{
public:
CustomWeakFormMagnetostatics(std::string material_iron_1, std::string material_iron_2,
CubicSpline* mu_inv_iron, std::string material_air,
std::string material_copper, double mu_vacuum,
double current_density, int order_inc = 3);
};
class FilterFluxDensity : public Hermes::Hermes2D::DXDYFilter < double >
{
public:
FilterFluxDensity(std::vector<MeshFunctionSharedPtr<double> > solutions);
virtual Func<double>* get_pt_value(double x, double y, bool use_MeshHashGrid = false, Element* e = NULL);
virtual MeshFunction<double>* clone() const;
protected:
void filter_fn(int n, double* x, double* y, const std::vector<const double *>& values, const std::vector<const double *>& dx, const std::vector<const double *>& dy, double* rslt, double* rslt_dx, double* rslt_dy);
};
| Unknown |
2D | hpfem/hermes-examples | 2d-advanced/maxwell/magnetostatics-sensor/main.cpp | .cpp | 4,548 | 112 | #include "definitions.h"
// This example shows how to handle stiff nonlinear problems.
//
// PDE: magnetostatics with nonlinear magnetic permeability
// curl[1/mu curl u] = current_density.
// The following parameters can be changed:
// Initial polynomial degree.
const int P_INIT = 2;
// Stopping criterion for the Newton's method.
const double NEWTON_TOL = 1e-9;
// Maximum allowed number of Newton iterations.
const int NEWTON_MAX_ITER = 1000;
// Problem parameters.
double MU_VACUUM = 4. * M_PI * 1e-7;
// Constant initial condition for the magnetic potential.
double INIT_COND = 0.0;
// Volume source term.
double CURRENT_DENSITY = 1e6;
// Material and boundary markers.
const std::string MAT_AIR = "2";
const std::string MAT_IRON_1 = "0";
const std::string MAT_IRON_2 = "3";
const std::string MAT_COPPER = "1";
const std::vector<std::string> BDY_DIRICHLET = { "0","16","17","18","19","21","22","23" };
int main(int argc, char* argv[])
{
// Define nonlinear magnetic permeability via a cubic spline.
std::vector<double> mu_inv_pts({ 0.,
0.0622676, 0.0699801, 0.0781449, 0.108779, 0.140252, 0.589805, 0.777016, 0.796086,
0.895488, 1.01564, 1.11306, 1.15599, 1.19261, 1.27821, 1.37143, 1.47334,
1.50096, 1.51305, 1.53748, 1.53964, 1.56167, 1.57542, 1.57922, 1.60284,
1.61321, 1.62708, 1.70197, 1.73799, 1.81436, 1.91181, 1.9277, 1.94428 });
std::vector<double> mu_inv_val({ 0.0000684097470207555,
0.0000527896701173514, 0.0000489615260328434, 0.0000457019592429927, 0.0000379594517136794,
0.0000326526347410973, 0.0000156557733795492, 0.0000168817970335306, 0.0000171729834624169,
0.0000195679781777907, 0.0000258374565607762, 0.0000383782871002901, 0.0000483917017909769,
0.0000607947084285784, 0.0001328584088079810, 0.0003721594926721800, 0.0014517580790337100,
0.0022977202020155600, 0.0029860551225775600, 0.0055377731506606600, 0.0060092903629010500,
0.0162330810712535000, 0.0246391595089908000, 0.0269898976812979000, 0.0411436282920046000,
0.0472806532295050000, 0.0553578051737405000, 0.0966940310774616000, 0.1153592807118130000,
0.1525031873166150000, 0.1956315475433570000, 0.2022547357946390000, 0.2090148086991960000
});
// Create the cubic spline (and plot it for visual control).
double bc_left = 0.;
double bc_right = 0.;
bool first_der_left = false;
bool first_der_right = false;
bool extrapolate_der_left = true;
bool extrapolate_der_right = true;
CubicSpline mu_inv_iron(mu_inv_pts, mu_inv_val, bc_left, bc_right, first_der_left, first_der_right,
extrapolate_der_left, extrapolate_der_right);
mu_inv_iron.calculate_coeffs();
// Load the mesh.
MeshSharedPtr mesh(new Mesh);
MeshReaderH2DXML mloader;
mloader.load("sensor.msh", mesh);
// View the mesh.
MeshView m_view("Mesh", new WinGeom(50, 0, 400, 400));
m_view.show(mesh);
// Initialize boundary conditions.
DefaultEssentialBCConst<double> bc_essential(BDY_DIRICHLET, 0.0);
EssentialBCs<double> bcs({ &bc_essential });
// Create an H1 space with default shapeset.
SpaceSharedPtr<double> space(new H1Space<double>(mesh, &bcs, P_INIT));
int ndof = space->get_num_dofs();
Hermes::Mixins::Loggable::Static::info("ndof: %d", ndof);
// Initialize the weak formulation
int order_inc = 3;
WeakFormSharedPtr<double> wf(new CustomWeakFormMagnetostatics(MAT_IRON_1, MAT_IRON_2, &mu_inv_iron, MAT_AIR, MAT_COPPER, MU_VACUUM, CURRENT_DENSITY, order_inc));
// Initialize the FE problem.
DiscreteProblem<double> dp(wf, space);
// Initialize the solution.
MeshFunctionSharedPtr<double> sln(new Solution<double>());
// Perform Newton's iteration.
Hermes::Hermes2D::NewtonSolver<double> newton(&dp);
newton.set_sufficient_improvement_factor(1.5);
newton.set_necessary_successful_steps_to_increase(1);
newton.set_max_steps_with_reused_jacobian(0);
newton.set_max_allowed_iterations(NEWTON_MAX_ITER);
newton.set_tolerance(NEWTON_TOL, Hermes::Solvers::ResidualNormAbsolute);
newton.solve();
// Translate the resulting coefficient vector into the Solution sln.
Solution<double>::vector_to_solution(newton.get_sln_vector(), space, sln);
// Visualise the solution and mesh.
ScalarView Sview1("Vector Potential", new WinGeom(450, 0, 400, 400));
Sview1.show(sln);
ScalarView Sview2("Flux Density", new WinGeom(850, 0, 400, 400));
MeshFunctionSharedPtr<double> flux_density(new FilterFluxDensity(std::vector<MeshFunctionSharedPtr<double> >({ sln, sln })));
Sview2.show(flux_density);
// Wait for all views to be closed.
View::wait();
return 0;
} | C++ |
2D | hpfem/hermes-examples | 2d-advanced/maxwell/magnetostatics-sensor/definitions.cpp | .cpp | 2,051 | 45 | #include "definitions.h"
CustomWeakFormMagnetostatics::CustomWeakFormMagnetostatics(std::string material_iron_1, std::string material_iron_2,
CubicSpline* mu_inv_iron, std::string material_air,
std::string material_copper, double mu_vacuum,
double current_density, int order_inc) : WeakForm<double>(1)
{
// Jacobian.
add_matrix_form(new DefaultJacobianMagnetostatics<double>(0, 0, std::vector<std::string>({ material_air, material_copper }),
1.0, nullptr, HERMES_NONSYM, HERMES_AXISYM_Y, order_inc));
add_matrix_form(new DefaultJacobianMagnetostatics<double>(0, 0, std::vector<std::string>({ material_iron_1, material_iron_2 }), 1.0,
mu_inv_iron, HERMES_NONSYM, HERMES_AXISYM_Y, order_inc));
// Residual.
add_vector_form(new DefaultResidualMagnetostatics<double>(0, std::vector<std::string>({ material_air, material_copper }),
1.0, nullptr, HERMES_AXISYM_Y, order_inc));
add_vector_form(new DefaultResidualMagnetostatics<double>(0, std::vector<std::string>({ material_iron_1, material_iron_2 }), 1.0,
mu_inv_iron, HERMES_AXISYM_Y, order_inc));
add_vector_form(new DefaultVectorFormVol<double>(0, material_copper, new Hermes2DFunction<double>(-current_density * mu_vacuum)));
}
FilterFluxDensity::FilterFluxDensity(std::vector<MeshFunctionSharedPtr<double> > solutions)
: DXDYFilter<double>(solutions)
{
}
Func<double>* FilterFluxDensity::get_pt_value(double x, double y, bool use_MeshHashGrid, Element* e)
{
return new Func<double>();
}
MeshFunction<double>* FilterFluxDensity::clone() const
{
std::vector<MeshFunctionSharedPtr<double> > fns;
for (int i = 0; i < this->solutions.size(); i++)
fns.push_back(this->solutions[i]->clone());
return new FilterFluxDensity(fns);
}
void FilterFluxDensity::filter_fn(int n, double* x, double* y, const std::vector<const double *>& values, const std::vector<const double *>& dx, const std::vector<const double *>& dy, double* rslt, double* rslt_dx, double* rslt_dy)
{
for (int i = 0; i < n; i++)
{
rslt[i] = std::sqrt(sqr(dy[0][i]) + sqr(dx[0][i]));
}
} | C++ |
2D | hpfem/hermes-examples | 2d-advanced/maxwell/magnetostatics-sensor/plot_spline.py | .py | 299 | 18 | # import libraries
import numpy, pylab
from pylab import *
data = numpy.loadtxt("spline.dat")
x = data[:, 0]
y = data[:, 1]
plot(x, y, '-o', label="cubic spline")
data = numpy.loadtxt("spline_der.dat")
x = data[:, 0]
y = data[:, 1]
plot(x, y, '-*', label="derivative")
legend()
# finalize
show()
| Python |
2D | M3RG-IITD/FNO-StressStrain | FNO-StressStrain.ipynb | .ipynb | 1,070,096 | 1,536 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"## @meermehran -- M3RG Lab -- Indian Institute of Technology, Delhi\n",
"## @date : 4July2022\n",
"\n",
"####################################--DESCRIPTION####################################################\n",
"## ##\n",
"## Using Fourier Neural Operator to predict the tensorial compoents of a stress and strain fields. ##\n",
"## ## \n",
"#####################################################################################################\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"## importing the necessary libraries\n",
"\n",
"import numpy as np\n",
"import scipy.io as sio\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"from torch.nn.parameter import Parameter\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import operator\n",
"from functools import reduce\n",
"from functools import partial\n",
"\n",
"from timeit import default_timer\n",
"from utility import *\n",
"from Adam import Adam\n",
"\n",
"torch.manual_seed(0)\n",
"np.random.seed(0)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model Architecture"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"class FouirerLayer(nn.Module):\n",
" def __init__(self, in_channels, out_channels, modes1, modes2):\n",
" super(FouirerLayer, self).__init__()\n",
"\n",
" \"\"\"\n",
" 2D Fourier layer. It does FFT, linear transform, and Inverse FFT. \n",
" \"\"\"\n",
"\n",
" self.in_channels = in_channels\n",
" self.out_channels = out_channels\n",
" self.modes1 = modes1\n",
" self.modes2 = modes2\n",
"\n",
" self.scale = (1 / (in_channels * out_channels))\n",
" self.weights1 = nn.Parameter(self.scale * torch.rand(in_channels, out_channels, self.modes1, self.modes2, dtype=torch.cfloat))\n",
" self.weights2 = nn.Parameter(self.scale * torch.rand(in_channels, out_channels, self.modes1, self.modes2, dtype=torch.cfloat))\n",
"\n",
" # Complex multiplication\n",
" def compl_mul2d(self, input, weights):\n",
" # (batch, in_channel, x,y ), (in_channel, out_channel, x,y) -> (batch, out_channel, x,y)\n",
" return torch.einsum(\"bixy,ioxy->boxy\", input, weights)\n",
"\n",
" def forward(self, x):\n",
" batchsize = x.shape[0]\n",
" #Compute Fourier coeffcients up to factor of e^(- something constant)\n",
" x_ft = torch.fft.rfft2(x)\n",
"\n",
" # Multiply relevant Fourier modes\n",
" out_ft = torch.zeros(batchsize, self.out_channels, x.size(-2), x.size(-1)//2 + 1, dtype=torch.cfloat, device=x.device)\n",
" out_ft[:, :, :self.modes1, :self.modes2] = \\\n",
" self.compl_mul2d(x_ft[:, :, :self.modes1, :self.modes2], self.weights1)\n",
" out_ft[:, :, -self.modes1:, :self.modes2] = \\\n",
" self.compl_mul2d(x_ft[:, :, -self.modes1:, :self.modes2], self.weights2)\n",
"\n",
" #Return to physical space\n",
" x = torch.fft.irfft2(out_ft, s=(x.size(-2), x.size(-1)))\n",
" return x\n",
"\n",
"class FNO2d(nn.Module):\n",
" def __init__(self, modes1, modes2, width, strain_channels):\n",
" super(FNO2d, self).__init__()\n",
"\n",
"\n",
" self.modes1 = modes1\n",
" self.modes2 = modes2\n",
" self.width = width\n",
" self.strain_channels = strain_channels\n",
" self.padding = 9 # pad the domain if input is non-periodic\n",
" self.fc0 = nn.Linear(3, self.width) ##input channels := [x y Material]\n",
"\n",
" self.conv0 = FouirerLayer(self.width, self.width, self.modes1, self.modes2)\n",
" self.conv1 = FouirerLayer(self.width, self.width, self.modes1, self.modes2)\n",
" self.conv2 = FouirerLayer(self.width, self.width, self.modes1, self.modes2)\n",
" self.conv3 = FouirerLayer(self.width, self.width, self.modes1, self.modes2)\n",
" self.w0 = nn.Conv2d(self.width, self.width, 1)\n",
" self.w1 = nn.Conv2d(self.width, self.width, 1)\n",
" self.w2 = nn.Conv2d(self.width, self.width, 1)\n",
" self.w3 = nn.Conv2d(self.width, self.width, 1)\n",
"\n",
" self.fc1 = nn.Linear(self.width, 128)\n",
" self.fc2 = nn.Linear(128, self.strain_channels) ## output channels := [xx,yy,xy]\n",
"\n",
" def forward(self, x):\n",
" grid = self.get_grid(x.shape, x.device)\n",
" x = torch.cat((x, grid), dim=-1)\n",
" x = self.fc0(x)\n",
" x = x.permute(0, 3, 1, 2)\n",
" x = F.pad(x, [0,self.padding, 0,self.padding])\n",
"\n",
" x1 = self.conv0(x)\n",
" x2 = self.w0(x)\n",
" x = x1 + x2\n",
" x = F.gelu(x)\n",
"\n",
" x1 = self.conv1(x)\n",
" x2 = self.w1(x)\n",
" x = x1 + x2\n",
" x = F.gelu(x)\n",
"\n",
" x1 = self.conv2(x)\n",
" x2 = self.w2(x)\n",
" x = x1 + x2\n",
" x = F.gelu(x)\n",
"\n",
" x1 = self.conv3(x)\n",
" x2 = self.w3(x)\n",
" x = x1 + x2\n",
"\n",
" x = x[..., :-self.padding, :-self.padding]\n",
" x = x.permute(0, 2, 3, 1)\n",
" x = self.fc1(x)\n",
" x = F.gelu(x)\n",
" x = self.fc2(x)\n",
" return x\n",
" \n",
" def get_grid(self, shape, device):\n",
" batchsize, size_x, size_y = shape[0], shape[1], shape[2]\n",
" gridx = torch.tensor(np.linspace(0, 1, size_x), dtype=torch.float)\n",
" gridx = gridx.reshape(1, size_x, 1, 1).repeat([batchsize, 1, size_y, 1])\n",
" gridy = torch.tensor(np.linspace(0, 1, size_y), dtype=torch.float)\n",
" gridy = gridy.reshape(1, 1, size_y, 1).repeat([batchsize, size_x, 1, 1])\n",
" return torch.cat((gridx, gridy), dim=-1).to(device)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reading the Data:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"##Loading data\n",
"\n",
"path1 = '/home/civil/mtech/cey217538/GitHub/FNOStressStrain/material.mat' \n",
"path2 = '/home/civil/mtech/cey217538/GitHub/FNOStressStrain/strainsv2.mat' #strain file\n",
"\n",
"material_dict = sio.loadmat(path1)\n",
"strains_dict = sio.loadmat(path2)\n",
"\n",
"material = material_dict['Emat'].astype(np.float32)\n",
"strains = strains_dict['strains'].astype(np.float32)\n",
"\n",
"\n",
"## convert to Tensors\n",
"material = torch.from_numpy(material)\n",
"strains = torch.from_numpy(strains)\n",
"\n",
"## Train and Test samples\n",
"ntrain = 1500\n",
"ntest = 200\n",
"\n",
"\n",
"## Train and Test Split\n",
"\n",
"## X - Input - Material Geometry\n",
"x_train = material[:2*ntrain:2]\n",
"x_test = material[-ntest:] \n",
"\n",
"## Y - Output - Strains [xx, yy, xy] -- 3 dimensional\n",
"y_train = strains[:2*ntrain:2]\n",
"y_test = strains[-ntest:]\n",
"\n",
"test_geo =material[-ntest:]\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"## hyp par settings\n",
"\n",
"batch_size = 20\n",
"learning_rate = 0.01\n",
"step_size = 100\n",
"epochs = 300\n",
"gamma = 0.5\n",
"modes =12\n",
"width = 32"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1500, 48, 48, 1]) torch.Size([1500, 48, 48, 3]) torch.Size([200, 48, 48, 1]) torch.Size([200, 48, 48, 3])\n"
]
}
],
"source": [
"x_normalizer = UnitGaussianNormalizer(x_train)\n",
"x_train = x_normalizer.encode(x_train)\n",
"x_test = x_normalizer.encode(x_test)\n",
"\n",
"y_normalizer = UnitGaussianNormalizer(y_train)\n",
"y_train = y_normalizer.encode(y_train)\n",
"\n",
"dim = 48 # image resolution\n",
"\n",
"x_train = x_train.reshape(ntrain,dim,dim,1)\n",
"x_test = x_test.reshape(ntest,dim,dim,1)\n",
"\n",
"print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MODEL PARAMETERS. :== 1188611\n"
]
}
],
"source": [
"strain_channels = 3\n",
"model = FNO2d(modes, modes, width, strain_channels).cuda()\n",
"print(f'MODEL PARAMETERS. :== {count_params(model)}')\n",
"\n",
"train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_train, y_train), batch_size=batch_size, shuffle=True)\n",
"test_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_test, y_test), batch_size=batch_size, shuffle=False)\n",
"optimizer = Adam(model.parameters(), lr=learning_rate, weight_decay=1e-4)\n",
"scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training--STRAIN MODEL"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Epoch :== 1 || TIME(sec):== 7.9913 || TrainError:== 0.350925 || TestError:== 0.217995\n",
" Epoch :== 2 || TIME(sec):== 1.4101 || TrainError:== 0.18432 || TestError:== 0.168914\n",
" Epoch :== 3 || TIME(sec):== 1.4128 || TrainError:== 0.153847 || TestError:== 0.152535\n",
" Epoch :== 4 || TIME(sec):== 1.4153 || TrainError:== 0.135319 || TestError:== 0.140093\n",
" Epoch :== 5 || TIME(sec):== 1.4149 || TrainError:== 0.125926 || TestError:== 0.139063\n",
" Epoch :== 6 || TIME(sec):== 1.4153 || TrainError:== 0.121228 || TestError:== 0.128347\n",
" Epoch :== 7 || TIME(sec):== 1.4104 || TrainError:== 0.1114 || TestError:== 0.124149\n",
" Epoch :== 8 || TIME(sec):== 1.4224 || TrainError:== 0.108548 || TestError:== 0.118425\n",
" Epoch :== 9 || TIME(sec):== 1.4131 || TrainError:== 0.104178 || TestError:== 0.115147\n",
" Epoch :== 10 || TIME(sec):== 1.4129 || TrainError:== 0.103069 || TestError:== 0.109688\n",
" Epoch :== 11 || TIME(sec):== 1.4175 || TrainError:== 0.098711 || TestError:== 0.111785\n",
" Epoch :== 12 || TIME(sec):== 1.418 || TrainError:== 0.098239 || TestError:== 0.10787\n",
" Epoch :== 13 || TIME(sec):== 1.4173 || TrainError:== 0.096085 || TestError:== 0.110067\n",
" Epoch :== 14 || TIME(sec):== 1.4018 || TrainError:== 0.095448 || TestError:== 0.108746\n",
" Epoch :== 15 || TIME(sec):== 1.3949 || TrainError:== 0.094249 || TestError:== 0.104576\n",
" Epoch :== 16 || TIME(sec):== 1.3929 || TrainError:== 0.090229 || TestError:== 0.102512\n",
" Epoch :== 17 || TIME(sec):== 1.3952 || TrainError:== 0.092203 || TestError:== 0.104304\n",
" Epoch :== 18 || TIME(sec):== 1.3864 || TrainError:== 0.091986 || TestError:== 0.106272\n",
" Epoch :== 19 || TIME(sec):== 1.418 || TrainError:== 0.089211 || TestError:== 0.109791\n",
" Epoch :== 20 || TIME(sec):== 1.4212 || TrainError:== 0.089781 || TestError:== 0.100921\n",
" Epoch :== 21 || TIME(sec):== 1.4163 || TrainError:== 0.085689 || TestError:== 0.096542\n",
" Epoch :== 22 || TIME(sec):== 1.404 || TrainError:== 0.085404 || TestError:== 0.096264\n",
" Epoch :== 23 || TIME(sec):== 1.4067 || TrainError:== 0.085444 || TestError:== 0.099432\n",
" Epoch :== 24 || TIME(sec):== 1.4116 || TrainError:== 0.08466 || TestError:== 0.100165\n",
" Epoch :== 25 || TIME(sec):== 1.4138 || TrainError:== 0.083657 || TestError:== 0.100089\n",
" Epoch :== 26 || TIME(sec):== 1.4123 || TrainError:== 0.084489 || TestError:== 0.09735\n",
" Epoch :== 27 || TIME(sec):== 1.4177 || TrainError:== 0.083213 || TestError:== 0.100435\n",
" Epoch :== 28 || TIME(sec):== 1.4055 || TrainError:== 0.082667 || TestError:== 0.098373\n",
" Epoch :== 29 || TIME(sec):== 1.4141 || TrainError:== 0.082297 || TestError:== 0.097739\n",
" Epoch :== 30 || TIME(sec):== 1.4337 || TrainError:== 0.08259 || TestError:== 0.095223\n",
" Epoch :== 31 || TIME(sec):== 1.4111 || TrainError:== 0.081243 || TestError:== 0.093834\n",
" Epoch :== 32 || TIME(sec):== 1.4112 || TrainError:== 0.080657 || TestError:== 0.098533\n",
" Epoch :== 33 || TIME(sec):== 1.4125 || TrainError:== 0.079266 || TestError:== 0.09434\n",
" Epoch :== 34 || TIME(sec):== 1.4128 || TrainError:== 0.079456 || TestError:== 0.096319\n",
" Epoch :== 35 || TIME(sec):== 1.4094 || TrainError:== 0.078126 || TestError:== 0.095503\n",
" Epoch :== 36 || TIME(sec):== 1.4161 || TrainError:== 0.077871 || TestError:== 0.093049\n",
" Epoch :== 37 || TIME(sec):== 1.4155 || TrainError:== 0.077503 || TestError:== 0.093798\n",
" Epoch :== 38 || TIME(sec):== 1.4094 || TrainError:== 0.077706 || TestError:== 0.093306\n",
" Epoch :== 39 || TIME(sec):== 1.4147 || TrainError:== 0.079248 || TestError:== 0.092633\n",
" Epoch :== 40 || TIME(sec):== 1.4098 || TrainError:== 0.077618 || TestError:== 0.093996\n",
" Epoch :== 41 || TIME(sec):== 1.428 || TrainError:== 0.079329 || TestError:== 0.094412\n",
" Epoch :== 42 || TIME(sec):== 1.4065 || TrainError:== 0.075969 || TestError:== 0.090551\n",
" Epoch :== 43 || TIME(sec):== 1.3991 || TrainError:== 0.075025 || TestError:== 0.093948\n",
" Epoch :== 44 || TIME(sec):== 1.3943 || TrainError:== 0.07689 || TestError:== 0.091207\n",
" Epoch :== 45 || TIME(sec):== 1.3866 || TrainError:== 0.076289 || TestError:== 0.093973\n",
" Epoch :== 46 || TIME(sec):== 1.3953 || TrainError:== 0.077248 || TestError:== 0.09104\n",
" Epoch :== 47 || TIME(sec):== 1.4123 || TrainError:== 0.078039 || TestError:== 0.093069\n",
" Epoch :== 48 || TIME(sec):== 1.4101 || TrainError:== 0.075659 || TestError:== 0.093646\n",
" Epoch :== 49 || TIME(sec):== 1.4052 || TrainError:== 0.074595 || TestError:== 0.090917\n",
" Epoch :== 50 || TIME(sec):== 1.4032 || TrainError:== 0.072958 || TestError:== 0.094056\n",
" Epoch :== 51 || TIME(sec):== 1.4048 || TrainError:== 0.073915 || TestError:== 0.090977\n",
" Epoch :== 52 || TIME(sec):== 1.4118 || TrainError:== 0.074049 || TestError:== 0.089698\n",
" Epoch :== 53 || TIME(sec):== 1.4057 || TrainError:== 0.073667 || TestError:== 0.089367\n",
" Epoch :== 54 || TIME(sec):== 1.4105 || TrainError:== 0.072864 || TestError:== 0.088312\n",
" Epoch :== 55 || TIME(sec):== 1.4148 || TrainError:== 0.071491 || TestError:== 0.090164\n",
" Epoch :== 56 || TIME(sec):== 1.4093 || TrainError:== 0.073659 || TestError:== 0.088957\n",
" Epoch :== 57 || TIME(sec):== 1.4093 || TrainError:== 0.073572 || TestError:== 0.089513\n",
" Epoch :== 58 || TIME(sec):== 1.414 || TrainError:== 0.0731 || TestError:== 0.090607\n",
" Epoch :== 59 || TIME(sec):== 1.4082 || TrainError:== 0.07202 || TestError:== 0.087713\n",
" Epoch :== 60 || TIME(sec):== 1.4097 || TrainError:== 0.071386 || TestError:== 0.091046\n",
" Epoch :== 61 || TIME(sec):== 1.4053 || TrainError:== 0.071908 || TestError:== 0.087355\n",
" Epoch :== 62 || TIME(sec):== 1.4179 || TrainError:== 0.072584 || TestError:== 0.090293\n",
" Epoch :== 63 || TIME(sec):== 1.403 || TrainError:== 0.073413 || TestError:== 0.090047\n",
" Epoch :== 64 || TIME(sec):== 1.4141 || TrainError:== 0.070929 || TestError:== 0.085404\n",
" Epoch :== 65 || TIME(sec):== 1.4023 || TrainError:== 0.071071 || TestError:== 0.088445\n",
" Epoch :== 66 || TIME(sec):== 1.4108 || TrainError:== 0.070473 || TestError:== 0.086825\n",
" Epoch :== 67 || TIME(sec):== 1.4084 || TrainError:== 0.071629 || TestError:== 0.08663\n",
" Epoch :== 68 || TIME(sec):== 1.4093 || TrainError:== 0.069942 || TestError:== 0.090551\n",
" Epoch :== 69 || TIME(sec):== 1.4045 || TrainError:== 0.072624 || TestError:== 0.087535\n",
" Epoch :== 70 || TIME(sec):== 1.4147 || TrainError:== 0.070415 || TestError:== 0.086076\n",
" Epoch :== 71 || TIME(sec):== 1.3886 || TrainError:== 0.070198 || TestError:== 0.087342\n",
" Epoch :== 72 || TIME(sec):== 1.3909 || TrainError:== 0.070682 || TestError:== 0.086156\n",
" Epoch :== 73 || TIME(sec):== 1.3851 || TrainError:== 0.069361 || TestError:== 0.086706\n",
" Epoch :== 74 || TIME(sec):== 1.4088 || TrainError:== 0.069447 || TestError:== 0.085864\n",
" Epoch :== 75 || TIME(sec):== 1.4074 || TrainError:== 0.069972 || TestError:== 0.085152\n",
" Epoch :== 76 || TIME(sec):== 1.4138 || TrainError:== 0.067448 || TestError:== 0.08947\n",
" Epoch :== 77 || TIME(sec):== 1.4056 || TrainError:== 0.068968 || TestError:== 0.087239\n",
" Epoch :== 78 || TIME(sec):== 1.4051 || TrainError:== 0.069517 || TestError:== 0.087429\n",
" Epoch :== 79 || TIME(sec):== 1.4082 || TrainError:== 0.069354 || TestError:== 0.091754\n",
" Epoch :== 80 || TIME(sec):== 1.4118 || TrainError:== 0.069699 || TestError:== 0.086808\n",
" Epoch :== 81 || TIME(sec):== 1.4133 || TrainError:== 0.068508 || TestError:== 0.085444\n",
" Epoch :== 82 || TIME(sec):== 1.4056 || TrainError:== 0.069843 || TestError:== 0.087367\n",
" Epoch :== 83 || TIME(sec):== 1.4099 || TrainError:== 0.070247 || TestError:== 0.087065\n",
" Epoch :== 84 || TIME(sec):== 1.4211 || TrainError:== 0.068559 || TestError:== 0.087836\n",
" Epoch :== 85 || TIME(sec):== 1.4129 || TrainError:== 0.066767 || TestError:== 0.085143\n",
" Epoch :== 86 || TIME(sec):== 1.4136 || TrainError:== 0.067397 || TestError:== 0.087337\n",
" Epoch :== 87 || TIME(sec):== 1.4129 || TrainError:== 0.070712 || TestError:== 0.088801\n",
" Epoch :== 88 || TIME(sec):== 1.411 || TrainError:== 0.070627 || TestError:== 0.085882\n",
" Epoch :== 89 || TIME(sec):== 1.4123 || TrainError:== 0.068107 || TestError:== 0.085774\n",
" Epoch :== 90 || TIME(sec):== 1.4084 || TrainError:== 0.06722 || TestError:== 0.086629\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Epoch :== 91 || TIME(sec):== 1.4199 || TrainError:== 0.065625 || TestError:== 0.084565\n",
" Epoch :== 92 || TIME(sec):== 1.4108 || TrainError:== 0.067614 || TestError:== 0.086082\n",
" Epoch :== 93 || TIME(sec):== 1.4135 || TrainError:== 0.069023 || TestError:== 0.082591\n",
" Epoch :== 94 || TIME(sec):== 1.4075 || TrainError:== 0.067842 || TestError:== 0.082965\n",
" Epoch :== 95 || TIME(sec):== 1.4113 || TrainError:== 0.065495 || TestError:== 0.086326\n",
" Epoch :== 96 || TIME(sec):== 1.4102 || TrainError:== 0.066806 || TestError:== 0.086495\n",
" Epoch :== 97 || TIME(sec):== 1.4035 || TrainError:== 0.066486 || TestError:== 0.089054\n",
" Epoch :== 98 || TIME(sec):== 1.3979 || TrainError:== 0.066923 || TestError:== 0.084911\n",
" Epoch :== 99 || TIME(sec):== 1.3936 || TrainError:== 0.066653 || TestError:== 0.08605\n",
" Epoch :== 100 || TIME(sec):== 1.3891 || TrainError:== 0.066794 || TestError:== 0.084556\n",
" Epoch :== 101 || TIME(sec):== 1.4001 || TrainError:== 0.05621 || TestError:== 0.07356\n",
" Epoch :== 102 || TIME(sec):== 1.4017 || TrainError:== 0.050335 || TestError:== 0.072774\n",
" Epoch :== 103 || TIME(sec):== 1.3973 || TrainError:== 0.049386 || TestError:== 0.071609\n",
" Epoch :== 104 || TIME(sec):== 1.4013 || TrainError:== 0.04947 || TestError:== 0.072972\n",
" Epoch :== 105 || TIME(sec):== 1.4166 || TrainError:== 0.050509 || TestError:== 0.072184\n",
" Epoch :== 106 || TIME(sec):== 1.3967 || TrainError:== 0.050298 || TestError:== 0.073073\n",
" Epoch :== 107 || TIME(sec):== 1.4113 || TrainError:== 0.050432 || TestError:== 0.0723\n",
" Epoch :== 108 || TIME(sec):== 1.4084 || TrainError:== 0.049777 || TestError:== 0.071898\n",
" Epoch :== 109 || TIME(sec):== 1.4128 || TrainError:== 0.050663 || TestError:== 0.07452\n",
" Epoch :== 110 || TIME(sec):== 1.4098 || TrainError:== 0.051284 || TestError:== 0.073541\n",
" Epoch :== 111 || TIME(sec):== 1.4165 || TrainError:== 0.051161 || TestError:== 0.073876\n",
" Epoch :== 112 || TIME(sec):== 1.4186 || TrainError:== 0.05056 || TestError:== 0.072698\n",
" Epoch :== 113 || TIME(sec):== 1.4115 || TrainError:== 0.050779 || TestError:== 0.074177\n",
" Epoch :== 114 || TIME(sec):== 1.4078 || TrainError:== 0.050669 || TestError:== 0.074395\n",
" Epoch :== 115 || TIME(sec):== 1.4416 || TrainError:== 0.051529 || TestError:== 0.073522\n",
" Epoch :== 116 || TIME(sec):== 1.4105 || TrainError:== 0.051912 || TestError:== 0.072366\n",
" Epoch :== 117 || TIME(sec):== 1.4159 || TrainError:== 0.050505 || TestError:== 0.073852\n",
" Epoch :== 118 || TIME(sec):== 1.4125 || TrainError:== 0.051581 || TestError:== 0.074332\n",
" Epoch :== 119 || TIME(sec):== 1.407 || TrainError:== 0.050683 || TestError:== 0.072618\n",
" Epoch :== 120 || TIME(sec):== 1.3952 || TrainError:== 0.04999 || TestError:== 0.072374\n",
" Epoch :== 121 || TIME(sec):== 1.4126 || TrainError:== 0.049375 || TestError:== 0.070298\n",
" Epoch :== 122 || TIME(sec):== 1.4196 || TrainError:== 0.050092 || TestError:== 0.072273\n",
" Epoch :== 123 || TIME(sec):== 1.4105 || TrainError:== 0.050367 || TestError:== 0.073113\n",
" Epoch :== 124 || TIME(sec):== 1.4144 || TrainError:== 0.049425 || TestError:== 0.073024\n",
" Epoch :== 125 || TIME(sec):== 1.4128 || TrainError:== 0.049241 || TestError:== 0.070053\n",
" Epoch :== 126 || TIME(sec):== 1.416 || TrainError:== 0.049438 || TestError:== 0.071958\n",
" Epoch :== 127 || TIME(sec):== 1.4064 || TrainError:== 0.049318 || TestError:== 0.071663\n",
" Epoch :== 128 || TIME(sec):== 1.4096 || TrainError:== 0.049222 || TestError:== 0.071487\n",
" Epoch :== 129 || TIME(sec):== 1.3917 || TrainError:== 0.050813 || TestError:== 0.072979\n",
" Epoch :== 130 || TIME(sec):== 1.3951 || TrainError:== 0.049947 || TestError:== 0.072753\n",
" Epoch :== 131 || TIME(sec):== 1.3926 || TrainError:== 0.049026 || TestError:== 0.072278\n",
" Epoch :== 132 || TIME(sec):== 1.4076 || TrainError:== 0.048579 || TestError:== 0.070173\n",
" Epoch :== 133 || TIME(sec):== 1.4259 || TrainError:== 0.049146 || TestError:== 0.071479\n",
" Epoch :== 134 || TIME(sec):== 1.4165 || TrainError:== 0.048994 || TestError:== 0.072451\n",
" Epoch :== 135 || TIME(sec):== 1.4094 || TrainError:== 0.048662 || TestError:== 0.072547\n",
" Epoch :== 136 || TIME(sec):== 1.4093 || TrainError:== 0.04949 || TestError:== 0.073776\n",
" Epoch :== 137 || TIME(sec):== 1.4217 || TrainError:== 0.049664 || TestError:== 0.070573\n",
" Epoch :== 138 || TIME(sec):== 1.414 || TrainError:== 0.048841 || TestError:== 0.071602\n",
" Epoch :== 139 || TIME(sec):== 1.419 || TrainError:== 0.049524 || TestError:== 0.072076\n",
" Epoch :== 140 || TIME(sec):== 1.4191 || TrainError:== 0.049232 || TestError:== 0.073506\n",
" Epoch :== 141 || TIME(sec):== 1.4244 || TrainError:== 0.049722 || TestError:== 0.070536\n",
" Epoch :== 142 || TIME(sec):== 1.413 || TrainError:== 0.048374 || TestError:== 0.072427\n",
" Epoch :== 143 || TIME(sec):== 1.4213 || TrainError:== 0.049533 || TestError:== 0.072077\n",
" Epoch :== 144 || TIME(sec):== 1.4185 || TrainError:== 0.04932 || TestError:== 0.071689\n",
" Epoch :== 145 || TIME(sec):== 1.4176 || TrainError:== 0.048769 || TestError:== 0.070349\n",
" Epoch :== 146 || TIME(sec):== 1.4091 || TrainError:== 0.047638 || TestError:== 0.072234\n",
" Epoch :== 147 || TIME(sec):== 1.4181 || TrainError:== 0.047936 || TestError:== 0.07157\n",
" Epoch :== 148 || TIME(sec):== 1.4175 || TrainError:== 0.048249 || TestError:== 0.070396\n",
" Epoch :== 149 || TIME(sec):== 1.4168 || TrainError:== 0.047692 || TestError:== 0.070327\n",
" Epoch :== 150 || TIME(sec):== 1.4136 || TrainError:== 0.048488 || TestError:== 0.076128\n",
" Epoch :== 151 || TIME(sec):== 1.4211 || TrainError:== 0.049472 || TestError:== 0.071999\n",
" Epoch :== 152 || TIME(sec):== 1.4111 || TrainError:== 0.04859 || TestError:== 0.070719\n",
" Epoch :== 153 || TIME(sec):== 1.4185 || TrainError:== 0.047784 || TestError:== 0.069992\n",
" Epoch :== 154 || TIME(sec):== 1.4116 || TrainError:== 0.046839 || TestError:== 0.070274\n",
" Epoch :== 155 || TIME(sec):== 1.4186 || TrainError:== 0.046639 || TestError:== 0.072594\n",
" Epoch :== 156 || TIME(sec):== 1.3916 || TrainError:== 0.048506 || TestError:== 0.071912\n",
" Epoch :== 157 || TIME(sec):== 1.3903 || TrainError:== 0.048709 || TestError:== 0.071946\n",
" Epoch :== 158 || TIME(sec):== 1.3834 || TrainError:== 0.050016 || TestError:== 0.072169\n",
" Epoch :== 159 || TIME(sec):== 1.4095 || TrainError:== 0.048262 || TestError:== 0.071395\n",
" Epoch :== 160 || TIME(sec):== 1.4188 || TrainError:== 0.047151 || TestError:== 0.070592\n",
" Epoch :== 161 || TIME(sec):== 1.4205 || TrainError:== 0.046449 || TestError:== 0.070299\n",
" Epoch :== 162 || TIME(sec):== 1.4127 || TrainError:== 0.0472 || TestError:== 0.071678\n",
" Epoch :== 163 || TIME(sec):== 1.411 || TrainError:== 0.050058 || TestError:== 0.071197\n",
" Epoch :== 164 || TIME(sec):== 1.4165 || TrainError:== 0.04867 || TestError:== 0.072941\n",
" Epoch :== 165 || TIME(sec):== 1.419 || TrainError:== 0.048193 || TestError:== 0.072301\n",
" Epoch :== 166 || TIME(sec):== 1.4212 || TrainError:== 0.046834 || TestError:== 0.068625\n",
" Epoch :== 167 || TIME(sec):== 1.4185 || TrainError:== 0.046765 || TestError:== 0.070828\n",
" Epoch :== 168 || TIME(sec):== 1.4185 || TrainError:== 0.048227 || TestError:== 0.072192\n",
" Epoch :== 169 || TIME(sec):== 1.4216 || TrainError:== 0.049189 || TestError:== 0.071258\n",
" Epoch :== 170 || TIME(sec):== 1.4204 || TrainError:== 0.047782 || TestError:== 0.070692\n",
" Epoch :== 171 || TIME(sec):== 1.4153 || TrainError:== 0.046971 || TestError:== 0.069023\n",
" Epoch :== 172 || TIME(sec):== 1.422 || TrainError:== 0.047287 || TestError:== 0.072515\n",
" Epoch :== 173 || TIME(sec):== 1.4179 || TrainError:== 0.046655 || TestError:== 0.068236\n",
" Epoch :== 174 || TIME(sec):== 1.4235 || TrainError:== 0.046354 || TestError:== 0.069892\n",
" Epoch :== 175 || TIME(sec):== 1.4173 || TrainError:== 0.046512 || TestError:== 0.068857\n",
" Epoch :== 176 || TIME(sec):== 1.4292 || TrainError:== 0.046763 || TestError:== 0.07139\n",
" Epoch :== 177 || TIME(sec):== 1.4143 || TrainError:== 0.049563 || TestError:== 0.071106\n",
" Epoch :== 178 || TIME(sec):== 1.4218 || TrainError:== 0.046951 || TestError:== 0.0711\n",
" Epoch :== 179 || TIME(sec):== 1.4222 || TrainError:== 0.046437 || TestError:== 0.070236\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Epoch :== 180 || TIME(sec):== 1.4182 || TrainError:== 0.046737 || TestError:== 0.069621\n",
" Epoch :== 181 || TIME(sec):== 1.416 || TrainError:== 0.047392 || TestError:== 0.071247\n",
" Epoch :== 182 || TIME(sec):== 1.4145 || TrainError:== 0.047158 || TestError:== 0.070652\n",
" Epoch :== 183 || TIME(sec):== 1.4058 || TrainError:== 0.047359 || TestError:== 0.070604\n",
" Epoch :== 184 || TIME(sec):== 1.4012 || TrainError:== 0.047205 || TestError:== 0.06905\n",
" Epoch :== 185 || TIME(sec):== 1.3947 || TrainError:== 0.04599 || TestError:== 0.070514\n",
" Epoch :== 186 || TIME(sec):== 1.4096 || TrainError:== 0.046897 || TestError:== 0.068214\n",
" Epoch :== 187 || TIME(sec):== 1.4248 || TrainError:== 0.0472 || TestError:== 0.070839\n",
" Epoch :== 188 || TIME(sec):== 1.4186 || TrainError:== 0.047952 || TestError:== 0.072411\n",
" Epoch :== 189 || TIME(sec):== 1.4084 || TrainError:== 0.048077 || TestError:== 0.06885\n",
" Epoch :== 190 || TIME(sec):== 1.4021 || TrainError:== 0.047202 || TestError:== 0.070264\n",
" Epoch :== 191 || TIME(sec):== 1.4096 || TrainError:== 0.046659 || TestError:== 0.068332\n",
" Epoch :== 192 || TIME(sec):== 1.4115 || TrainError:== 0.046207 || TestError:== 0.070518\n",
" Epoch :== 193 || TIME(sec):== 1.4269 || TrainError:== 0.045676 || TestError:== 0.068871\n",
" Epoch :== 194 || TIME(sec):== 1.4127 || TrainError:== 0.04563 || TestError:== 0.069526\n",
" Epoch :== 195 || TIME(sec):== 1.4077 || TrainError:== 0.045879 || TestError:== 0.069839\n",
" Epoch :== 196 || TIME(sec):== 1.4056 || TrainError:== 0.046372 || TestError:== 0.068333\n",
" Epoch :== 197 || TIME(sec):== 1.4033 || TrainError:== 0.046252 || TestError:== 0.068613\n",
" Epoch :== 198 || TIME(sec):== 1.4072 || TrainError:== 0.046096 || TestError:== 0.069068\n",
" Epoch :== 199 || TIME(sec):== 1.4082 || TrainError:== 0.046806 || TestError:== 0.069837\n",
" Epoch :== 200 || TIME(sec):== 1.4489 || TrainError:== 0.046877 || TestError:== 0.069847\n",
" Epoch :== 201 || TIME(sec):== 1.4248 || TrainError:== 0.039841 || TestError:== 0.063818\n",
" Epoch :== 202 || TIME(sec):== 1.4228 || TrainError:== 0.03617 || TestError:== 0.063007\n",
" Epoch :== 203 || TIME(sec):== 1.429 || TrainError:== 0.035329 || TestError:== 0.06319\n",
" Epoch :== 204 || TIME(sec):== 1.43 || TrainError:== 0.035409 || TestError:== 0.063182\n",
" Epoch :== 205 || TIME(sec):== 1.4229 || TrainError:== 0.035234 || TestError:== 0.063002\n",
" Epoch :== 206 || TIME(sec):== 1.4246 || TrainError:== 0.03558 || TestError:== 0.06225\n",
" Epoch :== 207 || TIME(sec):== 1.4229 || TrainError:== 0.035953 || TestError:== 0.063268\n",
" Epoch :== 208 || TIME(sec):== 1.4277 || TrainError:== 0.036385 || TestError:== 0.06434\n",
" Epoch :== 209 || TIME(sec):== 1.4204 || TrainError:== 0.036896 || TestError:== 0.063674\n",
" Epoch :== 210 || TIME(sec):== 1.4267 || TrainError:== 0.036425 || TestError:== 0.062491\n",
" Epoch :== 211 || TIME(sec):== 1.4234 || TrainError:== 0.036294 || TestError:== 0.063297\n",
" Epoch :== 212 || TIME(sec):== 1.4275 || TrainError:== 0.036149 || TestError:== 0.06276\n",
" Epoch :== 213 || TIME(sec):== 1.4178 || TrainError:== 0.035931 || TestError:== 0.063289\n",
" Epoch :== 214 || TIME(sec):== 1.4172 || TrainError:== 0.035526 || TestError:== 0.06217\n",
" Epoch :== 215 || TIME(sec):== 1.4023 || TrainError:== 0.035837 || TestError:== 0.063564\n",
" Epoch :== 216 || TIME(sec):== 1.4021 || TrainError:== 0.036085 || TestError:== 0.063205\n",
" Epoch :== 217 || TIME(sec):== 1.3945 || TrainError:== 0.036315 || TestError:== 0.063016\n",
" Epoch :== 218 || TIME(sec):== 1.4296 || TrainError:== 0.036447 || TestError:== 0.063142\n",
" Epoch :== 219 || TIME(sec):== 1.4255 || TrainError:== 0.037319 || TestError:== 0.064637\n",
" Epoch :== 220 || TIME(sec):== 1.4224 || TrainError:== 0.037145 || TestError:== 0.064135\n",
" Epoch :== 221 || TIME(sec):== 1.4192 || TrainError:== 0.037044 || TestError:== 0.06329\n",
" Epoch :== 222 || TIME(sec):== 1.4171 || TrainError:== 0.036117 || TestError:== 0.06259\n",
" Epoch :== 223 || TIME(sec):== 1.4265 || TrainError:== 0.035365 || TestError:== 0.063155\n",
" Epoch :== 224 || TIME(sec):== 1.4223 || TrainError:== 0.035685 || TestError:== 0.062065\n",
" Epoch :== 225 || TIME(sec):== 1.4152 || TrainError:== 0.03572 || TestError:== 0.062759\n",
" Epoch :== 226 || TIME(sec):== 1.421 || TrainError:== 0.035473 || TestError:== 0.062318\n",
" Epoch :== 227 || TIME(sec):== 1.418 || TrainError:== 0.03635 || TestError:== 0.063703\n",
" Epoch :== 228 || TIME(sec):== 1.4277 || TrainError:== 0.037121 || TestError:== 0.063633\n",
" Epoch :== 229 || TIME(sec):== 1.4193 || TrainError:== 0.036875 || TestError:== 0.063674\n",
" Epoch :== 230 || TIME(sec):== 1.418 || TrainError:== 0.035759 || TestError:== 0.062221\n",
" Epoch :== 231 || TIME(sec):== 1.4214 || TrainError:== 0.035412 || TestError:== 0.062795\n",
" Epoch :== 232 || TIME(sec):== 1.4221 || TrainError:== 0.035747 || TestError:== 0.06298\n",
" Epoch :== 233 || TIME(sec):== 1.4248 || TrainError:== 0.035672 || TestError:== 0.062252\n",
" Epoch :== 234 || TIME(sec):== 1.4203 || TrainError:== 0.035666 || TestError:== 0.061989\n",
" Epoch :== 235 || TIME(sec):== 1.423 || TrainError:== 0.035202 || TestError:== 0.06229\n",
" Epoch :== 236 || TIME(sec):== 1.4139 || TrainError:== 0.035432 || TestError:== 0.06169\n",
" Epoch :== 237 || TIME(sec):== 1.4018 || TrainError:== 0.035999 || TestError:== 0.063126\n",
" Epoch :== 238 || TIME(sec):== 1.4152 || TrainError:== 0.035565 || TestError:== 0.062487\n",
" Epoch :== 239 || TIME(sec):== 1.4211 || TrainError:== 0.035457 || TestError:== 0.062868\n",
" Epoch :== 240 || TIME(sec):== 1.4211 || TrainError:== 0.03599 || TestError:== 0.063371\n",
" Epoch :== 241 || TIME(sec):== 1.404 || TrainError:== 0.035883 || TestError:== 0.063607\n",
" Epoch :== 242 || TIME(sec):== 1.4259 || TrainError:== 0.035319 || TestError:== 0.062501\n",
" Epoch :== 243 || TIME(sec):== 1.4177 || TrainError:== 0.035724 || TestError:== 0.062743\n",
" Epoch :== 244 || TIME(sec):== 1.4157 || TrainError:== 0.035384 || TestError:== 0.062496\n",
" Epoch :== 245 || TIME(sec):== 1.4011 || TrainError:== 0.035378 || TestError:== 0.064014\n",
" Epoch :== 246 || TIME(sec):== 1.4033 || TrainError:== 0.036123 || TestError:== 0.063277\n",
" Epoch :== 247 || TIME(sec):== 1.3926 || TrainError:== 0.035395 || TestError:== 0.062933\n",
" Epoch :== 248 || TIME(sec):== 1.4174 || TrainError:== 0.035645 || TestError:== 0.061715\n",
" Epoch :== 249 || TIME(sec):== 1.4225 || TrainError:== 0.035244 || TestError:== 0.063482\n",
" Epoch :== 250 || TIME(sec):== 1.4236 || TrainError:== 0.035433 || TestError:== 0.063093\n",
" Epoch :== 251 || TIME(sec):== 1.4122 || TrainError:== 0.035134 || TestError:== 0.063334\n",
" Epoch :== 252 || TIME(sec):== 1.4148 || TrainError:== 0.035185 || TestError:== 0.061578\n",
" Epoch :== 253 || TIME(sec):== 1.4337 || TrainError:== 0.034456 || TestError:== 0.062125\n",
" Epoch :== 254 || TIME(sec):== 1.4163 || TrainError:== 0.034916 || TestError:== 0.061738\n",
" Epoch :== 255 || TIME(sec):== 1.4279 || TrainError:== 0.035025 || TestError:== 0.062451\n",
" Epoch :== 256 || TIME(sec):== 1.4222 || TrainError:== 0.034801 || TestError:== 0.06367\n",
" Epoch :== 257 || TIME(sec):== 1.4262 || TrainError:== 0.036321 || TestError:== 0.061603\n",
" Epoch :== 258 || TIME(sec):== 1.4253 || TrainError:== 0.035724 || TestError:== 0.063222\n",
" Epoch :== 259 || TIME(sec):== 1.431 || TrainError:== 0.035094 || TestError:== 0.062524\n",
" Epoch :== 260 || TIME(sec):== 1.4277 || TrainError:== 0.034446 || TestError:== 0.061971\n",
" Epoch :== 261 || TIME(sec):== 1.429 || TrainError:== 0.034715 || TestError:== 0.061326\n",
" Epoch :== 262 || TIME(sec):== 1.4278 || TrainError:== 0.03458 || TestError:== 0.063673\n",
" Epoch :== 263 || TIME(sec):== 1.4286 || TrainError:== 0.03584 || TestError:== 0.062904\n",
" Epoch :== 264 || TIME(sec):== 1.4248 || TrainError:== 0.035682 || TestError:== 0.061819\n",
" Epoch :== 265 || TIME(sec):== 1.4296 || TrainError:== 0.034243 || TestError:== 0.061969\n",
" Epoch :== 266 || TIME(sec):== 1.4275 || TrainError:== 0.034787 || TestError:== 0.061347\n",
" Epoch :== 267 || TIME(sec):== 1.432 || TrainError:== 0.034694 || TestError:== 0.062023\n",
" Epoch :== 268 || TIME(sec):== 1.4316 || TrainError:== 0.035302 || TestError:== 0.063658\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Epoch :== 269 || TIME(sec):== 1.4285 || TrainError:== 0.035842 || TestError:== 0.062631\n",
" Epoch :== 270 || TIME(sec):== 1.4255 || TrainError:== 0.035043 || TestError:== 0.062818\n",
" Epoch :== 271 || TIME(sec):== 1.4053 || TrainError:== 0.034632 || TestError:== 0.062607\n",
" Epoch :== 272 || TIME(sec):== 1.4097 || TrainError:== 0.034785 || TestError:== 0.062555\n",
" Epoch :== 273 || TIME(sec):== 1.4013 || TrainError:== 0.034645 || TestError:== 0.062103\n",
" Epoch :== 274 || TIME(sec):== 1.4232 || TrainError:== 0.034541 || TestError:== 0.061978\n",
" Epoch :== 275 || TIME(sec):== 1.4289 || TrainError:== 0.034089 || TestError:== 0.060813\n",
" Epoch :== 276 || TIME(sec):== 1.4218 || TrainError:== 0.034178 || TestError:== 0.062194\n",
" Epoch :== 277 || TIME(sec):== 1.4224 || TrainError:== 0.034396 || TestError:== 0.061367\n",
" Epoch :== 278 || TIME(sec):== 1.422 || TrainError:== 0.034889 || TestError:== 0.062092\n",
" Epoch :== 279 || TIME(sec):== 1.4239 || TrainError:== 0.034186 || TestError:== 0.062205\n",
" Epoch :== 280 || TIME(sec):== 1.4243 || TrainError:== 0.03432 || TestError:== 0.062166\n",
" Epoch :== 281 || TIME(sec):== 1.426 || TrainError:== 0.035113 || TestError:== 0.063508\n",
" Epoch :== 282 || TIME(sec):== 1.4334 || TrainError:== 0.034922 || TestError:== 0.064111\n",
" Epoch :== 283 || TIME(sec):== 1.4244 || TrainError:== 0.035088 || TestError:== 0.062116\n",
" Epoch :== 284 || TIME(sec):== 1.446 || TrainError:== 0.034359 || TestError:== 0.061525\n",
" Epoch :== 285 || TIME(sec):== 1.4197 || TrainError:== 0.034794 || TestError:== 0.062253\n",
" Epoch :== 286 || TIME(sec):== 1.4234 || TrainError:== 0.03431 || TestError:== 0.061935\n",
" Epoch :== 287 || TIME(sec):== 1.4114 || TrainError:== 0.034537 || TestError:== 0.061415\n",
" Epoch :== 288 || TIME(sec):== 1.4239 || TrainError:== 0.034138 || TestError:== 0.060508\n",
" Epoch :== 289 || TIME(sec):== 1.4322 || TrainError:== 0.034805 || TestError:== 0.062444\n",
" Epoch :== 290 || TIME(sec):== 1.4256 || TrainError:== 0.034819 || TestError:== 0.061642\n",
" Epoch :== 291 || TIME(sec):== 1.4318 || TrainError:== 0.03464 || TestError:== 0.062532\n",
" Epoch :== 292 || TIME(sec):== 1.4239 || TrainError:== 0.034603 || TestError:== 0.064083\n",
" Epoch :== 293 || TIME(sec):== 1.4301 || TrainError:== 0.034759 || TestError:== 0.061481\n",
" Epoch :== 294 || TIME(sec):== 1.422 || TrainError:== 0.034823 || TestError:== 0.061868\n",
" Epoch :== 295 || TIME(sec):== 1.425 || TrainError:== 0.034638 || TestError:== 0.061547\n",
" Epoch :== 296 || TIME(sec):== 1.4258 || TrainError:== 0.03391 || TestError:== 0.061232\n",
" Epoch :== 297 || TIME(sec):== 1.4298 || TrainError:== 0.034332 || TestError:== 0.061619\n",
" Epoch :== 298 || TIME(sec):== 1.4222 || TrainError:== 0.034367 || TestError:== 0.062283\n",
" Epoch :== 299 || TIME(sec):== 1.4223 || TrainError:== 0.034527 || TestError:== 0.06119\n",
" Epoch :== 300 || TIME(sec):== 1.4063 || TrainError:== 0.034328 || TestError:== 0.062151\n"
]
}
],
"source": [
"\n",
"myloss = L2Loss(size_average=False)\n",
"y_normalizer.cuda()\n",
"\n",
"## ep wise error\n",
"trainerror=[] \n",
"testerror=[]\n",
"\n",
"##Train Test Loop\n",
"for ep in range(epochs):\n",
" model.train()\n",
" t1 = default_timer()\n",
" train_l2 = 0\n",
" for x, y in train_loader:\n",
" x, y = x.cuda(), y.cuda()\n",
" \n",
" optimizer.zero_grad()\n",
"\n",
" out = model(x).reshape(batch_size, dim, dim,strain_channels)\n",
" out = y_normalizer.decode(out)\n",
" y = y_normalizer.decode(y)\n",
"\n",
" loss = myloss(out.view(batch_size,-1), y.view(batch_size,-1))\n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
" train_l2 += loss.item()\n",
"\n",
" scheduler.step()\n",
" model.eval()\n",
" test_l2 = 0.0\n",
" with torch.no_grad():\n",
" for x, y in test_loader:\n",
" x, y = x.cuda(), y.cuda()\n",
" out = model(x).reshape(batch_size, dim, dim,strain_channels)\n",
" out = y_normalizer.decode(out)\n",
"\n",
" test_l2 += myloss(out.view(batch_size,-1), y.view(batch_size,-1)).item()\n",
"\n",
" train_l2/= ntrain\n",
" test_l2 /= ntest\n",
" trainerror += [train_l2]\n",
" testerror += [test_l2]\n",
" t2 = default_timer()\n",
" print(f' Epoch :== {ep+1} || TIME(sec):== {np.round((t2-t1),4)} '\n",
" f'|| TrainError:== {np.round(train_l2,6)} || TestError:== {np.round(test_l2,6)}')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(7,6))\n",
"plt.plot(range(epochs),trainerror, label =\"TRAIN\")\n",
"plt.plot(range(epochs),testerror, label =\"TEST\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" TEST SET SHAPE :: torch.Size([200, 48, 48, 3])\n"
]
}
],
"source": [
"## testing\n",
"\n",
"prediction = torch.zeros(y_test.shape).cuda()\n",
"\n",
"counter = 0\n",
"print(f' TEST SET SHAPE :: {y_test.shape}')\n",
"\n",
"with torch.no_grad():\n",
" for x , y in test_loader:\n",
" x,y = x.cuda(), y.cuda()\n",
" out = model(x).reshape(batch_size,48,48,strain_channels) #out.shape[batchsize, 48,48, channels]\n",
" out = y_normalizer.decode(out)\n",
" prediction[counter*batch_size: (counter*batch_size) + batch_size] = out\n",
" counter+=1\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"zpred = toNumpy(prediction)[0]\n",
"ytest = toNumpy(y_test)[0]\n",
"xcor,ycor = getgrid(x_train) ## numpy arrays\n",
"xtest = toNumpy(test_geo)[0]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 547.2x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x1944 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"index=66\n",
"\n",
"geometry(xtest,index,pos='vert')\n",
"platter(ytest,zpred,index,name ='S')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x1764 with 7 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"contour(ytest,zpred,xcor,ycor,index,cmap ='Reds')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAGOCAYAAACABOR8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAGlUlEQVR4nO3aMW7iUBRA0XhE5yYbYH8sybM+b2Aa13+6aJQi4RKQx3BOFWFberI+unoo0xjjDQCu9WvvAQA4FuEAIBEOABLhACARDgCS07U3TtPk368AXswYY/r8mY0DgEQ4AEiu/qnqX2P7c+cx+M5lPu89wstatnXvEV7W0c/9Uc/ONL9/ed3GAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkp1seuszne8/xcMu27j0CB3XE8/4sfG//TzYOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AktMtDy3beu85Hu4yn/ce4UeO+M6fxdHPDvt51rNj4wAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOAJJpjHHdjdP0cePY/jxqnoe5zOe9RwCiZVv3HuElTfP7x99jjOnzdRsHAIlwAJAIBwCJcACQCAcAiXAAkAgHAIlwAJAIBwCJcACQCAcAiXAAkAgHAIlwAJAIBwCJcACQCAcAiXAAkAgHAIlwAJAIBwCJcACQCAcAiXAAkAgHAIlwAJAIBwCJcACQCAcAiXAAkAgHAIlwAJAIBwCJcACQCAcAiXAAkAgHAIlwAJAIBwCJcACQCAcAiXAAkAgHAIlwAJAIBwCJcACQCAcAyemWhy7z+d5zPNyyrXuP8CNHfOfPwtnhVs/67m0cACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJKdbHlq29d5zPNxlPu89wss64nl5Jt4/92bjACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AktPeAwCPdZnPe49ws2Vb9x7hR446/+/5/cvrNg4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgAS4QAgEQ4AEuEAIBEOABLhACARDgCS0y0PXebzvefgG8u27j3CzY5+Xo787uERbBwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTCAUAiHAAkwgFAIhwAJMIBQCIcACTTGOO6G6fpuhsBeBpjjOnzZzYOABLhACC5+qcqAHh7s3EAEAkHAIlwAJAIBwCJcACQCAcAiXAAkAgHAMlfB11R0XkvHY8AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 547.2x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x1944 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"index=33\n",
"\n",
"geometry(xtest,index,pos='vert')\n",
"platter(ytest,zpred,index,name ='S')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1152x1764 with 7 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"contour(ytest,zpred,xcor,ycor,index,cmap ='Reds')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ----------------------------------------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### STRESSES\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"##Loading data\n",
"\n",
"path1 = '/home/civil/mtech/cey217538/GitHub/FNOStressStrain/material.mat' \n",
"path2 = '/home/civil/mtech/cey217538/GitHub/FNOStressStrain/stressesv2.mat' #strain file\n",
"\n",
"material_dict = sio.loadmat(path1)\n",
"strains_dict = sio.loadmat(path2)\n",
"\n",
"material = material_dict['Emat'].astype(np.float32)\n",
"strains = strains_dict['stresses'].astype(np.float32)\n",
"\n",
"\n",
"## convert to Tensors\n",
"material = torch.from_numpy(material)\n",
"strains = torch.from_numpy(strains)\n",
"\n",
"## Train and Test samples\n",
"ntrain = 1500\n",
"ntest = 200\n",
"\n",
"\n",
"## Train and Test Split\n",
"\n",
"## X - Input - Material Geometry\n",
"x_train = material[:2*ntrain:2]\n",
"x_test = material[-ntest:] \n",
"\n",
"## Y - Output - Strains [xx, yy, xy] -- 3 dimensional\n",
"y_train = strains[:2*ntrain:2]\n",
"y_test = strains[-ntest:]\n",
"\n",
"test_geo =material[-ntest:]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"## hyp par settings\n",
"\n",
"batch_size = 20\n",
"learning_rate = 0.01\n",
"step_size = 100\n",
"epochs = 300\n",
"gamma = 0.5\n",
"modes =12\n",
"width = 32"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1500, 48, 48, 1]) torch.Size([1500, 48, 48, 3]) torch.Size([200, 48, 48, 1]) torch.Size([200, 48, 48, 3])\n"
]
}
],
"source": [
"x_normalizer = UnitGaussianNormalizer(x_train)\n",
"x_train = x_normalizer.encode(x_train)\n",
"x_test = x_normalizer.encode(x_test)\n",
"\n",
"y_normalizer = UnitGaussianNormalizer(y_train)\n",
"y_train = y_normalizer.encode(y_train)\n",
"\n",
"dim = 48 # image resolution\n",
"\n",
"x_train = x_train.reshape(ntrain,dim,dim,1)\n",
"x_test = x_test.reshape(ntest,dim,dim,1)\n",
"\n",
"print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MODEL PARAMETERS. :== 1188611\n"
]
}
],
"source": [
"strain_channels = 3\n",
"model = FNO2d(modes, modes, width, strain_channels).cuda()\n",
"print(f'MODEL PARAMETERS. :== {count_params(model)}')\n",
"\n",
"train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_train, y_train), batch_size=batch_size, shuffle=True)\n",
"test_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_test, y_test), batch_size=batch_size, shuffle=False)\n",
"optimizer = Adam(model.parameters(), lr=learning_rate, weight_decay=1e-4)\n",
"scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Epoch :== 1 || TIME(sec):== 1.4408 || TrainError:== 0.301799 || TestError:== 0.207855\n",
" Epoch :== 2 || TIME(sec):== 1.4105 || TrainError:== 0.182953 || TestError:== 0.178203\n",
" Epoch :== 3 || TIME(sec):== 1.4139 || TrainError:== 0.155127 || TestError:== 0.153384\n",
" Epoch :== 4 || TIME(sec):== 1.4171 || TrainError:== 0.137705 || TestError:== 0.139939\n",
" Epoch :== 5 || TIME(sec):== 1.421 || TrainError:== 0.129426 || TestError:== 0.139747\n",
" Epoch :== 6 || TIME(sec):== 1.4156 || TrainError:== 0.123962 || TestError:== 0.134305\n",
" Epoch :== 7 || TIME(sec):== 1.4184 || TrainError:== 0.120447 || TestError:== 0.13425\n",
" Epoch :== 8 || TIME(sec):== 1.4157 || TrainError:== 0.11764 || TestError:== 0.128528\n",
" Epoch :== 9 || TIME(sec):== 1.4168 || TrainError:== 0.114148 || TestError:== 0.131651\n",
" Epoch :== 10 || TIME(sec):== 1.415 || TrainError:== 0.110093 || TestError:== 0.122472\n",
" Epoch :== 11 || TIME(sec):== 1.4124 || TrainError:== 0.105396 || TestError:== 0.119929\n",
" Epoch :== 12 || TIME(sec):== 1.4164 || TrainError:== 0.103314 || TestError:== 0.117802\n",
" Epoch :== 13 || TIME(sec):== 1.4128 || TrainError:== 0.105569 || TestError:== 0.118638\n",
" Epoch :== 14 || TIME(sec):== 1.4044 || TrainError:== 0.101168 || TestError:== 0.128151\n",
" Epoch :== 15 || TIME(sec):== 1.4132 || TrainError:== 0.102375 || TestError:== 0.119849\n",
" Epoch :== 16 || TIME(sec):== 1.4213 || TrainError:== 0.099045 || TestError:== 0.120135\n",
" Epoch :== 17 || TIME(sec):== 1.4149 || TrainError:== 0.099024 || TestError:== 0.116212\n",
" Epoch :== 18 || TIME(sec):== 1.4072 || TrainError:== 0.097396 || TestError:== 0.113829\n",
" Epoch :== 19 || TIME(sec):== 1.4034 || TrainError:== 0.100317 || TestError:== 0.119\n",
" Epoch :== 20 || TIME(sec):== 1.415 || TrainError:== 0.093989 || TestError:== 0.117447\n",
" Epoch :== 21 || TIME(sec):== 1.3976 || TrainError:== 0.090808 || TestError:== 0.109264\n",
" Epoch :== 22 || TIME(sec):== 1.4068 || TrainError:== 0.091195 || TestError:== 0.110899\n",
" Epoch :== 23 || TIME(sec):== 1.3866 || TrainError:== 0.09221 || TestError:== 0.118056\n",
" Epoch :== 24 || TIME(sec):== 1.3883 || TrainError:== 0.091161 || TestError:== 0.108953\n",
" Epoch :== 25 || TIME(sec):== 1.3798 || TrainError:== 0.08665 || TestError:== 0.104974\n",
" Epoch :== 26 || TIME(sec):== 1.4117 || TrainError:== 0.088049 || TestError:== 0.111764\n",
" Epoch :== 27 || TIME(sec):== 1.4064 || TrainError:== 0.089176 || TestError:== 0.104391\n",
" Epoch :== 28 || TIME(sec):== 1.4099 || TrainError:== 0.086575 || TestError:== 0.108216\n",
" Epoch :== 29 || TIME(sec):== 1.4032 || TrainError:== 0.087652 || TestError:== 0.106157\n",
" Epoch :== 30 || TIME(sec):== 1.403 || TrainError:== 0.089365 || TestError:== 0.106902\n",
" Epoch :== 31 || TIME(sec):== 1.4108 || TrainError:== 0.084662 || TestError:== 0.108167\n",
" Epoch :== 32 || TIME(sec):== 1.4107 || TrainError:== 0.084274 || TestError:== 0.11101\n",
" Epoch :== 33 || TIME(sec):== 1.4168 || TrainError:== 0.08083 || TestError:== 0.103455\n",
" Epoch :== 34 || TIME(sec):== 1.4087 || TrainError:== 0.084012 || TestError:== 0.11034\n",
" Epoch :== 35 || TIME(sec):== 1.4138 || TrainError:== 0.079843 || TestError:== 0.10436\n",
" Epoch :== 36 || TIME(sec):== 1.4105 || TrainError:== 0.082072 || TestError:== 0.110148\n",
" Epoch :== 37 || TIME(sec):== 1.41 || TrainError:== 0.083571 || TestError:== 0.107864\n",
" Epoch :== 38 || TIME(sec):== 1.401 || TrainError:== 0.081916 || TestError:== 0.105253\n",
" Epoch :== 39 || TIME(sec):== 1.4081 || TrainError:== 0.078115 || TestError:== 0.104076\n",
" Epoch :== 40 || TIME(sec):== 1.4071 || TrainError:== 0.077009 || TestError:== 0.103478\n",
" Epoch :== 41 || TIME(sec):== 1.4129 || TrainError:== 0.081145 || TestError:== 0.107204\n",
" Epoch :== 42 || TIME(sec):== 1.4095 || TrainError:== 0.083563 || TestError:== 0.112688\n",
" Epoch :== 43 || TIME(sec):== 1.4157 || TrainError:== 0.08233 || TestError:== 0.102609\n",
" Epoch :== 44 || TIME(sec):== 1.4023 || TrainError:== 0.077067 || TestError:== 0.10517\n",
" Epoch :== 45 || TIME(sec):== 1.3929 || TrainError:== 0.075512 || TestError:== 0.098863\n",
" Epoch :== 46 || TIME(sec):== 1.3899 || TrainError:== 0.07551 || TestError:== 0.102489\n",
" Epoch :== 47 || TIME(sec):== 1.3879 || TrainError:== 0.074547 || TestError:== 0.102088\n",
" Epoch :== 48 || TIME(sec):== 1.3944 || TrainError:== 0.074709 || TestError:== 0.100993\n",
" Epoch :== 49 || TIME(sec):== 1.3918 || TrainError:== 0.079279 || TestError:== 0.106139\n",
" Epoch :== 50 || TIME(sec):== 1.3955 || TrainError:== 0.08034 || TestError:== 0.099375\n",
" Epoch :== 51 || TIME(sec):== 1.4092 || TrainError:== 0.075104 || TestError:== 0.097519\n",
" Epoch :== 52 || TIME(sec):== 1.402 || TrainError:== 0.074373 || TestError:== 0.10148\n",
" Epoch :== 53 || TIME(sec):== 1.4042 || TrainError:== 0.074746 || TestError:== 0.105212\n",
" Epoch :== 54 || TIME(sec):== 1.3891 || TrainError:== 0.075624 || TestError:== 0.101919\n",
" Epoch :== 55 || TIME(sec):== 1.3947 || TrainError:== 0.074036 || TestError:== 0.100678\n",
" Epoch :== 56 || TIME(sec):== 1.3832 || TrainError:== 0.074923 || TestError:== 0.098687\n",
" Epoch :== 57 || TIME(sec):== 1.4008 || TrainError:== 0.073997 || TestError:== 0.096104\n",
" Epoch :== 58 || TIME(sec):== 1.4004 || TrainError:== 0.074856 || TestError:== 0.098927\n",
" Epoch :== 59 || TIME(sec):== 1.3942 || TrainError:== 0.072372 || TestError:== 0.098303\n",
" Epoch :== 60 || TIME(sec):== 1.3974 || TrainError:== 0.07093 || TestError:== 0.099705\n",
" Epoch :== 61 || TIME(sec):== 1.3993 || TrainError:== 0.072551 || TestError:== 0.10065\n",
" Epoch :== 62 || TIME(sec):== 1.4172 || TrainError:== 0.077148 || TestError:== 0.100596\n",
" Epoch :== 63 || TIME(sec):== 1.4093 || TrainError:== 0.070711 || TestError:== 0.094939\n",
" Epoch :== 64 || TIME(sec):== 1.4119 || TrainError:== 0.069147 || TestError:== 0.0998\n",
" Epoch :== 65 || TIME(sec):== 1.477 || TrainError:== 0.072545 || TestError:== 0.09995\n",
" Epoch :== 66 || TIME(sec):== 1.4149 || TrainError:== 0.077108 || TestError:== 0.098192\n",
" Epoch :== 67 || TIME(sec):== 1.4123 || TrainError:== 0.070401 || TestError:== 0.10017\n",
" Epoch :== 68 || TIME(sec):== 1.3962 || TrainError:== 0.072497 || TestError:== 0.100064\n",
" Epoch :== 69 || TIME(sec):== 1.415 || TrainError:== 0.068436 || TestError:== 0.100258\n",
" Epoch :== 70 || TIME(sec):== 1.4043 || TrainError:== 0.071111 || TestError:== 0.097618\n",
" Epoch :== 71 || TIME(sec):== 1.413 || TrainError:== 0.076557 || TestError:== 0.101091\n",
" Epoch :== 72 || TIME(sec):== 1.4107 || TrainError:== 0.07194 || TestError:== 0.097651\n",
" Epoch :== 73 || TIME(sec):== 1.4178 || TrainError:== 0.069211 || TestError:== 0.097804\n",
" Epoch :== 74 || TIME(sec):== 1.4113 || TrainError:== 0.069181 || TestError:== 0.093473\n",
" Epoch :== 75 || TIME(sec):== 1.4172 || TrainError:== 0.068768 || TestError:== 0.10262\n",
" Epoch :== 76 || TIME(sec):== 1.4163 || TrainError:== 0.070783 || TestError:== 0.097306\n",
" Epoch :== 77 || TIME(sec):== 1.4165 || TrainError:== 0.068427 || TestError:== 0.093717\n",
" Epoch :== 78 || TIME(sec):== 1.4127 || TrainError:== 0.068635 || TestError:== 0.099964\n",
" Epoch :== 79 || TIME(sec):== 1.4149 || TrainError:== 0.072256 || TestError:== 0.09606\n",
" Epoch :== 80 || TIME(sec):== 1.4072 || TrainError:== 0.073751 || TestError:== 0.097379\n",
" Epoch :== 81 || TIME(sec):== 1.3978 || TrainError:== 0.070121 || TestError:== 0.096288\n",
" Epoch :== 82 || TIME(sec):== 1.4193 || TrainError:== 0.06806 || TestError:== 0.096107\n",
" Epoch :== 83 || TIME(sec):== 1.4097 || TrainError:== 0.073032 || TestError:== 0.101698\n",
" Epoch :== 84 || TIME(sec):== 1.4004 || TrainError:== 0.071428 || TestError:== 0.09642\n",
" Epoch :== 85 || TIME(sec):== 1.3928 || TrainError:== 0.067555 || TestError:== 0.106998\n",
" Epoch :== 86 || TIME(sec):== 1.3875 || TrainError:== 0.06604 || TestError:== 0.092898\n",
" Epoch :== 87 || TIME(sec):== 1.4096 || TrainError:== 0.06629 || TestError:== 0.093124\n",
" Epoch :== 88 || TIME(sec):== 1.4188 || TrainError:== 0.067413 || TestError:== 0.096954\n",
" Epoch :== 89 || TIME(sec):== 1.4237 || TrainError:== 0.072502 || TestError:== 0.093616\n",
" Epoch :== 90 || TIME(sec):== 1.4081 || TrainError:== 0.069667 || TestError:== 0.095488\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Epoch :== 91 || TIME(sec):== 1.4056 || TrainError:== 0.06932 || TestError:== 0.101306\n",
" Epoch :== 92 || TIME(sec):== 1.4237 || TrainError:== 0.070387 || TestError:== 0.094594\n",
" Epoch :== 93 || TIME(sec):== 1.4088 || TrainError:== 0.064997 || TestError:== 0.094393\n",
" Epoch :== 94 || TIME(sec):== 1.4141 || TrainError:== 0.066095 || TestError:== 0.09091\n",
" Epoch :== 95 || TIME(sec):== 1.4111 || TrainError:== 0.068183 || TestError:== 0.097881\n",
" Epoch :== 96 || TIME(sec):== 1.4126 || TrainError:== 0.066673 || TestError:== 0.095698\n",
" Epoch :== 97 || TIME(sec):== 1.4194 || TrainError:== 0.071291 || TestError:== 0.094463\n",
" Epoch :== 98 || TIME(sec):== 1.4171 || TrainError:== 0.067743 || TestError:== 0.092644\n",
" Epoch :== 99 || TIME(sec):== 1.4074 || TrainError:== 0.066057 || TestError:== 0.092499\n",
" Epoch :== 100 || TIME(sec):== 1.4182 || TrainError:== 0.063051 || TestError:== 0.089992\n",
" Epoch :== 101 || TIME(sec):== 1.4092 || TrainError:== 0.053904 || TestError:== 0.081397\n",
" Epoch :== 102 || TIME(sec):== 1.4139 || TrainError:== 0.049071 || TestError:== 0.081338\n",
" Epoch :== 103 || TIME(sec):== 1.4103 || TrainError:== 0.048309 || TestError:== 0.08009\n",
" Epoch :== 104 || TIME(sec):== 1.4197 || TrainError:== 0.048201 || TestError:== 0.081005\n",
" Epoch :== 105 || TIME(sec):== 1.4095 || TrainError:== 0.048638 || TestError:== 0.08112\n",
" Epoch :== 106 || TIME(sec):== 1.4122 || TrainError:== 0.04802 || TestError:== 0.081049\n",
" Epoch :== 107 || TIME(sec):== 1.4108 || TrainError:== 0.048237 || TestError:== 0.080648\n",
" Epoch :== 108 || TIME(sec):== 1.4097 || TrainError:== 0.048955 || TestError:== 0.083048\n",
" Epoch :== 109 || TIME(sec):== 1.4065 || TrainError:== 0.049867 || TestError:== 0.084206\n",
" Epoch :== 110 || TIME(sec):== 1.4065 || TrainError:== 0.052109 || TestError:== 0.080715\n",
" Epoch :== 111 || TIME(sec):== 1.4015 || TrainError:== 0.05182 || TestError:== 0.082371\n",
" Epoch :== 112 || TIME(sec):== 1.3984 || TrainError:== 0.049586 || TestError:== 0.084258\n",
" Epoch :== 113 || TIME(sec):== 1.3874 || TrainError:== 0.050648 || TestError:== 0.082633\n",
" Epoch :== 114 || TIME(sec):== 1.3974 || TrainError:== 0.050443 || TestError:== 0.084259\n",
" Epoch :== 115 || TIME(sec):== 1.4154 || TrainError:== 0.050939 || TestError:== 0.083294\n",
" Epoch :== 116 || TIME(sec):== 1.4093 || TrainError:== 0.048322 || TestError:== 0.082218\n",
" Epoch :== 117 || TIME(sec):== 1.4084 || TrainError:== 0.049841 || TestError:== 0.083961\n",
" Epoch :== 118 || TIME(sec):== 1.4063 || TrainError:== 0.051515 || TestError:== 0.087953\n",
" Epoch :== 119 || TIME(sec):== 1.412 || TrainError:== 0.050579 || TestError:== 0.082231\n",
" Epoch :== 120 || TIME(sec):== 1.417 || TrainError:== 0.047839 || TestError:== 0.080742\n",
" Epoch :== 121 || TIME(sec):== 1.4061 || TrainError:== 0.048792 || TestError:== 0.082916\n",
" Epoch :== 122 || TIME(sec):== 1.4103 || TrainError:== 0.05032 || TestError:== 0.086786\n",
" Epoch :== 123 || TIME(sec):== 1.4077 || TrainError:== 0.053885 || TestError:== 0.087075\n",
" Epoch :== 124 || TIME(sec):== 1.413 || TrainError:== 0.052513 || TestError:== 0.08548\n",
" Epoch :== 125 || TIME(sec):== 1.4138 || TrainError:== 0.050755 || TestError:== 0.083978\n",
" Epoch :== 126 || TIME(sec):== 1.4184 || TrainError:== 0.049282 || TestError:== 0.084558\n",
" Epoch :== 127 || TIME(sec):== 1.4094 || TrainError:== 0.048612 || TestError:== 0.082571\n",
" Epoch :== 128 || TIME(sec):== 1.4119 || TrainError:== 0.047774 || TestError:== 0.080034\n",
" Epoch :== 129 || TIME(sec):== 1.4116 || TrainError:== 0.04769 || TestError:== 0.078792\n",
" Epoch :== 130 || TIME(sec):== 1.4146 || TrainError:== 0.048482 || TestError:== 0.082396\n",
" Epoch :== 131 || TIME(sec):== 1.4162 || TrainError:== 0.054123 || TestError:== 0.086553\n",
" Epoch :== 132 || TIME(sec):== 1.4111 || TrainError:== 0.05406 || TestError:== 0.082117\n",
" Epoch :== 133 || TIME(sec):== 1.4138 || TrainError:== 0.049634 || TestError:== 0.079636\n",
" Epoch :== 134 || TIME(sec):== 1.4044 || TrainError:== 0.049027 || TestError:== 0.083184\n",
" Epoch :== 135 || TIME(sec):== 1.3989 || TrainError:== 0.051171 || TestError:== 0.086325\n",
" Epoch :== 136 || TIME(sec):== 1.4106 || TrainError:== 0.050962 || TestError:== 0.085651\n",
" Epoch :== 137 || TIME(sec):== 1.409 || TrainError:== 0.049179 || TestError:== 0.081594\n",
" Epoch :== 138 || TIME(sec):== 1.4068 || TrainError:== 0.050031 || TestError:== 0.083586\n",
" Epoch :== 139 || TIME(sec):== 1.3955 || TrainError:== 0.048565 || TestError:== 0.083031\n",
" Epoch :== 140 || TIME(sec):== 1.3974 || TrainError:== 0.047629 || TestError:== 0.080502\n",
" Epoch :== 141 || TIME(sec):== 1.3839 || TrainError:== 0.046833 || TestError:== 0.079739\n",
" Epoch :== 142 || TIME(sec):== 1.4014 || TrainError:== 0.0465 || TestError:== 0.080563\n",
" Epoch :== 143 || TIME(sec):== 1.4171 || TrainError:== 0.046751 || TestError:== 0.079743\n",
" Epoch :== 144 || TIME(sec):== 1.4053 || TrainError:== 0.048165 || TestError:== 0.082853\n",
" Epoch :== 145 || TIME(sec):== 1.408 || TrainError:== 0.048572 || TestError:== 0.081676\n",
" Epoch :== 146 || TIME(sec):== 1.4019 || TrainError:== 0.050267 || TestError:== 0.084752\n",
" Epoch :== 147 || TIME(sec):== 1.4063 || TrainError:== 0.051184 || TestError:== 0.084303\n",
" Epoch :== 148 || TIME(sec):== 1.42 || TrainError:== 0.050747 || TestError:== 0.082506\n",
" Epoch :== 149 || TIME(sec):== 1.412 || TrainError:== 0.0492 || TestError:== 0.081008\n",
" Epoch :== 150 || TIME(sec):== 1.4845 || TrainError:== 0.047931 || TestError:== 0.083347\n",
" Epoch :== 151 || TIME(sec):== 1.4054 || TrainError:== 0.048463 || TestError:== 0.081062\n",
" Epoch :== 152 || TIME(sec):== 1.4123 || TrainError:== 0.047965 || TestError:== 0.081717\n",
" Epoch :== 153 || TIME(sec):== 1.4036 || TrainError:== 0.047012 || TestError:== 0.080904\n",
" Epoch :== 154 || TIME(sec):== 1.4204 || TrainError:== 0.046403 || TestError:== 0.079126\n",
" Epoch :== 155 || TIME(sec):== 1.4086 || TrainError:== 0.046683 || TestError:== 0.081409\n",
" Epoch :== 156 || TIME(sec):== 1.4148 || TrainError:== 0.046187 || TestError:== 0.07911\n",
" Epoch :== 157 || TIME(sec):== 1.4089 || TrainError:== 0.045232 || TestError:== 0.078422\n",
" Epoch :== 158 || TIME(sec):== 1.4135 || TrainError:== 0.046004 || TestError:== 0.0798\n",
" Epoch :== 159 || TIME(sec):== 1.4156 || TrainError:== 0.045565 || TestError:== 0.081508\n",
" Epoch :== 160 || TIME(sec):== 1.4141 || TrainError:== 0.048414 || TestError:== 0.08022\n",
" Epoch :== 161 || TIME(sec):== 1.4048 || TrainError:== 0.050268 || TestError:== 0.075831\n",
" Epoch :== 162 || TIME(sec):== 1.4159 || TrainError:== 0.050102 || TestError:== 0.079166\n",
" Epoch :== 163 || TIME(sec):== 1.4126 || TrainError:== 0.049272 || TestError:== 0.083504\n",
" Epoch :== 164 || TIME(sec):== 1.4151 || TrainError:== 0.048008 || TestError:== 0.081658\n",
" Epoch :== 165 || TIME(sec):== 1.4086 || TrainError:== 0.045775 || TestError:== 0.07941\n",
" Epoch :== 166 || TIME(sec):== 1.4049 || TrainError:== 0.046485 || TestError:== 0.078383\n",
" Epoch :== 167 || TIME(sec):== 1.3908 || TrainError:== 0.047369 || TestError:== 0.07908\n",
" Epoch :== 168 || TIME(sec):== 1.3956 || TrainError:== 0.04781 || TestError:== 0.078335\n",
" Epoch :== 169 || TIME(sec):== 1.3922 || TrainError:== 0.047078 || TestError:== 0.081691\n",
" Epoch :== 170 || TIME(sec):== 1.4089 || TrainError:== 0.046931 || TestError:== 0.079164\n",
" Epoch :== 171 || TIME(sec):== 1.4109 || TrainError:== 0.044936 || TestError:== 0.078598\n",
" Epoch :== 172 || TIME(sec):== 1.4133 || TrainError:== 0.044921 || TestError:== 0.076441\n",
" Epoch :== 173 || TIME(sec):== 1.4028 || TrainError:== 0.044752 || TestError:== 0.078079\n",
" Epoch :== 174 || TIME(sec):== 1.4067 || TrainError:== 0.04577 || TestError:== 0.080698\n",
" Epoch :== 175 || TIME(sec):== 1.4219 || TrainError:== 0.047901 || TestError:== 0.078215\n",
" Epoch :== 176 || TIME(sec):== 1.4133 || TrainError:== 0.049664 || TestError:== 0.079705\n",
" Epoch :== 177 || TIME(sec):== 1.4152 || TrainError:== 0.049206 || TestError:== 0.079593\n",
" Epoch :== 178 || TIME(sec):== 1.4097 || TrainError:== 0.049507 || TestError:== 0.08496\n",
" Epoch :== 179 || TIME(sec):== 1.4123 || TrainError:== 0.051749 || TestError:== 0.086338\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Epoch :== 180 || TIME(sec):== 1.4137 || TrainError:== 0.049393 || TestError:== 0.082458\n",
" Epoch :== 181 || TIME(sec):== 1.4201 || TrainError:== 0.047156 || TestError:== 0.080214\n",
" Epoch :== 182 || TIME(sec):== 1.4178 || TrainError:== 0.045176 || TestError:== 0.08003\n",
" Epoch :== 183 || TIME(sec):== 1.419 || TrainError:== 0.045101 || TestError:== 0.081012\n",
" Epoch :== 184 || TIME(sec):== 1.4111 || TrainError:== 0.045646 || TestError:== 0.081177\n",
" Epoch :== 185 || TIME(sec):== 1.4069 || TrainError:== 0.045388 || TestError:== 0.079022\n",
" Epoch :== 186 || TIME(sec):== 1.4121 || TrainError:== 0.045442 || TestError:== 0.079148\n",
" Epoch :== 187 || TIME(sec):== 1.4102 || TrainError:== 0.045522 || TestError:== 0.080424\n",
" Epoch :== 188 || TIME(sec):== 1.4172 || TrainError:== 0.045501 || TestError:== 0.07898\n",
" Epoch :== 189 || TIME(sec):== 1.4214 || TrainError:== 0.049387 || TestError:== 0.088275\n",
" Epoch :== 190 || TIME(sec):== 1.3938 || TrainError:== 0.055002 || TestError:== 0.088112\n",
" Epoch :== 191 || TIME(sec):== 1.4114 || TrainError:== 0.052484 || TestError:== 0.083641\n",
" Epoch :== 192 || TIME(sec):== 1.4016 || TrainError:== 0.049345 || TestError:== 0.086283\n",
" Epoch :== 193 || TIME(sec):== 1.3985 || TrainError:== 0.047616 || TestError:== 0.082157\n",
" Epoch :== 194 || TIME(sec):== 1.3976 || TrainError:== 0.045786 || TestError:== 0.080652\n",
" Epoch :== 195 || TIME(sec):== 1.3942 || TrainError:== 0.045525 || TestError:== 0.081559\n",
" Epoch :== 196 || TIME(sec):== 1.4088 || TrainError:== 0.044675 || TestError:== 0.083235\n",
" Epoch :== 197 || TIME(sec):== 1.416 || TrainError:== 0.044461 || TestError:== 0.079272\n",
" Epoch :== 198 || TIME(sec):== 1.4092 || TrainError:== 0.044027 || TestError:== 0.080936\n",
" Epoch :== 199 || TIME(sec):== 1.3943 || TrainError:== 0.044585 || TestError:== 0.08017\n",
" Epoch :== 200 || TIME(sec):== 1.3928 || TrainError:== 0.045065 || TestError:== 0.078736\n",
" Epoch :== 201 || TIME(sec):== 1.39 || TrainError:== 0.039442 || TestError:== 0.074572\n",
" Epoch :== 202 || TIME(sec):== 1.4214 || TrainError:== 0.0361 || TestError:== 0.073881\n",
" Epoch :== 203 || TIME(sec):== 1.4183 || TrainError:== 0.035132 || TestError:== 0.073063\n",
" Epoch :== 204 || TIME(sec):== 1.4241 || TrainError:== 0.035073 || TestError:== 0.074134\n",
" Epoch :== 205 || TIME(sec):== 1.3964 || TrainError:== 0.035079 || TestError:== 0.074254\n",
" Epoch :== 206 || TIME(sec):== 1.4089 || TrainError:== 0.034885 || TestError:== 0.074041\n",
" Epoch :== 207 || TIME(sec):== 1.4148 || TrainError:== 0.035254 || TestError:== 0.075787\n",
" Epoch :== 208 || TIME(sec):== 1.4208 || TrainError:== 0.035551 || TestError:== 0.073707\n",
" Epoch :== 209 || TIME(sec):== 1.4047 || TrainError:== 0.035116 || TestError:== 0.073579\n",
" Epoch :== 210 || TIME(sec):== 1.4183 || TrainError:== 0.035535 || TestError:== 0.073159\n",
" Epoch :== 211 || TIME(sec):== 1.4181 || TrainError:== 0.035589 || TestError:== 0.073616\n",
" Epoch :== 212 || TIME(sec):== 1.4204 || TrainError:== 0.036037 || TestError:== 0.073803\n",
" Epoch :== 213 || TIME(sec):== 1.4136 || TrainError:== 0.036114 || TestError:== 0.074633\n",
" Epoch :== 214 || TIME(sec):== 1.422 || TrainError:== 0.036643 || TestError:== 0.078409\n",
" Epoch :== 215 || TIME(sec):== 1.415 || TrainError:== 0.040355 || TestError:== 0.074343\n",
" Epoch :== 216 || TIME(sec):== 1.4175 || TrainError:== 0.038364 || TestError:== 0.072554\n",
" Epoch :== 217 || TIME(sec):== 1.4132 || TrainError:== 0.035921 || TestError:== 0.072794\n",
" Epoch :== 218 || TIME(sec):== 1.4147 || TrainError:== 0.036251 || TestError:== 0.072611\n",
" Epoch :== 219 || TIME(sec):== 1.4127 || TrainError:== 0.036213 || TestError:== 0.073532\n",
" Epoch :== 220 || TIME(sec):== 1.4171 || TrainError:== 0.035968 || TestError:== 0.073562\n",
" Epoch :== 221 || TIME(sec):== 1.4152 || TrainError:== 0.037383 || TestError:== 0.072777\n",
" Epoch :== 222 || TIME(sec):== 1.4169 || TrainError:== 0.035967 || TestError:== 0.072747\n",
" Epoch :== 223 || TIME(sec):== 1.4131 || TrainError:== 0.035297 || TestError:== 0.072548\n",
" Epoch :== 224 || TIME(sec):== 1.4044 || TrainError:== 0.034853 || TestError:== 0.072595\n",
" Epoch :== 225 || TIME(sec):== 1.3954 || TrainError:== 0.035603 || TestError:== 0.073054\n",
" Epoch :== 226 || TIME(sec):== 1.3924 || TrainError:== 0.036189 || TestError:== 0.074807\n",
" Epoch :== 227 || TIME(sec):== 1.3939 || TrainError:== 0.035798 || TestError:== 0.072938\n",
" Epoch :== 228 || TIME(sec):== 1.4172 || TrainError:== 0.035296 || TestError:== 0.072251\n",
" Epoch :== 229 || TIME(sec):== 1.4102 || TrainError:== 0.035844 || TestError:== 0.072599\n",
" Epoch :== 230 || TIME(sec):== 1.4067 || TrainError:== 0.035936 || TestError:== 0.073365\n",
" Epoch :== 231 || TIME(sec):== 1.4051 || TrainError:== 0.036156 || TestError:== 0.072292\n",
" Epoch :== 232 || TIME(sec):== 1.411 || TrainError:== 0.034627 || TestError:== 0.070911\n",
" Epoch :== 233 || TIME(sec):== 1.3922 || TrainError:== 0.035373 || TestError:== 0.072843\n",
" Epoch :== 234 || TIME(sec):== 1.4147 || TrainError:== 0.038059 || TestError:== 0.073045\n",
" Epoch :== 235 || TIME(sec):== 1.4834 || TrainError:== 0.038902 || TestError:== 0.074834\n",
" Epoch :== 236 || TIME(sec):== 1.4183 || TrainError:== 0.037906 || TestError:== 0.073976\n",
" Epoch :== 237 || TIME(sec):== 1.4096 || TrainError:== 0.036879 || TestError:== 0.075093\n",
" Epoch :== 238 || TIME(sec):== 1.4073 || TrainError:== 0.035652 || TestError:== 0.073769\n",
" Epoch :== 239 || TIME(sec):== 1.4177 || TrainError:== 0.035604 || TestError:== 0.072591\n",
" Epoch :== 240 || TIME(sec):== 1.4074 || TrainError:== 0.03456 || TestError:== 0.072409\n",
" Epoch :== 241 || TIME(sec):== 1.4153 || TrainError:== 0.034654 || TestError:== 0.072495\n",
" Epoch :== 242 || TIME(sec):== 1.4174 || TrainError:== 0.034927 || TestError:== 0.072077\n",
" Epoch :== 243 || TIME(sec):== 1.4214 || TrainError:== 0.034447 || TestError:== 0.071386\n",
" Epoch :== 244 || TIME(sec):== 1.4153 || TrainError:== 0.034311 || TestError:== 0.071886\n",
" Epoch :== 245 || TIME(sec):== 1.4131 || TrainError:== 0.034695 || TestError:== 0.073495\n",
" Epoch :== 246 || TIME(sec):== 1.4145 || TrainError:== 0.036678 || TestError:== 0.074588\n",
" Epoch :== 247 || TIME(sec):== 1.4196 || TrainError:== 0.036233 || TestError:== 0.072436\n",
" Epoch :== 248 || TIME(sec):== 1.4099 || TrainError:== 0.03521 || TestError:== 0.073178\n",
" Epoch :== 249 || TIME(sec):== 1.4139 || TrainError:== 0.034841 || TestError:== 0.073244\n",
" Epoch :== 250 || TIME(sec):== 1.4159 || TrainError:== 0.034414 || TestError:== 0.069394\n",
" Epoch :== 251 || TIME(sec):== 1.4179 || TrainError:== 0.034788 || TestError:== 0.071925\n",
" Epoch :== 252 || TIME(sec):== 1.4185 || TrainError:== 0.035456 || TestError:== 0.074193\n",
" Epoch :== 253 || TIME(sec):== 1.4181 || TrainError:== 0.03546 || TestError:== 0.072505\n",
" Epoch :== 254 || TIME(sec):== 1.4039 || TrainError:== 0.035116 || TestError:== 0.070996\n",
" Epoch :== 255 || TIME(sec):== 1.3943 || TrainError:== 0.034505 || TestError:== 0.072524\n",
" Epoch :== 256 || TIME(sec):== 1.3879 || TrainError:== 0.035891 || TestError:== 0.072155\n",
" Epoch :== 257 || TIME(sec):== 1.4051 || TrainError:== 0.035745 || TestError:== 0.072389\n",
" Epoch :== 258 || TIME(sec):== 1.4193 || TrainError:== 0.038232 || TestError:== 0.073548\n",
" Epoch :== 259 || TIME(sec):== 1.416 || TrainError:== 0.037601 || TestError:== 0.075992\n",
" Epoch :== 260 || TIME(sec):== 1.4146 || TrainError:== 0.036674 || TestError:== 0.072487\n",
" Epoch :== 261 || TIME(sec):== 1.4108 || TrainError:== 0.035113 || TestError:== 0.072433\n",
" Epoch :== 262 || TIME(sec):== 1.4192 || TrainError:== 0.034121 || TestError:== 0.070707\n",
" Epoch :== 263 || TIME(sec):== 1.4163 || TrainError:== 0.034792 || TestError:== 0.070032\n",
" Epoch :== 264 || TIME(sec):== 1.4209 || TrainError:== 0.034723 || TestError:== 0.069873\n",
" Epoch :== 265 || TIME(sec):== 1.4165 || TrainError:== 0.034045 || TestError:== 0.071448\n",
" Epoch :== 266 || TIME(sec):== 1.4193 || TrainError:== 0.035377 || TestError:== 0.071684\n",
" Epoch :== 267 || TIME(sec):== 1.4227 || TrainError:== 0.035441 || TestError:== 0.070546\n",
" Epoch :== 268 || TIME(sec):== 1.4225 || TrainError:== 0.035138 || TestError:== 0.071172\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Epoch :== 269 || TIME(sec):== 1.4177 || TrainError:== 0.034685 || TestError:== 0.070787\n",
" Epoch :== 270 || TIME(sec):== 1.4123 || TrainError:== 0.034286 || TestError:== 0.069375\n",
" Epoch :== 271 || TIME(sec):== 1.4041 || TrainError:== 0.034769 || TestError:== 0.07058\n",
" Epoch :== 272 || TIME(sec):== 1.4203 || TrainError:== 0.034464 || TestError:== 0.079099\n",
" Epoch :== 273 || TIME(sec):== 1.4189 || TrainError:== 0.037804 || TestError:== 0.073949\n",
" Epoch :== 274 || TIME(sec):== 1.4254 || TrainError:== 0.036271 || TestError:== 0.070013\n",
" Epoch :== 275 || TIME(sec):== 1.4185 || TrainError:== 0.035527 || TestError:== 0.071161\n",
" Epoch :== 276 || TIME(sec):== 1.4155 || TrainError:== 0.034424 || TestError:== 0.070259\n",
" Epoch :== 277 || TIME(sec):== 1.4185 || TrainError:== 0.034618 || TestError:== 0.071863\n",
" Epoch :== 278 || TIME(sec):== 1.4158 || TrainError:== 0.034652 || TestError:== 0.070377\n",
" Epoch :== 279 || TIME(sec):== 1.4034 || TrainError:== 0.034705 || TestError:== 0.071567\n",
" Epoch :== 280 || TIME(sec):== 1.4017 || TrainError:== 0.03393 || TestError:== 0.069391\n",
" Epoch :== 281 || TIME(sec):== 1.3898 || TrainError:== 0.034434 || TestError:== 0.072984\n",
" Epoch :== 282 || TIME(sec):== 1.4223 || TrainError:== 0.034482 || TestError:== 0.073224\n",
" Epoch :== 283 || TIME(sec):== 1.4206 || TrainError:== 0.035402 || TestError:== 0.071817\n",
" Epoch :== 284 || TIME(sec):== 1.4202 || TrainError:== 0.034513 || TestError:== 0.07042\n",
" Epoch :== 285 || TIME(sec):== 1.4055 || TrainError:== 0.034618 || TestError:== 0.069596\n",
" Epoch :== 286 || TIME(sec):== 1.4127 || TrainError:== 0.034997 || TestError:== 0.070153\n",
" Epoch :== 287 || TIME(sec):== 1.4174 || TrainError:== 0.034464 || TestError:== 0.069499\n",
" Epoch :== 288 || TIME(sec):== 1.4168 || TrainError:== 0.034243 || TestError:== 0.07147\n",
" Epoch :== 289 || TIME(sec):== 1.418 || TrainError:== 0.035121 || TestError:== 0.070942\n",
" Epoch :== 290 || TIME(sec):== 1.4182 || TrainError:== 0.034085 || TestError:== 0.070512\n",
" Epoch :== 291 || TIME(sec):== 1.4136 || TrainError:== 0.034118 || TestError:== 0.070721\n",
" Epoch :== 292 || TIME(sec):== 1.4178 || TrainError:== 0.034709 || TestError:== 0.072361\n",
" Epoch :== 293 || TIME(sec):== 1.4131 || TrainError:== 0.034311 || TestError:== 0.071663\n",
" Epoch :== 294 || TIME(sec):== 1.4159 || TrainError:== 0.034882 || TestError:== 0.073204\n",
" Epoch :== 295 || TIME(sec):== 1.4174 || TrainError:== 0.036036 || TestError:== 0.073541\n",
" Epoch :== 296 || TIME(sec):== 1.4269 || TrainError:== 0.035339 || TestError:== 0.069366\n",
" Epoch :== 297 || TIME(sec):== 1.4154 || TrainError:== 0.034348 || TestError:== 0.069936\n",
" Epoch :== 298 || TIME(sec):== 1.4203 || TrainError:== 0.034282 || TestError:== 0.067873\n",
" Epoch :== 299 || TIME(sec):== 1.413 || TrainError:== 0.033834 || TestError:== 0.068295\n",
" Epoch :== 300 || TIME(sec):== 1.4186 || TrainError:== 0.033486 || TestError:== 0.0678\n"
]
}
],
"source": [
"\n",
"myloss = L2Loss(size_average=False)\n",
"y_normalizer.cuda()\n",
"\n",
"## ep wise error\n",
"trainerror=[] \n",
"testerror=[]\n",
"\n",
"##Train Test Loop\n",
"for ep in range(epochs):\n",
" model.train()\n",
" t1 = default_timer()\n",
" train_l2 = 0\n",
" for x, y in train_loader:\n",
" x, y = x.cuda(), y.cuda()\n",
" \n",
" optimizer.zero_grad()\n",
"\n",
" out = model(x).reshape(batch_size, dim, dim,strain_channels)\n",
" out = y_normalizer.decode(out)\n",
" y = y_normalizer.decode(y)\n",
"\n",
" loss = myloss(out.view(batch_size,-1), y.view(batch_size,-1))\n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
" train_l2 += loss.item()\n",
"\n",
" scheduler.step()\n",
" model.eval()\n",
" test_l2 = 0.0\n",
" with torch.no_grad():\n",
" for x, y in test_loader:\n",
" x, y = x.cuda(), y.cuda()\n",
" out = model(x).reshape(batch_size, dim, dim,strain_channels)\n",
" out = y_normalizer.decode(out)\n",
"\n",
" test_l2 += myloss(out.view(batch_size,-1), y.view(batch_size,-1)).item()\n",
"\n",
" train_l2/= ntrain\n",
" test_l2 /= ntest\n",
" trainerror += [train_l2]\n",
" testerror += [test_l2]\n",
" t2 = default_timer()\n",
" print(f' Epoch :== {ep+1} || TIME(sec):== {np.round((t2-t1),4)} '\n",
" f'|| TrainError:== {np.round(train_l2,6)} || TestError:== {np.round(test_l2,6)}')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(7,6))\n",
"plt.plot(range(epochs),trainerror, label =\"TRAIN\")\n",
"plt.plot(range(epochs),testerror, label =\"TEST\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" TEST SET SHAPE :: (200, 48, 48, 3)\n"
]
}
],
"source": [
"## testing\n",
"\n",
"prediction = torch.zeros(ytest.shape).cuda()\n",
"\n",
"counter = 0\n",
"print(f' TEST SET SHAPE :: {ytest.shape}')\n",
"\n",
"with torch.no_grad():\n",
" for x , y in test_loader:\n",
" x,y = x.cuda(), y.cuda()\n",
" out = model(x).reshape(batch_size,48,48,strain_channels) #out.shape[batchsize, 48,48, channels]\n",
" out = y_normalizer.decode(out)\n",
" prediction[counter*batch_size: (counter*batch_size) + batch_size] = out\n",
" counter+=1"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"zpred = toNumpy(prediction)[0]\n",
"ytest = toNumpy(y_test)[0]\n",
"xcor,ycor = getgrid(x_train) ## numpy arrays\n",
"xtest = toNumpy(test_geo)[0]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 547.2x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x1944 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"index=55\n",
"geometry(xtest,index,pos='vert')\n",
"platter(ytest,zpred,index,name ='S')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x1764 with 7 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"index =22\n",
"contour(ytest,zpred,xcor,ycor,index,cmap ='plasma_r')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
},
"vscode": {
"interpreter": {
"hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Unknown |
2D | M3RG-IITD/FNO-StressStrain | Adam.py | .py | 5,328 | 141 | import math
import torch
from torch import Tensor
from typing import List, Optional
from torch.optim.optimizer import Optimizer
def adam(params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
max_exp_avg_sqs: List[Tensor],
state_steps: List[int],
*,
amsgrad: bool,
beta1: float,
beta2: float,
lr: float,
weight_decay: float,
eps: float):
r"""Functional API that performs Adam algorithm computation.
See :class:`~torch.optim.Adam` for details.
"""
for i, param in enumerate(params):
grad = grads[i]
exp_avg = exp_avgs[i]
exp_avg_sq = exp_avg_sqs[i]
step = state_steps[i]
bias_correction1 = 1 - beta1 ** step
bias_correction2 = 1 - beta2 ** step
if weight_decay != 0:
grad = grad.add(param, alpha=weight_decay)
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad.conj(), value=1 - beta2)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])
# Use the max. for normalizing running avg. of gradient
denom = (max_exp_avg_sqs[i].sqrt() / math.sqrt(bias_correction2)).add_(eps)
else:
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)
step_size = lr / bias_correction1
param.addcdiv_(exp_avg, denom, value=-step_size)
class Adam(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0, amsgrad=False):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
if not 0.0 <= weight_decay:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
defaults = dict(lr=lr, betas=betas, eps=eps,
weight_decay=weight_decay, amsgrad=amsgrad)
super(Adam, self).__init__(params, defaults)
def __setstate__(self, state):
super(Adam, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
@torch.no_grad()
def step(self, closure=None):
"""Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
params_with_grad = []
grads = []
exp_avgs = []
exp_avg_sqs = []
max_exp_avg_sqs = []
state_steps = []
beta1, beta2 = group['betas']
for p in group['params']:
if p.grad is not None:
params_with_grad.append(p)
if p.grad.is_sparse:
raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
grads.append(p.grad)
state = self.state[p]
# Lazy state initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
if group['amsgrad']:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
exp_avgs.append(state['exp_avg'])
exp_avg_sqs.append(state['exp_avg_sq'])
if group['amsgrad']:
max_exp_avg_sqs.append(state['max_exp_avg_sq'])
# update the steps for each param group update
state['step'] += 1
# record the step after step update
state_steps.append(state['step'])
adam(params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
max_exp_avg_sqs,
state_steps,
amsgrad=group['amsgrad'],
beta1=beta1,
beta2=beta2,
lr=group['lr'],
weight_decay=group['weight_decay'],
eps=group['eps'])
return loss
| Python |
2D | M3RG-IITD/FNO-StressStrain | utility.py | .py | 11,347 | 339 | ## @meermehran || M3RG Lab || Indian Institute of Technology, Delhi
import torch
import numpy as np
import scipy.io
import h5py
import torch.nn as nn
import operator
from functools import reduce
from functools import partial
import matplotlib as mpl
import matplotlib.pyplot as plt
##### UTILITLY FUNCTIONS #####
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# normalization, pointwise gaussian
class UnitGaussianNormalizer(object):
def __init__(self, x, eps=0.00001):
super(UnitGaussianNormalizer, self).__init__()
# x could be in shape of ntrain*n or ntrain*T*n or ntrain*n*T
self.mean = torch.mean(x, 0)
self.std = torch.std(x, 0)
self.eps = eps
def encode(self, x):
x = (x - self.mean) / (self.std + self.eps)
return x
def decode(self, x, sample_idx=None):
if sample_idx is None:
std = self.std + self.eps # n
mean = self.mean
else:
if len(self.mean.shape) == len(sample_idx[0].shape):
std = self.std[sample_idx] + self.eps # batch*n
mean = self.mean[sample_idx]
if len(self.mean.shape) > len(sample_idx[0].shape):
std = self.std[:,sample_idx]+ self.eps # T*batch*n
mean = self.mean[:,sample_idx]
# x is in shape of batch*n or T*batch*n
x = (x * std) + mean
return x
def cuda(self):
self.mean = self.mean.cuda()
self.std = self.std.cuda()
def cpu(self):
self.mean = self.mean.cpu()
self.std = self.std.cpu()
#loss function
class L2Loss(object):
def __init__(self, d=2, p=2, size_average=True, reduction=True):
super(L2Loss, self).__init__()
#Dimension and Lp-norm type are postive
assert d > 0 and p > 0
self.d = d
self.p = p
self.reduction = reduction
self.size_average = size_average
def abs(self, x, y):
num_examples = x.size()[0]
#Assume uniform mesh
h = 1.0 / (x.size()[1] - 1.0)
all_norms = (h**(self.d/self.p))*torch.norm(x.view(num_examples,-1) - y.view(num_examples,-1), self.p, 1)
if self.reduction:
if self.size_average:
return torch.mean(all_norms)
else:
return torch.sum(all_norms)
return all_norms
def rel(self, x, y):
num_examples = x.size()[0]
diff_norms = torch.norm(x.reshape(num_examples,-1) - y.reshape(num_examples,-1), self.p, 1)
y_norms = torch.norm(y.reshape(num_examples,-1), self.p, 1)
if self.reduction:
if self.size_average:
return torch.mean(diff_norms/y_norms)
else:
return torch.sum(diff_norms/y_norms)
return diff_norms/y_norms
def __call__(self, x, y):
return self.rel(x, y)
# print the number of parameters
def count_params(model):
c = 0
for p in list(model.parameters()):
c += reduce(operator.mul, list(p.size()))
return c
def testing(model,trainX, trainY, testX,testY, batch_size = 20,W =48,H =48, strain_channels =3):
''' Returns the predictions for TestSet'''
n = trainX.shape[0]
assert trainX.shape[0]==trainY.shape[0]
assert testX.shape[0] == testY.shape[0]
X_encoder = UnitGaussianNormalizer(trainX)
testX = X_encoder.encode(testX)
Y_encoder = UnitGaussianNormalizer(trainY)
testY = Y_encoder.encode(testY)
testloader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(testX,testY), batch_size = batch_size, shuffle =False)
prediction = torch.zeros(testY.shape).cuda()
Y_encoder.cuda()
counter = 0
print(f' TEST SET SHAPE :: {testY.shape}')
with torch.no_grad():
for x , y in testloader:
x,y = x.cuda(), y.cuda()
# x= x.unsqueeze(-1)
out = model(x).reshape(batch_size,W,H,strain_channels) #out.shape[batchsize, 48,48, channels]
out = Y_encoder.decode(out)
prediction[counter*batch_size: (counter*batch_size) + batch_size] = out
counter+=1
return prediction
def getgrid(x_train):
''' GET THE GRID X and Y as per the dimensions of the input'''
shape=torch.tensor(x_train.shape)
batchsize, size_x, size_y = shape[0], shape[1], shape[2]
gridx = torch.tensor(np.linspace(0, 1, size_x), dtype=torch.float)
gridx = gridx.reshape(1, size_x, 1, 1).repeat([batchsize, 1, size_y, 1])
gridy = torch.tensor(np.linspace(0, 1, size_y), dtype=torch.float)
gridy = gridy.reshape(1, 1, size_y, 1).repeat([batchsize, size_x, 1, 1])
zzz=torch.cat((gridx, gridy), dim=-1)
xcor=zzz.permute(0,3,1,2)[0][0].cpu().detach().numpy()
ycor=zzz.permute(0,3,1,2)[0][1].cpu().detach().numpy()
return xcor,ycor
def toNumpy(*args):
'''Directly convert gpu tensor to cpu numpy array'''
arr = [x.cpu().detach().numpy() for x in args]
return arr[:]
def geometry(xtest,index, pos = 'vert'):
'''MATERIAL GEOMETRY'''
fig,ax =plt.subplots(1, figsize = (7.6,7))
im = plt.pcolormesh(xtest[index],cmap ='Reds')
ax.xaxis.set_tick_params(width =0)
ax.yaxis.set_tick_params(width=0)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_aspect('equal')
for spine in ax.spines:
ax.spines[spine].set_linewidth(3)
pos1 = ax.get_position()
# if pos == 'vert':
# ax2 = fig.add_axes([pos1.x0,0.03,pos1.width,.05])
# else:
# ax2 = fig.add_axes([0.92,0.11,cbarwidth,0.77])
cmap =plt.cm.Reds
# bounds = [0,100,1000]
# norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
# cb = mpl.colorbar.ColorbarBase(ax2,cmap = cmap,norm = norm, spacing = 'uniform', orientation ='horizontal')
# cb.ax.tick_params(size = 0, labelsize =15)
plt.show()
def platter(ytest, zpred, index, hline=24, vline=24,name = 'S'):
'''Plot Values along cross sectional lines-Horizontal and Vertical Line'''
from matplotlib import ticker
from matplotlib.ticker import AutoMinorLocator, MultipleLocator
## crack is along horizontal axis. [y - axis is horizontal]
if name == 'S':
variable ='Stress'
else:
variable ='Strain'
tt = ytest[index] ## FEM
zz = zpred[index] ##ML
t11 = tt[...,0] ##FEM
t22 = tt[...,1] ##FEM
t33 = tt[...,2] ##FEM
z11 = zz[...,0] ##ML
z22 = zz[...,1] ##ML
z33 = zz[...,2] ##ML
# hline = 24 ## along crack path-x-axis
lt11 = t11[hline,:]
lz11 = z11[hline,:]
lt22 = t22[hline,:]
lz22 = z22[hline,:]
lt33 = t33[hline,:]
lz33 = z33[hline,:]
horizontal = np.array([[lt11, lz11],[lt22,lz22],[lt33,lz33]])
# vline = 24
lt11 = t11[:,vline]
lz11 = z11[:,vline]
lt22 = t22[:,vline]
lz22 = z22[:,vline]
lt33 = t33[:,vline]
lz33 = z33[:,vline]
vertical = np.array([[lt11, lz11],[lt22,lz22],[lt33,lz33]])
fig, ax = plt.subplots(3,2, figsize=(20,27))
c = 0
for i in range(ax.shape[0]):
ax[i,0].plot(range(48), horizontal[i][0][:],linewidth=6, color='red', label='FEM-Hor')
ax[i,0].plot(range(48), horizontal[i][1],linewidth=4, color='black', label='ML-Hor', linestyle ='dashed')
# ax[i,0].tick_params(axis = 'both')
ax[i,0].xaxis.set_tick_params(which='major', size=10, width=3, direction='in', top='on')
ax[i,0].xaxis.set_tick_params(which='minor', size=5, width=1.5, direction='in', top='on')
ax[i,0].yaxis.set_tick_params(which='major', size=10, width=3, direction='in', right='on')
ax[i,0].yaxis.set_tick_params(which='minor', size=5, width=1.5, direction='in', right='on')
M = 6
yticks = ticker.MaxNLocator(M)
ax[i,0].yaxis.set_major_locator(yticks)
minor = AutoMinorLocator()
ax[i,0].xaxis.set_minor_locator(minor)
minor = AutoMinorLocator()
ax[i,0].yaxis.set_minor_locator(minor)
ax[i,0].yaxis.set_label_coords(-.1, .5)
ax[i,0].set_xlabel('L', labelpad=20)
ax[i,0].set_ylabel(f'{variable}', labelpad=20)
ax[i,0].set_xlim(0, 48)
ax[i,0].legend()
ax[i,1].plot(range(48), vertical[i][0][:],linewidth=6, color='red', label='FEM-Vert')
ax[i,1].plot(range(48), vertical[i][1][:],linewidth=4, color='black', label='ML-Vert', linestyle ='dashed')
ax[i,1].xaxis.set_tick_params(which='major', size=10, width=3, direction='in', top='on')
ax[i,1].xaxis.set_tick_params(which='minor', size=5, width=1.5, direction='in', top='on')
ax[i,1].yaxis.set_tick_params(which='major', size=10, width=3, direction='in', right='on')
ax[i,1].yaxis.set_tick_params(which='minor', size=5, width=1.5, direction='in', right='on')
ax[i,1].set_xlabel('L', labelpad=15)
ax[i,1].set_ylabel(f'{variable}', labelpad=15)
ax[i,1].set_xlim(0, 48)
ax[i,1].legend()
M = 6
yticks = ticker.MaxNLocator(M)
ax[i,1].yaxis.set_major_locator(yticks)
minor = AutoMinorLocator()
ax[i,1].xaxis.set_minor_locator(minor)
minor = AutoMinorLocator()
ax[i,1].yaxis.set_minor_locator(minor)
ax[i,1].yaxis.set_label_coords(-.12, .5)
plt.show()
def contour(zpred, ytest,xcor,ycor, index,cmap = 'Reds'):
'''MAP OF TENSOR COMPONENT WISE'''
R = zpred[index].shape[-1]
fig, ax = plt.subplots(R,2, figsize = (16,7*(R+0.5)))
counter = 0
index = index
# cmap = 'plasma_r'
# cmap =cmap
comb = [ zpred[index], ytest[index]]
_min, _max = np.min(comb), np.max(comb)
cbarwidth = 0.03
for i in range(1):
for j in range(R):
# _min, _max = minmax(zpred[index][...,j], ytest[index][...,j])
ax1 = ax[j,i]
pl1 = ax1.pcolormesh(xcor, ycor, ytest[index][:,:,counter], cmap = cmap, vmin=_min, vmax= _max,shading ='auto')
ax1.set_yticklabels([])
ax1.set_xticklabels([])
ax1.xaxis.set_tick_params(width =0)
ax1.yaxis.set_tick_params(width=0)
ax1.set_aspect('equal')
for spine in ax1.spines:
ax1.spines[spine].set_linewidth(3)
# ax1.spines['right'].set_linewidth(3)
axx2 = ax[j,i+1]
pcm2 = ax[j,i+1].pcolormesh(xcor, ycor,zpred[index][:,:,counter], cmap=cmap,vmin = _min, vmax = _max,shading='auto')
axx2.axis('on')
axx2.set_yticklabels([])
axx2.set_xticklabels([])
axx2.xaxis.set_tick_params(width =0)
axx2.yaxis.set_tick_params(width=0)
axx2.set_aspect('equal')
for spine in axx2.spines:
axx2.spines[spine].set_linewidth(3)
counter+=1
pos1 = ax1.get_position()
aaa = plt.axes([0.94,pos1.y0,cbarwidth,0.74])
colorbar =plt.colorbar(pl1, cax = aaa)
colorbar.ax.tick_params(labelsize=30)
# colorbar.set_label("Strain", labelpad =-90, x =0.2,y = 1.05, rotation =0,size = 25, weight = 20) ## - left +rigjt
# colorbar.ax.set_title('Strain', fontdict={'font':'25'})
colorbar.outline.set_linewidth(3)
plt.show()
def minmax(arr1, arr2):
combined = np.array([arr1,arr2])
min, max = np.amin(combined) , np.amax(combined)
return min, max
| Python |
2D | M3RG-IITD/FNO-StressStrain | abaqus/simulation.py | .py | 19,394 | 395 |
# Crack simulation using Stress Intensity Factor
# https://iopscience.iop.org/article/10.1088/2399-1984/ab36f0/pdf (ARTICLE FOR MATERIAL PROPERTY)
from abaqusConstants import *
from caeModules import *
import abaqus
from part import *
from material import *
from section import *
from assembly import *
from step import *
from interaction import *
from load import *
from mesh import *
from optimization import *
from job import *
from sketch import *
from visualization import *
from connectorBehavior import *
import random
def getPyScriptPath():
pyName = abaqus.getCurrentScriptPath()
print pyName
return os.path.dirname(pyName)
def getPyScriptName():
pyName = abaqus.getCurrentScriptPath()
pyName = pyName.replace('/','\\')
toReturn = pyName[pyName.rfind('\\')+1:pyName.rfind('.py')]
return toReturn
def equals(a,b):
tol = 5e-5*(abs(a)+abs(b))/2
if(abs(a-b)<=tol):
return 1
else:
return 0
def isEdgeExternal(instance,edge,sizeRVE):
currentCentroid=edge.pointOn
isStraight = 0
try:
currentEdge.getCurvature(0)
except:
isStraight = 1
if(isStraight==0):
return 0
if(equals(currentCentroid[0][0],sizeRVE[0]) or equals(currentCentroid[0][1],sizeRVE[1]) or equals(currentCentroid[0][0],0) or equals(currentCentroid[0][1],0)):
vert = edge.getVertices()
if(not len(vert)==2):
return 0
point1 = instance.vertices[vert[0]].pointOn[0]
point2 = instance.vertices[vert[1]].pointOn[0]
if(equals(point1[0],0) and equals(point2[0],0)):
return 1
elif(equals(point1[1],0) and equals(point2[1],0)):
return 1
elif(equals(point1[0],sizeRVE[0]) and equals(point2[0],sizeRVE[0])):
return 1
elif(equals(point1[1],sizeRVE[1]) and equals(point2[1],sizeRVE[1])):
return 1
else:
return 0
else:
return 0
def main():
# Units: N, mm, s
pyPath = getPyScriptPath()
if(pyPath==''):
raise AbaqusException, 'abaqus replay file not found'
os.chdir(pyPath)
#modelName = getPyScriptName()
modelName = 'Model-1'
mdb = Mdb(modelName+'.cae')
myModel = mdb.Model(name=modelName)
myAssembly = myModel.rootAssembly
cliCommand("""session.journalOptions.setValues(replayGeometry=COORDINATE, recoverGeometry=COORDINATE)""")
mappedMeshFlag = 1
#------- CREATE GEOMETRY
elem_size = 0.125
totalsize = 1.0
fac =1
mesh_size = 0.02*fac
plate_thickness = 1.0e-3
numCells = totalsize/elem_size
total_numCells = numCells**2
#------- CREATE GEOMETRY: PLATES
GeomName = 'Plate'
partNames={}
partNames[GeomName]=[]
for i in range(int(total_numCells)):
if i%8 == 0:
m=i/8
k = 0
point2=((k+1)*elem_size, (m+1)*elem_size)
point1=(k*elem_size, m*elem_size)
k = k+1
PlateName = GeomName +'-'+str(i+1)
partNames[GeomName].append(PlateName)
myModel.ConstrainedSketch(name='__profile__', sheetSize=5.0)
myModel.sketches['__profile__'].rectangle(point1=point1,
point2=point2)
myModel.Part(dimensionality=TWO_D_PLANAR, name=PlateName, type=
DEFORMABLE_BODY)
myModel.parts[PlateName].BaseShell(sketch=
myModel.sketches['__profile__'])
del myModel.sketches['__profile__']
#------ CREATE GEOMETRY: CRACK WIRE
extra_size = 0.001
crack_size = 0.2
part_crack = 'initial_crack'
myModel.ConstrainedSketch(name='__profile__', sheetSize=5.0)
myModel.sketches['__profile__'].Line(point1=(0.0, totalsize/2+extra_size), point2=(
crack_size, totalsize/2+extra_size))
myModel.Part(dimensionality=TWO_D_PLANAR, name=part_crack, type=
DEFORMABLE_BODY)
myModel.parts[part_crack].BaseWire(sketch=myModel.sketches['__profile__'])
del myModel.sketches['__profile__']
#------- CREATE GEOMETRY: SET INSTANCES
inst=[]
for i in range(1,int(total_numCells)+1):
PlateName = GeomName +'-'+str(i)
print PlateName
p = myModel.parts[PlateName]
instanceName = PlateName
currentSet=0
myAttributes = p.queryAttributes()
if(myAttributes['numShellFaces']==0 and myAttributes['numWireEdges']>0):
currentSet = p.Set(name='Set_'+PlateName+str(j),edges=p.edges)
else:
currentSet = p.Set(name='Set_'+PlateName,faces=p.faces)
myAssembly.Instance(name=instanceName,part=myModel.parts[PlateName],autoOffset=OFF,dependent=ON)
inst.append(myAssembly.instances[instanceName])
#------- CREATE GEOMETRY: SET CRACK WIRE
instanceName = part_crack
p = myModel.parts[part_crack]
currentSet=0
myAttributes = p.queryAttributes()
if(myAttributes['numShellFaces']==0 and myAttributes['numWireEdges']>0):
currentSet = p.Set(name='Set_'+part_crack,edges=p.edges)
else:
currentSet = p.Set(name='Set_'+part_crack,faces=p.faces)
myAssembly.Instance(name=instanceName,part=myModel.parts[part_crack],autoOffset=OFF,dependent=ON)
inst.append(myAssembly.instances[instanceName])
#------ CREATE MATERIAL PROPERTY: SOFT MATERIAL
vol = plate_thickness*totalsize
E_SOFT = 100.0 # MPa
NU_SOFT = 0.33
SOFT_CRTICICAL_STRAIN = 0.4
SIGMA_SOFT = E_SOFT*SOFT_CRTICICAL_STRAIN
G_SOFT = 0.5*E_SOFT*SOFT_CRTICICAL_STRAIN*SOFT_CRTICICAL_STRAIN*vol
myModel.Material(name='Soft_Material')
myModel.materials['Soft_Material'].Elastic(dependencies=0,
moduli=LONG_TERM, noCompression=OFF, noTension=OFF, table=((E_SOFT, NU_SOFT), )
, temperatureDependency=OFF, type=ISOTROPIC)
myModel.materials['Soft_Material'].MaxpsDamageInitiation(table=(
(SIGMA_SOFT, ), ))
myModel.materials['Soft_Material'].maxpsDamageInitiation.DamageEvolution(
type=ENERGY, table=((G_SOFT, ), ))
myModel.materials['Soft_Material'].maxpsDamageInitiation.DamageStabilizationCohesive(
cohesiveCoeff=0.0005)
myModel.materials['Soft_Material'].setValues(materialIdentifier='')
#------ CREATE MATERIAL PROPERTY: STIFF MATERIAL
E_STIFF = 1000.0 # MPa
NU_STIFF = 0.33
STIFF_CRTICICAL_STRAIN = 0.04
SIGMA_STIFF = E_STIFF*STIFF_CRTICICAL_STRAIN
G_STIFF = 0.5*E_STIFF*STIFF_CRTICICAL_STRAIN*STIFF_CRTICICAL_STRAIN*vol
myModel.Material(name='Stiff_Material')
myModel.materials['Stiff_Material'].Elastic(dependencies=0,
moduli=LONG_TERM, noCompression=OFF, noTension=OFF, table=((E_STIFF, NU_STIFF),
), temperatureDependency=OFF, type=ISOTROPIC)
myModel.materials['Stiff_Material'].MaxpsDamageInitiation(table=(
(SIGMA_STIFF, ), ))
myModel.materials['Stiff_Material'].maxpsDamageInitiation.DamageEvolution(
type=ENERGY, table=((G_STIFF, ), ))
myModel.materials['Stiff_Material'].maxpsDamageInitiation.DamageStabilizationCohesive(
cohesiveCoeff=0.0005)
myModel.materials['Stiff_Material'].setValues(materialIdentifier=
'')
#------ CREATE MATERIAL PROPERTY: CREATE SECTION
myModel.HomogeneousSolidSection(material='Stiff_Material', name=
'Section-Stiff', thickness=plate_thickness)
myModel.HomogeneousSolidSection(material='Soft_Material', name=
'Section-Soft', thickness=plate_thickness)
#------ CREATE MATERIAL PROPERTY: ASSIGN SECTION PROPERTY
'''
ORIENTATION OF PLATES
57 58 59 60 61 62 63 64
49 50 51 52 53 54 55 56
41 42 43 44 45 46 47 48
33 34 35 36 37 38 39 40
25 26 27 28 29 30 30 32
17 18 19 20 21 22 23 24
9 10 11 12 13 14 15 16
1 2 3 4 5 6 7 8
'''
#------ CREATE MATERIAL PROPERTY: THIS IS WHERE THE SHUFFLING IS NEEDED BETWEEN STIFF AND SOFT
# randlist=[]
# randlist+=random.sample(range(1, 65), 32)
# rlist=set(randlist)
# for j in range(1, int(total_numCells)+1):
# ii = j-1
# if j in rlist:
# # print myModel.parts[partNames[GeomName][j]].sets.keys()
# region = myModel.parts[partNames[GeomName][ii]].sets['Set_'+partNames[GeomName][ii]]
# myModel.parts[partNames[GeomName][ii]].SectionAssignment(region=region,
# sectionName='Section-Soft', offset=0.0)
# else:
# # print myModel.parts[partNames[GeomName][j]].sets.keys()
# region = myModel.parts[partNames[GeomName][ii]].sets['Set_'+partNames[GeomName][ii]]
# myModel.parts[partNames[GeomName][ii]].SectionAssignment(region=region,
# sectionName='Section-Stiff', offset=0.0)
mesh = [25, 23, 40, 13, 44, 35, 26, 8, 28, 30, 6, 21, 27, 53, 29, 20, 17, 47, 55, 34, 31, 54, 58, 57, 36, 56, 46, 38, 45, 52, 48, 11]
# mat55=[233, 218, 203, 188, 173, 158, 129, 131, 133, 135, 137, 139, 141, 142, 143, 144, 114, 116, 118, 120, 122, 124, 126, 127, 128, 110, 93, 76, 59, 42, 25, 8]
total=[x for x in range(1,65)]
for i in total:
ii=i-1
if i in mesh:
region = myModel.parts[partNames[GeomName][ii]].sets['Set_'+partNames[GeomName][ii]]
myModel.parts[partNames[GeomName][ii]].SectionAssignment(region=region,
sectionName='Section-Soft', offset=0.0)
else:
region = myModel.parts[partNames[GeomName][ii]].sets['Set_'+partNames[GeomName][ii]]
myModel.parts[partNames[GeomName][ii]].SectionAssignment(region=region,
sectionName='Section-Stiff', offset=0.0)
#------ CREATE ASSEMBLY
myAssembly.DatumCsysByDefault(CARTESIAN)
sizeCube = [totalsize,totalsize]
#------ CREATE ASSEMBLY: CREATE SETS OF SURFACE
PlateSurf = {}
PlateSurfInstName = {}
PlateSurf[GeomName]=[]
PlateSurfInstName[GeomName]=[]
for j in range(int(total_numCells)):
myAssembly.Instance(name=partNames[GeomName][j],part=myModel.parts[partNames[GeomName][j]],autoOffset=OFF,dependent=ON)
currentInclFaces=[]
for j in range(int(total_numCells)):
currentInst = myAssembly.instances[partNames[GeomName][j]]
faces = currentInst.faces
name='Surf_'+partNames[GeomName][j]
surfEdges=[]
surfEdgesId = faces[0].getEdges()
for tmpId in surfEdgesId:
if(not isEdgeExternal(currentInst,currentInst.edges[tmpId],sizeCube)):
surfEdges.append(currentInst.edges[tmpId:tmpId+1])
s = myAssembly.Surface(side1Edges=surfEdges,name=name)
PlateSurf[GeomName].append(s)
PlateSurfInstName[GeomName].append(currentInst.name)
for i in range(1,int(total_numCells)):
part_name = 'Part-'+str(i)
next_plate = 'Plate-'+str(i+1)
if i == 1:
previous_part = 'Plate-'+str(i)
else:
previous_part = 'Part-'+str(i-1)+'-1'
myAssembly.InstanceFromBooleanMerge(domain=GEOMETRY,
instances=(myAssembly.instances[previous_part],
myAssembly.instances[next_plate]),
keepIntersections=ON, name=part_name, originalInstances=DELETE)
allFaces=[]
for inst in myAssembly.instances.values():
faces=inst.faces
allFaces.append(faces[0:len(faces)])
setAllFaces = myAssembly.Set(name='allRVE',faces=allFaces)
#------ CREATE ASSEMBLY: CREATE REFERENCE POINTS
refpoint1 = myAssembly.ReferencePoint(point=(0.5*sizeCube[0],-0.1*sizeCube[1],0.0))
refpoint2 = myAssembly.ReferencePoint(point=(0.5*sizeCube[0],1.1*sizeCube[1],0.0))
myAssembly.Set(name='refpoint1',referencePoints=(myAssembly.referencePoints[refpoint1.id],))
myAssembly.Set(name='refpoint2',referencePoints=(myAssembly.referencePoints[refpoint2.id],))
myAssembly.Surface(name='Surf_Bottom', side1Edges=myAssembly.instances[part_name+'-1'].edges.findAt(
((0.96875, 0.0, 0.0), ), ((0.84375, 0.0, 0.0), ), ((0.71875, 0.0, 0.0), ),
((0.59375, 0.0, 0.0), ), ((0.46875, 0.0, 0.0), ), ((0.34375, 0.0, 0.0), ),
((0.21875, 0.0, 0.0), ), ((0.09375, 0.0, 0.0), ), ))
myAssembly.Surface(name='Surf_Top', side1Edges=myAssembly.instances[part_name+'-1'].edges.findAt(
((0.90625, 1.0, 0.0), ), ((0.78125, 1.0, 0.0), ), ((0.65625, 1.0, 0.0), ),
((0.53125, 1.0, 0.0), ), ((0.40625, 1.0, 0.0), ), ((0.28125, 1.0, 0.0), ),
((0.15625, 1.0, 0.0), ), ((0.03125, 1.0, 0.0), ), ))
# myAssembly.Surface(name='Surf_HalfTop', side1Edges=myAssembly.instances[part_name+'-1'].edges.findAt(
# ((0.28125, 1.0, 0.0), ), ((0.15625, 1.0, 0.0), ), ((0.03125, 1.0, 0.0), )))
#------ CREATE STEP
myModel.StaticStep(adaptiveDampingRatio=0.05,continueDampingFactors=False,
timePeriod=2.0, initialInc=1e-05, maxNumInc=10000000, minInc=1e-10, maxInc=2.0, name='Step-1',
nlgeom=OFF, previous='Initial', stabilizationMagnitude=0.0002,
stabilizationMethod=DISSIPATED_ENERGY_FRACTION)
#------ CREATE STEP: CHANGE F-OUTPUT
myModel.fieldOutputRequests['F-Output-1'].setValues(
numIntervals=100, variables=('S', 'PE', 'PEEQ', 'PEMAG', 'LE', 'U', 'RF',
'CF', 'PHILSM', 'PSILSM', 'STATUSXFEM', 'IVOL', 'EVOL'))
#------ CREATE STEP: CHANGE SOLVER CONTROLS
myModel.steps['Step-1'].control.setValues(
allowPropagation=OFF, resetDefaultValues=OFF, timeIncrementation=(10.0,
20.0, 9.0, 16.0, 10.0, 4.0, 12.0, 10.0, 6.0, 3.0, 50.0))
#------ CREATE INTERACTIONS: COUPLINGS
myModel.Coupling(controlPoint=myAssembly.sets['refpoint1'],
couplingType=KINEMATIC, influenceRadius=WHOLE_SURFACE, localCsys=None,
name='Coupling-1', surface=myAssembly.surfaces['Surf_Bottom'], u1=ON, u2=ON, ur3=ON)
myModel.Coupling(controlPoint=myAssembly.sets['refpoint2'],
couplingType=KINEMATIC, influenceRadius=WHOLE_SURFACE, localCsys=None,
name='Coupling-2', surface=myAssembly.surfaces['Surf_Top'], u1=ON, u2=ON, ur3=ON)
#------ CREATE INTERACTIONS: XFEM
myAssembly.engineeringFeatures.XFEMCrack(crackDomain=myAssembly.sets['allRVE'],
crackLocation=myAssembly.instances[part_crack].sets['Set_'+part_crack]
, name='Crack-1')
#------ CREATE BOUNDARY CONDITIONS: FIXED AT BOTTOM FACES
myModel.DisplacementBC(amplitude=UNSET, createStepName='Step-1', distributionType=UNIFORM,
fieldName='', fixed=OFF, localCsys=None, name='BC-1',
region=myAssembly.sets['refpoint1'], u1=0.0, u2=0.0, ur3=0.0)
#------ CREATE BOUNDARY CONDITIONS: LOADING AT TOP FACES
Srate = 0.2 # STRAIN RATE
myModel.EquallySpacedAmplitude(begin=0.0, data=(0.0, 1.0), fixedInterval=1.0, name='Amp-1',
smooth=SOLVER_DEFAULT, timeSpan=STEP)
myModel.DisplacementBC(amplitude='Amp-1', createStepName='Step-1', distributionType=UNIFORM,
fieldName='', fixed=OFF, localCsys=None, name='BC-2',
region=myAssembly.sets['refpoint2'], u1=0.0, u2=sizeCube[0]*Srate, ur3=0.0)
myAssembly.regenerate()
#------ CREATE BOUNDARY CONDITIONS: ROLLER AT SIDE FACES
e1 = myAssembly.instances['Part-63-1'].edges
edges1 = e1.findAt(((1.0, 0.96875, 0.0), ), ((0.0, 0.90625, 0.0), ), ((1.0,
0.84375, 0.0), ), ((0.0, 0.78125, 0.0), ), ((1.0, 0.71875, 0.0), ), ((
0.0, 0.65625, 0.0), ), ((1.0, 0.59375, 0.0), ), ((0.0, 0.53125, 0.0),
), ((1.0, 0.46875, 0.0), ), ((0.0, 0.40625, 0.0), ), ((1.0, 0.34375,
0.0), ), ((0.0, 0.28125, 0.0), ), ((1.0, 0.21875, 0.0), ), ((0.0,
0.15625, 0.0), ), ((1.0, 0.09375, 0.0), ), ((0.0, 0.03125, 0.0), ))
myAssembly.Set(edges=edges1, name='Set_LRedges')
region = myAssembly.sets['Set_LRedges']
myModel.DisplacementBC(name='BC-3',
createStepName='Step-1', region=region, u1=0.0, u2=UNSET, ur3=0.0,
amplitude=UNSET, fixed=OFF, distributionType=UNIFORM, fieldName='',
localCsys=None)
#------ CREATE MESHES
deviationFactor=0.07
minSizeFactor=0.1
remeshFlag = NO
myModel.parts[part_name].seedPart(size=mesh_size,
deviationFactor=deviationFactor, minSizeFactor=minSizeFactor)
#------ CREATE MESHES: ELEMENT SHAPES (TRI, QUAD, QUAD_DOMINATED)
f1 = myModel.parts[part_name].faces
faces1 = f1.findAt(((0.916667,
0.916667, 0.0), ), ((0.791667, 0.916667, 0.0), ), ((0.666667, 0.916667,
0.0), ), ((0.541667, 0.916667, 0.0), ), ((0.416667, 0.916667, 0.0), ), ((
0.291667, 0.916667, 0.0), ), ((0.166667, 0.916667, 0.0), ), ((0.041667,
0.916667, 0.0), ), ((0.916667, 0.791667, 0.0), ), ((0.791667, 0.791667,
0.0), ), ((0.666667, 0.791667, 0.0), ), ((0.541667, 0.791667, 0.0), ), ((
0.416667, 0.791667, 0.0), ), ((0.291667, 0.791667, 0.0), ), ((0.166667,
0.791667, 0.0), ), ((0.041667, 0.791667, 0.0), ), ((0.916667, 0.666667,
0.0), ), ((0.791667, 0.666667, 0.0), ), ((0.666667, 0.666667, 0.0), ), ((
0.541667, 0.666667, 0.0), ), ((0.416667, 0.666667, 0.0), ), ((0.291667,
0.666667, 0.0), ), ((0.166667, 0.666667, 0.0), ), ((0.041667, 0.666667,
0.0), ), ((0.916667, 0.541667, 0.0), ), ((0.791667, 0.541667, 0.0), ), ((
0.666667, 0.541667, 0.0), ), ((0.541667, 0.541667, 0.0), ), ((0.416667,
0.541667, 0.0), ), ((0.291667, 0.541667, 0.0), ), ((0.166667, 0.541667,
0.0), ), ((0.041667, 0.541667, 0.0), ), ((0.916667, 0.416667, 0.0), ), ((
0.791667, 0.416667, 0.0), ), ((0.666667, 0.416667, 0.0), ), ((0.541667,
0.416667, 0.0), ), ((0.416667, 0.416667, 0.0), ), ((0.291667, 0.416667,
0.0), ), ((0.166667, 0.416667, 0.0), ), ((0.041667, 0.416667, 0.0), ), ((
0.916667, 0.291667, 0.0), ), ((0.791667, 0.291667, 0.0), ), ((0.666667,
0.291667, 0.0), ), ((0.541667, 0.291667, 0.0), ), ((0.416667, 0.291667,
0.0), ), ((0.291667, 0.291667, 0.0), ), ((0.166667, 0.291667, 0.0), ), ((
0.041667, 0.291667, 0.0), ), ((0.916667, 0.166667, 0.0), ), ((0.791667,
0.166667, 0.0), ), ((0.666667, 0.166667, 0.0), ), ((0.541667, 0.166667,
0.0), ), ((0.416667, 0.166667, 0.0), ), ((0.291667, 0.166667, 0.0), ), ((
0.166667, 0.166667, 0.0), ), ((0.041667, 0.166667, 0.0), ), ((0.916667,
0.041667, 0.0), ), ((0.791667, 0.041667, 0.0), ), ((0.666667, 0.041667,
0.0), ), ((0.541667, 0.041667, 0.0), ), ((0.416667, 0.041667, 0.0), ), ((
0.291667, 0.041667, 0.0), ), ((0.166667, 0.041667, 0.0), ), ((0.041667,
0.041667, 0.0), ), )
myModel.parts[part_name].setMeshControls(algorithm=MEDIAL_AXIS, elemShape=QUAD, regions=faces1)
#------ CREATE MESHES: ELEMENT TYPES(PlaneStress = CPS4R, PlaneStrain = CPE4R)
myModel.parts[part_name].setElementType(elemTypes=(ElemType(elemCode=CPS4R, elemLibrary=STANDARD,
secondOrderAccuracy=OFF, hourglassControl=ENHANCED, distortionControl=DEFAULT, elemDeletion=ON),
ElemType(elemCode=CPE4R, elemLibrary=STANDARD)), regions=(faces1, ))
myModel.parts[part_name].generateMesh()
#------ CREATE JOB
job_name = '50Soft_'+getPyScriptName()
mdb.Job(atTime=None, contactPrint=OFF, description='', echoPrint=OFF, explicitPrecision=SINGLE,
getMemoryFromAnalysis=True, historyPrint=OFF, memory=90, memoryUnits=PERCENTAGE, model=modelName,
modelPrint=OFF, multiprocessingMode=DEFAULT, name=job_name, nodalOutputPrecision=SINGLE, numCpus=6,
numDomains=6, numGPUs=0, queue=None, resultsFormat=ODB,
scratch='', type=ANALYSIS, userSubroutine='', waitHours=0, waitMinutes=0)
mdb.jobs[job_name].writeInput(consistencyChecking=OFF)
if __name__=='__main__':
main()
| Python |
2D | M3RG-IITD/FNO-StressStrain | benchmarking/UNet.ipynb | .ipynb | 198,781 | 1,871 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "e7a4212e-365b-4ab5-9dc6-1ef148e975ea",
"metadata": {},
"outputs": [],
"source": [
"## @meermehran -- M3RG Lab -- Indian Institute of Technology, Delhi\n",
"## @date : 28Sept2022\n",
"\n",
"####################################--DESCRIPTION####################################################\n",
"## ##\n",
"## -----------------------------Benchmarking- FNO-v-ResNet-UNet----------------------------------- ##\n",
"## ## \n",
"#####################################################################################################"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "4255cce4-817a-449d-ae12-8f09110a9f1f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DEVICE:= cuda\n"
]
}
],
"source": [
"\n",
"import numpy as np\n",
"import torch\n",
"import torchvision\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"from torch.nn.parameter import Parameter\n",
"from Adam import Adam\n",
"from sklearn.metrics import r2_score\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import operator\n",
"from functools import reduce\n",
"from functools import partial\n",
"import scipy.io as sio\n",
"\n",
"from timeit import default_timer\n",
"from utility import *\n",
"\n",
"\n",
"torch.manual_seed(0)\n",
"np.random.seed(0)\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"print('DEVICE:=', device)"
]
},
{
"cell_type": "markdown",
"id": "731479a5-7d99-4030-b5ef-f232a1797069",
"metadata": {},
"source": [
"#### MODEL"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5531896e-122c-4d67-ba97-aed16af2faaa",
"metadata": {},
"outputs": [],
"source": [
"class Block(nn.Module):\n",
" def __init__(self, in_ch, out_ch):\n",
" super().__init__()\n",
" self.conv1 = nn.Conv2d(in_ch, out_ch, kernel_size=3,stride = 1, padding=1)#3)\n",
" self.relu = nn.ReLU()\n",
" self.conv2 = nn.Conv2d(out_ch, out_ch,kernel_size=3,stride = 1, padding=1)# 3)\n",
" \n",
" def forward(self, x):\n",
" return self.conv2(self.relu(self.conv1(x)))\n",
"\n",
"\n",
"class Encoder(nn.Module):\n",
" def __init__(self, chs=(3,64,128,256,512,1024)):\n",
" super().__init__()\n",
" self.enc_blocks = nn.ModuleList([Block(chs[i], chs[i+1]) for i in range(len(chs)-1)])\n",
" self.pool = nn.MaxPool2d(2)\n",
" \n",
" def forward(self, x):\n",
" ftrs = []\n",
" for block in self.enc_blocks:\n",
"# print(\"before ConvBloc\", x.shape)\n",
" x = block(x)\n",
"# print(f'after conv block {x.shape}')\n",
" \n",
" ftrs.append(x)\n",
" x = self.pool(x)\n",
" return ftrs\n",
"\n",
"\n",
"class Decoder(nn.Module):\n",
" def __init__(self, chs=(1024, 512, 256, 128, 64)):\n",
" super().__init__()\n",
" self.chs = chs\n",
" self.upconvs = nn.ModuleList([nn.ConvTranspose2d(chs[i], chs[i+1], 2, 2) for i in range(len(chs)-1)])\n",
" self.dec_blocks = nn.ModuleList([Block(chs[i], chs[i+1]) for i in range(len(chs)-1)]) \n",
" \n",
" def forward(self, x, encoder_features):\n",
" for i in range(len(self.chs)-1):\n",
" x = self.upconvs[i](x)\n",
" # print(encoder_features[i].shape, x.shape)\n",
" enc_ftrs = self.crop(encoder_features[i], x)\n",
" x = torch.cat([x, enc_ftrs], dim=1)\n",
" x = self.dec_blocks[i](x)\n",
" return x\n",
" \n",
" def crop(self, enc_ftrs, x):\n",
" _, _, H, W = x.shape\n",
" enc_ftrs = torchvision.transforms.CenterCrop([H, W])(enc_ftrs)\n",
" return enc_ftrs\n",
"\n",
"\n",
"class UNet(nn.Module):\n",
" def __init__(self, enc_chs=(1,64,128,256,512,1024), dec_chs=(1024, 512, 256, 128, 64), num_class=3, retain_dim=False, out_sz=(48,48)):\n",
" super().__init__()\n",
" self.encoder = Encoder(enc_chs)\n",
" self.decoder = Decoder(dec_chs)\n",
" self.head = nn.Conv2d(dec_chs[-1], num_class, 1)\n",
" self.retain_dim = retain_dim\n",
" self.out_sz = out_sz\n",
"\n",
" def forward(self, x):\n",
" enc_ftrs = self.encoder(x)\n",
" out = self.decoder(enc_ftrs[::-1][0], enc_ftrs[::-1][1:])\n",
" out = self.head(out)\n",
" if self.retain_dim:\n",
" out = F.interpolate(out, self.out_sz)\n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "74777b75-ab7b-4c12-919d-8347cc884f36",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5e244aed-18dc-4864-83af-cd6a007b4237",
"metadata": {},
"outputs": [],
"source": [
"######################################################\n",
"### Data Loading\n",
"######################################################\n",
"\n",
"\n",
"X_Data = '/DATA1/meer/data/material-v3-3350.mat'\n",
"Y_Data = '/DATA1/meer/data/strain-v3-3350.mat'\n",
"\n",
"ntrain =1200\n",
"ntest = 200\n",
"batch_size = 20\n",
"dim = 48\n",
"strain_channels = 3\n",
"\n",
"\n",
"################################################################\n",
"# load data and data normalization\n",
"################################################################\n",
"\n",
"reader = MatReader(X_Data) \n",
"x_train = reader.read_field('Emat')[:ntrain]\n",
"x_test = reader.read_field('Emat')[-ntest:]\n",
"x_test_plot = reader.read_field('Emat')[-ntest:]\n",
"\n",
"\n",
"reader.load_file(Y_Data) \n",
"y_train = reader.read_field('strain')[:ntrain]# \n",
"y_test_strain = reader.read_field('strain')[-ntest:] \n",
"\n",
"\n",
"######################################################\n",
"### Normalization\n",
"######################################################\n",
"\n",
"x_normalizer = UnitGaussianNormalizer(x_train)\n",
"x_train = x_normalizer.encode(x_train)\n",
"x_test = x_normalizer.encode(x_test)\n",
"\n",
"y_normalizer = UnitGaussianNormalizer(y_train)\n",
"y_train = y_normalizer.encode(y_train)\n",
"\n",
"\n",
"\n",
"train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_train, y_train), batch_size=batch_size, shuffle=True)\n",
"test_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_test, y_test_strain), batch_size=batch_size, shuffle=False)\n",
"\n",
"\n",
"\n",
"if device=='cpu':\n",
" y_normalizer.cpu()\n",
"else:\n",
" y_normalizer.cuda()\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e7bc424d-f27d-4a18-8403-30dd7de840ac",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" x_train Shape := torch.Size([1200, 48, 48])\n",
" y_train Shape := torch.Size([1200, 48, 48, 3])\n",
" x_test Shape := torch.Size([200, 48, 48])\n",
" y_test_strain Shape := torch.Size([200, 48, 48, 3])\n"
]
}
],
"source": [
"print(f' x_train Shape := {x_train.shape}')\n",
"print(f' y_train Shape := {y_train.shape}')\n",
"print(f' x_test Shape := {x_test.shape}')\n",
"print(f' y_test_strain Shape := {y_test_strain.shape}')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "db7410d2-79bb-4c0e-8ab4-4c26f175d606",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MODEL PARAMETERS. :== 31030723\n",
"Epochs:= 500--- LR:=1e-05---BatchSize:= 20\n"
]
}
],
"source": [
"# ################################################################\n",
"## Model\n",
"# ################################################################\n",
"\n",
"model = UNet().to(device)\n",
"print(f'MODEL PARAMETERS. :== {count_params(model)}')\n",
"learning_rate = 1e-5\n",
"optimizer = torch.optim.RMSprop(model.parameters(), lr=learning_rate, weight_decay= 1e-8, momentum = 0.9)\n",
"scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=80, gamma=0.5) ### gamma =0.1\n",
"epochs = 500\n",
"\n",
"print(f'Epochs:= {epochs}--- LR:={learning_rate}---BatchSize:= {batch_size}')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "eca6d4b6-9327-4833-9df1-4cec65fc3927",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Epoch :== 0 || TIME(sec):== 1.4218 || TrainError:== 0.710358 || TestError:== 0.709678\n",
" Epoch :== 1 || TIME(sec):== 1.4467 || TrainError:== 0.710142 || TestError:== 0.709732\n",
" Epoch :== 2 || TIME(sec):== 1.4643 || TrainError:== 0.710017 || TestError:== 0.709638\n",
" Epoch :== 3 || TIME(sec):== 1.4742 || TrainError:== 0.710015 || TestError:== 0.7096\n",
" Epoch :== 4 || TIME(sec):== 1.4629 || TrainError:== 0.709919 || TestError:== 0.709567\n",
" Epoch :== 5 || TIME(sec):== 1.4745 || TrainError:== 0.709775 || TestError:== 0.70792\n",
" Epoch :== 6 || TIME(sec):== 1.4576 || TrainError:== 0.706147 || TestError:== 0.703627\n",
" Epoch :== 7 || TIME(sec):== 1.4813 || TrainError:== 0.701564 || TestError:== 0.697456\n",
" Epoch :== 8 || TIME(sec):== 1.465 || TrainError:== 0.696075 || TestError:== 0.692739\n",
" Epoch :== 9 || TIME(sec):== 1.4604 || TrainError:== 0.69177 || TestError:== 0.688926\n",
" Epoch :== 10 || TIME(sec):== 1.4794 || TrainError:== 0.688464 || TestError:== 0.68587\n",
" Epoch :== 11 || TIME(sec):== 1.4622 || TrainError:== 0.684956 || TestError:== 0.683381\n",
" Epoch :== 12 || TIME(sec):== 1.4807 || TrainError:== 0.680969 || TestError:== 0.676335\n",
" Epoch :== 13 || TIME(sec):== 1.4742 || TrainError:== 0.672624 || TestError:== 0.666328\n",
" Epoch :== 14 || TIME(sec):== 1.4749 || TrainError:== 0.659036 || TestError:== 0.652033\n",
" Epoch :== 15 || TIME(sec):== 1.4646 || TrainError:== 0.64333 || TestError:== 0.638991\n",
" Epoch :== 16 || TIME(sec):== 1.4827 || TrainError:== 0.627972 || TestError:== 0.625344\n",
" Epoch :== 17 || TIME(sec):== 1.4705 || TrainError:== 0.613552 || TestError:== 0.610584\n",
" Epoch :== 18 || TIME(sec):== 1.4915 || TrainError:== 0.596822 || TestError:== 0.593832\n",
" Epoch :== 19 || TIME(sec):== 1.4667 || TrainError:== 0.578772 || TestError:== 0.575523\n",
" Epoch :== 20 || TIME(sec):== 1.4631 || TrainError:== 0.563769 || TestError:== 0.561249\n",
" Epoch :== 21 || TIME(sec):== 1.4808 || TrainError:== 0.547608 || TestError:== 0.545591\n",
" Epoch :== 22 || TIME(sec):== 1.4772 || TrainError:== 0.531233 || TestError:== 0.532528\n",
" Epoch :== 23 || TIME(sec):== 1.4632 || TrainError:== 0.51597 || TestError:== 0.517781\n",
" Epoch :== 24 || TIME(sec):== 1.473 || TrainError:== 0.499168 || TestError:== 0.504759\n",
" Epoch :== 25 || TIME(sec):== 1.4664 || TrainError:== 0.481557 || TestError:== 0.487017\n",
" Epoch :== 26 || TIME(sec):== 1.4882 || TrainError:== 0.463419 || TestError:== 0.465903\n",
" Epoch :== 27 || TIME(sec):== 1.4857 || TrainError:== 0.44652 || TestError:== 0.454226\n",
" Epoch :== 28 || TIME(sec):== 1.4905 || TrainError:== 0.430608 || TestError:== 0.437503\n",
" Epoch :== 29 || TIME(sec):== 1.4876 || TrainError:== 0.41585 || TestError:== 0.428777\n",
" Epoch :== 30 || TIME(sec):== 1.4781 || TrainError:== 0.404997 || TestError:== 0.420284\n",
" Epoch :== 31 || TIME(sec):== 1.4649 || TrainError:== 0.393086 || TestError:== 0.405906\n",
" Epoch :== 32 || TIME(sec):== 1.4864 || TrainError:== 0.382888 || TestError:== 0.400006\n",
" Epoch :== 33 || TIME(sec):== 1.484 || TrainError:== 0.376194 || TestError:== 0.39276\n",
" Epoch :== 34 || TIME(sec):== 1.477 || TrainError:== 0.365011 || TestError:== 0.384653\n",
" Epoch :== 35 || TIME(sec):== 1.4787 || TrainError:== 0.357439 || TestError:== 0.379794\n",
" Epoch :== 36 || TIME(sec):== 1.4747 || TrainError:== 0.35089 || TestError:== 0.372806\n",
" Epoch :== 37 || TIME(sec):== 1.4791 || TrainError:== 0.344159 || TestError:== 0.364505\n",
" Epoch :== 38 || TIME(sec):== 1.4755 || TrainError:== 0.336659 || TestError:== 0.363334\n",
" Epoch :== 39 || TIME(sec):== 1.4764 || TrainError:== 0.330438 || TestError:== 0.356816\n",
" Epoch :== 40 || TIME(sec):== 1.4775 || TrainError:== 0.324107 || TestError:== 0.354324\n",
" Epoch :== 41 || TIME(sec):== 1.4771 || TrainError:== 0.317099 || TestError:== 0.347226\n",
" Epoch :== 42 || TIME(sec):== 1.4683 || TrainError:== 0.309896 || TestError:== 0.34322\n",
" Epoch :== 43 || TIME(sec):== 1.4794 || TrainError:== 0.304748 || TestError:== 0.340135\n",
" Epoch :== 44 || TIME(sec):== 1.4791 || TrainError:== 0.299393 || TestError:== 0.333306\n",
" Epoch :== 45 || TIME(sec):== 1.4799 || TrainError:== 0.29558 || TestError:== 0.329826\n",
" Epoch :== 46 || TIME(sec):== 1.4917 || TrainError:== 0.289688 || TestError:== 0.326626\n",
" Epoch :== 47 || TIME(sec):== 1.4652 || TrainError:== 0.285037 || TestError:== 0.322442\n",
" Epoch :== 48 || TIME(sec):== 1.4799 || TrainError:== 0.281055 || TestError:== 0.318142\n",
" Epoch :== 49 || TIME(sec):== 1.4702 || TrainError:== 0.278162 || TestError:== 0.314722\n",
" Epoch :== 50 || TIME(sec):== 1.4748 || TrainError:== 0.272783 || TestError:== 0.314073\n",
" Epoch :== 51 || TIME(sec):== 1.4882 || TrainError:== 0.270584 || TestError:== 0.311319\n",
" Epoch :== 52 || TIME(sec):== 1.4777 || TrainError:== 0.267581 || TestError:== 0.308373\n",
" Epoch :== 53 || TIME(sec):== 1.4816 || TrainError:== 0.262651 || TestError:== 0.305969\n",
" Epoch :== 54 || TIME(sec):== 1.4743 || TrainError:== 0.259782 || TestError:== 0.304578\n",
" Epoch :== 55 || TIME(sec):== 1.4704 || TrainError:== 0.257852 || TestError:== 0.30197\n",
" Epoch :== 56 || TIME(sec):== 1.4711 || TrainError:== 0.254442 || TestError:== 0.303063\n",
" Epoch :== 57 || TIME(sec):== 1.461 || TrainError:== 0.251304 || TestError:== 0.297607\n",
" Epoch :== 58 || TIME(sec):== 1.47 || TrainError:== 0.250594 || TestError:== 0.297597\n",
" Epoch :== 59 || TIME(sec):== 1.469 || TrainError:== 0.247084 || TestError:== 0.29394\n",
" Epoch :== 60 || TIME(sec):== 1.4668 || TrainError:== 0.243602 || TestError:== 0.292156\n",
" Epoch :== 61 || TIME(sec):== 1.4688 || TrainError:== 0.242139 || TestError:== 0.29483\n",
" Epoch :== 62 || TIME(sec):== 1.4694 || TrainError:== 0.239344 || TestError:== 0.290267\n",
" Epoch :== 63 || TIME(sec):== 1.4648 || TrainError:== 0.237649 || TestError:== 0.287367\n",
" Epoch :== 64 || TIME(sec):== 1.4621 || TrainError:== 0.234007 || TestError:== 0.285759\n",
" Epoch :== 65 || TIME(sec):== 1.4537 || TrainError:== 0.232643 || TestError:== 0.283441\n",
" Epoch :== 66 || TIME(sec):== 1.4694 || TrainError:== 0.231003 || TestError:== 0.285314\n",
" Epoch :== 67 || TIME(sec):== 1.4615 || TrainError:== 0.228366 || TestError:== 0.285046\n",
" Epoch :== 68 || TIME(sec):== 1.4763 || TrainError:== 0.22825 || TestError:== 0.280596\n",
" Epoch :== 69 || TIME(sec):== 1.4697 || TrainError:== 0.223852 || TestError:== 0.279802\n",
" Epoch :== 70 || TIME(sec):== 1.4732 || TrainError:== 0.222171 || TestError:== 0.278426\n",
" Epoch :== 71 || TIME(sec):== 1.4847 || TrainError:== 0.222579 || TestError:== 0.276307\n",
" Epoch :== 72 || TIME(sec):== 1.4785 || TrainError:== 0.219555 || TestError:== 0.275326\n",
" Epoch :== 73 || TIME(sec):== 1.478 || TrainError:== 0.217038 || TestError:== 0.275554\n",
" Epoch :== 74 || TIME(sec):== 1.484 || TrainError:== 0.216714 || TestError:== 0.273637\n",
" Epoch :== 75 || TIME(sec):== 1.4711 || TrainError:== 0.214957 || TestError:== 0.273057\n",
" Epoch :== 76 || TIME(sec):== 1.4757 || TrainError:== 0.211611 || TestError:== 0.271593\n",
" Epoch :== 77 || TIME(sec):== 1.4757 || TrainError:== 0.210815 || TestError:== 0.27188\n",
" Epoch :== 78 || TIME(sec):== 1.4806 || TrainError:== 0.208598 || TestError:== 0.269344\n",
" Epoch :== 79 || TIME(sec):== 1.4803 || TrainError:== 0.208769 || TestError:== 0.268213\n",
" Epoch :== 80 || TIME(sec):== 1.4632 || TrainError:== 0.198129 || TestError:== 0.260849\n",
" Epoch :== 81 || TIME(sec):== 1.4801 || TrainError:== 0.194501 || TestError:== 0.259934\n",
" Epoch :== 82 || TIME(sec):== 1.4702 || TrainError:== 0.193381 || TestError:== 0.25989\n",
" Epoch :== 83 || TIME(sec):== 1.4798 || TrainError:== 0.192188 || TestError:== 0.259018\n",
" Epoch :== 84 || TIME(sec):== 1.4886 || TrainError:== 0.190973 || TestError:== 0.257972\n",
" Epoch :== 85 || TIME(sec):== 1.4665 || TrainError:== 0.189993 || TestError:== 0.257336\n",
" Epoch :== 86 || TIME(sec):== 1.4774 || TrainError:== 0.189034 || TestError:== 0.256484\n",
" Epoch :== 87 || TIME(sec):== 1.4851 || TrainError:== 0.187147 || TestError:== 0.256227\n",
" Epoch :== 88 || TIME(sec):== 1.4806 || TrainError:== 0.186279 || TestError:== 0.254927\n",
" Epoch :== 89 || TIME(sec):== 1.4805 || TrainError:== 0.184913 || TestError:== 0.254806\n",
" Epoch :== 90 || TIME(sec):== 1.473 || TrainError:== 0.183211 || TestError:== 0.253279\n",
" Epoch :== 91 || TIME(sec):== 1.4779 || TrainError:== 0.18277 || TestError:== 0.253348\n",
" Epoch :== 92 || TIME(sec):== 1.4749 || TrainError:== 0.18181 || TestError:== 0.252145\n",
" Epoch :== 93 || TIME(sec):== 1.4667 || TrainError:== 0.17994 || TestError:== 0.251789\n",
" Epoch :== 94 || TIME(sec):== 1.4736 || TrainError:== 0.179029 || TestError:== 0.251096\n",
" Epoch :== 95 || TIME(sec):== 1.491 || TrainError:== 0.178117 || TestError:== 0.25083\n",
" Epoch :== 96 || TIME(sec):== 1.4679 || TrainError:== 0.176748 || TestError:== 0.249685\n",
" Epoch :== 97 || TIME(sec):== 1.4895 || TrainError:== 0.176278 || TestError:== 0.249068\n",
" Epoch :== 98 || TIME(sec):== 1.4799 || TrainError:== 0.174582 || TestError:== 0.247551\n",
" Epoch :== 99 || TIME(sec):== 1.4858 || TrainError:== 0.173091 || TestError:== 0.247443\n",
" Epoch :== 100 || TIME(sec):== 1.4717 || TrainError:== 0.172499 || TestError:== 0.246661\n",
" Epoch :== 101 || TIME(sec):== 1.4783 || TrainError:== 0.171424 || TestError:== 0.247281\n",
" Epoch :== 102 || TIME(sec):== 1.4742 || TrainError:== 0.170224 || TestError:== 0.245984\n",
" Epoch :== 103 || TIME(sec):== 1.484 || TrainError:== 0.169288 || TestError:== 0.245804\n",
" Epoch :== 104 || TIME(sec):== 1.4733 || TrainError:== 0.168947 || TestError:== 0.244045\n",
" Epoch :== 105 || TIME(sec):== 1.4575 || TrainError:== 0.167165 || TestError:== 0.24409\n",
" Epoch :== 106 || TIME(sec):== 1.4745 || TrainError:== 0.167376 || TestError:== 0.243714\n",
" Epoch :== 107 || TIME(sec):== 1.4531 || TrainError:== 0.165285 || TestError:== 0.241573\n",
" Epoch :== 108 || TIME(sec):== 1.4671 || TrainError:== 0.164439 || TestError:== 0.241768\n",
" Epoch :== 109 || TIME(sec):== 1.4691 || TrainError:== 0.164415 || TestError:== 0.242074\n",
" Epoch :== 110 || TIME(sec):== 1.4637 || TrainError:== 0.162723 || TestError:== 0.241403\n",
" Epoch :== 111 || TIME(sec):== 1.4586 || TrainError:== 0.161948 || TestError:== 0.240691\n",
" Epoch :== 112 || TIME(sec):== 1.4681 || TrainError:== 0.160718 || TestError:== 0.240212\n",
" Epoch :== 113 || TIME(sec):== 1.4837 || TrainError:== 0.160574 || TestError:== 0.23969\n",
" Epoch :== 114 || TIME(sec):== 1.4559 || TrainError:== 0.159775 || TestError:== 0.239175\n",
" Epoch :== 115 || TIME(sec):== 1.465 || TrainError:== 0.158952 || TestError:== 0.23903\n",
" Epoch :== 116 || TIME(sec):== 1.4651 || TrainError:== 0.158452 || TestError:== 0.23916\n",
" Epoch :== 117 || TIME(sec):== 1.4726 || TrainError:== 0.156552 || TestError:== 0.238684\n",
" Epoch :== 118 || TIME(sec):== 1.4595 || TrainError:== 0.156266 || TestError:== 0.237755\n",
" Epoch :== 119 || TIME(sec):== 1.4568 || TrainError:== 0.155843 || TestError:== 0.236503\n",
" Epoch :== 120 || TIME(sec):== 1.4665 || TrainError:== 0.154684 || TestError:== 0.237517\n",
" Epoch :== 121 || TIME(sec):== 1.4703 || TrainError:== 0.154566 || TestError:== 0.23619\n",
" Epoch :== 122 || TIME(sec):== 1.4585 || TrainError:== 0.1537 || TestError:== 0.236139\n",
" Epoch :== 123 || TIME(sec):== 1.4753 || TrainError:== 0.15309 || TestError:== 0.235468\n",
" Epoch :== 124 || TIME(sec):== 1.4618 || TrainError:== 0.15199 || TestError:== 0.235267\n",
" Epoch :== 125 || TIME(sec):== 1.4694 || TrainError:== 0.152218 || TestError:== 0.235502\n",
" Epoch :== 126 || TIME(sec):== 1.4709 || TrainError:== 0.150827 || TestError:== 0.235233\n",
" Epoch :== 127 || TIME(sec):== 1.4535 || TrainError:== 0.150269 || TestError:== 0.234939\n",
" Epoch :== 128 || TIME(sec):== 1.4661 || TrainError:== 0.150102 || TestError:== 0.234701\n",
" Epoch :== 129 || TIME(sec):== 1.4695 || TrainError:== 0.148956 || TestError:== 0.234691\n",
" Epoch :== 130 || TIME(sec):== 1.4563 || TrainError:== 0.147972 || TestError:== 0.23288\n",
" Epoch :== 131 || TIME(sec):== 1.4744 || TrainError:== 0.147148 || TestError:== 0.233338\n",
" Epoch :== 132 || TIME(sec):== 1.4707 || TrainError:== 0.147645 || TestError:== 0.232254\n",
" Epoch :== 133 || TIME(sec):== 1.4814 || TrainError:== 0.145457 || TestError:== 0.232122\n",
" Epoch :== 134 || TIME(sec):== 1.4657 || TrainError:== 0.146182 || TestError:== 0.231686\n",
" Epoch :== 135 || TIME(sec):== 1.4915 || TrainError:== 0.145192 || TestError:== 0.231195\n",
" Epoch :== 136 || TIME(sec):== 1.4794 || TrainError:== 0.143629 || TestError:== 0.230872\n",
" Epoch :== 137 || TIME(sec):== 1.4644 || TrainError:== 0.144464 || TestError:== 0.231797\n",
" Epoch :== 138 || TIME(sec):== 1.4727 || TrainError:== 0.143899 || TestError:== 0.230783\n",
" Epoch :== 139 || TIME(sec):== 1.4573 || TrainError:== 0.142066 || TestError:== 0.23092\n",
" Epoch :== 140 || TIME(sec):== 1.4639 || TrainError:== 0.142195 || TestError:== 0.230343\n",
" Epoch :== 141 || TIME(sec):== 1.4647 || TrainError:== 0.141444 || TestError:== 0.229821\n",
" Epoch :== 142 || TIME(sec):== 1.4591 || TrainError:== 0.14124 || TestError:== 0.230651\n",
" Epoch :== 143 || TIME(sec):== 1.4608 || TrainError:== 0.141075 || TestError:== 0.229406\n",
" Epoch :== 144 || TIME(sec):== 1.4589 || TrainError:== 0.140655 || TestError:== 0.230335\n",
" Epoch :== 145 || TIME(sec):== 1.4683 || TrainError:== 0.139359 || TestError:== 0.229196\n",
" Epoch :== 146 || TIME(sec):== 1.4572 || TrainError:== 0.138702 || TestError:== 0.228175\n",
" Epoch :== 147 || TIME(sec):== 1.4745 || TrainError:== 0.137945 || TestError:== 0.227852\n",
" Epoch :== 148 || TIME(sec):== 1.4626 || TrainError:== 0.138441 || TestError:== 0.229269\n",
" Epoch :== 149 || TIME(sec):== 1.4812 || TrainError:== 0.138531 || TestError:== 0.230439\n",
" Epoch :== 150 || TIME(sec):== 1.4755 || TrainError:== 0.138386 || TestError:== 0.229031\n",
" Epoch :== 151 || TIME(sec):== 1.4763 || TrainError:== 0.136366 || TestError:== 0.227917\n",
" Epoch :== 152 || TIME(sec):== 1.4626 || TrainError:== 0.135809 || TestError:== 0.227585\n",
" Epoch :== 153 || TIME(sec):== 1.4776 || TrainError:== 0.135284 || TestError:== 0.226328\n",
" Epoch :== 154 || TIME(sec):== 1.4754 || TrainError:== 0.134942 || TestError:== 0.226441\n",
" Epoch :== 155 || TIME(sec):== 1.4808 || TrainError:== 0.135134 || TestError:== 0.227785\n",
" Epoch :== 156 || TIME(sec):== 1.4715 || TrainError:== 0.134664 || TestError:== 0.225872\n",
" Epoch :== 157 || TIME(sec):== 1.4572 || TrainError:== 0.133709 || TestError:== 0.22595\n",
" Epoch :== 158 || TIME(sec):== 1.4653 || TrainError:== 0.132547 || TestError:== 0.225721\n",
" Epoch :== 159 || TIME(sec):== 1.4558 || TrainError:== 0.132823 || TestError:== 0.226854\n",
" Epoch :== 160 || TIME(sec):== 1.4635 || TrainError:== 0.128546 || TestError:== 0.222934\n",
" Epoch :== 161 || TIME(sec):== 1.4721 || TrainError:== 0.126389 || TestError:== 0.222714\n",
" Epoch :== 162 || TIME(sec):== 1.4651 || TrainError:== 0.125841 || TestError:== 0.222954\n",
" Epoch :== 163 || TIME(sec):== 1.4636 || TrainError:== 0.125931 || TestError:== 0.223304\n",
" Epoch :== 164 || TIME(sec):== 1.4639 || TrainError:== 0.125642 || TestError:== 0.223027\n",
" Epoch :== 165 || TIME(sec):== 1.4701 || TrainError:== 0.125307 || TestError:== 0.223163\n",
" Epoch :== 166 || TIME(sec):== 1.4654 || TrainError:== 0.124852 || TestError:== 0.222288\n",
" Epoch :== 167 || TIME(sec):== 1.4639 || TrainError:== 0.124551 || TestError:== 0.222728\n",
" Epoch :== 168 || TIME(sec):== 1.4672 || TrainError:== 0.124316 || TestError:== 0.22253\n",
" Epoch :== 169 || TIME(sec):== 1.4654 || TrainError:== 0.123853 || TestError:== 0.222207\n",
" Epoch :== 170 || TIME(sec):== 1.464 || TrainError:== 0.123408 || TestError:== 0.22241\n",
" Epoch :== 171 || TIME(sec):== 1.4688 || TrainError:== 0.122889 || TestError:== 0.221772\n",
" Epoch :== 172 || TIME(sec):== 1.4613 || TrainError:== 0.122788 || TestError:== 0.222355\n",
" Epoch :== 173 || TIME(sec):== 1.4798 || TrainError:== 0.122266 || TestError:== 0.222235\n",
" Epoch :== 174 || TIME(sec):== 1.484 || TrainError:== 0.122105 || TestError:== 0.221522\n",
" Epoch :== 175 || TIME(sec):== 1.4827 || TrainError:== 0.121411 || TestError:== 0.222227\n",
" Epoch :== 176 || TIME(sec):== 1.4791 || TrainError:== 0.121611 || TestError:== 0.221682\n",
" Epoch :== 177 || TIME(sec):== 1.4863 || TrainError:== 0.121009 || TestError:== 0.221445\n",
" Epoch :== 178 || TIME(sec):== 1.4773 || TrainError:== 0.120517 || TestError:== 0.221209\n",
" Epoch :== 179 || TIME(sec):== 1.4724 || TrainError:== 0.120114 || TestError:== 0.221363\n",
" Epoch :== 180 || TIME(sec):== 1.4783 || TrainError:== 0.120007 || TestError:== 0.221132\n",
" Epoch :== 181 || TIME(sec):== 1.4883 || TrainError:== 0.119808 || TestError:== 0.220883\n",
" Epoch :== 182 || TIME(sec):== 1.4718 || TrainError:== 0.119403 || TestError:== 0.221276\n",
" Epoch :== 183 || TIME(sec):== 1.4847 || TrainError:== 0.118961 || TestError:== 0.221066\n",
" Epoch :== 184 || TIME(sec):== 1.4731 || TrainError:== 0.118455 || TestError:== 0.220946\n",
" Epoch :== 185 || TIME(sec):== 1.4888 || TrainError:== 0.118192 || TestError:== 0.221041\n",
" Epoch :== 186 || TIME(sec):== 1.4795 || TrainError:== 0.118034 || TestError:== 0.220544\n",
" Epoch :== 187 || TIME(sec):== 1.4748 || TrainError:== 0.117978 || TestError:== 0.220755\n",
" Epoch :== 188 || TIME(sec):== 1.4676 || TrainError:== 0.117508 || TestError:== 0.220322\n",
" Epoch :== 189 || TIME(sec):== 1.4829 || TrainError:== 0.117351 || TestError:== 0.220229\n",
" Epoch :== 190 || TIME(sec):== 1.4688 || TrainError:== 0.116993 || TestError:== 0.22054\n",
" Epoch :== 191 || TIME(sec):== 1.4821 || TrainError:== 0.11674 || TestError:== 0.220532\n",
" Epoch :== 192 || TIME(sec):== 1.4806 || TrainError:== 0.116113 || TestError:== 0.22032\n",
" Epoch :== 193 || TIME(sec):== 1.4852 || TrainError:== 0.116021 || TestError:== 0.220304\n",
" Epoch :== 194 || TIME(sec):== 1.4694 || TrainError:== 0.115872 || TestError:== 0.219999\n",
" Epoch :== 195 || TIME(sec):== 1.4797 || TrainError:== 0.115355 || TestError:== 0.220327\n",
" Epoch :== 196 || TIME(sec):== 1.4853 || TrainError:== 0.115409 || TestError:== 0.219446\n",
" Epoch :== 197 || TIME(sec):== 1.477 || TrainError:== 0.114815 || TestError:== 0.219507\n",
" Epoch :== 198 || TIME(sec):== 1.465 || TrainError:== 0.114824 || TestError:== 0.219739\n",
" Epoch :== 199 || TIME(sec):== 1.4782 || TrainError:== 0.114126 || TestError:== 0.219635\n",
" Epoch :== 200 || TIME(sec):== 1.4816 || TrainError:== 0.114035 || TestError:== 0.219631\n",
" Epoch :== 201 || TIME(sec):== 1.4676 || TrainError:== 0.113685 || TestError:== 0.219401\n",
" Epoch :== 202 || TIME(sec):== 1.4879 || TrainError:== 0.11349 || TestError:== 0.219447\n",
" Epoch :== 203 || TIME(sec):== 1.476 || TrainError:== 0.113648 || TestError:== 0.219137\n",
" Epoch :== 204 || TIME(sec):== 1.4713 || TrainError:== 0.113062 || TestError:== 0.219245\n",
" Epoch :== 205 || TIME(sec):== 1.4671 || TrainError:== 0.112757 || TestError:== 0.219228\n",
" Epoch :== 206 || TIME(sec):== 1.4799 || TrainError:== 0.112422 || TestError:== 0.218833\n",
" Epoch :== 207 || TIME(sec):== 1.4762 || TrainError:== 0.112574 || TestError:== 0.219716\n",
" Epoch :== 208 || TIME(sec):== 1.4739 || TrainError:== 0.112385 || TestError:== 0.218934\n",
" Epoch :== 209 || TIME(sec):== 1.4824 || TrainError:== 0.111651 || TestError:== 0.218937\n",
" Epoch :== 210 || TIME(sec):== 1.4756 || TrainError:== 0.1118 || TestError:== 0.219016\n",
" Epoch :== 211 || TIME(sec):== 1.4794 || TrainError:== 0.11145 || TestError:== 0.218815\n",
" Epoch :== 212 || TIME(sec):== 1.4846 || TrainError:== 0.111349 || TestError:== 0.218877\n",
" Epoch :== 213 || TIME(sec):== 1.482 || TrainError:== 0.111125 || TestError:== 0.21879\n",
" Epoch :== 214 || TIME(sec):== 1.4796 || TrainError:== 0.110589 || TestError:== 0.218578\n",
" Epoch :== 215 || TIME(sec):== 1.4769 || TrainError:== 0.110683 || TestError:== 0.218343\n",
" Epoch :== 216 || TIME(sec):== 1.487 || TrainError:== 0.110149 || TestError:== 0.21885\n",
" Epoch :== 217 || TIME(sec):== 1.4747 || TrainError:== 0.110276 || TestError:== 0.21824\n",
" Epoch :== 218 || TIME(sec):== 1.48 || TrainError:== 0.109926 || TestError:== 0.218462\n",
" Epoch :== 219 || TIME(sec):== 1.4735 || TrainError:== 0.109623 || TestError:== 0.218869\n",
" Epoch :== 220 || TIME(sec):== 1.4751 || TrainError:== 0.109643 || TestError:== 0.218178\n",
" Epoch :== 221 || TIME(sec):== 1.4727 || TrainError:== 0.109028 || TestError:== 0.21854\n",
" Epoch :== 222 || TIME(sec):== 1.4969 || TrainError:== 0.109079 || TestError:== 0.217771\n",
" Epoch :== 223 || TIME(sec):== 1.51 || TrainError:== 0.109193 || TestError:== 0.218087\n",
" Epoch :== 224 || TIME(sec):== 1.5272 || TrainError:== 0.108419 || TestError:== 0.217922\n",
" Epoch :== 225 || TIME(sec):== 1.5147 || TrainError:== 0.108296 || TestError:== 0.218099\n",
" Epoch :== 226 || TIME(sec):== 1.5107 || TrainError:== 0.108064 || TestError:== 0.217758\n",
" Epoch :== 227 || TIME(sec):== 1.5139 || TrainError:== 0.107769 || TestError:== 0.217935\n",
" Epoch :== 228 || TIME(sec):== 1.5156 || TrainError:== 0.107735 || TestError:== 0.217837\n",
" Epoch :== 229 || TIME(sec):== 1.5125 || TrainError:== 0.10768 || TestError:== 0.217939\n",
" Epoch :== 230 || TIME(sec):== 1.5112 || TrainError:== 0.107308 || TestError:== 0.217828\n",
" Epoch :== 231 || TIME(sec):== 1.5155 || TrainError:== 0.10717 || TestError:== 0.218457\n",
" Epoch :== 232 || TIME(sec):== 1.5171 || TrainError:== 0.106894 || TestError:== 0.217471\n",
" Epoch :== 233 || TIME(sec):== 1.5158 || TrainError:== 0.106425 || TestError:== 0.217814\n",
" Epoch :== 234 || TIME(sec):== 1.5305 || TrainError:== 0.106687 || TestError:== 0.217564\n",
" Epoch :== 235 || TIME(sec):== 1.532 || TrainError:== 0.106277 || TestError:== 0.217503\n",
" Epoch :== 236 || TIME(sec):== 1.5302 || TrainError:== 0.106289 || TestError:== 0.217568\n",
" Epoch :== 237 || TIME(sec):== 1.5201 || TrainError:== 0.10636 || TestError:== 0.217073\n",
" Epoch :== 238 || TIME(sec):== 1.5225 || TrainError:== 0.10568 || TestError:== 0.217193\n",
" Epoch :== 239 || TIME(sec):== 1.5171 || TrainError:== 0.105703 || TestError:== 0.21753\n",
" Epoch :== 240 || TIME(sec):== 1.5236 || TrainError:== 0.10359 || TestError:== 0.216466\n",
" Epoch :== 241 || TIME(sec):== 1.5244 || TrainError:== 0.102921 || TestError:== 0.216345\n",
" Epoch :== 242 || TIME(sec):== 1.5378 || TrainError:== 0.102818 || TestError:== 0.216499\n",
" Epoch :== 243 || TIME(sec):== 1.5373 || TrainError:== 0.102723 || TestError:== 0.216568\n",
" Epoch :== 244 || TIME(sec):== 1.5218 || TrainError:== 0.102631 || TestError:== 0.216665\n",
" Epoch :== 245 || TIME(sec):== 1.5246 || TrainError:== 0.102611 || TestError:== 0.216657\n",
" Epoch :== 246 || TIME(sec):== 1.5018 || TrainError:== 0.102445 || TestError:== 0.216469\n",
" Epoch :== 247 || TIME(sec):== 1.4615 || TrainError:== 0.102297 || TestError:== 0.216598\n",
" Epoch :== 248 || TIME(sec):== 1.4728 || TrainError:== 0.102079 || TestError:== 0.216508\n",
" Epoch :== 249 || TIME(sec):== 1.4634 || TrainError:== 0.101992 || TestError:== 0.216336\n",
" Epoch :== 250 || TIME(sec):== 1.4638 || TrainError:== 0.101781 || TestError:== 0.216585\n",
" Epoch :== 251 || TIME(sec):== 1.4736 || TrainError:== 0.101643 || TestError:== 0.216317\n",
" Epoch :== 252 || TIME(sec):== 1.4738 || TrainError:== 0.101466 || TestError:== 0.216401\n",
" Epoch :== 253 || TIME(sec):== 1.4683 || TrainError:== 0.101271 || TestError:== 0.216369\n",
" Epoch :== 254 || TIME(sec):== 1.4805 || TrainError:== 0.10106 || TestError:== 0.216445\n",
" Epoch :== 255 || TIME(sec):== 1.4545 || TrainError:== 0.10107 || TestError:== 0.216303\n",
" Epoch :== 256 || TIME(sec):== 1.461 || TrainError:== 0.100881 || TestError:== 0.216358\n",
" Epoch :== 257 || TIME(sec):== 1.4605 || TrainError:== 0.100674 || TestError:== 0.216326\n",
" Epoch :== 258 || TIME(sec):== 1.4755 || TrainError:== 0.10053 || TestError:== 0.21633\n",
" Epoch :== 259 || TIME(sec):== 1.4811 || TrainError:== 0.100461 || TestError:== 0.216233\n",
" Epoch :== 260 || TIME(sec):== 1.4668 || TrainError:== 0.100214 || TestError:== 0.216357\n",
" Epoch :== 261 || TIME(sec):== 1.4638 || TrainError:== 0.100047 || TestError:== 0.216309\n",
" Epoch :== 262 || TIME(sec):== 1.4597 || TrainError:== 0.099891 || TestError:== 0.216371\n",
" Epoch :== 263 || TIME(sec):== 1.4682 || TrainError:== 0.099819 || TestError:== 0.216461\n",
" Epoch :== 264 || TIME(sec):== 1.465 || TrainError:== 0.099664 || TestError:== 0.216105\n",
" Epoch :== 265 || TIME(sec):== 1.4736 || TrainError:== 0.099483 || TestError:== 0.216288\n",
" Epoch :== 266 || TIME(sec):== 1.4658 || TrainError:== 0.099314 || TestError:== 0.216176\n",
" Epoch :== 267 || TIME(sec):== 1.464 || TrainError:== 0.099266 || TestError:== 0.216207\n",
" Epoch :== 268 || TIME(sec):== 1.4712 || TrainError:== 0.098993 || TestError:== 0.216167\n",
" Epoch :== 269 || TIME(sec):== 1.4664 || TrainError:== 0.098899 || TestError:== 0.216253\n",
" Epoch :== 270 || TIME(sec):== 1.4594 || TrainError:== 0.098841 || TestError:== 0.216123\n",
" Epoch :== 271 || TIME(sec):== 1.4716 || TrainError:== 0.098611 || TestError:== 0.216067\n",
" Epoch :== 272 || TIME(sec):== 1.4655 || TrainError:== 0.098391 || TestError:== 0.216192\n",
" Epoch :== 273 || TIME(sec):== 1.4604 || TrainError:== 0.098307 || TestError:== 0.21612\n",
" Epoch :== 274 || TIME(sec):== 1.4556 || TrainError:== 0.098189 || TestError:== 0.215985\n",
" Epoch :== 275 || TIME(sec):== 1.4589 || TrainError:== 0.098107 || TestError:== 0.215846\n",
" Epoch :== 276 || TIME(sec):== 1.4737 || TrainError:== 0.097964 || TestError:== 0.21608\n",
" Epoch :== 277 || TIME(sec):== 1.4632 || TrainError:== 0.097798 || TestError:== 0.216031\n",
" Epoch :== 278 || TIME(sec):== 1.4595 || TrainError:== 0.097589 || TestError:== 0.215915\n",
" Epoch :== 279 || TIME(sec):== 1.4567 || TrainError:== 0.097477 || TestError:== 0.216093\n",
" Epoch :== 280 || TIME(sec):== 1.4634 || TrainError:== 0.097427 || TestError:== 0.216035\n",
" Epoch :== 281 || TIME(sec):== 1.4521 || TrainError:== 0.097284 || TestError:== 0.21601\n",
" Epoch :== 282 || TIME(sec):== 1.4707 || TrainError:== 0.097189 || TestError:== 0.21598\n",
" Epoch :== 283 || TIME(sec):== 1.4656 || TrainError:== 0.097166 || TestError:== 0.215817\n",
" Epoch :== 284 || TIME(sec):== 1.4581 || TrainError:== 0.096753 || TestError:== 0.215929\n",
" Epoch :== 285 || TIME(sec):== 1.4556 || TrainError:== 0.096751 || TestError:== 0.216036\n",
" Epoch :== 286 || TIME(sec):== 1.4589 || TrainError:== 0.096672 || TestError:== 0.216107\n",
" Epoch :== 287 || TIME(sec):== 1.4687 || TrainError:== 0.096544 || TestError:== 0.216026\n",
" Epoch :== 288 || TIME(sec):== 1.47 || TrainError:== 0.096367 || TestError:== 0.215873\n",
" Epoch :== 289 || TIME(sec):== 1.4646 || TrainError:== 0.096181 || TestError:== 0.215884\n",
" Epoch :== 290 || TIME(sec):== 1.4527 || TrainError:== 0.096027 || TestError:== 0.215956\n",
" Epoch :== 291 || TIME(sec):== 1.456 || TrainError:== 0.096045 || TestError:== 0.215932\n",
" Epoch :== 292 || TIME(sec):== 1.4586 || TrainError:== 0.095905 || TestError:== 0.215944\n",
" Epoch :== 293 || TIME(sec):== 1.4466 || TrainError:== 0.095807 || TestError:== 0.215724\n",
" Epoch :== 294 || TIME(sec):== 1.4639 || TrainError:== 0.095618 || TestError:== 0.215643\n",
" Epoch :== 295 || TIME(sec):== 1.4658 || TrainError:== 0.095476 || TestError:== 0.215743\n",
" Epoch :== 296 || TIME(sec):== 1.4476 || TrainError:== 0.095415 || TestError:== 0.21583\n",
" Epoch :== 297 || TIME(sec):== 1.4592 || TrainError:== 0.095282 || TestError:== 0.21577\n",
" Epoch :== 298 || TIME(sec):== 1.4513 || TrainError:== 0.095317 || TestError:== 0.215696\n",
" Epoch :== 299 || TIME(sec):== 1.4706 || TrainError:== 0.095081 || TestError:== 0.215577\n",
" Epoch :== 300 || TIME(sec):== 1.4649 || TrainError:== 0.094813 || TestError:== 0.215624\n",
" Epoch :== 301 || TIME(sec):== 1.4553 || TrainError:== 0.094769 || TestError:== 0.215734\n",
" Epoch :== 302 || TIME(sec):== 1.4646 || TrainError:== 0.094779 || TestError:== 0.215955\n",
" Epoch :== 303 || TIME(sec):== 1.4713 || TrainError:== 0.094601 || TestError:== 0.21581\n",
" Epoch :== 304 || TIME(sec):== 1.4708 || TrainError:== 0.094423 || TestError:== 0.215777\n",
" Epoch :== 305 || TIME(sec):== 1.4774 || TrainError:== 0.094322 || TestError:== 0.215801\n",
" Epoch :== 306 || TIME(sec):== 1.4529 || TrainError:== 0.094286 || TestError:== 0.21562\n",
" Epoch :== 307 || TIME(sec):== 1.4568 || TrainError:== 0.094131 || TestError:== 0.215562\n",
" Epoch :== 308 || TIME(sec):== 1.4633 || TrainError:== 0.093968 || TestError:== 0.215437\n",
" Epoch :== 309 || TIME(sec):== 1.4637 || TrainError:== 0.093818 || TestError:== 0.215565\n",
" Epoch :== 310 || TIME(sec):== 1.4688 || TrainError:== 0.093691 || TestError:== 0.215606\n",
" Epoch :== 311 || TIME(sec):== 1.4593 || TrainError:== 0.093658 || TestError:== 0.215748\n",
" Epoch :== 312 || TIME(sec):== 1.4794 || TrainError:== 0.093442 || TestError:== 0.2157\n",
" Epoch :== 313 || TIME(sec):== 1.4635 || TrainError:== 0.093565 || TestError:== 0.21574\n",
" Epoch :== 314 || TIME(sec):== 1.4589 || TrainError:== 0.093415 || TestError:== 0.215651\n",
" Epoch :== 315 || TIME(sec):== 1.4613 || TrainError:== 0.093278 || TestError:== 0.215635\n",
" Epoch :== 316 || TIME(sec):== 1.4709 || TrainError:== 0.093104 || TestError:== 0.215636\n",
" Epoch :== 317 || TIME(sec):== 1.4622 || TrainError:== 0.093027 || TestError:== 0.215696\n",
" Epoch :== 318 || TIME(sec):== 1.4624 || TrainError:== 0.092906 || TestError:== 0.215557\n",
" Epoch :== 319 || TIME(sec):== 1.4795 || TrainError:== 0.092667 || TestError:== 0.215591\n",
" Epoch :== 320 || TIME(sec):== 1.4651 || TrainError:== 0.091912 || TestError:== 0.215373\n",
" Epoch :== 321 || TIME(sec):== 1.4725 || TrainError:== 0.091734 || TestError:== 0.215437\n",
" Epoch :== 322 || TIME(sec):== 1.4729 || TrainError:== 0.091667 || TestError:== 0.215399\n",
" Epoch :== 323 || TIME(sec):== 1.4727 || TrainError:== 0.091622 || TestError:== 0.215381\n",
" Epoch :== 324 || TIME(sec):== 1.4639 || TrainError:== 0.09159 || TestError:== 0.215457\n",
" Epoch :== 325 || TIME(sec):== 1.4779 || TrainError:== 0.091544 || TestError:== 0.215422\n",
" Epoch :== 326 || TIME(sec):== 1.476 || TrainError:== 0.091442 || TestError:== 0.215399\n",
" Epoch :== 327 || TIME(sec):== 1.4679 || TrainError:== 0.091339 || TestError:== 0.215444\n",
" Epoch :== 328 || TIME(sec):== 1.4661 || TrainError:== 0.091258 || TestError:== 0.215395\n",
" Epoch :== 329 || TIME(sec):== 1.4633 || TrainError:== 0.091158 || TestError:== 0.215352\n",
" Epoch :== 330 || TIME(sec):== 1.4642 || TrainError:== 0.091096 || TestError:== 0.215393\n",
" Epoch :== 331 || TIME(sec):== 1.4751 || TrainError:== 0.090999 || TestError:== 0.215411\n",
" Epoch :== 332 || TIME(sec):== 1.4687 || TrainError:== 0.090902 || TestError:== 0.215347\n",
" Epoch :== 333 || TIME(sec):== 1.46 || TrainError:== 0.090848 || TestError:== 0.215372\n",
" Epoch :== 334 || TIME(sec):== 1.46 || TrainError:== 0.090754 || TestError:== 0.215385\n",
" Epoch :== 335 || TIME(sec):== 1.4763 || TrainError:== 0.090673 || TestError:== 0.215378\n",
" Epoch :== 336 || TIME(sec):== 1.4576 || TrainError:== 0.090569 || TestError:== 0.215474\n",
" Epoch :== 337 || TIME(sec):== 1.4714 || TrainError:== 0.09049 || TestError:== 0.215459\n",
" Epoch :== 338 || TIME(sec):== 1.4668 || TrainError:== 0.090409 || TestError:== 0.215262\n",
" Epoch :== 339 || TIME(sec):== 1.4744 || TrainError:== 0.090346 || TestError:== 0.21544\n",
" Epoch :== 340 || TIME(sec):== 1.4596 || TrainError:== 0.090268 || TestError:== 0.215293\n",
" Epoch :== 341 || TIME(sec):== 1.4737 || TrainError:== 0.090142 || TestError:== 0.215386\n",
" Epoch :== 342 || TIME(sec):== 1.4649 || TrainError:== 0.090093 || TestError:== 0.215459\n",
" Epoch :== 343 || TIME(sec):== 1.4586 || TrainError:== 0.090029 || TestError:== 0.215452\n",
" Epoch :== 344 || TIME(sec):== 1.4609 || TrainError:== 0.08992 || TestError:== 0.215411\n",
" Epoch :== 345 || TIME(sec):== 1.4642 || TrainError:== 0.089832 || TestError:== 0.21538\n",
" Epoch :== 346 || TIME(sec):== 1.4688 || TrainError:== 0.089785 || TestError:== 0.215471\n",
" Epoch :== 347 || TIME(sec):== 1.4748 || TrainError:== 0.089663 || TestError:== 0.215414\n",
" Epoch :== 348 || TIME(sec):== 1.4581 || TrainError:== 0.089596 || TestError:== 0.215378\n",
" Epoch :== 349 || TIME(sec):== 1.4634 || TrainError:== 0.089545 || TestError:== 0.21547\n",
" Epoch :== 350 || TIME(sec):== 1.4605 || TrainError:== 0.089462 || TestError:== 0.215481\n",
" Epoch :== 351 || TIME(sec):== 1.4744 || TrainError:== 0.089363 || TestError:== 0.215424\n",
" Epoch :== 352 || TIME(sec):== 1.4534 || TrainError:== 0.089294 || TestError:== 0.215416\n",
" Epoch :== 353 || TIME(sec):== 1.4603 || TrainError:== 0.089203 || TestError:== 0.215431\n",
" Epoch :== 354 || TIME(sec):== 1.4807 || TrainError:== 0.089141 || TestError:== 0.215354\n",
" Epoch :== 355 || TIME(sec):== 1.4637 || TrainError:== 0.089051 || TestError:== 0.215473\n",
" Epoch :== 356 || TIME(sec):== 1.4727 || TrainError:== 0.088961 || TestError:== 0.215423\n",
" Epoch :== 357 || TIME(sec):== 1.467 || TrainError:== 0.088908 || TestError:== 0.215503\n",
" Epoch :== 358 || TIME(sec):== 1.4655 || TrainError:== 0.088792 || TestError:== 0.215473\n",
" Epoch :== 359 || TIME(sec):== 1.4625 || TrainError:== 0.088757 || TestError:== 0.215364\n",
" Epoch :== 360 || TIME(sec):== 1.4716 || TrainError:== 0.088673 || TestError:== 0.215482\n",
" Epoch :== 361 || TIME(sec):== 1.4725 || TrainError:== 0.088598 || TestError:== 0.215423\n",
" Epoch :== 362 || TIME(sec):== 1.4722 || TrainError:== 0.088503 || TestError:== 0.215397\n",
" Epoch :== 363 || TIME(sec):== 1.4708 || TrainError:== 0.088424 || TestError:== 0.215487\n",
" Epoch :== 364 || TIME(sec):== 1.4618 || TrainError:== 0.08834 || TestError:== 0.215474\n",
" Epoch :== 365 || TIME(sec):== 1.4772 || TrainError:== 0.088285 || TestError:== 0.215549\n",
" Epoch :== 366 || TIME(sec):== 1.4727 || TrainError:== 0.088208 || TestError:== 0.215395\n",
" Epoch :== 367 || TIME(sec):== 1.469 || TrainError:== 0.088131 || TestError:== 0.215366\n",
" Epoch :== 368 || TIME(sec):== 1.4691 || TrainError:== 0.088066 || TestError:== 0.21542\n",
" Epoch :== 369 || TIME(sec):== 1.4769 || TrainError:== 0.087991 || TestError:== 0.215482\n",
" Epoch :== 370 || TIME(sec):== 1.4675 || TrainError:== 0.087901 || TestError:== 0.215418\n",
" Epoch :== 371 || TIME(sec):== 1.4686 || TrainError:== 0.087827 || TestError:== 0.215493\n",
" Epoch :== 372 || TIME(sec):== 1.4745 || TrainError:== 0.08778 || TestError:== 0.215414\n",
" Epoch :== 373 || TIME(sec):== 1.4656 || TrainError:== 0.087662 || TestError:== 0.215444\n",
" Epoch :== 374 || TIME(sec):== 1.4703 || TrainError:== 0.087581 || TestError:== 0.215463\n",
" Epoch :== 375 || TIME(sec):== 1.4744 || TrainError:== 0.087558 || TestError:== 0.215439\n",
" Epoch :== 376 || TIME(sec):== 1.467 || TrainError:== 0.087456 || TestError:== 0.215448\n",
" Epoch :== 377 || TIME(sec):== 1.4658 || TrainError:== 0.087398 || TestError:== 0.215424\n",
" Epoch :== 378 || TIME(sec):== 1.4703 || TrainError:== 0.087288 || TestError:== 0.215476\n",
" Epoch :== 379 || TIME(sec):== 1.4834 || TrainError:== 0.087274 || TestError:== 0.21543\n",
" Epoch :== 380 || TIME(sec):== 1.461 || TrainError:== 0.08717 || TestError:== 0.215386\n",
" Epoch :== 381 || TIME(sec):== 1.473 || TrainError:== 0.08709 || TestError:== 0.215449\n",
" Epoch :== 382 || TIME(sec):== 1.4738 || TrainError:== 0.087068 || TestError:== 0.215435\n",
" Epoch :== 383 || TIME(sec):== 1.4663 || TrainError:== 0.086939 || TestError:== 0.215362\n",
" Epoch :== 384 || TIME(sec):== 1.461 || TrainError:== 0.086892 || TestError:== 0.215475\n",
" Epoch :== 385 || TIME(sec):== 1.4685 || TrainError:== 0.086769 || TestError:== 0.215436\n",
" Epoch :== 386 || TIME(sec):== 1.4737 || TrainError:== 0.08673 || TestError:== 0.215436\n",
" Epoch :== 387 || TIME(sec):== 1.4798 || TrainError:== 0.086664 || TestError:== 0.215496\n",
" Epoch :== 388 || TIME(sec):== 1.4681 || TrainError:== 0.0866 || TestError:== 0.215492\n",
" Epoch :== 389 || TIME(sec):== 1.4718 || TrainError:== 0.086516 || TestError:== 0.215527\n",
" Epoch :== 390 || TIME(sec):== 1.4709 || TrainError:== 0.086469 || TestError:== 0.215567\n",
" Epoch :== 391 || TIME(sec):== 1.4667 || TrainError:== 0.086474 || TestError:== 0.215434\n",
" Epoch :== 392 || TIME(sec):== 1.4699 || TrainError:== 0.086328 || TestError:== 0.215438\n",
" Epoch :== 393 || TIME(sec):== 1.4715 || TrainError:== 0.086233 || TestError:== 0.215381\n",
" Epoch :== 394 || TIME(sec):== 1.4631 || TrainError:== 0.086149 || TestError:== 0.215415\n",
" Epoch :== 395 || TIME(sec):== 1.4668 || TrainError:== 0.086173 || TestError:== 0.215413\n",
" Epoch :== 396 || TIME(sec):== 1.4751 || TrainError:== 0.086068 || TestError:== 0.215411\n",
" Epoch :== 397 || TIME(sec):== 1.4609 || TrainError:== 0.08596 || TestError:== 0.215484\n",
" Epoch :== 398 || TIME(sec):== 1.4722 || TrainError:== 0.08591 || TestError:== 0.215479\n",
" Epoch :== 399 || TIME(sec):== 1.4709 || TrainError:== 0.085821 || TestError:== 0.215373\n",
" Epoch :== 400 || TIME(sec):== 1.4565 || TrainError:== 0.08547 || TestError:== 0.21539\n",
" Epoch :== 401 || TIME(sec):== 1.4674 || TrainError:== 0.085381 || TestError:== 0.21536\n",
" Epoch :== 402 || TIME(sec):== 1.4597 || TrainError:== 0.085359 || TestError:== 0.215437\n",
" Epoch :== 403 || TIME(sec):== 1.4545 || TrainError:== 0.085325 || TestError:== 0.21541\n",
" Epoch :== 404 || TIME(sec):== 1.4604 || TrainError:== 0.085288 || TestError:== 0.215432\n",
" Epoch :== 405 || TIME(sec):== 1.4633 || TrainError:== 0.085233 || TestError:== 0.215395\n",
" Epoch :== 406 || TIME(sec):== 1.4594 || TrainError:== 0.085199 || TestError:== 0.215401\n",
" Epoch :== 407 || TIME(sec):== 1.4669 || TrainError:== 0.085141 || TestError:== 0.215375\n",
" Epoch :== 408 || TIME(sec):== 1.4751 || TrainError:== 0.085105 || TestError:== 0.215396\n",
" Epoch :== 409 || TIME(sec):== 1.4673 || TrainError:== 0.085049 || TestError:== 0.21542\n",
" Epoch :== 410 || TIME(sec):== 1.4645 || TrainError:== 0.085009 || TestError:== 0.21544\n",
" Epoch :== 411 || TIME(sec):== 1.456 || TrainError:== 0.084958 || TestError:== 0.215471\n",
" Epoch :== 412 || TIME(sec):== 1.4633 || TrainError:== 0.084913 || TestError:== 0.215453\n",
" Epoch :== 413 || TIME(sec):== 1.4534 || TrainError:== 0.084857 || TestError:== 0.215451\n",
" Epoch :== 414 || TIME(sec):== 1.4613 || TrainError:== 0.084809 || TestError:== 0.215444\n",
" Epoch :== 415 || TIME(sec):== 1.4652 || TrainError:== 0.084759 || TestError:== 0.215477\n",
" Epoch :== 416 || TIME(sec):== 1.474 || TrainError:== 0.084717 || TestError:== 0.215498\n",
" Epoch :== 417 || TIME(sec):== 1.4605 || TrainError:== 0.084659 || TestError:== 0.215445\n",
" Epoch :== 418 || TIME(sec):== 1.4592 || TrainError:== 0.084612 || TestError:== 0.215438\n",
" Epoch :== 419 || TIME(sec):== 1.4543 || TrainError:== 0.084581 || TestError:== 0.215419\n",
" Epoch :== 420 || TIME(sec):== 1.4532 || TrainError:== 0.084521 || TestError:== 0.21543\n",
" Epoch :== 421 || TIME(sec):== 1.4554 || TrainError:== 0.084476 || TestError:== 0.215436\n",
" Epoch :== 422 || TIME(sec):== 1.459 || TrainError:== 0.08444 || TestError:== 0.215478\n",
" Epoch :== 423 || TIME(sec):== 1.4631 || TrainError:== 0.084392 || TestError:== 0.21547\n",
" Epoch :== 424 || TIME(sec):== 1.4571 || TrainError:== 0.084334 || TestError:== 0.21545\n",
" Epoch :== 425 || TIME(sec):== 1.4541 || TrainError:== 0.084303 || TestError:== 0.215482\n",
" Epoch :== 426 || TIME(sec):== 1.4513 || TrainError:== 0.084244 || TestError:== 0.215481\n",
" Epoch :== 427 || TIME(sec):== 1.4622 || TrainError:== 0.084193 || TestError:== 0.215497\n",
" Epoch :== 428 || TIME(sec):== 1.4569 || TrainError:== 0.084137 || TestError:== 0.215488\n",
" Epoch :== 429 || TIME(sec):== 1.4547 || TrainError:== 0.084103 || TestError:== 0.215528\n",
" Epoch :== 430 || TIME(sec):== 1.4554 || TrainError:== 0.084071 || TestError:== 0.215506\n",
" Epoch :== 431 || TIME(sec):== 1.4561 || TrainError:== 0.084011 || TestError:== 0.215483\n",
" Epoch :== 432 || TIME(sec):== 1.4628 || TrainError:== 0.083952 || TestError:== 0.215542\n",
" Epoch :== 433 || TIME(sec):== 1.447 || TrainError:== 0.083915 || TestError:== 0.215491\n",
" Epoch :== 434 || TIME(sec):== 1.456 || TrainError:== 0.083867 || TestError:== 0.215508\n",
" Epoch :== 435 || TIME(sec):== 1.4624 || TrainError:== 0.083829 || TestError:== 0.215479\n",
" Epoch :== 436 || TIME(sec):== 1.4542 || TrainError:== 0.083768 || TestError:== 0.21548\n",
" Epoch :== 437 || TIME(sec):== 1.4498 || TrainError:== 0.08374 || TestError:== 0.215473\n",
" Epoch :== 438 || TIME(sec):== 1.4434 || TrainError:== 0.083692 || TestError:== 0.2155\n",
" Epoch :== 439 || TIME(sec):== 1.4564 || TrainError:== 0.083639 || TestError:== 0.215536\n",
" Epoch :== 440 || TIME(sec):== 1.4537 || TrainError:== 0.083597 || TestError:== 0.215557\n",
" Epoch :== 441 || TIME(sec):== 1.4516 || TrainError:== 0.083549 || TestError:== 0.215551\n",
" Epoch :== 442 || TIME(sec):== 1.4591 || TrainError:== 0.083502 || TestError:== 0.215529\n",
" Epoch :== 443 || TIME(sec):== 1.4554 || TrainError:== 0.083457 || TestError:== 0.215566\n",
" Epoch :== 444 || TIME(sec):== 1.4506 || TrainError:== 0.083413 || TestError:== 0.215579\n",
" Epoch :== 445 || TIME(sec):== 1.4562 || TrainError:== 0.083359 || TestError:== 0.215542\n",
" Epoch :== 446 || TIME(sec):== 1.4641 || TrainError:== 0.083328 || TestError:== 0.215599\n",
" Epoch :== 447 || TIME(sec):== 1.4658 || TrainError:== 0.083274 || TestError:== 0.215552\n",
" Epoch :== 448 || TIME(sec):== 1.4662 || TrainError:== 0.083224 || TestError:== 0.2156\n",
" Epoch :== 449 || TIME(sec):== 1.4616 || TrainError:== 0.083182 || TestError:== 0.215554\n",
" Epoch :== 450 || TIME(sec):== 1.4512 || TrainError:== 0.083144 || TestError:== 0.215567\n",
" Epoch :== 451 || TIME(sec):== 1.4644 || TrainError:== 0.083083 || TestError:== 0.215535\n",
" Epoch :== 452 || TIME(sec):== 1.4635 || TrainError:== 0.083051 || TestError:== 0.215565\n",
" Epoch :== 453 || TIME(sec):== 1.4581 || TrainError:== 0.083009 || TestError:== 0.215539\n",
" Epoch :== 454 || TIME(sec):== 1.4563 || TrainError:== 0.082975 || TestError:== 0.215596\n",
" Epoch :== 455 || TIME(sec):== 1.4521 || TrainError:== 0.08291 || TestError:== 0.215593\n",
" Epoch :== 456 || TIME(sec):== 1.4566 || TrainError:== 0.082873 || TestError:== 0.215592\n",
" Epoch :== 457 || TIME(sec):== 1.4501 || TrainError:== 0.082829 || TestError:== 0.215654\n",
" Epoch :== 458 || TIME(sec):== 1.4547 || TrainError:== 0.082782 || TestError:== 0.215596\n",
" Epoch :== 459 || TIME(sec):== 1.463 || TrainError:== 0.082732 || TestError:== 0.215618\n",
" Epoch :== 460 || TIME(sec):== 1.4574 || TrainError:== 0.082706 || TestError:== 0.215623\n",
" Epoch :== 461 || TIME(sec):== 1.4553 || TrainError:== 0.082647 || TestError:== 0.215592\n",
" Epoch :== 462 || TIME(sec):== 1.4646 || TrainError:== 0.082613 || TestError:== 0.21561\n",
" Epoch :== 463 || TIME(sec):== 1.4648 || TrainError:== 0.082573 || TestError:== 0.21557\n",
" Epoch :== 464 || TIME(sec):== 1.4454 || TrainError:== 0.082519 || TestError:== 0.215596\n",
" Epoch :== 465 || TIME(sec):== 1.4554 || TrainError:== 0.082473 || TestError:== 0.215585\n",
" Epoch :== 466 || TIME(sec):== 1.4562 || TrainError:== 0.08243 || TestError:== 0.21563\n",
" Epoch :== 467 || TIME(sec):== 1.4485 || TrainError:== 0.082389 || TestError:== 0.21563\n",
" Epoch :== 468 || TIME(sec):== 1.4476 || TrainError:== 0.082338 || TestError:== 0.215646\n",
" Epoch :== 469 || TIME(sec):== 1.4527 || TrainError:== 0.082288 || TestError:== 0.215601\n",
" Epoch :== 470 || TIME(sec):== 1.4633 || TrainError:== 0.082263 || TestError:== 0.215617\n",
" Epoch :== 471 || TIME(sec):== 1.4546 || TrainError:== 0.082205 || TestError:== 0.21569\n",
" Epoch :== 472 || TIME(sec):== 1.4549 || TrainError:== 0.082168 || TestError:== 0.215693\n",
" Epoch :== 473 || TIME(sec):== 1.472 || TrainError:== 0.082114 || TestError:== 0.215629\n",
" Epoch :== 474 || TIME(sec):== 1.4546 || TrainError:== 0.082081 || TestError:== 0.215611\n",
" Epoch :== 475 || TIME(sec):== 1.4553 || TrainError:== 0.082036 || TestError:== 0.215652\n",
" Epoch :== 476 || TIME(sec):== 1.4544 || TrainError:== 0.081995 || TestError:== 0.215633\n",
" Epoch :== 477 || TIME(sec):== 1.447 || TrainError:== 0.081955 || TestError:== 0.215672\n",
" Epoch :== 478 || TIME(sec):== 1.4485 || TrainError:== 0.081911 || TestError:== 0.21567\n",
" Epoch :== 479 || TIME(sec):== 1.4475 || TrainError:== 0.081857 || TestError:== 0.215646\n",
" Epoch :== 480 || TIME(sec):== 1.4464 || TrainError:== 0.081664 || TestError:== 0.215635\n",
" Epoch :== 481 || TIME(sec):== 1.4501 || TrainError:== 0.081626 || TestError:== 0.215627\n",
" Epoch :== 482 || TIME(sec):== 1.4465 || TrainError:== 0.081609 || TestError:== 0.215641\n",
" Epoch :== 483 || TIME(sec):== 1.4469 || TrainError:== 0.08158 || TestError:== 0.215617\n",
" Epoch :== 484 || TIME(sec):== 1.4435 || TrainError:== 0.081563 || TestError:== 0.215626\n",
" Epoch :== 485 || TIME(sec):== 1.4563 || TrainError:== 0.081534 || TestError:== 0.215665\n",
" Epoch :== 486 || TIME(sec):== 1.4552 || TrainError:== 0.08151 || TestError:== 0.215622\n",
" Epoch :== 487 || TIME(sec):== 1.445 || TrainError:== 0.081486 || TestError:== 0.215649\n",
" Epoch :== 488 || TIME(sec):== 1.4535 || TrainError:== 0.081455 || TestError:== 0.215646\n",
" Epoch :== 489 || TIME(sec):== 1.4525 || TrainError:== 0.081426 || TestError:== 0.215646\n",
" Epoch :== 490 || TIME(sec):== 1.4473 || TrainError:== 0.081399 || TestError:== 0.215664\n",
" Epoch :== 491 || TIME(sec):== 1.4521 || TrainError:== 0.081379 || TestError:== 0.215659\n",
" Epoch :== 492 || TIME(sec):== 1.4483 || TrainError:== 0.081349 || TestError:== 0.21566\n",
" Epoch :== 493 || TIME(sec):== 1.4531 || TrainError:== 0.081325 || TestError:== 0.215658\n",
" Epoch :== 494 || TIME(sec):== 1.4529 || TrainError:== 0.081296 || TestError:== 0.215683\n",
" Epoch :== 495 || TIME(sec):== 1.4541 || TrainError:== 0.081273 || TestError:== 0.215681\n",
" Epoch :== 496 || TIME(sec):== 1.4492 || TrainError:== 0.081247 || TestError:== 0.215683\n",
" Epoch :== 497 || TIME(sec):== 1.4659 || TrainError:== 0.081212 || TestError:== 0.215682\n",
" Epoch :== 498 || TIME(sec):== 1.4692 || TrainError:== 0.081192 || TestError:== 0.215676\n",
" Epoch :== 499 || TIME(sec):== 1.4515 || TrainError:== 0.081164 || TestError:== 0.215683\n"
]
}
],
"source": [
"\n",
"# ep wise error\n",
"trainerror=[] \n",
"testerror=[]\n",
"\n",
"##Train Test Loop\n",
"for ep in range(epochs):\n",
" model.train()\n",
" t1 = default_timer()\n",
" train_l2 = 0\n",
" for x, y in train_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" x =x.reshape(x.shape[0], 1, dim, dim)\n",
"\n",
" optimizer.zero_grad()\n",
"\n",
" out = model(x).reshape(batch_size, dim,dim,strain_channels)\n",
" \n",
"\n",
" out = y_normalizer.decode(out)\n",
" y = y_normalizer.decode(y)\n",
" \n",
"\n",
" loss = lossfunc(y.view(batch_size,-1),out.view(batch_size,-1))\n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
" train_l2 += loss.item()\n",
"\n",
" scheduler.step()\n",
" model.eval()\n",
" test_l2 = 0.0\n",
" with torch.no_grad():\n",
" for x, y in test_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" x =x.reshape(x.shape[0], 1, dim, dim)\n",
" out = model(x).reshape(batch_size, dim, dim,strain_channels)\n",
" out = y_normalizer.decode(out)\n",
"\n",
" test_l2 += lossfunc(y.view(batch_size,-1),out.view(batch_size,-1)).item()\n",
"\n",
" train_l2/= ntrain\n",
" test_l2 /= ntest\n",
" trainerror += [train_l2]\n",
" testerror += [test_l2]\n",
" t2 = default_timer()\n",
" print(f' Epoch :== {ep} || TIME(sec):== {np.round((t2-t1),4)} '\n",
" f'|| TrainError:== {np.round(train_l2,6)} || TestError:== {np.round(test_l2,6)}')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "d5586ab9-0400-405c-84de-782c6d1bb529",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 700x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(7,6))\n",
"plt.plot(range(epochs),trainerror,linewidth = 4,label =\"TRAIN\")\n",
"plt.plot(range(epochs),testerror, linewidth = 4,label =\"TEST\")\n",
"plt.xlabel('Epoch', fontsize =20)\n",
"plt.ylabel('Loss', fontsize =20)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "047854a9-45c7-4195-a77b-cce2e18b8459",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 14,
"id": "f905e8b5-f013-4996-9b79-b48d018230be",
"metadata": {},
"outputs": [],
"source": [
"# trainn = np.array(trainerror)\n",
"# testnn = np.array(testerror)\n",
"# torch.save(model.state_dict(),'UNet_STRAIN_N1200_ep1200.pt')\n",
"# np.save('trainUNET_STRAIN', trainn)\n",
"# np.save('testUNET_STRAIN', testnn)"
]
},
{
"cell_type": "markdown",
"id": "a089b9fc-3f06-4f74-9a6f-d94cff8016f7",
"metadata": {},
"source": [
"#### Testing-Strains"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "436d7686-fbcf-492d-9535-aff6d4b7f8ea",
"metadata": {},
"outputs": [],
"source": [
"prediction = torch.zeros(y_test_strain.shape)\n",
"c = 0\n",
"with torch.no_grad():\n",
" for x,y in test_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" x =x.reshape(x.shape[0], 1, dim, dim)\n",
" out = model(x).reshape(batch_size, dim, dim,strain_channels)\n",
" out = y_normalizer.decode(out)\n",
" prediction[c*batch_size :c*batch_size+batch_size] = out\n",
" c+=1\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "03560fcc-fa19-4acf-ac7a-d4a99155c94d",
"metadata": {},
"outputs": [],
"source": [
"strain_act = y_test_strain.reshape(ntest, -1)\n",
"strain_pred = prediction.reshape(ntest,-1)\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "a55e6bfb-6065-4ca2-816e-3975ec39f5d4",
"metadata": {},
"outputs": [],
"source": [
"r2_strain =[]\n",
"for i in range(strain_act.shape[0]):\n",
" act = strain_act[i]\n",
" pred = strain_pred[i]\n",
" r2 = r2_score(act,pred)\n",
" # print(r2)\n",
" # break\n",
" r2_strain += [r2]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "b1841a31-34f2-4a1a-ad57-fc140e572822",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9443775309476833 0.03338474697973763\n"
]
}
],
"source": [
"r2_avg_strain = np.average(r2_strain)\n",
"r2_std_strain = np.std(r2_strain)\n",
"\n",
"print(r2_avg_strain,r2_std_strain)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "8c0c7d6c-0510-4e56-a5fd-49716123e01c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.94\n",
"0.03\n"
]
}
],
"source": [
"##R2 mean and std\n",
"\n",
"print(np.round((r2_avg_strain),2))\n",
"print(np.round((r2_std_strain),2))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f0d860db-6087-4771-8927-3e806af4d34d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(0.2157)\n"
]
}
],
"source": [
"## total test error\n",
"loss = lossfunc(y_test_strain, prediction)\n",
"print(loss/ntest)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "775c4f21-9e79-46e1-a61a-7371dd753869",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "b3fafe51-7fc5-410c-8aae-d7a7cac832e5",
"metadata": {},
"source": [
"### STRESS"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "082df5f7-468f-48b4-b188-929d5d3eb012",
"metadata": {},
"outputs": [],
"source": [
"######################################################\n",
"### Data Loading & Hyperparameters\n",
"######################################################\n",
"\n",
"\n",
"X_Data = '/DATA1/meer/data/material-v3-3350.mat'\n",
"Y_Data = '/DATA1/meer/data/stress-v3-3350.mat'\n",
"\n",
"\n",
"ntrain =1200\n",
"ntest = 200\n",
"batch_size =20\n",
"\n",
"\n",
"################################################################\n",
"# load data and data normalization\n",
"################################################################\n",
"\n",
"reader = MatReader(X_Data) \n",
"x_train = reader.read_field('Emat')[:ntrain]\n",
"x_test = reader.read_field('Emat')[-ntest:]\n",
"x_test_plot = reader.read_field('Emat')[-ntest:]\n",
"\n",
"reader.load_file(Y_Data) \n",
"y_train = reader.read_field('stress')[:ntrain]# \n",
"y_test_stress = reader.read_field('stress')[-ntest:] \n",
"\n",
"######################################################\n",
"### Normalization\n",
"######################################################\n",
"\n",
"x_normalizer = UnitGaussianNormalizer(x_train)\n",
"x_train = x_normalizer.encode(x_train)\n",
"x_test = x_normalizer.encode(x_test)\n",
"\n",
"y_normalizer = UnitGaussianNormalizer(y_train)\n",
"y_train = y_normalizer.encode(y_train)\n",
"\n",
"\n",
"train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_train, y_train), batch_size=batch_size, shuffle=True)\n",
"test_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_test, y_test_stress), batch_size=batch_size, shuffle=False)\n",
"\n",
"\n",
"\n",
"if device=='cpu':\n",
" y_normalizer.cpu()\n",
"else:\n",
" y_normalizer.cuda()\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "ade762d9-d323-4378-94fe-5291530c0212",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" x_train Shape := torch.Size([1200, 48, 48])\n",
" y_train Shape := torch.Size([1200, 48, 48, 3])\n",
" x_test Shape := torch.Size([200, 48, 48])\n",
" y_test_stress Shape := torch.Size([200, 48, 48, 3])\n"
]
}
],
"source": [
"print(f' x_train Shape := {x_train.shape}')\n",
"print(f' y_train Shape := {y_train.shape}')\n",
"print(f' x_test Shape := {x_test.shape}')\n",
"print(f' y_test_stress Shape := {y_test_stress.shape}')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "5d5005cf-90af-4d73-9e6a-9d840accd7c5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MODEL PARAMETERS. :== 31030723\n",
"Epochs:= 500--- LR:=1e-05---BatchSize:= 20\n"
]
}
],
"source": [
"# ################################################################\n",
"## Model\n",
"# ################################################################\n",
"\n",
"model = UNet().to(device)\n",
"print(f'MODEL PARAMETERS. :== {count_params(model)}')\n",
"learning_rate = 1e-5\n",
"optimizer = torch.optim.RMSprop(model.parameters(), lr=learning_rate, weight_decay= 1e-8, momentum = 0.9)\n",
"scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=80, gamma=0.5) ### gamma =0.1\n",
"epochs = 500\n",
"\n",
"print(f'Epochs:= {epochs}--- LR:={learning_rate}---BatchSize:= {batch_size}')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "3332d3cf-04f5-4108-9374-d296147666cd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Epoch :== 0 || TIME(sec):== 1.4424 || TrainError:== 0.531698 || TestError:== 0.533993\n",
" Epoch :== 1 || TIME(sec):== 1.4629 || TrainError:== 0.531504 || TestError:== 0.533824\n",
" Epoch :== 2 || TIME(sec):== 1.4549 || TrainError:== 0.531432 || TestError:== 0.533798\n",
" Epoch :== 3 || TIME(sec):== 1.4498 || TrainError:== 0.531344 || TestError:== 0.533759\n",
" Epoch :== 4 || TIME(sec):== 1.4634 || TrainError:== 0.531135 || TestError:== 0.533337\n",
" Epoch :== 5 || TIME(sec):== 1.444 || TrainError:== 0.530578 || TestError:== 0.53135\n",
" Epoch :== 6 || TIME(sec):== 1.4771 || TrainError:== 0.529693 || TestError:== 0.529972\n",
" Epoch :== 7 || TIME(sec):== 1.454 || TrainError:== 0.525608 || TestError:== 0.524158\n",
" Epoch :== 8 || TIME(sec):== 1.4641 || TrainError:== 0.520263 || TestError:== 0.519836\n",
" Epoch :== 9 || TIME(sec):== 1.4598 || TrainError:== 0.516465 || TestError:== 0.516924\n",
" Epoch :== 10 || TIME(sec):== 1.4715 || TrainError:== 0.5127 || TestError:== 0.510038\n",
" Epoch :== 11 || TIME(sec):== 1.47 || TrainError:== 0.498689 || TestError:== 0.493766\n",
" Epoch :== 12 || TIME(sec):== 1.4813 || TrainError:== 0.483886 || TestError:== 0.484532\n",
" Epoch :== 13 || TIME(sec):== 1.4749 || TrainError:== 0.475108 || TestError:== 0.478955\n",
" Epoch :== 14 || TIME(sec):== 1.4525 || TrainError:== 0.469532 || TestError:== 0.47097\n",
" Epoch :== 15 || TIME(sec):== 1.4557 || TrainError:== 0.462132 || TestError:== 0.465524\n",
" Epoch :== 16 || TIME(sec):== 1.4606 || TrainError:== 0.453018 || TestError:== 0.458192\n",
" Epoch :== 17 || TIME(sec):== 1.4622 || TrainError:== 0.44454 || TestError:== 0.451876\n",
" Epoch :== 18 || TIME(sec):== 1.4654 || TrainError:== 0.43632 || TestError:== 0.441372\n",
" Epoch :== 19 || TIME(sec):== 1.4657 || TrainError:== 0.426353 || TestError:== 0.44088\n",
" Epoch :== 20 || TIME(sec):== 1.4709 || TrainError:== 0.421146 || TestError:== 0.433586\n",
" Epoch :== 21 || TIME(sec):== 1.4659 || TrainError:== 0.413421 || TestError:== 0.426418\n",
" Epoch :== 22 || TIME(sec):== 1.4701 || TrainError:== 0.406626 || TestError:== 0.420919\n",
" Epoch :== 23 || TIME(sec):== 1.466 || TrainError:== 0.397983 || TestError:== 0.41477\n",
" Epoch :== 24 || TIME(sec):== 1.4605 || TrainError:== 0.392528 || TestError:== 0.408669\n",
" Epoch :== 25 || TIME(sec):== 1.4683 || TrainError:== 0.386098 || TestError:== 0.40414\n",
" Epoch :== 26 || TIME(sec):== 1.4581 || TrainError:== 0.379048 || TestError:== 0.400112\n",
" Epoch :== 27 || TIME(sec):== 1.4664 || TrainError:== 0.37125 || TestError:== 0.389514\n",
" Epoch :== 28 || TIME(sec):== 1.4627 || TrainError:== 0.363935 || TestError:== 0.382536\n",
" Epoch :== 29 || TIME(sec):== 1.469 || TrainError:== 0.360282 || TestError:== 0.375463\n",
" Epoch :== 30 || TIME(sec):== 1.4806 || TrainError:== 0.348398 || TestError:== 0.366629\n",
" Epoch :== 31 || TIME(sec):== 1.4677 || TrainError:== 0.342604 || TestError:== 0.360159\n",
" Epoch :== 32 || TIME(sec):== 1.4575 || TrainError:== 0.335317 || TestError:== 0.354109\n",
" Epoch :== 33 || TIME(sec):== 1.4714 || TrainError:== 0.329533 || TestError:== 0.347928\n",
" Epoch :== 34 || TIME(sec):== 1.4651 || TrainError:== 0.325187 || TestError:== 0.346729\n",
" Epoch :== 35 || TIME(sec):== 1.4712 || TrainError:== 0.32003 || TestError:== 0.338539\n",
" Epoch :== 36 || TIME(sec):== 1.4706 || TrainError:== 0.314184 || TestError:== 0.336639\n",
" Epoch :== 37 || TIME(sec):== 1.4682 || TrainError:== 0.309808 || TestError:== 0.334919\n",
" Epoch :== 38 || TIME(sec):== 1.4627 || TrainError:== 0.306801 || TestError:== 0.330715\n",
" Epoch :== 39 || TIME(sec):== 1.4653 || TrainError:== 0.302137 || TestError:== 0.32551\n",
" Epoch :== 40 || TIME(sec):== 1.4607 || TrainError:== 0.297329 || TestError:== 0.324181\n",
" Epoch :== 41 || TIME(sec):== 1.4662 || TrainError:== 0.293167 || TestError:== 0.319888\n",
" Epoch :== 42 || TIME(sec):== 1.4639 || TrainError:== 0.289533 || TestError:== 0.320493\n",
" Epoch :== 43 || TIME(sec):== 1.4615 || TrainError:== 0.287157 || TestError:== 0.314019\n",
" Epoch :== 44 || TIME(sec):== 1.4661 || TrainError:== 0.280964 || TestError:== 0.311603\n",
" Epoch :== 45 || TIME(sec):== 1.4684 || TrainError:== 0.276674 || TestError:== 0.30868\n",
" Epoch :== 46 || TIME(sec):== 1.466 || TrainError:== 0.273875 || TestError:== 0.304654\n",
" Epoch :== 47 || TIME(sec):== 1.4709 || TrainError:== 0.270845 || TestError:== 0.304227\n",
" Epoch :== 48 || TIME(sec):== 1.4572 || TrainError:== 0.265631 || TestError:== 0.298499\n",
" Epoch :== 49 || TIME(sec):== 1.4659 || TrainError:== 0.262804 || TestError:== 0.295944\n",
" Epoch :== 50 || TIME(sec):== 1.463 || TrainError:== 0.257536 || TestError:== 0.291864\n",
" Epoch :== 51 || TIME(sec):== 1.4643 || TrainError:== 0.25443 || TestError:== 0.290582\n",
" Epoch :== 52 || TIME(sec):== 1.4673 || TrainError:== 0.251645 || TestError:== 0.286568\n",
" Epoch :== 53 || TIME(sec):== 1.4716 || TrainError:== 0.246696 || TestError:== 0.284411\n",
" Epoch :== 54 || TIME(sec):== 1.4627 || TrainError:== 0.244383 || TestError:== 0.282461\n",
" Epoch :== 55 || TIME(sec):== 1.4742 || TrainError:== 0.24278 || TestError:== 0.278346\n",
" Epoch :== 56 || TIME(sec):== 1.4723 || TrainError:== 0.237515 || TestError:== 0.276933\n",
" Epoch :== 57 || TIME(sec):== 1.4718 || TrainError:== 0.234343 || TestError:== 0.274642\n",
" Epoch :== 58 || TIME(sec):== 1.4731 || TrainError:== 0.232383 || TestError:== 0.271151\n",
" Epoch :== 59 || TIME(sec):== 1.4742 || TrainError:== 0.229464 || TestError:== 0.269217\n",
" Epoch :== 60 || TIME(sec):== 1.4727 || TrainError:== 0.226367 || TestError:== 0.266827\n",
" Epoch :== 61 || TIME(sec):== 1.4702 || TrainError:== 0.224655 || TestError:== 0.264506\n",
" Epoch :== 62 || TIME(sec):== 1.4767 || TrainError:== 0.222016 || TestError:== 0.263901\n",
" Epoch :== 63 || TIME(sec):== 1.4667 || TrainError:== 0.221523 || TestError:== 0.262383\n",
" Epoch :== 64 || TIME(sec):== 1.4753 || TrainError:== 0.217129 || TestError:== 0.259347\n",
" Epoch :== 65 || TIME(sec):== 1.4661 || TrainError:== 0.21599 || TestError:== 0.262773\n",
" Epoch :== 66 || TIME(sec):== 1.4698 || TrainError:== 0.212311 || TestError:== 0.255525\n",
" Epoch :== 67 || TIME(sec):== 1.4654 || TrainError:== 0.210537 || TestError:== 0.253145\n",
" Epoch :== 68 || TIME(sec):== 1.469 || TrainError:== 0.208985 || TestError:== 0.255622\n",
" Epoch :== 69 || TIME(sec):== 1.4707 || TrainError:== 0.207798 || TestError:== 0.251946\n",
" Epoch :== 70 || TIME(sec):== 1.4804 || TrainError:== 0.205228 || TestError:== 0.248746\n",
" Epoch :== 71 || TIME(sec):== 1.4792 || TrainError:== 0.203218 || TestError:== 0.250401\n",
" Epoch :== 72 || TIME(sec):== 1.4737 || TrainError:== 0.200561 || TestError:== 0.248801\n",
" Epoch :== 73 || TIME(sec):== 1.471 || TrainError:== 0.199633 || TestError:== 0.244746\n",
" Epoch :== 74 || TIME(sec):== 1.4663 || TrainError:== 0.198651 || TestError:== 0.250228\n",
" Epoch :== 75 || TIME(sec):== 1.4646 || TrainError:== 0.195654 || TestError:== 0.243016\n",
" Epoch :== 76 || TIME(sec):== 1.4772 || TrainError:== 0.194687 || TestError:== 0.243182\n",
" Epoch :== 77 || TIME(sec):== 1.4665 || TrainError:== 0.193047 || TestError:== 0.240193\n",
" Epoch :== 78 || TIME(sec):== 1.464 || TrainError:== 0.190646 || TestError:== 0.243716\n",
" Epoch :== 79 || TIME(sec):== 1.4752 || TrainError:== 0.189499 || TestError:== 0.237598\n",
" Epoch :== 80 || TIME(sec):== 1.463 || TrainError:== 0.182291 || TestError:== 0.233582\n",
" Epoch :== 81 || TIME(sec):== 1.467 || TrainError:== 0.179643 || TestError:== 0.232961\n",
" Epoch :== 82 || TIME(sec):== 1.4781 || TrainError:== 0.178289 || TestError:== 0.231925\n",
" Epoch :== 83 || TIME(sec):== 1.46 || TrainError:== 0.17721 || TestError:== 0.231671\n",
" Epoch :== 84 || TIME(sec):== 1.4722 || TrainError:== 0.176489 || TestError:== 0.230973\n",
" Epoch :== 85 || TIME(sec):== 1.4643 || TrainError:== 0.175478 || TestError:== 0.230533\n",
" Epoch :== 86 || TIME(sec):== 1.4662 || TrainError:== 0.174048 || TestError:== 0.229476\n",
" Epoch :== 87 || TIME(sec):== 1.4616 || TrainError:== 0.172908 || TestError:== 0.227692\n",
" Epoch :== 88 || TIME(sec):== 1.4796 || TrainError:== 0.171828 || TestError:== 0.22874\n",
" Epoch :== 89 || TIME(sec):== 1.4705 || TrainError:== 0.171413 || TestError:== 0.227233\n",
" Epoch :== 90 || TIME(sec):== 1.4747 || TrainError:== 0.169973 || TestError:== 0.22693\n",
" Epoch :== 91 || TIME(sec):== 1.4625 || TrainError:== 0.169109 || TestError:== 0.226999\n",
" Epoch :== 92 || TIME(sec):== 1.4742 || TrainError:== 0.168218 || TestError:== 0.224645\n",
" Epoch :== 93 || TIME(sec):== 1.4724 || TrainError:== 0.167197 || TestError:== 0.224572\n",
" Epoch :== 94 || TIME(sec):== 1.4608 || TrainError:== 0.166324 || TestError:== 0.224586\n",
" Epoch :== 95 || TIME(sec):== 1.4754 || TrainError:== 0.165093 || TestError:== 0.2242\n",
" Epoch :== 96 || TIME(sec):== 1.4654 || TrainError:== 0.163751 || TestError:== 0.222656\n",
" Epoch :== 97 || TIME(sec):== 1.4632 || TrainError:== 0.162831 || TestError:== 0.222802\n",
" Epoch :== 98 || TIME(sec):== 1.4716 || TrainError:== 0.162146 || TestError:== 0.221885\n",
" Epoch :== 99 || TIME(sec):== 1.4714 || TrainError:== 0.161795 || TestError:== 0.221742\n",
" Epoch :== 100 || TIME(sec):== 1.464 || TrainError:== 0.160059 || TestError:== 0.221344\n",
" Epoch :== 101 || TIME(sec):== 1.4641 || TrainError:== 0.160469 || TestError:== 0.221305\n",
" Epoch :== 102 || TIME(sec):== 1.4675 || TrainError:== 0.158583 || TestError:== 0.220014\n",
" Epoch :== 103 || TIME(sec):== 1.482 || TrainError:== 0.157839 || TestError:== 0.219799\n",
" Epoch :== 104 || TIME(sec):== 1.4715 || TrainError:== 0.156925 || TestError:== 0.218916\n",
" Epoch :== 105 || TIME(sec):== 1.4807 || TrainError:== 0.156347 || TestError:== 0.218404\n",
" Epoch :== 106 || TIME(sec):== 1.4672 || TrainError:== 0.155437 || TestError:== 0.218477\n",
" Epoch :== 107 || TIME(sec):== 1.4653 || TrainError:== 0.15502 || TestError:== 0.217389\n",
" Epoch :== 108 || TIME(sec):== 1.469 || TrainError:== 0.154248 || TestError:== 0.217715\n",
" Epoch :== 109 || TIME(sec):== 1.4598 || TrainError:== 0.153736 || TestError:== 0.217161\n",
" Epoch :== 110 || TIME(sec):== 1.4704 || TrainError:== 0.152336 || TestError:== 0.21714\n",
" Epoch :== 111 || TIME(sec):== 1.473 || TrainError:== 0.151179 || TestError:== 0.215627\n",
" Epoch :== 112 || TIME(sec):== 1.4647 || TrainError:== 0.151529 || TestError:== 0.215744\n",
" Epoch :== 113 || TIME(sec):== 1.4967 || TrainError:== 0.150955 || TestError:== 0.215481\n",
" Epoch :== 114 || TIME(sec):== 1.4719 || TrainError:== 0.149138 || TestError:== 0.215297\n",
" Epoch :== 115 || TIME(sec):== 1.4782 || TrainError:== 0.148472 || TestError:== 0.214459\n",
" Epoch :== 116 || TIME(sec):== 1.475 || TrainError:== 0.147788 || TestError:== 0.214019\n",
" Epoch :== 117 || TIME(sec):== 1.4864 || TrainError:== 0.147489 || TestError:== 0.213687\n",
" Epoch :== 118 || TIME(sec):== 1.4719 || TrainError:== 0.147463 || TestError:== 0.214151\n",
" Epoch :== 119 || TIME(sec):== 1.4549 || TrainError:== 0.146154 || TestError:== 0.2139\n",
" Epoch :== 120 || TIME(sec):== 1.464 || TrainError:== 0.14517 || TestError:== 0.213778\n",
" Epoch :== 121 || TIME(sec):== 1.4547 || TrainError:== 0.144744 || TestError:== 0.212528\n",
" Epoch :== 122 || TIME(sec):== 1.4646 || TrainError:== 0.144379 || TestError:== 0.211954\n",
" Epoch :== 123 || TIME(sec):== 1.4575 || TrainError:== 0.143613 || TestError:== 0.21287\n",
" Epoch :== 124 || TIME(sec):== 1.4565 || TrainError:== 0.142675 || TestError:== 0.212728\n",
" Epoch :== 125 || TIME(sec):== 1.4624 || TrainError:== 0.142499 || TestError:== 0.212838\n",
" Epoch :== 126 || TIME(sec):== 1.4612 || TrainError:== 0.141367 || TestError:== 0.212022\n",
" Epoch :== 127 || TIME(sec):== 1.4663 || TrainError:== 0.141289 || TestError:== 0.211509\n",
" Epoch :== 128 || TIME(sec):== 1.4676 || TrainError:== 0.140092 || TestError:== 0.210522\n",
" Epoch :== 129 || TIME(sec):== 1.4584 || TrainError:== 0.139848 || TestError:== 0.212422\n",
" Epoch :== 130 || TIME(sec):== 1.4574 || TrainError:== 0.140201 || TestError:== 0.210408\n",
" Epoch :== 131 || TIME(sec):== 1.4618 || TrainError:== 0.139195 || TestError:== 0.210266\n",
" Epoch :== 132 || TIME(sec):== 1.4506 || TrainError:== 0.13832 || TestError:== 0.209879\n",
" Epoch :== 133 || TIME(sec):== 1.4604 || TrainError:== 0.137149 || TestError:== 0.208812\n",
" Epoch :== 134 || TIME(sec):== 1.454 || TrainError:== 0.136365 || TestError:== 0.208824\n",
" Epoch :== 135 || TIME(sec):== 1.4689 || TrainError:== 0.136908 || TestError:== 0.208659\n",
" Epoch :== 136 || TIME(sec):== 1.4487 || TrainError:== 0.136287 || TestError:== 0.209851\n",
" Epoch :== 137 || TIME(sec):== 1.4608 || TrainError:== 0.135331 || TestError:== 0.20879\n",
" Epoch :== 138 || TIME(sec):== 1.4627 || TrainError:== 0.13536 || TestError:== 0.208435\n",
" Epoch :== 139 || TIME(sec):== 1.4547 || TrainError:== 0.134388 || TestError:== 0.206592\n",
" Epoch :== 140 || TIME(sec):== 1.4488 || TrainError:== 0.134125 || TestError:== 0.207206\n",
" Epoch :== 141 || TIME(sec):== 1.4482 || TrainError:== 0.133354 || TestError:== 0.206687\n",
" Epoch :== 142 || TIME(sec):== 1.4269 || TrainError:== 0.132738 || TestError:== 0.207193\n",
" Epoch :== 143 || TIME(sec):== 1.4266 || TrainError:== 0.132502 || TestError:== 0.206317\n",
" Epoch :== 144 || TIME(sec):== 1.4638 || TrainError:== 0.132413 || TestError:== 0.206499\n",
" Epoch :== 145 || TIME(sec):== 1.4689 || TrainError:== 0.131131 || TestError:== 0.208\n",
" Epoch :== 146 || TIME(sec):== 1.4642 || TrainError:== 0.130585 || TestError:== 0.205493\n",
" Epoch :== 147 || TIME(sec):== 1.4581 || TrainError:== 0.130405 || TestError:== 0.20733\n",
" Epoch :== 148 || TIME(sec):== 1.4507 || TrainError:== 0.129915 || TestError:== 0.205914\n",
" Epoch :== 149 || TIME(sec):== 1.4619 || TrainError:== 0.12964 || TestError:== 0.205585\n",
" Epoch :== 150 || TIME(sec):== 1.4557 || TrainError:== 0.129601 || TestError:== 0.205906\n",
" Epoch :== 151 || TIME(sec):== 1.4633 || TrainError:== 0.128562 || TestError:== 0.206292\n",
" Epoch :== 152 || TIME(sec):== 1.4629 || TrainError:== 0.12757 || TestError:== 0.205316\n",
" Epoch :== 153 || TIME(sec):== 1.4461 || TrainError:== 0.127227 || TestError:== 0.205249\n",
" Epoch :== 154 || TIME(sec):== 1.4704 || TrainError:== 0.126809 || TestError:== 0.204592\n",
" Epoch :== 155 || TIME(sec):== 1.4611 || TrainError:== 0.126346 || TestError:== 0.204364\n",
" Epoch :== 156 || TIME(sec):== 1.4597 || TrainError:== 0.126505 || TestError:== 0.204287\n",
" Epoch :== 157 || TIME(sec):== 1.46 || TrainError:== 0.12583 || TestError:== 0.205157\n",
" Epoch :== 158 || TIME(sec):== 1.461 || TrainError:== 0.125609 || TestError:== 0.20439\n",
" Epoch :== 159 || TIME(sec):== 1.4652 || TrainError:== 0.125091 || TestError:== 0.20466\n",
" Epoch :== 160 || TIME(sec):== 1.4605 || TrainError:== 0.121923 || TestError:== 0.202046\n",
" Epoch :== 161 || TIME(sec):== 1.4691 || TrainError:== 0.120457 || TestError:== 0.202178\n",
" Epoch :== 162 || TIME(sec):== 1.4716 || TrainError:== 0.120156 || TestError:== 0.202096\n",
" Epoch :== 163 || TIME(sec):== 1.4475 || TrainError:== 0.119803 || TestError:== 0.202292\n",
" Epoch :== 164 || TIME(sec):== 1.4569 || TrainError:== 0.119571 || TestError:== 0.202098\n",
" Epoch :== 165 || TIME(sec):== 1.4615 || TrainError:== 0.119304 || TestError:== 0.202038\n",
" Epoch :== 166 || TIME(sec):== 1.46 || TrainError:== 0.11929 || TestError:== 0.20238\n",
" Epoch :== 167 || TIME(sec):== 1.4597 || TrainError:== 0.118875 || TestError:== 0.201497\n",
" Epoch :== 168 || TIME(sec):== 1.4685 || TrainError:== 0.118283 || TestError:== 0.201926\n",
" Epoch :== 169 || TIME(sec):== 1.46 || TrainError:== 0.118019 || TestError:== 0.201661\n",
" Epoch :== 170 || TIME(sec):== 1.454 || TrainError:== 0.117881 || TestError:== 0.201912\n",
" Epoch :== 171 || TIME(sec):== 1.4475 || TrainError:== 0.11741 || TestError:== 0.201962\n",
" Epoch :== 172 || TIME(sec):== 1.4531 || TrainError:== 0.116925 || TestError:== 0.201045\n",
" Epoch :== 173 || TIME(sec):== 1.4623 || TrainError:== 0.116719 || TestError:== 0.20133\n",
" Epoch :== 174 || TIME(sec):== 1.4604 || TrainError:== 0.116461 || TestError:== 0.201422\n",
" Epoch :== 175 || TIME(sec):== 1.448 || TrainError:== 0.116523 || TestError:== 0.200703\n",
" Epoch :== 176 || TIME(sec):== 1.4586 || TrainError:== 0.116135 || TestError:== 0.200623\n",
" Epoch :== 177 || TIME(sec):== 1.4534 || TrainError:== 0.115648 || TestError:== 0.201135\n",
" Epoch :== 178 || TIME(sec):== 1.4555 || TrainError:== 0.115336 || TestError:== 0.200872\n",
" Epoch :== 179 || TIME(sec):== 1.4558 || TrainError:== 0.115116 || TestError:== 0.200904\n",
" Epoch :== 180 || TIME(sec):== 1.4573 || TrainError:== 0.114684 || TestError:== 0.200967\n",
" Epoch :== 181 || TIME(sec):== 1.4594 || TrainError:== 0.114323 || TestError:== 0.200516\n",
" Epoch :== 182 || TIME(sec):== 1.4498 || TrainError:== 0.1141 || TestError:== 0.200452\n",
" Epoch :== 183 || TIME(sec):== 1.4558 || TrainError:== 0.113794 || TestError:== 0.200892\n",
" Epoch :== 184 || TIME(sec):== 1.4708 || TrainError:== 0.113627 || TestError:== 0.200658\n",
" Epoch :== 185 || TIME(sec):== 1.4719 || TrainError:== 0.113427 || TestError:== 0.199991\n",
" Epoch :== 186 || TIME(sec):== 1.4713 || TrainError:== 0.113164 || TestError:== 0.200569\n",
" Epoch :== 187 || TIME(sec):== 1.4547 || TrainError:== 0.11289 || TestError:== 0.200812\n",
" Epoch :== 188 || TIME(sec):== 1.4617 || TrainError:== 0.112649 || TestError:== 0.200428\n",
" Epoch :== 189 || TIME(sec):== 1.4552 || TrainError:== 0.112355 || TestError:== 0.199839\n",
" Epoch :== 190 || TIME(sec):== 1.4581 || TrainError:== 0.111905 || TestError:== 0.200116\n",
" Epoch :== 191 || TIME(sec):== 1.4621 || TrainError:== 0.111789 || TestError:== 0.1997\n",
" Epoch :== 192 || TIME(sec):== 1.4792 || TrainError:== 0.112016 || TestError:== 0.200093\n",
" Epoch :== 193 || TIME(sec):== 1.4794 || TrainError:== 0.111266 || TestError:== 0.199508\n",
" Epoch :== 194 || TIME(sec):== 1.4761 || TrainError:== 0.110915 || TestError:== 0.20014\n",
" Epoch :== 195 || TIME(sec):== 1.4828 || TrainError:== 0.110648 || TestError:== 0.199273\n",
" Epoch :== 196 || TIME(sec):== 1.4849 || TrainError:== 0.110248 || TestError:== 0.199964\n",
" Epoch :== 197 || TIME(sec):== 1.4716 || TrainError:== 0.110242 || TestError:== 0.199467\n",
" Epoch :== 198 || TIME(sec):== 1.4756 || TrainError:== 0.11026 || TestError:== 0.199215\n",
" Epoch :== 199 || TIME(sec):== 1.4799 || TrainError:== 0.109724 || TestError:== 0.199239\n",
" Epoch :== 200 || TIME(sec):== 1.4703 || TrainError:== 0.109365 || TestError:== 0.199355\n",
" Epoch :== 201 || TIME(sec):== 1.472 || TrainError:== 0.109517 || TestError:== 0.199464\n",
" Epoch :== 202 || TIME(sec):== 1.4759 || TrainError:== 0.109234 || TestError:== 0.199322\n",
" Epoch :== 203 || TIME(sec):== 1.4652 || TrainError:== 0.108697 || TestError:== 0.199287\n",
" Epoch :== 204 || TIME(sec):== 1.47 || TrainError:== 0.108422 || TestError:== 0.199492\n",
" Epoch :== 205 || TIME(sec):== 1.4793 || TrainError:== 0.108483 || TestError:== 0.198937\n",
" Epoch :== 206 || TIME(sec):== 1.4762 || TrainError:== 0.108152 || TestError:== 0.199008\n",
" Epoch :== 207 || TIME(sec):== 1.4782 || TrainError:== 0.107961 || TestError:== 0.198993\n",
" Epoch :== 208 || TIME(sec):== 1.4763 || TrainError:== 0.107807 || TestError:== 0.199713\n",
" Epoch :== 209 || TIME(sec):== 1.4667 || TrainError:== 0.107619 || TestError:== 0.199222\n",
" Epoch :== 210 || TIME(sec):== 1.4901 || TrainError:== 0.107045 || TestError:== 0.199179\n",
" Epoch :== 211 || TIME(sec):== 1.4623 || TrainError:== 0.107001 || TestError:== 0.199126\n",
" Epoch :== 212 || TIME(sec):== 1.4972 || TrainError:== 0.106836 || TestError:== 0.198994\n",
" Epoch :== 213 || TIME(sec):== 1.4771 || TrainError:== 0.106922 || TestError:== 0.198753\n",
" Epoch :== 214 || TIME(sec):== 1.4748 || TrainError:== 0.106762 || TestError:== 0.198719\n",
" Epoch :== 215 || TIME(sec):== 1.4944 || TrainError:== 0.10651 || TestError:== 0.198426\n",
" Epoch :== 216 || TIME(sec):== 1.4725 || TrainError:== 0.106194 || TestError:== 0.198774\n",
" Epoch :== 217 || TIME(sec):== 1.4867 || TrainError:== 0.105965 || TestError:== 0.198655\n",
" Epoch :== 218 || TIME(sec):== 1.4788 || TrainError:== 0.105772 || TestError:== 0.198211\n",
" Epoch :== 219 || TIME(sec):== 1.4746 || TrainError:== 0.105441 || TestError:== 0.198379\n",
" Epoch :== 220 || TIME(sec):== 1.4851 || TrainError:== 0.105206 || TestError:== 0.198165\n",
" Epoch :== 221 || TIME(sec):== 1.4752 || TrainError:== 0.104813 || TestError:== 0.198176\n",
" Epoch :== 222 || TIME(sec):== 1.4723 || TrainError:== 0.1046 || TestError:== 0.198532\n",
" Epoch :== 223 || TIME(sec):== 1.4756 || TrainError:== 0.104674 || TestError:== 0.198669\n",
" Epoch :== 224 || TIME(sec):== 1.4755 || TrainError:== 0.10467 || TestError:== 0.198348\n",
" Epoch :== 225 || TIME(sec):== 1.4619 || TrainError:== 0.104269 || TestError:== 0.198537\n",
" Epoch :== 226 || TIME(sec):== 1.4654 || TrainError:== 0.103997 || TestError:== 0.198203\n",
" Epoch :== 227 || TIME(sec):== 1.4572 || TrainError:== 0.103787 || TestError:== 0.197984\n",
" Epoch :== 228 || TIME(sec):== 1.4146 || TrainError:== 0.103449 || TestError:== 0.19787\n",
" Epoch :== 229 || TIME(sec):== 1.4099 || TrainError:== 0.103342 || TestError:== 0.19779\n",
" Epoch :== 230 || TIME(sec):== 1.4272 || TrainError:== 0.103516 || TestError:== 0.198011\n",
" Epoch :== 231 || TIME(sec):== 1.4208 || TrainError:== 0.103149 || TestError:== 0.19813\n",
" Epoch :== 232 || TIME(sec):== 1.4205 || TrainError:== 0.102919 || TestError:== 0.197563\n",
" Epoch :== 233 || TIME(sec):== 1.4288 || TrainError:== 0.102627 || TestError:== 0.19774\n",
" Epoch :== 234 || TIME(sec):== 1.4156 || TrainError:== 0.102678 || TestError:== 0.197754\n",
" Epoch :== 235 || TIME(sec):== 1.4157 || TrainError:== 0.102216 || TestError:== 0.198241\n",
" Epoch :== 236 || TIME(sec):== 1.4078 || TrainError:== 0.102103 || TestError:== 0.197923\n",
" Epoch :== 237 || TIME(sec):== 1.4337 || TrainError:== 0.102032 || TestError:== 0.197619\n",
" Epoch :== 238 || TIME(sec):== 1.4734 || TrainError:== 0.101825 || TestError:== 0.197751\n",
" Epoch :== 239 || TIME(sec):== 1.482 || TrainError:== 0.101326 || TestError:== 0.197369\n",
" Epoch :== 240 || TIME(sec):== 1.4727 || TrainError:== 0.100112 || TestError:== 0.19658\n",
" Epoch :== 241 || TIME(sec):== 1.4694 || TrainError:== 0.099624 || TestError:== 0.196951\n",
" Epoch :== 242 || TIME(sec):== 1.4755 || TrainError:== 0.099457 || TestError:== 0.197111\n",
" Epoch :== 243 || TIME(sec):== 1.4704 || TrainError:== 0.099382 || TestError:== 0.197141\n",
" Epoch :== 244 || TIME(sec):== 1.4821 || TrainError:== 0.099328 || TestError:== 0.196852\n",
" Epoch :== 245 || TIME(sec):== 1.478 || TrainError:== 0.099191 || TestError:== 0.197131\n",
" Epoch :== 246 || TIME(sec):== 1.4794 || TrainError:== 0.099128 || TestError:== 0.197191\n",
" Epoch :== 247 || TIME(sec):== 1.4866 || TrainError:== 0.098965 || TestError:== 0.196934\n",
" Epoch :== 248 || TIME(sec):== 1.4797 || TrainError:== 0.098758 || TestError:== 0.196692\n",
" Epoch :== 249 || TIME(sec):== 1.4761 || TrainError:== 0.098598 || TestError:== 0.196839\n",
" Epoch :== 250 || TIME(sec):== 1.4735 || TrainError:== 0.098464 || TestError:== 0.196837\n",
" Epoch :== 251 || TIME(sec):== 1.4778 || TrainError:== 0.09836 || TestError:== 0.196892\n",
" Epoch :== 252 || TIME(sec):== 1.4743 || TrainError:== 0.098175 || TestError:== 0.196738\n",
" Epoch :== 253 || TIME(sec):== 1.483 || TrainError:== 0.098046 || TestError:== 0.196885\n",
" Epoch :== 254 || TIME(sec):== 1.4864 || TrainError:== 0.097895 || TestError:== 0.197051\n",
" Epoch :== 255 || TIME(sec):== 1.4875 || TrainError:== 0.097793 || TestError:== 0.196788\n",
" Epoch :== 256 || TIME(sec):== 1.476 || TrainError:== 0.097629 || TestError:== 0.197011\n",
" Epoch :== 257 || TIME(sec):== 1.4722 || TrainError:== 0.097555 || TestError:== 0.196521\n",
" Epoch :== 258 || TIME(sec):== 1.4647 || TrainError:== 0.09732 || TestError:== 0.196702\n",
" Epoch :== 259 || TIME(sec):== 1.4684 || TrainError:== 0.097193 || TestError:== 0.196995\n",
" Epoch :== 260 || TIME(sec):== 1.4703 || TrainError:== 0.097113 || TestError:== 0.196461\n",
" Epoch :== 261 || TIME(sec):== 1.4861 || TrainError:== 0.097071 || TestError:== 0.197221\n",
" Epoch :== 262 || TIME(sec):== 1.4733 || TrainError:== 0.096929 || TestError:== 0.196398\n",
" Epoch :== 263 || TIME(sec):== 1.4892 || TrainError:== 0.096745 || TestError:== 0.197065\n",
" Epoch :== 264 || TIME(sec):== 1.476 || TrainError:== 0.096566 || TestError:== 0.196797\n",
" Epoch :== 265 || TIME(sec):== 1.4761 || TrainError:== 0.096518 || TestError:== 0.196744\n",
" Epoch :== 266 || TIME(sec):== 1.4665 || TrainError:== 0.096341 || TestError:== 0.196688\n",
" Epoch :== 267 || TIME(sec):== 1.467 || TrainError:== 0.096138 || TestError:== 0.196556\n",
" Epoch :== 268 || TIME(sec):== 1.4793 || TrainError:== 0.096084 || TestError:== 0.196388\n",
" Epoch :== 269 || TIME(sec):== 1.4841 || TrainError:== 0.095902 || TestError:== 0.196577\n",
" Epoch :== 270 || TIME(sec):== 1.4839 || TrainError:== 0.095748 || TestError:== 0.196699\n",
" Epoch :== 271 || TIME(sec):== 1.4743 || TrainError:== 0.095676 || TestError:== 0.196709\n",
" Epoch :== 272 || TIME(sec):== 1.4691 || TrainError:== 0.095586 || TestError:== 0.196502\n",
" Epoch :== 273 || TIME(sec):== 1.4847 || TrainError:== 0.095423 || TestError:== 0.196865\n",
" Epoch :== 274 || TIME(sec):== 1.4834 || TrainError:== 0.095314 || TestError:== 0.196696\n",
" Epoch :== 275 || TIME(sec):== 1.464 || TrainError:== 0.095276 || TestError:== 0.19671\n",
" Epoch :== 276 || TIME(sec):== 1.4781 || TrainError:== 0.095141 || TestError:== 0.196892\n",
" Epoch :== 277 || TIME(sec):== 1.4749 || TrainError:== 0.09496 || TestError:== 0.19613\n",
" Epoch :== 278 || TIME(sec):== 1.4736 || TrainError:== 0.094869 || TestError:== 0.196278\n",
" Epoch :== 279 || TIME(sec):== 1.4791 || TrainError:== 0.094673 || TestError:== 0.196658\n",
" Epoch :== 280 || TIME(sec):== 1.487 || TrainError:== 0.094598 || TestError:== 0.196821\n",
" Epoch :== 281 || TIME(sec):== 1.4761 || TrainError:== 0.094404 || TestError:== 0.196731\n",
" Epoch :== 282 || TIME(sec):== 1.4727 || TrainError:== 0.094359 || TestError:== 0.196489\n",
" Epoch :== 283 || TIME(sec):== 1.4707 || TrainError:== 0.094273 || TestError:== 0.196656\n",
" Epoch :== 284 || TIME(sec):== 1.4708 || TrainError:== 0.094187 || TestError:== 0.196718\n",
" Epoch :== 285 || TIME(sec):== 1.4806 || TrainError:== 0.094062 || TestError:== 0.196619\n",
" Epoch :== 286 || TIME(sec):== 1.4903 || TrainError:== 0.09388 || TestError:== 0.196496\n",
" Epoch :== 287 || TIME(sec):== 1.4693 || TrainError:== 0.093778 || TestError:== 0.196399\n",
" Epoch :== 288 || TIME(sec):== 1.4823 || TrainError:== 0.093711 || TestError:== 0.196356\n",
" Epoch :== 289 || TIME(sec):== 1.4691 || TrainError:== 0.093689 || TestError:== 0.196478\n",
" Epoch :== 290 || TIME(sec):== 1.4711 || TrainError:== 0.093511 || TestError:== 0.19646\n",
" Epoch :== 291 || TIME(sec):== 1.4763 || TrainError:== 0.093355 || TestError:== 0.196295\n",
" Epoch :== 292 || TIME(sec):== 1.4821 || TrainError:== 0.093282 || TestError:== 0.196382\n",
" Epoch :== 293 || TIME(sec):== 1.4697 || TrainError:== 0.093109 || TestError:== 0.196506\n",
" Epoch :== 294 || TIME(sec):== 1.4757 || TrainError:== 0.092943 || TestError:== 0.196295\n",
" Epoch :== 295 || TIME(sec):== 1.4653 || TrainError:== 0.09284 || TestError:== 0.196305\n",
" Epoch :== 296 || TIME(sec):== 1.4709 || TrainError:== 0.092739 || TestError:== 0.19626\n",
" Epoch :== 297 || TIME(sec):== 1.4738 || TrainError:== 0.092691 || TestError:== 0.196125\n",
" Epoch :== 298 || TIME(sec):== 1.4764 || TrainError:== 0.092718 || TestError:== 0.196292\n",
" Epoch :== 299 || TIME(sec):== 1.478 || TrainError:== 0.092478 || TestError:== 0.196257\n",
" Epoch :== 300 || TIME(sec):== 1.4854 || TrainError:== 0.092348 || TestError:== 0.196432\n",
" Epoch :== 301 || TIME(sec):== 1.4794 || TrainError:== 0.09215 || TestError:== 0.196297\n",
" Epoch :== 302 || TIME(sec):== 1.4597 || TrainError:== 0.09211 || TestError:== 0.19599\n",
" Epoch :== 303 || TIME(sec):== 1.4722 || TrainError:== 0.092082 || TestError:== 0.196181\n",
" Epoch :== 304 || TIME(sec):== 1.4758 || TrainError:== 0.09201 || TestError:== 0.196409\n",
" Epoch :== 305 || TIME(sec):== 1.475 || TrainError:== 0.091869 || TestError:== 0.196417\n",
" Epoch :== 306 || TIME(sec):== 1.4779 || TrainError:== 0.091657 || TestError:== 0.195964\n",
" Epoch :== 307 || TIME(sec):== 1.4764 || TrainError:== 0.091649 || TestError:== 0.196779\n",
" Epoch :== 308 || TIME(sec):== 1.4791 || TrainError:== 0.091533 || TestError:== 0.196143\n",
" Epoch :== 309 || TIME(sec):== 1.4726 || TrainError:== 0.091429 || TestError:== 0.196171\n",
" Epoch :== 310 || TIME(sec):== 1.4742 || TrainError:== 0.091397 || TestError:== 0.196518\n",
" Epoch :== 311 || TIME(sec):== 1.471 || TrainError:== 0.091279 || TestError:== 0.196213\n",
" Epoch :== 312 || TIME(sec):== 1.4781 || TrainError:== 0.091087 || TestError:== 0.196191\n",
" Epoch :== 313 || TIME(sec):== 1.4865 || TrainError:== 0.090999 || TestError:== 0.196244\n",
" Epoch :== 314 || TIME(sec):== 1.4723 || TrainError:== 0.090994 || TestError:== 0.195873\n",
" Epoch :== 315 || TIME(sec):== 1.4823 || TrainError:== 0.090922 || TestError:== 0.1962\n",
" Epoch :== 316 || TIME(sec):== 1.4996 || TrainError:== 0.090812 || TestError:== 0.196504\n",
" Epoch :== 317 || TIME(sec):== 1.5151 || TrainError:== 0.090706 || TestError:== 0.196043\n",
" Epoch :== 318 || TIME(sec):== 1.4954 || TrainError:== 0.090463 || TestError:== 0.196103\n",
" Epoch :== 319 || TIME(sec):== 1.4673 || TrainError:== 0.090393 || TestError:== 0.196019\n",
" Epoch :== 320 || TIME(sec):== 1.4723 || TrainError:== 0.089834 || TestError:== 0.196087\n",
" Epoch :== 321 || TIME(sec):== 1.4749 || TrainError:== 0.089583 || TestError:== 0.195884\n",
" Epoch :== 322 || TIME(sec):== 1.4822 || TrainError:== 0.089513 || TestError:== 0.195986\n",
" Epoch :== 323 || TIME(sec):== 1.475 || TrainError:== 0.089462 || TestError:== 0.195951\n",
" Epoch :== 324 || TIME(sec):== 1.464 || TrainError:== 0.089388 || TestError:== 0.196065\n",
" Epoch :== 325 || TIME(sec):== 1.4678 || TrainError:== 0.08934 || TestError:== 0.195984\n",
" Epoch :== 326 || TIME(sec):== 1.4658 || TrainError:== 0.089293 || TestError:== 0.196071\n",
" Epoch :== 327 || TIME(sec):== 1.4699 || TrainError:== 0.089196 || TestError:== 0.196021\n",
" Epoch :== 328 || TIME(sec):== 1.4605 || TrainError:== 0.089119 || TestError:== 0.195929\n",
" Epoch :== 329 || TIME(sec):== 1.461 || TrainError:== 0.089037 || TestError:== 0.19594\n",
" Epoch :== 330 || TIME(sec):== 1.4749 || TrainError:== 0.088968 || TestError:== 0.196111\n",
" Epoch :== 331 || TIME(sec):== 1.4701 || TrainError:== 0.088894 || TestError:== 0.19603\n",
" Epoch :== 332 || TIME(sec):== 1.4709 || TrainError:== 0.088823 || TestError:== 0.196058\n",
" Epoch :== 333 || TIME(sec):== 1.4662 || TrainError:== 0.088743 || TestError:== 0.196028\n",
" Epoch :== 334 || TIME(sec):== 1.4699 || TrainError:== 0.088669 || TestError:== 0.196069\n",
" Epoch :== 335 || TIME(sec):== 1.4745 || TrainError:== 0.0886 || TestError:== 0.195927\n",
" Epoch :== 336 || TIME(sec):== 1.4609 || TrainError:== 0.088517 || TestError:== 0.195973\n",
" Epoch :== 337 || TIME(sec):== 1.468 || TrainError:== 0.088449 || TestError:== 0.196153\n",
" Epoch :== 338 || TIME(sec):== 1.471 || TrainError:== 0.088372 || TestError:== 0.195999\n",
" Epoch :== 339 || TIME(sec):== 1.4657 || TrainError:== 0.088296 || TestError:== 0.196157\n",
" Epoch :== 340 || TIME(sec):== 1.4736 || TrainError:== 0.088236 || TestError:== 0.196167\n",
" Epoch :== 341 || TIME(sec):== 1.4754 || TrainError:== 0.088178 || TestError:== 0.196101\n",
" Epoch :== 342 || TIME(sec):== 1.4664 || TrainError:== 0.088091 || TestError:== 0.19612\n",
" Epoch :== 343 || TIME(sec):== 1.4632 || TrainError:== 0.088004 || TestError:== 0.196206\n",
" Epoch :== 344 || TIME(sec):== 1.462 || TrainError:== 0.08794 || TestError:== 0.196033\n",
" Epoch :== 345 || TIME(sec):== 1.4699 || TrainError:== 0.087866 || TestError:== 0.195944\n",
" Epoch :== 346 || TIME(sec):== 1.4767 || TrainError:== 0.087779 || TestError:== 0.196066\n",
" Epoch :== 347 || TIME(sec):== 1.4645 || TrainError:== 0.087726 || TestError:== 0.196073\n",
" Epoch :== 348 || TIME(sec):== 1.4729 || TrainError:== 0.087664 || TestError:== 0.196051\n",
" Epoch :== 349 || TIME(sec):== 1.4657 || TrainError:== 0.087565 || TestError:== 0.196101\n",
" Epoch :== 350 || TIME(sec):== 1.4602 || TrainError:== 0.087506 || TestError:== 0.196139\n",
" Epoch :== 351 || TIME(sec):== 1.4652 || TrainError:== 0.087423 || TestError:== 0.196133\n",
" Epoch :== 352 || TIME(sec):== 1.4781 || TrainError:== 0.087355 || TestError:== 0.196116\n",
" Epoch :== 353 || TIME(sec):== 1.4669 || TrainError:== 0.087302 || TestError:== 0.196104\n",
" Epoch :== 354 || TIME(sec):== 1.474 || TrainError:== 0.087246 || TestError:== 0.196129\n",
" Epoch :== 355 || TIME(sec):== 1.485 || TrainError:== 0.08714 || TestError:== 0.196052\n",
" Epoch :== 356 || TIME(sec):== 1.4719 || TrainError:== 0.087092 || TestError:== 0.196122\n",
" Epoch :== 357 || TIME(sec):== 1.4753 || TrainError:== 0.087037 || TestError:== 0.196155\n",
" Epoch :== 358 || TIME(sec):== 1.4798 || TrainError:== 0.086969 || TestError:== 0.196019\n",
" Epoch :== 359 || TIME(sec):== 1.4756 || TrainError:== 0.086875 || TestError:== 0.19618\n",
" Epoch :== 360 || TIME(sec):== 1.4718 || TrainError:== 0.086792 || TestError:== 0.196036\n",
" Epoch :== 361 || TIME(sec):== 1.4664 || TrainError:== 0.086739 || TestError:== 0.196055\n",
" Epoch :== 362 || TIME(sec):== 1.472 || TrainError:== 0.086681 || TestError:== 0.196208\n",
" Epoch :== 363 || TIME(sec):== 1.4703 || TrainError:== 0.086634 || TestError:== 0.196006\n",
" Epoch :== 364 || TIME(sec):== 1.4667 || TrainError:== 0.086536 || TestError:== 0.196218\n",
" Epoch :== 365 || TIME(sec):== 1.4824 || TrainError:== 0.086471 || TestError:== 0.196073\n",
" Epoch :== 366 || TIME(sec):== 1.4737 || TrainError:== 0.086402 || TestError:== 0.196062\n",
" Epoch :== 367 || TIME(sec):== 1.4701 || TrainError:== 0.086334 || TestError:== 0.196113\n",
" Epoch :== 368 || TIME(sec):== 1.4647 || TrainError:== 0.086294 || TestError:== 0.196102\n",
" Epoch :== 369 || TIME(sec):== 1.463 || TrainError:== 0.086201 || TestError:== 0.196107\n",
" Epoch :== 370 || TIME(sec):== 1.466 || TrainError:== 0.086127 || TestError:== 0.196145\n",
" Epoch :== 371 || TIME(sec):== 1.467 || TrainError:== 0.08608 || TestError:== 0.195983\n",
" Epoch :== 372 || TIME(sec):== 1.4506 || TrainError:== 0.086029 || TestError:== 0.196197\n",
" Epoch :== 373 || TIME(sec):== 1.4536 || TrainError:== 0.085955 || TestError:== 0.196251\n",
" Epoch :== 374 || TIME(sec):== 1.4564 || TrainError:== 0.085882 || TestError:== 0.196041\n",
" Epoch :== 375 || TIME(sec):== 1.4548 || TrainError:== 0.085832 || TestError:== 0.196131\n",
" Epoch :== 376 || TIME(sec):== 1.4506 || TrainError:== 0.085776 || TestError:== 0.196009\n",
" Epoch :== 377 || TIME(sec):== 1.4564 || TrainError:== 0.085709 || TestError:== 0.196101\n",
" Epoch :== 378 || TIME(sec):== 1.4598 || TrainError:== 0.085632 || TestError:== 0.195991\n",
" Epoch :== 379 || TIME(sec):== 1.4591 || TrainError:== 0.085573 || TestError:== 0.196153\n",
" Epoch :== 380 || TIME(sec):== 1.4563 || TrainError:== 0.085523 || TestError:== 0.196222\n",
" Epoch :== 381 || TIME(sec):== 1.4643 || TrainError:== 0.085449 || TestError:== 0.196103\n",
" Epoch :== 382 || TIME(sec):== 1.4478 || TrainError:== 0.085359 || TestError:== 0.196236\n",
" Epoch :== 383 || TIME(sec):== 1.4536 || TrainError:== 0.085293 || TestError:== 0.196207\n",
" Epoch :== 384 || TIME(sec):== 1.4555 || TrainError:== 0.085231 || TestError:== 0.195928\n",
" Epoch :== 385 || TIME(sec):== 1.4614 || TrainError:== 0.085165 || TestError:== 0.196187\n",
" Epoch :== 386 || TIME(sec):== 1.4546 || TrainError:== 0.085104 || TestError:== 0.1962\n",
" Epoch :== 387 || TIME(sec):== 1.4653 || TrainError:== 0.085076 || TestError:== 0.196111\n",
" Epoch :== 388 || TIME(sec):== 1.4624 || TrainError:== 0.085016 || TestError:== 0.196129\n",
" Epoch :== 389 || TIME(sec):== 1.469 || TrainError:== 0.084943 || TestError:== 0.196243\n",
" Epoch :== 390 || TIME(sec):== 1.4631 || TrainError:== 0.084912 || TestError:== 0.196084\n",
" Epoch :== 391 || TIME(sec):== 1.4708 || TrainError:== 0.084833 || TestError:== 0.196072\n",
" Epoch :== 392 || TIME(sec):== 1.4631 || TrainError:== 0.084754 || TestError:== 0.196048\n",
" Epoch :== 393 || TIME(sec):== 1.4515 || TrainError:== 0.084696 || TestError:== 0.196208\n",
" Epoch :== 394 || TIME(sec):== 1.4665 || TrainError:== 0.08464 || TestError:== 0.196134\n",
" Epoch :== 395 || TIME(sec):== 1.4654 || TrainError:== 0.084553 || TestError:== 0.196123\n",
" Epoch :== 396 || TIME(sec):== 1.47 || TrainError:== 0.084505 || TestError:== 0.196106\n",
" Epoch :== 397 || TIME(sec):== 1.4549 || TrainError:== 0.08443 || TestError:== 0.196086\n",
" Epoch :== 398 || TIME(sec):== 1.4529 || TrainError:== 0.084395 || TestError:== 0.196245\n",
" Epoch :== 399 || TIME(sec):== 1.4569 || TrainError:== 0.084317 || TestError:== 0.196084\n",
" Epoch :== 400 || TIME(sec):== 1.4749 || TrainError:== 0.084029 || TestError:== 0.196058\n",
" Epoch :== 401 || TIME(sec):== 1.4631 || TrainError:== 0.083928 || TestError:== 0.196117\n",
" Epoch :== 402 || TIME(sec):== 1.4594 || TrainError:== 0.083899 || TestError:== 0.19613\n",
" Epoch :== 403 || TIME(sec):== 1.465 || TrainError:== 0.083867 || TestError:== 0.196122\n",
" Epoch :== 404 || TIME(sec):== 1.4592 || TrainError:== 0.083832 || TestError:== 0.196039\n",
" Epoch :== 405 || TIME(sec):== 1.4549 || TrainError:== 0.083801 || TestError:== 0.196098\n",
" Epoch :== 406 || TIME(sec):== 1.463 || TrainError:== 0.083759 || TestError:== 0.196121\n",
" Epoch :== 407 || TIME(sec):== 1.461 || TrainError:== 0.083719 || TestError:== 0.196112\n",
" Epoch :== 408 || TIME(sec):== 1.4791 || TrainError:== 0.083674 || TestError:== 0.196118\n",
" Epoch :== 409 || TIME(sec):== 1.4754 || TrainError:== 0.083633 || TestError:== 0.196098\n",
" Epoch :== 410 || TIME(sec):== 1.4709 || TrainError:== 0.083595 || TestError:== 0.196148\n",
" Epoch :== 411 || TIME(sec):== 1.481 || TrainError:== 0.083554 || TestError:== 0.196103\n",
" Epoch :== 412 || TIME(sec):== 1.4814 || TrainError:== 0.083515 || TestError:== 0.19616\n",
" Epoch :== 413 || TIME(sec):== 1.4697 || TrainError:== 0.083468 || TestError:== 0.19613\n",
" Epoch :== 414 || TIME(sec):== 1.4628 || TrainError:== 0.083426 || TestError:== 0.196161\n",
" Epoch :== 415 || TIME(sec):== 1.4723 || TrainError:== 0.083389 || TestError:== 0.196204\n",
" Epoch :== 416 || TIME(sec):== 1.4745 || TrainError:== 0.083355 || TestError:== 0.196176\n",
" Epoch :== 417 || TIME(sec):== 1.4718 || TrainError:== 0.083307 || TestError:== 0.196155\n",
" Epoch :== 418 || TIME(sec):== 1.4836 || TrainError:== 0.083264 || TestError:== 0.196153\n",
" Epoch :== 419 || TIME(sec):== 1.4834 || TrainError:== 0.083221 || TestError:== 0.196143\n",
" Epoch :== 420 || TIME(sec):== 1.4803 || TrainError:== 0.083182 || TestError:== 0.196167\n",
" Epoch :== 421 || TIME(sec):== 1.4734 || TrainError:== 0.083143 || TestError:== 0.196183\n",
" Epoch :== 422 || TIME(sec):== 1.4758 || TrainError:== 0.083104 || TestError:== 0.196201\n",
" Epoch :== 423 || TIME(sec):== 1.4826 || TrainError:== 0.083063 || TestError:== 0.196199\n",
" Epoch :== 424 || TIME(sec):== 1.4732 || TrainError:== 0.083028 || TestError:== 0.196218\n",
" Epoch :== 425 || TIME(sec):== 1.4643 || TrainError:== 0.082974 || TestError:== 0.19623\n",
" Epoch :== 426 || TIME(sec):== 1.4729 || TrainError:== 0.082935 || TestError:== 0.196198\n",
" Epoch :== 427 || TIME(sec):== 1.4777 || TrainError:== 0.0829 || TestError:== 0.196204\n",
" Epoch :== 428 || TIME(sec):== 1.4786 || TrainError:== 0.082858 || TestError:== 0.196152\n",
" Epoch :== 429 || TIME(sec):== 1.4752 || TrainError:== 0.082824 || TestError:== 0.196256\n",
" Epoch :== 430 || TIME(sec):== 1.4701 || TrainError:== 0.082781 || TestError:== 0.196206\n",
" Epoch :== 431 || TIME(sec):== 1.4665 || TrainError:== 0.082739 || TestError:== 0.196186\n",
" Epoch :== 432 || TIME(sec):== 1.4669 || TrainError:== 0.082699 || TestError:== 0.196272\n",
" Epoch :== 433 || TIME(sec):== 1.4827 || TrainError:== 0.082663 || TestError:== 0.196221\n",
" Epoch :== 434 || TIME(sec):== 1.4732 || TrainError:== 0.082614 || TestError:== 0.19625\n",
" Epoch :== 435 || TIME(sec):== 1.472 || TrainError:== 0.082581 || TestError:== 0.196252\n",
" Epoch :== 436 || TIME(sec):== 1.4614 || TrainError:== 0.082538 || TestError:== 0.196265\n",
" Epoch :== 437 || TIME(sec):== 1.4785 || TrainError:== 0.082497 || TestError:== 0.196298\n",
" Epoch :== 438 || TIME(sec):== 1.4763 || TrainError:== 0.082468 || TestError:== 0.196252\n",
" Epoch :== 439 || TIME(sec):== 1.4617 || TrainError:== 0.082426 || TestError:== 0.196255\n",
" Epoch :== 440 || TIME(sec):== 1.4811 || TrainError:== 0.082382 || TestError:== 0.196232\n",
" Epoch :== 441 || TIME(sec):== 1.462 || TrainError:== 0.082339 || TestError:== 0.196244\n",
" Epoch :== 442 || TIME(sec):== 1.4747 || TrainError:== 0.082294 || TestError:== 0.196213\n",
" Epoch :== 443 || TIME(sec):== 1.4652 || TrainError:== 0.082254 || TestError:== 0.19623\n",
" Epoch :== 444 || TIME(sec):== 1.4759 || TrainError:== 0.082219 || TestError:== 0.19629\n",
" Epoch :== 445 || TIME(sec):== 1.4725 || TrainError:== 0.082181 || TestError:== 0.196231\n",
" Epoch :== 446 || TIME(sec):== 1.4835 || TrainError:== 0.082148 || TestError:== 0.196252\n",
" Epoch :== 447 || TIME(sec):== 1.4795 || TrainError:== 0.082107 || TestError:== 0.196278\n",
" Epoch :== 448 || TIME(sec):== 1.4778 || TrainError:== 0.08207 || TestError:== 0.196294\n",
" Epoch :== 449 || TIME(sec):== 1.4746 || TrainError:== 0.082026 || TestError:== 0.1963\n",
" Epoch :== 450 || TIME(sec):== 1.4685 || TrainError:== 0.081992 || TestError:== 0.196277\n",
" Epoch :== 451 || TIME(sec):== 1.4816 || TrainError:== 0.081945 || TestError:== 0.196319\n",
" Epoch :== 452 || TIME(sec):== 1.4646 || TrainError:== 0.081914 || TestError:== 0.19627\n",
" Epoch :== 453 || TIME(sec):== 1.4725 || TrainError:== 0.081869 || TestError:== 0.196297\n",
" Epoch :== 454 || TIME(sec):== 1.4699 || TrainError:== 0.081832 || TestError:== 0.196352\n",
" Epoch :== 455 || TIME(sec):== 1.4644 || TrainError:== 0.081793 || TestError:== 0.196307\n",
" Epoch :== 456 || TIME(sec):== 1.4723 || TrainError:== 0.081746 || TestError:== 0.196324\n",
" Epoch :== 457 || TIME(sec):== 1.4723 || TrainError:== 0.081713 || TestError:== 0.196247\n",
" Epoch :== 458 || TIME(sec):== 1.4653 || TrainError:== 0.081677 || TestError:== 0.196353\n",
" Epoch :== 459 || TIME(sec):== 1.467 || TrainError:== 0.081639 || TestError:== 0.196321\n",
" Epoch :== 460 || TIME(sec):== 1.4691 || TrainError:== 0.081593 || TestError:== 0.196332\n",
" Epoch :== 461 || TIME(sec):== 1.5042 || TrainError:== 0.081564 || TestError:== 0.196351\n",
" Epoch :== 462 || TIME(sec):== 1.527 || TrainError:== 0.081524 || TestError:== 0.196299\n",
" Epoch :== 463 || TIME(sec):== 1.521 || TrainError:== 0.081486 || TestError:== 0.196351\n",
" Epoch :== 464 || TIME(sec):== 1.5085 || TrainError:== 0.08145 || TestError:== 0.196382\n",
" Epoch :== 465 || TIME(sec):== 1.513 || TrainError:== 0.081421 || TestError:== 0.196284\n",
" Epoch :== 466 || TIME(sec):== 1.5177 || TrainError:== 0.081364 || TestError:== 0.196369\n",
" Epoch :== 467 || TIME(sec):== 1.506 || TrainError:== 0.08134 || TestError:== 0.196373\n",
" Epoch :== 468 || TIME(sec):== 1.5217 || TrainError:== 0.081289 || TestError:== 0.196324\n",
" Epoch :== 469 || TIME(sec):== 1.5187 || TrainError:== 0.08126 || TestError:== 0.196375\n",
" Epoch :== 470 || TIME(sec):== 1.5114 || TrainError:== 0.081221 || TestError:== 0.196346\n",
" Epoch :== 471 || TIME(sec):== 1.5157 || TrainError:== 0.081178 || TestError:== 0.19637\n",
" Epoch :== 472 || TIME(sec):== 1.5113 || TrainError:== 0.081146 || TestError:== 0.196393\n",
" Epoch :== 473 || TIME(sec):== 1.5127 || TrainError:== 0.0811 || TestError:== 0.196381\n",
" Epoch :== 474 || TIME(sec):== 1.5075 || TrainError:== 0.081066 || TestError:== 0.196322\n",
" Epoch :== 475 || TIME(sec):== 1.5111 || TrainError:== 0.08104 || TestError:== 0.196422\n",
" Epoch :== 476 || TIME(sec):== 1.5086 || TrainError:== 0.080996 || TestError:== 0.196365\n",
" Epoch :== 477 || TIME(sec):== 1.5112 || TrainError:== 0.080963 || TestError:== 0.196505\n",
" Epoch :== 478 || TIME(sec):== 1.5188 || TrainError:== 0.080923 || TestError:== 0.196435\n",
" Epoch :== 479 || TIME(sec):== 1.5258 || TrainError:== 0.080879 || TestError:== 0.196447\n",
" Epoch :== 480 || TIME(sec):== 1.5072 || TrainError:== 0.080717 || TestError:== 0.196405\n",
" Epoch :== 481 || TIME(sec):== 1.5277 || TrainError:== 0.080685 || TestError:== 0.196397\n",
" Epoch :== 482 || TIME(sec):== 1.5129 || TrainError:== 0.080668 || TestError:== 0.196339\n",
" Epoch :== 483 || TIME(sec):== 1.5128 || TrainError:== 0.080653 || TestError:== 0.196387\n",
" Epoch :== 484 || TIME(sec):== 1.5093 || TrainError:== 0.080627 || TestError:== 0.196416\n",
" Epoch :== 485 || TIME(sec):== 1.5127 || TrainError:== 0.080608 || TestError:== 0.196409\n",
" Epoch :== 486 || TIME(sec):== 1.5146 || TrainError:== 0.080584 || TestError:== 0.196376\n",
" Epoch :== 487 || TIME(sec):== 1.5182 || TrainError:== 0.08056 || TestError:== 0.196396\n",
" Epoch :== 488 || TIME(sec):== 1.5249 || TrainError:== 0.08054 || TestError:== 0.196438\n",
" Epoch :== 489 || TIME(sec):== 1.5213 || TrainError:== 0.080517 || TestError:== 0.196406\n",
" Epoch :== 490 || TIME(sec):== 1.5079 || TrainError:== 0.080494 || TestError:== 0.196377\n",
" Epoch :== 491 || TIME(sec):== 1.5139 || TrainError:== 0.080474 || TestError:== 0.196421\n",
" Epoch :== 492 || TIME(sec):== 1.5243 || TrainError:== 0.080449 || TestError:== 0.196416\n",
" Epoch :== 493 || TIME(sec):== 1.5159 || TrainError:== 0.080428 || TestError:== 0.19643\n",
" Epoch :== 494 || TIME(sec):== 1.5114 || TrainError:== 0.080407 || TestError:== 0.196416\n",
" Epoch :== 495 || TIME(sec):== 1.5189 || TrainError:== 0.080381 || TestError:== 0.196461\n",
" Epoch :== 496 || TIME(sec):== 1.511 || TrainError:== 0.080363 || TestError:== 0.196437\n",
" Epoch :== 497 || TIME(sec):== 1.518 || TrainError:== 0.080338 || TestError:== 0.196415\n",
" Epoch :== 498 || TIME(sec):== 1.5224 || TrainError:== 0.080315 || TestError:== 0.19647\n",
" Epoch :== 499 || TIME(sec):== 1.5212 || TrainError:== 0.080295 || TestError:== 0.196449\n"
]
}
],
"source": [
"\n",
"trainerror=[] \n",
"testerror=[]\n",
"dim =48\n",
"strain_channels = 3\n",
"##Train Test Loop\n",
"for ep in range(epochs):\n",
" model.train()\n",
" t1 = default_timer()\n",
" train_l2 = 0\n",
" for x, y in train_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" x =x.reshape(x.shape[0], 1, dim, dim)\n",
"\n",
" optimizer.zero_grad()\n",
"\n",
" out = model(x).reshape(batch_size, dim, dim,strain_channels)\n",
" \n",
"\n",
" out = y_normalizer.decode(out)\n",
" y = y_normalizer.decode(y)\n",
" \n",
"\n",
" loss = lossfunc(y.view(batch_size,-1),out.view(batch_size,-1))\n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
" train_l2 += loss.item()\n",
"\n",
" scheduler.step()\n",
" model.eval()\n",
" test_l2 = 0.0\n",
" with torch.no_grad():\n",
" for x, y in test_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" x =x.reshape(x.shape[0], 1, dim, dim)\n",
" out = model(x).reshape(batch_size, dim, dim,strain_channels)\n",
" out = y_normalizer.decode(out)\n",
"\n",
" test_l2 += lossfunc(y.view(batch_size,-1),out.view(batch_size,-1)).item()\n",
"\n",
" train_l2/= ntrain\n",
" test_l2 /= ntest\n",
" trainerror += [train_l2]\n",
" testerror += [test_l2]\n",
" t2 = default_timer()\n",
" print(f' Epoch :== {ep} || TIME(sec):== {np.round((t2-t1),4)} '\n",
" f'|| TrainError:== {np.round(train_l2,6)} || TestError:== {np.round(test_l2,6)}')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "467f94c1-eca1-40d3-8624-cfb55c3e9d85",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 700x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(7,6))\n",
"plt.plot(range(epochs),trainerror,linewidth = 4,label =\"TRAIN\")\n",
"plt.plot(range(epochs),testerror, linewidth = 4,label =\"TEST\")\n",
"plt.xlabel('Epoch', fontsize =20)\n",
"plt.ylabel('Loss', fontsize =20)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "04411583-9ec3-4d2f-8889-f22bd2aacf32",
"metadata": {},
"outputs": [],
"source": [
"# trainn = np.array(trainerror)\n",
"# testnn = np.array(testerror)\n",
"# torch.save(model.state_dict(),'UNet_STRESS_N1200_ep1200.pt')\n",
"# np.save('trainUNET_STRESS', trainn)\n",
"# np.save('testUNET_STRESS', testnn)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "48543fba-f336-46e5-bbdb-02d006c4c6a8",
"metadata": {},
"outputs": [],
"source": [
"prediction = torch.zeros(y_test_stress.shape)\n",
"c = 0\n",
"with torch.no_grad():\n",
" for x,y in test_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" x =x.reshape(x.shape[0], 1, dim, dim)\n",
" out = model(x).reshape(batch_size, dim, dim,strain_channels)\n",
" out = y_normalizer.decode(out)\n",
" prediction[c*batch_size :c*batch_size+batch_size] = out\n",
" c+=1\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "d63a3bcf-3b93-4e76-bafa-7eab798307dc",
"metadata": {},
"outputs": [],
"source": [
"stress_act = y_test_stress.reshape(ntest, -1)\n",
"stress_pred = prediction.reshape(ntest,-1)\n",
"\n",
"r2_stress =[]\n",
"for i in range(stress_act.shape[0]):\n",
" act = stress_act[i]\n",
" pred = stress_pred[i]\n",
" r2 = r2_score(act,pred)\n",
" r2_stress += [r2]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "962a9227-391e-4584-a0eb-74ca212f72e9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9320553105736218 0.07767333359342457\n"
]
}
],
"source": [
"r2_avg_stress = np.average(r2_stress)\n",
"r2_std_stress = np.std(r2_stress)\n",
"\n",
"print(r2_avg_stress,r2_std_stress)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "98480fff-3f28-4f45-8f74-6f16ad3079fb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.93\n",
"0.08\n"
]
}
],
"source": [
"print(np.round((r2_avg_stress),2))\n",
"print(np.round((r2_std_stress),2))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "6b6acc82-9615-4a55-b819-76ff7b8fdfab",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(0.1964)\n"
]
}
],
"source": [
"loss = lossfunc(y_test_stress, prediction)\n",
"print(loss/ntest)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c3125cc1-0c23-4054-8434-7df90b2526a9",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "462e72e0-3aa6-4e48-81ec-e66c85561507",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
2D | M3RG-IITD/FNO-StressStrain | benchmarking/FNO.ipynb | .ipynb | 205,084 | 2,034 | {
"cells": [
{
"cell_type": "code",
"execution_count": 46,
"id": "6699f0bf-c2e8-4fc4-b19a-7c98ccdfe7ef",
"metadata": {},
"outputs": [],
"source": [
"## @meermehran -- M3RG Lab -- Indian Institute of Technology, Delhi\n",
"## @date : 28Sept2022\n",
"\n",
"####################################--DESCRIPTION####################################################\n",
"## ##\n",
"## -----------------------------Benchmarking- FNO-v-ResNet-UNet----------------------------------- ##\n",
"## ## \n",
"#####################################################################################################"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b9bb88ad-8210-4219-bacb-621d2cecf80d",
"metadata": {},
"outputs": [],
"source": [
"\n",
"import numpy as np\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"from torch.nn.parameter import Parameter\n",
"from Adam import Adam\n",
"from sklearn.metrics import r2_score\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import operator\n",
"from functools import reduce\n",
"from functools import partial\n",
"import scipy.io as sio\n",
"\n",
"from timeit import default_timer\n",
"from utility import *\n",
"\n",
"device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
"torch.manual_seed(0)\n",
"np.random.seed(0)\n"
]
},
{
"cell_type": "markdown",
"id": "f572a735-438b-483f-a527-5bf732a62db6",
"metadata": {},
"source": [
"# STRAINS"
]
},
{
"cell_type": "markdown",
"id": "5cb72ceb-41a2-4822-9328-0a70bb54e502",
"metadata": {},
"source": [
"#### Model "
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "50e6f10f-a96f-443f-af7d-f4a368d3e880",
"metadata": {},
"outputs": [],
"source": [
"class SpectralConv2d(nn.Module):\n",
" def __init__(self, in_channels, out_channels, modes1, modes2):\n",
" super(SpectralConv2d, self).__init__()\n",
"\n",
" \"\"\"\n",
" 2D Fourier layer. It does FFT, linear transform, and Inverse FFT. \n",
" \"\"\"\n",
"\n",
" self.in_channels = in_channels\n",
" self.out_channels = out_channels\n",
" self.modes1 = modes1 #Number of Fourier modes to multiply, at most floor(N/2) + 1\n",
" self.modes2 = modes2\n",
"\n",
" self.scale = (1 / (in_channels * out_channels))\n",
" self.weights1 = nn.Parameter(self.scale * torch.rand(in_channels, out_channels, self.modes1, self.modes2, dtype=torch.cfloat))\n",
" self.weights2 = nn.Parameter(self.scale * torch.rand(in_channels, out_channels, self.modes1, self.modes2, dtype=torch.cfloat))\n",
"\n",
" # Complex multiplication\n",
" def compl_mul2d(self, input, weights):\n",
" # (batch, in_channel, x,y ), (in_channel, out_channel, x,y) -> (batch, out_channel, x,y)\n",
" return torch.einsum(\"bixy,ioxy->boxy\", input, weights)\n",
"\n",
" def forward(self, x):\n",
" batchsize = x.shape[0]\n",
" #Compute Fourier coeffcients up to factor of e^(- something constant)\n",
" x_ft = torch.fft.rfft2(x)\n",
"\n",
" # Multiply relevant Fourier modes\n",
" out_ft = torch.zeros(batchsize, self.out_channels, x.size(-2), x.size(-1)//2 + 1, dtype=torch.cfloat, device=x.device)\n",
" out_ft[:, :, :self.modes1, :self.modes2] = \\\n",
" self.compl_mul2d(x_ft[:, :, :self.modes1, :self.modes2], self.weights1)\n",
" out_ft[:, :, -self.modes1:, :self.modes2] = \\\n",
" self.compl_mul2d(x_ft[:, :, -self.modes1:, :self.modes2], self.weights2)\n",
"\n",
" #Return to physical space\n",
" x = torch.fft.irfft2(out_ft, s=(x.size(-2), x.size(-1)))\n",
" return x\n",
"\n",
"class FNO2d(nn.Module):\n",
" def __init__(self, modes1, modes2, width):\n",
" super(FNO2d, self).__init__()\n",
"\n",
" self.modes1 = modes1\n",
" self.modes2 = modes2\n",
" self.width = width\n",
" self.padding = 9 \n",
" self.fc0 = nn.Linear(3, self.width) \n",
" self.conv0 = SpectralConv2d(self.width, self.width, self.modes1, self.modes2)\n",
" self.conv1 = SpectralConv2d(self.width, self.width, self.modes1, self.modes2)\n",
" self.conv2 = SpectralConv2d(self.width, self.width, self.modes1, self.modes2)\n",
" self.conv3 = SpectralConv2d(self.width, self.width, self.modes1, self.modes2)\n",
" self.w0 = nn.Conv2d(self.width, self.width, 1)\n",
" self.w1 = nn.Conv2d(self.width, self.width, 1)\n",
" self.w2 = nn.Conv2d(self.width, self.width, 1)\n",
" self.w3 = nn.Conv2d(self.width, self.width, 1)\n",
"\n",
" self.fc1 = nn.Linear(self.width, 128)\n",
" self.fc2 = nn.Linear(128, 3)\n",
"\n",
" def forward(self, x):\n",
" grid = self.get_grid(x.shape, x.device)\n",
" x = torch.cat((x, grid), dim=-1)\n",
" x = self.fc0(x)\n",
" x = x.permute(0, 3, 1, 2)\n",
" x = F.pad(x, [0,self.padding, 0,self.padding])\n",
"\n",
" x1 = self.conv0(x)\n",
" x2 = self.w0(x)\n",
" x = x1 + x2\n",
" x = F.gelu(x)\n",
"\n",
" x1 = self.conv1(x)\n",
" x2 = self.w1(x)\n",
" x = x1 + x2\n",
" x = F.gelu(x)\n",
"\n",
" x1 = self.conv2(x)\n",
" x2 = self.w2(x)\n",
" x = x1 + x2\n",
" x = F.gelu(x)\n",
"\n",
" x1 = self.conv3(x)\n",
" x2 = self.w3(x)\n",
" x = x1 + x2\n",
"\n",
" x = x[..., :-self.padding, :-self.padding]\n",
" x = x.permute(0, 2, 3, 1)\n",
" x = self.fc1(x)\n",
" x = F.gelu(x)\n",
" x = self.fc2(x)\n",
" \n",
" return x #[20,48,48,3]\n",
" \n",
" def get_grid(self, shape, device):\n",
" batchsize, size_x, size_y = shape[0], shape[1], shape[2]\n",
" gridx = torch.tensor(np.linspace(0, 1, size_x), dtype=torch.float)\n",
" gridx = gridx.reshape(1, size_x, 1, 1).repeat([batchsize, 1, size_y, 1])\n",
" gridy = torch.tensor(np.linspace(0, 1, size_y), dtype=torch.float)\n",
" gridy = gridy.reshape(1, 1, size_y, 1).repeat([batchsize, size_x, 1, 1])\n",
" return torch.cat((gridx, gridy), dim=-1).to(device)\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "4c584116-c125-4831-9ab7-7ad58243c376",
"metadata": {},
"outputs": [],
"source": [
"######################################################\n",
"### Data Loading\n",
"######################################################\n",
"\n",
"\n",
"X_Data = '/DATA1/meer/data/material-v3-3350.mat'\n",
"Y_Data = '/DATA1/meer/data/strain-v3-3350.mat'\n",
"\n",
"\n",
"ntrain =1200\n",
"ntest = 200\n",
"batch_size =20\n",
"dim = 48\n",
"strain_channels = 3\n",
"modes = 12\n",
"width = 32\n",
"learning_rate = 0.001\n",
"epochs = 500\n",
"step_size = 80\n",
"gamma = 0.75\n",
"\n",
"\n",
"################################################################\n",
"# load data and data normalization\n",
"################################################################\n",
"reader = MatReader(X_Data) \n",
"x_train = reader.read_field('Emat')[:ntrain]\n",
"x_test = reader.read_field('Emat')[-ntest:]\n",
"x_test_plot = reader.read_field('Emat')[-ntest:]\n",
"\n",
"reader.load_file(Y_Data) \n",
"y_train = reader.read_field('strain')[:ntrain]# \n",
"y_test_strain = reader.read_field('strain')[-ntest:] \n",
"\n",
"\n",
"######################################################\n",
"### Normalization\n",
"######################################################\n",
"\n",
"x_normalizer = UnitGaussianNormalizer(x_train)\n",
"x_train = x_normalizer.encode(x_train)\n",
"x_test = x_normalizer.encode(x_test)\n",
"\n",
"y_normalizer = UnitGaussianNormalizer(y_train)\n",
"y_train = y_normalizer.encode(y_train)\n",
"\n",
"x_train = x_train.reshape(ntrain,dim,dim,1)\n",
"x_test = x_test.reshape(ntest,dim,dim,1)\n",
"\n",
"train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_train, y_train), batch_size=batch_size, shuffle=True)\n",
"test_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_test, y_test_strain), batch_size=batch_size, shuffle=False)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e375543-8c10-4c77-a52c-77d63555da85",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 18,
"id": "3b1c728f-0055-4673-a3bd-f664ab1c22bd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" x_train Shape := torch.Size([1200, 48, 48, 1])\n",
" y_train Shape := torch.Size([1200, 48, 48, 3])\n",
" x_test Shape := torch.Size([200, 48, 48, 1])\n",
" y_test_strain Shape := torch.Size([200, 48, 48, 3])\n"
]
}
],
"source": [
"print(f' x_train Shape := {x_train.shape}')\n",
"print(f' y_train Shape := {y_train.shape}')\n",
"print(f' x_test Shape := {x_test.shape}')\n",
"print(f' y_test_strain Shape := {y_test_strain.shape}')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "bd25756b-58b9-4190-80cd-e0fcf1ff80f7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1188611\n"
]
}
],
"source": [
"\n",
"# ################################################################\n",
"## Model\n",
"# ################################################################\n",
"model = FNO2d(modes, modes, width).to(device)\n",
"print(count_params(model))\n",
"\n",
"optimizer = Adam(model.parameters(), lr=learning_rate, weight_decay=1e-3)\n",
"scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma)\n",
"\n",
"\n",
"if device=='cpu':\n",
" y_normalizer.cpu()\n",
"else:\n",
" y_normalizer.cuda()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "3c6352e5-57e9-4a47-b7c0-8b612bf8006c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Epoch :== 1 || TIME(sec):== 3.3157 || TrainError:== 0.526526 || TestError:== 0.377169\n",
" Epoch :== 2 || TIME(sec):== 0.9273 || TrainError:== 0.322762 || TestError:== 0.279678\n",
" Epoch :== 3 || TIME(sec):== 1.0052 || TrainError:== 0.251047 || TestError:== 0.232322\n",
" Epoch :== 4 || TIME(sec):== 1.0805 || TrainError:== 0.218143 || TestError:== 0.208831\n",
" Epoch :== 5 || TIME(sec):== 1.098 || TrainError:== 0.198247 || TestError:== 0.195459\n",
" Epoch :== 6 || TIME(sec):== 1.1138 || TrainError:== 0.184442 || TestError:== 0.184594\n",
" Epoch :== 7 || TIME(sec):== 1.0765 || TrainError:== 0.174827 || TestError:== 0.177758\n",
" Epoch :== 8 || TIME(sec):== 1.0307 || TrainError:== 0.166908 || TestError:== 0.16968\n",
" Epoch :== 9 || TIME(sec):== 1.0396 || TrainError:== 0.160436 || TestError:== 0.164688\n",
" Epoch :== 10 || TIME(sec):== 1.0524 || TrainError:== 0.154699 || TestError:== 0.160578\n",
" Epoch :== 11 || TIME(sec):== 1.0622 || TrainError:== 0.151216 || TestError:== 0.156269\n",
" Epoch :== 12 || TIME(sec):== 1.0806 || TrainError:== 0.147008 || TestError:== 0.154328\n",
" Epoch :== 13 || TIME(sec):== 1.0415 || TrainError:== 0.143871 || TestError:== 0.149384\n",
" Epoch :== 14 || TIME(sec):== 0.9164 || TrainError:== 0.140102 || TestError:== 0.148897\n",
" Epoch :== 15 || TIME(sec):== 0.9213 || TrainError:== 0.137972 || TestError:== 0.146926\n",
" Epoch :== 16 || TIME(sec):== 0.9234 || TrainError:== 0.135787 || TestError:== 0.142104\n",
" Epoch :== 17 || TIME(sec):== 0.9068 || TrainError:== 0.131554 || TestError:== 0.140266\n",
" Epoch :== 18 || TIME(sec):== 0.9129 || TrainError:== 0.129551 || TestError:== 0.139617\n",
" Epoch :== 19 || TIME(sec):== 1.036 || TrainError:== 0.128837 || TestError:== 0.136978\n",
" Epoch :== 20 || TIME(sec):== 0.9713 || TrainError:== 0.126488 || TestError:== 0.136521\n",
" Epoch :== 21 || TIME(sec):== 0.9987 || TrainError:== 0.124688 || TestError:== 0.135681\n",
" Epoch :== 22 || TIME(sec):== 1.0333 || TrainError:== 0.123387 || TestError:== 0.132446\n",
" Epoch :== 23 || TIME(sec):== 1.0374 || TrainError:== 0.12063 || TestError:== 0.131876\n",
" Epoch :== 24 || TIME(sec):== 1.0296 || TrainError:== 0.119372 || TestError:== 0.129894\n",
" Epoch :== 25 || TIME(sec):== 1.0234 || TrainError:== 0.118406 || TestError:== 0.131462\n",
" Epoch :== 26 || TIME(sec):== 1.036 || TrainError:== 0.117567 || TestError:== 0.127668\n",
" Epoch :== 27 || TIME(sec):== 1.0305 || TrainError:== 0.115388 || TestError:== 0.126633\n",
" Epoch :== 28 || TIME(sec):== 1.0206 || TrainError:== 0.11424 || TestError:== 0.126735\n",
" Epoch :== 29 || TIME(sec):== 1.0376 || TrainError:== 0.113562 || TestError:== 0.125668\n",
" Epoch :== 30 || TIME(sec):== 1.0209 || TrainError:== 0.112596 || TestError:== 0.124345\n",
" Epoch :== 31 || TIME(sec):== 1.012 || TrainError:== 0.111447 || TestError:== 0.125087\n",
" Epoch :== 32 || TIME(sec):== 1.0252 || TrainError:== 0.111103 || TestError:== 0.12349\n",
" Epoch :== 33 || TIME(sec):== 1.038 || TrainError:== 0.108894 || TestError:== 0.122418\n",
" Epoch :== 34 || TIME(sec):== 1.0278 || TrainError:== 0.108847 || TestError:== 0.121514\n",
" Epoch :== 35 || TIME(sec):== 1.0298 || TrainError:== 0.107501 || TestError:== 0.122429\n",
" Epoch :== 36 || TIME(sec):== 1.0258 || TrainError:== 0.10736 || TestError:== 0.120461\n",
" Epoch :== 37 || TIME(sec):== 1.0278 || TrainError:== 0.106642 || TestError:== 0.120733\n",
" Epoch :== 38 || TIME(sec):== 1.0279 || TrainError:== 0.105784 || TestError:== 0.118586\n",
" Epoch :== 39 || TIME(sec):== 1.0228 || TrainError:== 0.104493 || TestError:== 0.117775\n",
" Epoch :== 40 || TIME(sec):== 1.024 || TrainError:== 0.103898 || TestError:== 0.11875\n",
" Epoch :== 41 || TIME(sec):== 1.0286 || TrainError:== 0.10399 || TestError:== 0.118616\n",
" Epoch :== 42 || TIME(sec):== 1.0273 || TrainError:== 0.103089 || TestError:== 0.117633\n",
" Epoch :== 43 || TIME(sec):== 1.0276 || TrainError:== 0.1032 || TestError:== 0.1178\n",
" Epoch :== 44 || TIME(sec):== 1.0301 || TrainError:== 0.101941 || TestError:== 0.117267\n",
" Epoch :== 45 || TIME(sec):== 1.0189 || TrainError:== 0.101661 || TestError:== 0.117079\n",
" Epoch :== 46 || TIME(sec):== 1.0166 || TrainError:== 0.101409 || TestError:== 0.115038\n",
" Epoch :== 47 || TIME(sec):== 1.0282 || TrainError:== 0.099707 || TestError:== 0.114692\n",
" Epoch :== 48 || TIME(sec):== 1.0177 || TrainError:== 0.101469 || TestError:== 0.114395\n",
" Epoch :== 49 || TIME(sec):== 1.0302 || TrainError:== 0.099077 || TestError:== 0.114492\n",
" Epoch :== 50 || TIME(sec):== 1.0184 || TrainError:== 0.099224 || TestError:== 0.115646\n",
" Epoch :== 51 || TIME(sec):== 1.0213 || TrainError:== 0.099192 || TestError:== 0.113954\n",
" Epoch :== 52 || TIME(sec):== 1.0295 || TrainError:== 0.099473 || TestError:== 0.113826\n",
" Epoch :== 53 || TIME(sec):== 1.0201 || TrainError:== 0.097698 || TestError:== 0.114877\n",
" Epoch :== 54 || TIME(sec):== 1.0242 || TrainError:== 0.09681 || TestError:== 0.111637\n",
" Epoch :== 55 || TIME(sec):== 1.0278 || TrainError:== 0.095895 || TestError:== 0.111834\n",
" Epoch :== 56 || TIME(sec):== 1.0259 || TrainError:== 0.096325 || TestError:== 0.112278\n",
" Epoch :== 57 || TIME(sec):== 1.0166 || TrainError:== 0.095146 || TestError:== 0.111428\n",
" Epoch :== 58 || TIME(sec):== 1.0159 || TrainError:== 0.094206 || TestError:== 0.110623\n",
" Epoch :== 59 || TIME(sec):== 1.0136 || TrainError:== 0.094256 || TestError:== 0.1101\n",
" Epoch :== 60 || TIME(sec):== 1.0103 || TrainError:== 0.094286 || TestError:== 0.110522\n",
" Epoch :== 61 || TIME(sec):== 1.0195 || TrainError:== 0.094111 || TestError:== 0.110597\n",
" Epoch :== 62 || TIME(sec):== 1.0234 || TrainError:== 0.094938 || TestError:== 0.110099\n",
" Epoch :== 63 || TIME(sec):== 1.0239 || TrainError:== 0.094233 || TestError:== 0.110629\n",
" Epoch :== 64 || TIME(sec):== 1.0203 || TrainError:== 0.092672 || TestError:== 0.107866\n",
" Epoch :== 65 || TIME(sec):== 1.0229 || TrainError:== 0.092666 || TestError:== 0.109746\n",
" Epoch :== 66 || TIME(sec):== 1.0235 || TrainError:== 0.093318 || TestError:== 0.108606\n",
" Epoch :== 67 || TIME(sec):== 1.0117 || TrainError:== 0.092121 || TestError:== 0.111154\n",
" Epoch :== 68 || TIME(sec):== 1.0227 || TrainError:== 0.092413 || TestError:== 0.108337\n",
" Epoch :== 69 || TIME(sec):== 1.0248 || TrainError:== 0.091727 || TestError:== 0.108176\n",
" Epoch :== 70 || TIME(sec):== 1.0111 || TrainError:== 0.090573 || TestError:== 0.106963\n",
" Epoch :== 71 || TIME(sec):== 1.0295 || TrainError:== 0.091568 || TestError:== 0.109071\n",
" Epoch :== 72 || TIME(sec):== 0.9886 || TrainError:== 0.090632 || TestError:== 0.107881\n",
" Epoch :== 73 || TIME(sec):== 0.8346 || TrainError:== 0.09077 || TestError:== 0.10811\n",
" Epoch :== 74 || TIME(sec):== 0.8354 || TrainError:== 0.090272 || TestError:== 0.108853\n",
" Epoch :== 75 || TIME(sec):== 0.8338 || TrainError:== 0.089993 || TestError:== 0.107876\n",
" Epoch :== 76 || TIME(sec):== 0.8169 || TrainError:== 0.089617 || TestError:== 0.106545\n",
" Epoch :== 77 || TIME(sec):== 0.8354 || TrainError:== 0.088167 || TestError:== 0.105929\n",
" Epoch :== 78 || TIME(sec):== 0.8341 || TrainError:== 0.088668 || TestError:== 0.106089\n",
" Epoch :== 79 || TIME(sec):== 0.8223 || TrainError:== 0.089246 || TestError:== 0.108035\n",
" Epoch :== 80 || TIME(sec):== 0.8138 || TrainError:== 0.088521 || TestError:== 0.106062\n",
" Epoch :== 81 || TIME(sec):== 0.8235 || TrainError:== 0.085152 || TestError:== 0.103208\n",
" Epoch :== 82 || TIME(sec):== 0.8279 || TrainError:== 0.083755 || TestError:== 0.10313\n",
" Epoch :== 83 || TIME(sec):== 0.8362 || TrainError:== 0.083762 || TestError:== 0.102688\n",
" Epoch :== 84 || TIME(sec):== 0.8331 || TrainError:== 0.083324 || TestError:== 0.103138\n",
" Epoch :== 85 || TIME(sec):== 0.8349 || TrainError:== 0.084505 || TestError:== 0.10347\n",
" Epoch :== 86 || TIME(sec):== 0.8404 || TrainError:== 0.08378 || TestError:== 0.102179\n",
" Epoch :== 87 || TIME(sec):== 0.8365 || TrainError:== 0.083845 || TestError:== 0.102903\n",
" Epoch :== 88 || TIME(sec):== 0.8332 || TrainError:== 0.083946 || TestError:== 0.105169\n",
" Epoch :== 89 || TIME(sec):== 0.832 || TrainError:== 0.084322 || TestError:== 0.10249\n",
" Epoch :== 90 || TIME(sec):== 0.8414 || TrainError:== 0.083261 || TestError:== 0.102927\n",
" Epoch :== 91 || TIME(sec):== 0.8427 || TrainError:== 0.082654 || TestError:== 0.101267\n",
" Epoch :== 92 || TIME(sec):== 0.8205 || TrainError:== 0.082623 || TestError:== 0.102464\n",
" Epoch :== 93 || TIME(sec):== 0.8255 || TrainError:== 0.082574 || TestError:== 0.101648\n",
" Epoch :== 94 || TIME(sec):== 0.8232 || TrainError:== 0.082282 || TestError:== 0.101816\n",
" Epoch :== 95 || TIME(sec):== 0.8236 || TrainError:== 0.082366 || TestError:== 0.10144\n",
" Epoch :== 96 || TIME(sec):== 0.828 || TrainError:== 0.081856 || TestError:== 0.102497\n",
" Epoch :== 97 || TIME(sec):== 0.827 || TrainError:== 0.081938 || TestError:== 0.100803\n",
" Epoch :== 98 || TIME(sec):== 0.8175 || TrainError:== 0.081877 || TestError:== 0.100844\n",
" Epoch :== 99 || TIME(sec):== 0.824 || TrainError:== 0.082332 || TestError:== 0.101738\n",
" Epoch :== 100 || TIME(sec):== 0.8284 || TrainError:== 0.082252 || TestError:== 0.100804\n",
" Epoch :== 101 || TIME(sec):== 0.8212 || TrainError:== 0.081649 || TestError:== 0.100069\n",
" Epoch :== 102 || TIME(sec):== 0.8179 || TrainError:== 0.080711 || TestError:== 0.09999\n",
" Epoch :== 103 || TIME(sec):== 0.8127 || TrainError:== 0.080467 || TestError:== 0.100475\n",
" Epoch :== 104 || TIME(sec):== 0.8157 || TrainError:== 0.080876 || TestError:== 0.100688\n",
" Epoch :== 105 || TIME(sec):== 0.8141 || TrainError:== 0.081226 || TestError:== 0.100043\n",
" Epoch :== 106 || TIME(sec):== 0.8137 || TrainError:== 0.080523 || TestError:== 0.101693\n",
" Epoch :== 107 || TIME(sec):== 0.8226 || TrainError:== 0.080745 || TestError:== 0.101596\n",
" Epoch :== 108 || TIME(sec):== 0.8163 || TrainError:== 0.080883 || TestError:== 0.100896\n",
" Epoch :== 109 || TIME(sec):== 0.8164 || TrainError:== 0.079686 || TestError:== 0.100852\n",
" Epoch :== 110 || TIME(sec):== 0.8179 || TrainError:== 0.079809 || TestError:== 0.099847\n",
" Epoch :== 111 || TIME(sec):== 0.9712 || TrainError:== 0.079868 || TestError:== 0.0996\n",
" Epoch :== 112 || TIME(sec):== 1.0555 || TrainError:== 0.079855 || TestError:== 0.100086\n",
" Epoch :== 113 || TIME(sec):== 1.0547 || TrainError:== 0.079276 || TestError:== 0.09845\n",
" Epoch :== 114 || TIME(sec):== 1.048 || TrainError:== 0.078846 || TestError:== 0.098716\n",
" Epoch :== 115 || TIME(sec):== 1.0512 || TrainError:== 0.078588 || TestError:== 0.098241\n",
" Epoch :== 116 || TIME(sec):== 1.0019 || TrainError:== 0.078698 || TestError:== 0.098441\n",
" Epoch :== 117 || TIME(sec):== 0.971 || TrainError:== 0.078331 || TestError:== 0.099656\n",
" Epoch :== 118 || TIME(sec):== 0.9804 || TrainError:== 0.078126 || TestError:== 0.098199\n",
" Epoch :== 119 || TIME(sec):== 0.9659 || TrainError:== 0.077665 || TestError:== 0.0983\n",
" Epoch :== 120 || TIME(sec):== 0.9762 || TrainError:== 0.078591 || TestError:== 0.099679\n",
" Epoch :== 121 || TIME(sec):== 0.9776 || TrainError:== 0.079247 || TestError:== 0.097894\n",
" Epoch :== 122 || TIME(sec):== 0.984 || TrainError:== 0.077593 || TestError:== 0.098695\n",
" Epoch :== 123 || TIME(sec):== 0.976 || TrainError:== 0.078001 || TestError:== 0.098328\n",
" Epoch :== 124 || TIME(sec):== 0.9776 || TrainError:== 0.077366 || TestError:== 0.097544\n",
" Epoch :== 125 || TIME(sec):== 0.9756 || TrainError:== 0.077333 || TestError:== 0.097777\n",
" Epoch :== 126 || TIME(sec):== 0.9748 || TrainError:== 0.07767 || TestError:== 0.098013\n",
" Epoch :== 127 || TIME(sec):== 0.9723 || TrainError:== 0.077507 || TestError:== 0.098098\n",
" Epoch :== 128 || TIME(sec):== 0.9764 || TrainError:== 0.077237 || TestError:== 0.097281\n",
" Epoch :== 129 || TIME(sec):== 0.9787 || TrainError:== 0.076037 || TestError:== 0.098868\n",
" Epoch :== 130 || TIME(sec):== 0.9735 || TrainError:== 0.076856 || TestError:== 0.096074\n",
" Epoch :== 131 || TIME(sec):== 0.9768 || TrainError:== 0.0761 || TestError:== 0.098822\n",
" Epoch :== 132 || TIME(sec):== 0.9767 || TrainError:== 0.077039 || TestError:== 0.098534\n",
" Epoch :== 133 || TIME(sec):== 0.9771 || TrainError:== 0.07603 || TestError:== 0.097869\n",
" Epoch :== 134 || TIME(sec):== 0.9767 || TrainError:== 0.076459 || TestError:== 0.097272\n",
" Epoch :== 135 || TIME(sec):== 0.9771 || TrainError:== 0.07682 || TestError:== 0.097684\n",
" Epoch :== 136 || TIME(sec):== 0.9719 || TrainError:== 0.075655 || TestError:== 0.097524\n",
" Epoch :== 137 || TIME(sec):== 0.9703 || TrainError:== 0.07705 || TestError:== 0.09703\n",
" Epoch :== 138 || TIME(sec):== 0.9758 || TrainError:== 0.075363 || TestError:== 0.096503\n",
" Epoch :== 139 || TIME(sec):== 0.9771 || TrainError:== 0.075202 || TestError:== 0.097609\n",
" Epoch :== 140 || TIME(sec):== 0.9763 || TrainError:== 0.075658 || TestError:== 0.097972\n",
" Epoch :== 141 || TIME(sec):== 0.979 || TrainError:== 0.075666 || TestError:== 0.096345\n",
" Epoch :== 142 || TIME(sec):== 1.0327 || TrainError:== 0.074927 || TestError:== 0.096391\n",
" Epoch :== 143 || TIME(sec):== 0.9781 || TrainError:== 0.075972 || TestError:== 0.096256\n",
" Epoch :== 144 || TIME(sec):== 0.9766 || TrainError:== 0.075313 || TestError:== 0.095491\n",
" Epoch :== 145 || TIME(sec):== 0.9723 || TrainError:== 0.073901 || TestError:== 0.095351\n",
" Epoch :== 146 || TIME(sec):== 0.9771 || TrainError:== 0.074866 || TestError:== 0.095962\n",
" Epoch :== 147 || TIME(sec):== 0.9162 || TrainError:== 0.0742 || TestError:== 0.096094\n",
" Epoch :== 148 || TIME(sec):== 0.8074 || TrainError:== 0.074166 || TestError:== 0.095627\n",
" Epoch :== 149 || TIME(sec):== 0.8131 || TrainError:== 0.074674 || TestError:== 0.095527\n",
" Epoch :== 150 || TIME(sec):== 0.8157 || TrainError:== 0.074171 || TestError:== 0.095908\n",
" Epoch :== 151 || TIME(sec):== 0.8152 || TrainError:== 0.073572 || TestError:== 0.095365\n",
" Epoch :== 152 || TIME(sec):== 0.8184 || TrainError:== 0.073773 || TestError:== 0.096513\n",
" Epoch :== 153 || TIME(sec):== 0.8081 || TrainError:== 0.074039 || TestError:== 0.094911\n",
" Epoch :== 154 || TIME(sec):== 0.8231 || TrainError:== 0.073241 || TestError:== 0.095654\n",
" Epoch :== 155 || TIME(sec):== 0.8113 || TrainError:== 0.073618 || TestError:== 0.094495\n",
" Epoch :== 156 || TIME(sec):== 0.8113 || TrainError:== 0.072604 || TestError:== 0.094956\n",
" Epoch :== 157 || TIME(sec):== 0.8348 || TrainError:== 0.073507 || TestError:== 0.094558\n",
" Epoch :== 158 || TIME(sec):== 0.8159 || TrainError:== 0.073396 || TestError:== 0.094985\n",
" Epoch :== 159 || TIME(sec):== 0.8148 || TrainError:== 0.074805 || TestError:== 0.095924\n",
" Epoch :== 160 || TIME(sec):== 0.8107 || TrainError:== 0.073692 || TestError:== 0.094166\n",
" Epoch :== 161 || TIME(sec):== 0.8151 || TrainError:== 0.070033 || TestError:== 0.091695\n",
" Epoch :== 162 || TIME(sec):== 0.8196 || TrainError:== 0.068629 || TestError:== 0.091738\n",
" Epoch :== 163 || TIME(sec):== 0.9188 || TrainError:== 0.068553 || TestError:== 0.092311\n",
" Epoch :== 164 || TIME(sec):== 1.0004 || TrainError:== 0.069031 || TestError:== 0.091701\n",
" Epoch :== 165 || TIME(sec):== 1.0171 || TrainError:== 0.06895 || TestError:== 0.092157\n",
" Epoch :== 166 || TIME(sec):== 1.032 || TrainError:== 0.069081 || TestError:== 0.092061\n",
" Epoch :== 167 || TIME(sec):== 1.0159 || TrainError:== 0.068831 || TestError:== 0.092281\n",
" Epoch :== 168 || TIME(sec):== 1.0245 || TrainError:== 0.069182 || TestError:== 0.092327\n",
" Epoch :== 169 || TIME(sec):== 1.0325 || TrainError:== 0.070726 || TestError:== 0.091551\n",
" Epoch :== 170 || TIME(sec):== 1.0274 || TrainError:== 0.069736 || TestError:== 0.090951\n",
" Epoch :== 171 || TIME(sec):== 1.0274 || TrainError:== 0.068855 || TestError:== 0.090532\n",
" Epoch :== 172 || TIME(sec):== 0.979 || TrainError:== 0.068723 || TestError:== 0.092192\n",
" Epoch :== 173 || TIME(sec):== 0.9688 || TrainError:== 0.068999 || TestError:== 0.092292\n",
" Epoch :== 174 || TIME(sec):== 0.9672 || TrainError:== 0.069061 || TestError:== 0.091804\n",
" Epoch :== 175 || TIME(sec):== 0.971 || TrainError:== 0.068392 || TestError:== 0.092727\n",
" Epoch :== 176 || TIME(sec):== 0.9667 || TrainError:== 0.068936 || TestError:== 0.092807\n",
" Epoch :== 177 || TIME(sec):== 0.9765 || TrainError:== 0.068667 || TestError:== 0.091342\n",
" Epoch :== 178 || TIME(sec):== 0.9633 || TrainError:== 0.068437 || TestError:== 0.091407\n",
" Epoch :== 179 || TIME(sec):== 0.971 || TrainError:== 0.068234 || TestError:== 0.090587\n",
" Epoch :== 180 || TIME(sec):== 0.9679 || TrainError:== 0.067828 || TestError:== 0.092603\n",
" Epoch :== 181 || TIME(sec):== 0.9664 || TrainError:== 0.068147 || TestError:== 0.091719\n",
" Epoch :== 182 || TIME(sec):== 0.969 || TrainError:== 0.067734 || TestError:== 0.090568\n",
" Epoch :== 183 || TIME(sec):== 1.0148 || TrainError:== 0.067539 || TestError:== 0.090331\n",
" Epoch :== 184 || TIME(sec):== 1.0222 || TrainError:== 0.067797 || TestError:== 0.092008\n",
" Epoch :== 185 || TIME(sec):== 1.0237 || TrainError:== 0.068378 || TestError:== 0.091291\n",
" Epoch :== 186 || TIME(sec):== 1.0322 || TrainError:== 0.068847 || TestError:== 0.091084\n",
" Epoch :== 187 || TIME(sec):== 1.0127 || TrainError:== 0.068714 || TestError:== 0.091226\n",
" Epoch :== 188 || TIME(sec):== 0.9806 || TrainError:== 0.067792 || TestError:== 0.090088\n",
" Epoch :== 189 || TIME(sec):== 0.8772 || TrainError:== 0.067423 || TestError:== 0.091211\n",
" Epoch :== 190 || TIME(sec):== 0.8251 || TrainError:== 0.068513 || TestError:== 0.090674\n",
" Epoch :== 191 || TIME(sec):== 0.8161 || TrainError:== 0.067219 || TestError:== 0.08987\n",
" Epoch :== 192 || TIME(sec):== 0.8373 || TrainError:== 0.06691 || TestError:== 0.090585\n",
" Epoch :== 193 || TIME(sec):== 0.8906 || TrainError:== 0.067788 || TestError:== 0.091423\n",
" Epoch :== 194 || TIME(sec):== 0.8553 || TrainError:== 0.06745 || TestError:== 0.089148\n",
" Epoch :== 195 || TIME(sec):== 0.8558 || TrainError:== 0.066861 || TestError:== 0.090215\n",
" Epoch :== 196 || TIME(sec):== 0.8569 || TrainError:== 0.066915 || TestError:== 0.090167\n",
" Epoch :== 197 || TIME(sec):== 0.8426 || TrainError:== 0.06738 || TestError:== 0.092086\n",
" Epoch :== 198 || TIME(sec):== 0.8328 || TrainError:== 0.06685 || TestError:== 0.089358\n",
" Epoch :== 199 || TIME(sec):== 0.8378 || TrainError:== 0.066615 || TestError:== 0.090008\n",
" Epoch :== 200 || TIME(sec):== 0.8362 || TrainError:== 0.067137 || TestError:== 0.089803\n",
" Epoch :== 201 || TIME(sec):== 0.8373 || TrainError:== 0.067023 || TestError:== 0.090048\n",
" Epoch :== 202 || TIME(sec):== 0.8409 || TrainError:== 0.066751 || TestError:== 0.090193\n",
" Epoch :== 203 || TIME(sec):== 0.8384 || TrainError:== 0.067725 || TestError:== 0.090046\n",
" Epoch :== 204 || TIME(sec):== 0.8389 || TrainError:== 0.06694 || TestError:== 0.088986\n",
" Epoch :== 205 || TIME(sec):== 0.8353 || TrainError:== 0.066348 || TestError:== 0.089001\n",
" Epoch :== 206 || TIME(sec):== 0.8335 || TrainError:== 0.066951 || TestError:== 0.090777\n",
" Epoch :== 207 || TIME(sec):== 0.8525 || TrainError:== 0.067648 || TestError:== 0.089801\n",
" Epoch :== 208 || TIME(sec):== 0.8523 || TrainError:== 0.066466 || TestError:== 0.089441\n",
" Epoch :== 209 || TIME(sec):== 0.8531 || TrainError:== 0.065623 || TestError:== 0.090491\n",
" Epoch :== 210 || TIME(sec):== 0.8525 || TrainError:== 0.06673 || TestError:== 0.089321\n",
" Epoch :== 211 || TIME(sec):== 0.8529 || TrainError:== 0.066446 || TestError:== 0.090635\n",
" Epoch :== 212 || TIME(sec):== 0.8178 || TrainError:== 0.065967 || TestError:== 0.089347\n",
" Epoch :== 213 || TIME(sec):== 0.8164 || TrainError:== 0.065684 || TestError:== 0.088824\n",
" Epoch :== 214 || TIME(sec):== 0.814 || TrainError:== 0.066 || TestError:== 0.088779\n",
" Epoch :== 215 || TIME(sec):== 0.8072 || TrainError:== 0.06521 || TestError:== 0.08966\n",
" Epoch :== 216 || TIME(sec):== 0.8118 || TrainError:== 0.065371 || TestError:== 0.08983\n",
" Epoch :== 217 || TIME(sec):== 0.8137 || TrainError:== 0.065863 || TestError:== 0.090054\n",
" Epoch :== 218 || TIME(sec):== 0.8128 || TrainError:== 0.065989 || TestError:== 0.088994\n",
" Epoch :== 219 || TIME(sec):== 1.004 || TrainError:== 0.066414 || TestError:== 0.089092\n",
" Epoch :== 220 || TIME(sec):== 1.0556 || TrainError:== 0.065663 || TestError:== 0.089109\n",
" Epoch :== 221 || TIME(sec):== 1.0231 || TrainError:== 0.065791 || TestError:== 0.089209\n",
" Epoch :== 222 || TIME(sec):== 1.0338 || TrainError:== 0.065204 || TestError:== 0.088342\n",
" Epoch :== 223 || TIME(sec):== 1.0444 || TrainError:== 0.06563 || TestError:== 0.089117\n",
" Epoch :== 224 || TIME(sec):== 1.0166 || TrainError:== 0.065453 || TestError:== 0.088579\n",
" Epoch :== 225 || TIME(sec):== 1.0332 || TrainError:== 0.065073 || TestError:== 0.088077\n",
" Epoch :== 226 || TIME(sec):== 1.0309 || TrainError:== 0.065957 || TestError:== 0.089155\n",
" Epoch :== 227 || TIME(sec):== 1.0238 || TrainError:== 0.065299 || TestError:== 0.089582\n",
" Epoch :== 228 || TIME(sec):== 1.0343 || TrainError:== 0.065411 || TestError:== 0.088677\n",
" Epoch :== 229 || TIME(sec):== 1.0262 || TrainError:== 0.065046 || TestError:== 0.092455\n",
" Epoch :== 230 || TIME(sec):== 0.9976 || TrainError:== 0.065952 || TestError:== 0.088027\n",
" Epoch :== 231 || TIME(sec):== 0.9653 || TrainError:== 0.064898 || TestError:== 0.087608\n",
" Epoch :== 232 || TIME(sec):== 0.9779 || TrainError:== 0.065166 || TestError:== 0.090523\n",
" Epoch :== 233 || TIME(sec):== 0.9723 || TrainError:== 0.066236 || TestError:== 0.089218\n",
" Epoch :== 234 || TIME(sec):== 0.9726 || TrainError:== 0.064666 || TestError:== 0.089221\n",
" Epoch :== 235 || TIME(sec):== 0.9745 || TrainError:== 0.064604 || TestError:== 0.087888\n",
" Epoch :== 236 || TIME(sec):== 0.9722 || TrainError:== 0.064487 || TestError:== 0.088017\n",
" Epoch :== 237 || TIME(sec):== 0.9709 || TrainError:== 0.064317 || TestError:== 0.087794\n",
" Epoch :== 238 || TIME(sec):== 0.9724 || TrainError:== 0.065068 || TestError:== 0.089322\n",
" Epoch :== 239 || TIME(sec):== 0.9786 || TrainError:== 0.065505 || TestError:== 0.087113\n",
" Epoch :== 240 || TIME(sec):== 0.9791 || TrainError:== 0.06516 || TestError:== 0.090121\n",
" Epoch :== 241 || TIME(sec):== 0.9698 || TrainError:== 0.062836 || TestError:== 0.086624\n",
" Epoch :== 242 || TIME(sec):== 0.9755 || TrainError:== 0.061029 || TestError:== 0.085966\n",
" Epoch :== 243 || TIME(sec):== 1.0089 || TrainError:== 0.06085 || TestError:== 0.085874\n",
" Epoch :== 244 || TIME(sec):== 1.008 || TrainError:== 0.060735 || TestError:== 0.086018\n",
" Epoch :== 245 || TIME(sec):== 1.0268 || TrainError:== 0.061548 || TestError:== 0.087171\n",
" Epoch :== 246 || TIME(sec):== 1.0205 || TrainError:== 0.062335 || TestError:== 0.087974\n",
" Epoch :== 247 || TIME(sec):== 1.016 || TrainError:== 0.0627 || TestError:== 0.085838\n",
" Epoch :== 248 || TIME(sec):== 1.0331 || TrainError:== 0.061363 || TestError:== 0.08581\n",
" Epoch :== 249 || TIME(sec):== 1.0225 || TrainError:== 0.060702 || TestError:== 0.086234\n",
" Epoch :== 250 || TIME(sec):== 1.0286 || TrainError:== 0.061848 || TestError:== 0.087022\n",
" Epoch :== 251 || TIME(sec):== 1.039 || TrainError:== 0.061683 || TestError:== 0.085835\n",
" Epoch :== 252 || TIME(sec):== 0.9858 || TrainError:== 0.061723 || TestError:== 0.08574\n",
" Epoch :== 253 || TIME(sec):== 1.0068 || TrainError:== 0.061247 || TestError:== 0.087103\n",
" Epoch :== 254 || TIME(sec):== 0.9841 || TrainError:== 0.061845 || TestError:== 0.086908\n",
" Epoch :== 255 || TIME(sec):== 0.9953 || TrainError:== 0.062084 || TestError:== 0.085881\n",
" Epoch :== 256 || TIME(sec):== 0.9716 || TrainError:== 0.061284 || TestError:== 0.085839\n",
" Epoch :== 257 || TIME(sec):== 0.9732 || TrainError:== 0.061473 || TestError:== 0.085719\n",
" Epoch :== 258 || TIME(sec):== 0.9321 || TrainError:== 0.061614 || TestError:== 0.08574\n",
" Epoch :== 259 || TIME(sec):== 0.9493 || TrainError:== 0.061199 || TestError:== 0.085513\n",
" Epoch :== 260 || TIME(sec):== 1.0044 || TrainError:== 0.062153 || TestError:== 0.08616\n",
" Epoch :== 261 || TIME(sec):== 1.0264 || TrainError:== 0.06127 || TestError:== 0.085294\n",
" Epoch :== 262 || TIME(sec):== 1.0122 || TrainError:== 0.061419 || TestError:== 0.086412\n",
" Epoch :== 263 || TIME(sec):== 1.043 || TrainError:== 0.061488 || TestError:== 0.085484\n",
" Epoch :== 264 || TIME(sec):== 1.0305 || TrainError:== 0.060739 || TestError:== 0.085755\n",
" Epoch :== 265 || TIME(sec):== 1.0405 || TrainError:== 0.061222 || TestError:== 0.086389\n",
" Epoch :== 266 || TIME(sec):== 1.0226 || TrainError:== 0.061308 || TestError:== 0.085902\n",
" Epoch :== 267 || TIME(sec):== 0.9848 || TrainError:== 0.061536 || TestError:== 0.086196\n",
" Epoch :== 268 || TIME(sec):== 0.9814 || TrainError:== 0.061395 || TestError:== 0.085761\n",
" Epoch :== 269 || TIME(sec):== 0.9813 || TrainError:== 0.06116 || TestError:== 0.086269\n",
" Epoch :== 270 || TIME(sec):== 0.9793 || TrainError:== 0.060947 || TestError:== 0.085717\n",
" Epoch :== 271 || TIME(sec):== 0.9737 || TrainError:== 0.060889 || TestError:== 0.085619\n",
" Epoch :== 272 || TIME(sec):== 0.8231 || TrainError:== 0.060984 || TestError:== 0.086644\n",
" Epoch :== 273 || TIME(sec):== 0.8269 || TrainError:== 0.060805 || TestError:== 0.085024\n",
" Epoch :== 274 || TIME(sec):== 0.8229 || TrainError:== 0.060689 || TestError:== 0.085345\n",
" Epoch :== 275 || TIME(sec):== 0.8208 || TrainError:== 0.060674 || TestError:== 0.085417\n",
" Epoch :== 276 || TIME(sec):== 0.8125 || TrainError:== 0.060768 || TestError:== 0.084772\n",
" Epoch :== 277 || TIME(sec):== 0.8283 || TrainError:== 0.060371 || TestError:== 0.085206\n",
" Epoch :== 278 || TIME(sec):== 0.8225 || TrainError:== 0.060741 || TestError:== 0.085622\n",
" Epoch :== 279 || TIME(sec):== 0.828 || TrainError:== 0.060055 || TestError:== 0.085045\n",
" Epoch :== 280 || TIME(sec):== 0.8236 || TrainError:== 0.060749 || TestError:== 0.084605\n",
" Epoch :== 281 || TIME(sec):== 0.8256 || TrainError:== 0.060762 || TestError:== 0.085471\n",
" Epoch :== 282 || TIME(sec):== 0.819 || TrainError:== 0.06063 || TestError:== 0.085547\n",
" Epoch :== 283 || TIME(sec):== 0.825 || TrainError:== 0.060374 || TestError:== 0.085122\n",
" Epoch :== 284 || TIME(sec):== 0.8182 || TrainError:== 0.061403 || TestError:== 0.085844\n",
" Epoch :== 285 || TIME(sec):== 0.8271 || TrainError:== 0.060413 || TestError:== 0.0847\n",
" Epoch :== 286 || TIME(sec):== 0.816 || TrainError:== 0.060467 || TestError:== 0.08522\n",
" Epoch :== 287 || TIME(sec):== 0.8214 || TrainError:== 0.060191 || TestError:== 0.085382\n",
" Epoch :== 288 || TIME(sec):== 0.8199 || TrainError:== 0.060329 || TestError:== 0.085022\n",
" Epoch :== 289 || TIME(sec):== 0.821 || TrainError:== 0.059768 || TestError:== 0.085991\n",
" Epoch :== 290 || TIME(sec):== 0.8252 || TrainError:== 0.060821 || TestError:== 0.085434\n",
" Epoch :== 291 || TIME(sec):== 0.8277 || TrainError:== 0.060645 || TestError:== 0.084734\n",
" Epoch :== 292 || TIME(sec):== 0.8299 || TrainError:== 0.06082 || TestError:== 0.085982\n",
" Epoch :== 293 || TIME(sec):== 0.8289 || TrainError:== 0.060261 || TestError:== 0.085428\n",
" Epoch :== 294 || TIME(sec):== 0.8205 || TrainError:== 0.060192 || TestError:== 0.084712\n",
" Epoch :== 295 || TIME(sec):== 0.8144 || TrainError:== 0.059413 || TestError:== 0.084679\n",
" Epoch :== 296 || TIME(sec):== 0.8141 || TrainError:== 0.059134 || TestError:== 0.085854\n",
" Epoch :== 297 || TIME(sec):== 0.8194 || TrainError:== 0.060094 || TestError:== 0.084498\n",
" Epoch :== 298 || TIME(sec):== 0.8271 || TrainError:== 0.060849 || TestError:== 0.085377\n",
" Epoch :== 299 || TIME(sec):== 0.8255 || TrainError:== 0.059912 || TestError:== 0.084937\n",
" Epoch :== 300 || TIME(sec):== 0.8319 || TrainError:== 0.06058 || TestError:== 0.085397\n",
" Epoch :== 301 || TIME(sec):== 0.8276 || TrainError:== 0.059698 || TestError:== 0.085539\n",
" Epoch :== 302 || TIME(sec):== 1.0047 || TrainError:== 0.060282 || TestError:== 0.08367\n",
" Epoch :== 303 || TIME(sec):== 0.9936 || TrainError:== 0.059381 || TestError:== 0.084617\n",
" Epoch :== 304 || TIME(sec):== 0.9589 || TrainError:== 0.059706 || TestError:== 0.084124\n",
" Epoch :== 305 || TIME(sec):== 0.964 || TrainError:== 0.059498 || TestError:== 0.084666\n",
" Epoch :== 306 || TIME(sec):== 1.0218 || TrainError:== 0.06027 || TestError:== 0.086778\n",
" Epoch :== 307 || TIME(sec):== 0.987 || TrainError:== 0.059961 || TestError:== 0.084191\n",
" Epoch :== 308 || TIME(sec):== 0.9685 || TrainError:== 0.059276 || TestError:== 0.084882\n",
" Epoch :== 309 || TIME(sec):== 0.9741 || TrainError:== 0.059338 || TestError:== 0.084523\n",
" Epoch :== 310 || TIME(sec):== 0.9722 || TrainError:== 0.059414 || TestError:== 0.084912\n",
" Epoch :== 311 || TIME(sec):== 1.0028 || TrainError:== 0.059634 || TestError:== 0.084697\n",
" Epoch :== 312 || TIME(sec):== 1.0046 || TrainError:== 0.060853 || TestError:== 0.084839\n",
" Epoch :== 313 || TIME(sec):== 0.9712 || TrainError:== 0.059691 || TestError:== 0.084664\n",
" Epoch :== 314 || TIME(sec):== 0.9373 || TrainError:== 0.059013 || TestError:== 0.08401\n",
" Epoch :== 315 || TIME(sec):== 0.9729 || TrainError:== 0.059398 || TestError:== 0.084457\n",
" Epoch :== 316 || TIME(sec):== 0.9355 || TrainError:== 0.059481 || TestError:== 0.083967\n",
" Epoch :== 317 || TIME(sec):== 0.8288 || TrainError:== 0.059001 || TestError:== 0.084785\n",
" Epoch :== 318 || TIME(sec):== 0.8338 || TrainError:== 0.059356 || TestError:== 0.085262\n",
" Epoch :== 319 || TIME(sec):== 0.8399 || TrainError:== 0.059646 || TestError:== 0.085049\n",
" Epoch :== 320 || TIME(sec):== 0.842 || TrainError:== 0.060262 || TestError:== 0.084865\n",
" Epoch :== 321 || TIME(sec):== 0.8307 || TrainError:== 0.057742 || TestError:== 0.083087\n",
" Epoch :== 322 || TIME(sec):== 0.8369 || TrainError:== 0.056827 || TestError:== 0.082792\n",
" Epoch :== 323 || TIME(sec):== 0.8369 || TrainError:== 0.056389 || TestError:== 0.082468\n",
" Epoch :== 324 || TIME(sec):== 0.8461 || TrainError:== 0.056281 || TestError:== 0.082792\n",
" Epoch :== 325 || TIME(sec):== 0.8357 || TrainError:== 0.056393 || TestError:== 0.082527\n",
" Epoch :== 326 || TIME(sec):== 0.8362 || TrainError:== 0.056751 || TestError:== 0.082564\n",
" Epoch :== 327 || TIME(sec):== 0.841 || TrainError:== 0.056534 || TestError:== 0.082759\n",
" Epoch :== 328 || TIME(sec):== 0.8393 || TrainError:== 0.056532 || TestError:== 0.083\n",
" Epoch :== 329 || TIME(sec):== 0.8364 || TrainError:== 0.056736 || TestError:== 0.083634\n",
" Epoch :== 330 || TIME(sec):== 0.8387 || TrainError:== 0.057254 || TestError:== 0.083363\n",
" Epoch :== 331 || TIME(sec):== 0.835 || TrainError:== 0.057392 || TestError:== 0.084078\n",
" Epoch :== 332 || TIME(sec):== 0.8356 || TrainError:== 0.057288 || TestError:== 0.082148\n",
" Epoch :== 333 || TIME(sec):== 0.8372 || TrainError:== 0.05684 || TestError:== 0.083315\n",
" Epoch :== 334 || TIME(sec):== 0.8354 || TrainError:== 0.056877 || TestError:== 0.082824\n",
" Epoch :== 335 || TIME(sec):== 0.8444 || TrainError:== 0.057052 || TestError:== 0.082684\n",
" Epoch :== 336 || TIME(sec):== 0.8363 || TrainError:== 0.056792 || TestError:== 0.082895\n",
" Epoch :== 337 || TIME(sec):== 0.8341 || TrainError:== 0.056845 || TestError:== 0.082991\n",
" Epoch :== 338 || TIME(sec):== 0.8383 || TrainError:== 0.056732 || TestError:== 0.083206\n",
" Epoch :== 339 || TIME(sec):== 0.8383 || TrainError:== 0.056571 || TestError:== 0.083479\n",
" Epoch :== 340 || TIME(sec):== 0.8356 || TrainError:== 0.056681 || TestError:== 0.082995\n",
" Epoch :== 341 || TIME(sec):== 0.8352 || TrainError:== 0.057292 || TestError:== 0.08304\n",
" Epoch :== 342 || TIME(sec):== 0.8331 || TrainError:== 0.057157 || TestError:== 0.083308\n",
" Epoch :== 343 || TIME(sec):== 0.8373 || TrainError:== 0.056917 || TestError:== 0.083343\n",
" Epoch :== 344 || TIME(sec):== 0.8443 || TrainError:== 0.056954 || TestError:== 0.08293\n",
" Epoch :== 345 || TIME(sec):== 0.8354 || TrainError:== 0.056722 || TestError:== 0.083115\n",
" Epoch :== 346 || TIME(sec):== 0.8366 || TrainError:== 0.056953 || TestError:== 0.082828\n",
" Epoch :== 347 || TIME(sec):== 0.8341 || TrainError:== 0.057372 || TestError:== 0.083467\n",
" Epoch :== 348 || TIME(sec):== 0.8378 || TrainError:== 0.056644 || TestError:== 0.083308\n",
" Epoch :== 349 || TIME(sec):== 0.8369 || TrainError:== 0.056922 || TestError:== 0.082586\n",
" Epoch :== 350 || TIME(sec):== 0.8479 || TrainError:== 0.056212 || TestError:== 0.082534\n",
" Epoch :== 351 || TIME(sec):== 0.8362 || TrainError:== 0.056394 || TestError:== 0.082325\n",
" Epoch :== 352 || TIME(sec):== 0.8496 || TrainError:== 0.056667 || TestError:== 0.083136\n",
" Epoch :== 353 || TIME(sec):== 0.8953 || TrainError:== 0.05692 || TestError:== 0.082693\n",
" Epoch :== 354 || TIME(sec):== 0.8797 || TrainError:== 0.056367 || TestError:== 0.08233\n",
" Epoch :== 355 || TIME(sec):== 0.856 || TrainError:== 0.056133 || TestError:== 0.08283\n",
" Epoch :== 356 || TIME(sec):== 0.8975 || TrainError:== 0.056805 || TestError:== 0.082863\n",
" Epoch :== 357 || TIME(sec):== 0.9836 || TrainError:== 0.056191 || TestError:== 0.082678\n",
" Epoch :== 358 || TIME(sec):== 0.9762 || TrainError:== 0.056083 || TestError:== 0.082974\n",
" Epoch :== 359 || TIME(sec):== 0.9551 || TrainError:== 0.056525 || TestError:== 0.082528\n",
" Epoch :== 360 || TIME(sec):== 0.9444 || TrainError:== 0.056389 || TestError:== 0.083709\n",
" Epoch :== 361 || TIME(sec):== 0.9511 || TrainError:== 0.057618 || TestError:== 0.083238\n",
" Epoch :== 362 || TIME(sec):== 0.9402 || TrainError:== 0.056719 || TestError:== 0.083937\n",
" Epoch :== 363 || TIME(sec):== 0.859 || TrainError:== 0.056612 || TestError:== 0.082479\n",
" Epoch :== 364 || TIME(sec):== 0.828 || TrainError:== 0.056008 || TestError:== 0.082433\n",
" Epoch :== 365 || TIME(sec):== 0.8338 || TrainError:== 0.055908 || TestError:== 0.081701\n",
" Epoch :== 366 || TIME(sec):== 0.8251 || TrainError:== 0.056104 || TestError:== 0.083288\n",
" Epoch :== 367 || TIME(sec):== 0.8301 || TrainError:== 0.056355 || TestError:== 0.082423\n",
" Epoch :== 368 || TIME(sec):== 0.8287 || TrainError:== 0.056603 || TestError:== 0.082548\n",
" Epoch :== 369 || TIME(sec):== 0.8309 || TrainError:== 0.0564 || TestError:== 0.083211\n",
" Epoch :== 370 || TIME(sec):== 0.827 || TrainError:== 0.056323 || TestError:== 0.082709\n",
" Epoch :== 371 || TIME(sec):== 0.8246 || TrainError:== 0.05661 || TestError:== 0.082339\n",
" Epoch :== 372 || TIME(sec):== 0.8298 || TrainError:== 0.055985 || TestError:== 0.083068\n",
" Epoch :== 373 || TIME(sec):== 0.825 || TrainError:== 0.056087 || TestError:== 0.082727\n",
" Epoch :== 374 || TIME(sec):== 0.8322 || TrainError:== 0.055952 || TestError:== 0.081571\n",
" Epoch :== 375 || TIME(sec):== 0.8867 || TrainError:== 0.056809 || TestError:== 0.0822\n",
" Epoch :== 376 || TIME(sec):== 0.9031 || TrainError:== 0.055844 || TestError:== 0.082341\n",
" Epoch :== 377 || TIME(sec):== 0.9112 || TrainError:== 0.055879 || TestError:== 0.082177\n",
" Epoch :== 378 || TIME(sec):== 0.903 || TrainError:== 0.055618 || TestError:== 0.0821\n",
" Epoch :== 379 || TIME(sec):== 0.9033 || TrainError:== 0.055547 || TestError:== 0.082021\n",
" Epoch :== 380 || TIME(sec):== 0.9018 || TrainError:== 0.055307 || TestError:== 0.082284\n",
" Epoch :== 381 || TIME(sec):== 0.9031 || TrainError:== 0.056543 || TestError:== 0.082107\n",
" Epoch :== 382 || TIME(sec):== 0.8453 || TrainError:== 0.056012 || TestError:== 0.081979\n",
" Epoch :== 383 || TIME(sec):== 0.8201 || TrainError:== 0.056224 || TestError:== 0.083148\n",
" Epoch :== 384 || TIME(sec):== 0.8183 || TrainError:== 0.055815 || TestError:== 0.08212\n",
" Epoch :== 385 || TIME(sec):== 0.8344 || TrainError:== 0.055748 || TestError:== 0.082245\n",
" Epoch :== 386 || TIME(sec):== 0.8253 || TrainError:== 0.055822 || TestError:== 0.082151\n",
" Epoch :== 387 || TIME(sec):== 0.8167 || TrainError:== 0.055871 || TestError:== 0.082259\n",
" Epoch :== 388 || TIME(sec):== 0.8311 || TrainError:== 0.056123 || TestError:== 0.082627\n",
" Epoch :== 389 || TIME(sec):== 0.8231 || TrainError:== 0.05629 || TestError:== 0.081744\n",
" Epoch :== 390 || TIME(sec):== 0.8319 || TrainError:== 0.055426 || TestError:== 0.082938\n",
" Epoch :== 391 || TIME(sec):== 0.8262 || TrainError:== 0.055712 || TestError:== 0.081775\n",
" Epoch :== 392 || TIME(sec):== 0.8307 || TrainError:== 0.055681 || TestError:== 0.081635\n",
" Epoch :== 393 || TIME(sec):== 0.818 || TrainError:== 0.055405 || TestError:== 0.082\n",
" Epoch :== 394 || TIME(sec):== 0.8163 || TrainError:== 0.055584 || TestError:== 0.081762\n",
" Epoch :== 395 || TIME(sec):== 0.8256 || TrainError:== 0.0554 || TestError:== 0.082212\n",
" Epoch :== 396 || TIME(sec):== 0.8283 || TrainError:== 0.055805 || TestError:== 0.082673\n",
" Epoch :== 397 || TIME(sec):== 0.8219 || TrainError:== 0.056008 || TestError:== 0.082705\n",
" Epoch :== 398 || TIME(sec):== 0.8211 || TrainError:== 0.055755 || TestError:== 0.082269\n",
" Epoch :== 399 || TIME(sec):== 0.8321 || TrainError:== 0.055585 || TestError:== 0.081698\n",
" Epoch :== 400 || TIME(sec):== 0.8454 || TrainError:== 0.055603 || TestError:== 0.082047\n",
" Epoch :== 401 || TIME(sec):== 1.0967 || TrainError:== 0.054257 || TestError:== 0.081424\n",
" Epoch :== 402 || TIME(sec):== 0.8297 || TrainError:== 0.053452 || TestError:== 0.080467\n",
" Epoch :== 403 || TIME(sec):== 0.8301 || TrainError:== 0.053114 || TestError:== 0.080872\n",
" Epoch :== 404 || TIME(sec):== 0.8329 || TrainError:== 0.053273 || TestError:== 0.080431\n",
" Epoch :== 405 || TIME(sec):== 0.8195 || TrainError:== 0.053407 || TestError:== 0.080799\n",
" Epoch :== 406 || TIME(sec):== 0.8345 || TrainError:== 0.053548 || TestError:== 0.08054\n",
" Epoch :== 407 || TIME(sec):== 0.8245 || TrainError:== 0.053616 || TestError:== 0.081159\n",
" Epoch :== 408 || TIME(sec):== 0.8221 || TrainError:== 0.053691 || TestError:== 0.080803\n",
" Epoch :== 409 || TIME(sec):== 0.8212 || TrainError:== 0.053656 || TestError:== 0.080954\n",
" Epoch :== 410 || TIME(sec):== 0.8271 || TrainError:== 0.053836 || TestError:== 0.080865\n",
" Epoch :== 411 || TIME(sec):== 0.8257 || TrainError:== 0.053549 || TestError:== 0.080794\n",
" Epoch :== 412 || TIME(sec):== 0.8291 || TrainError:== 0.053529 || TestError:== 0.080653\n",
" Epoch :== 413 || TIME(sec):== 0.8228 || TrainError:== 0.053391 || TestError:== 0.081131\n",
" Epoch :== 414 || TIME(sec):== 0.8225 || TrainError:== 0.053511 || TestError:== 0.080986\n",
" Epoch :== 415 || TIME(sec):== 0.8251 || TrainError:== 0.053393 || TestError:== 0.080913\n",
" Epoch :== 416 || TIME(sec):== 0.8147 || TrainError:== 0.053754 || TestError:== 0.080748\n",
" Epoch :== 417 || TIME(sec):== 0.8221 || TrainError:== 0.053925 || TestError:== 0.080808\n",
" Epoch :== 418 || TIME(sec):== 0.8197 || TrainError:== 0.053468 || TestError:== 0.080388\n",
" Epoch :== 419 || TIME(sec):== 0.8179 || TrainError:== 0.053546 || TestError:== 0.081497\n",
" Epoch :== 420 || TIME(sec):== 0.8287 || TrainError:== 0.053805 || TestError:== 0.080318\n",
" Epoch :== 421 || TIME(sec):== 0.8307 || TrainError:== 0.053497 || TestError:== 0.081701\n",
" Epoch :== 422 || TIME(sec):== 0.819 || TrainError:== 0.053675 || TestError:== 0.080815\n",
" Epoch :== 423 || TIME(sec):== 0.823 || TrainError:== 0.053386 || TestError:== 0.080616\n",
" Epoch :== 424 || TIME(sec):== 0.8242 || TrainError:== 0.053692 || TestError:== 0.080984\n",
" Epoch :== 425 || TIME(sec):== 0.8242 || TrainError:== 0.053924 || TestError:== 0.081985\n",
" Epoch :== 426 || TIME(sec):== 0.8316 || TrainError:== 0.053901 || TestError:== 0.080999\n",
" Epoch :== 427 || TIME(sec):== 0.8267 || TrainError:== 0.053851 || TestError:== 0.080522\n",
" Epoch :== 428 || TIME(sec):== 0.831 || TrainError:== 0.053717 || TestError:== 0.081227\n",
" Epoch :== 429 || TIME(sec):== 0.8205 || TrainError:== 0.05353 || TestError:== 0.080637\n",
" Epoch :== 430 || TIME(sec):== 0.8255 || TrainError:== 0.053731 || TestError:== 0.080704\n",
" Epoch :== 431 || TIME(sec):== 0.8212 || TrainError:== 0.053463 || TestError:== 0.081439\n",
" Epoch :== 432 || TIME(sec):== 0.8238 || TrainError:== 0.053438 || TestError:== 0.080803\n",
" Epoch :== 433 || TIME(sec):== 0.8249 || TrainError:== 0.053542 || TestError:== 0.080984\n",
" Epoch :== 434 || TIME(sec):== 0.8271 || TrainError:== 0.053278 || TestError:== 0.080344\n",
" Epoch :== 435 || TIME(sec):== 0.829 || TrainError:== 0.053395 || TestError:== 0.080444\n",
" Epoch :== 436 || TIME(sec):== 0.8274 || TrainError:== 0.054019 || TestError:== 0.080223\n",
" Epoch :== 437 || TIME(sec):== 0.8158 || TrainError:== 0.053557 || TestError:== 0.081792\n",
" Epoch :== 438 || TIME(sec):== 0.8169 || TrainError:== 0.054317 || TestError:== 0.080942\n",
" Epoch :== 439 || TIME(sec):== 0.8239 || TrainError:== 0.053411 || TestError:== 0.08027\n",
" Epoch :== 440 || TIME(sec):== 0.8217 || TrainError:== 0.053039 || TestError:== 0.080191\n",
" Epoch :== 441 || TIME(sec):== 0.8217 || TrainError:== 0.053035 || TestError:== 0.080083\n",
" Epoch :== 442 || TIME(sec):== 0.8765 || TrainError:== 0.053525 || TestError:== 0.081152\n",
" Epoch :== 443 || TIME(sec):== 0.936 || TrainError:== 0.053616 || TestError:== 0.080243\n",
" Epoch :== 444 || TIME(sec):== 0.9332 || TrainError:== 0.052829 || TestError:== 0.081209\n",
" Epoch :== 445 || TIME(sec):== 0.9441 || TrainError:== 0.05339 || TestError:== 0.080972\n",
" Epoch :== 446 || TIME(sec):== 0.9357 || TrainError:== 0.053546 || TestError:== 0.080341\n",
" Epoch :== 447 || TIME(sec):== 0.9393 || TrainError:== 0.052961 || TestError:== 0.080683\n",
" Epoch :== 448 || TIME(sec):== 0.9437 || TrainError:== 0.0527 || TestError:== 0.080374\n",
" Epoch :== 449 || TIME(sec):== 0.9444 || TrainError:== 0.052747 || TestError:== 0.079624\n",
" Epoch :== 450 || TIME(sec):== 0.9464 || TrainError:== 0.052973 || TestError:== 0.080423\n",
" Epoch :== 451 || TIME(sec):== 0.9389 || TrainError:== 0.053249 || TestError:== 0.080219\n",
" Epoch :== 452 || TIME(sec):== 0.9501 || TrainError:== 0.053246 || TestError:== 0.080612\n",
" Epoch :== 453 || TIME(sec):== 0.9579 || TrainError:== 0.05309 || TestError:== 0.080566\n",
" Epoch :== 454 || TIME(sec):== 0.9417 || TrainError:== 0.053215 || TestError:== 0.080161\n",
" Epoch :== 455 || TIME(sec):== 0.9105 || TrainError:== 0.053214 || TestError:== 0.080428\n",
" Epoch :== 456 || TIME(sec):== 0.8266 || TrainError:== 0.053043 || TestError:== 0.080564\n",
" Epoch :== 457 || TIME(sec):== 0.8362 || TrainError:== 0.052952 || TestError:== 0.080311\n",
" Epoch :== 458 || TIME(sec):== 0.8732 || TrainError:== 0.052787 || TestError:== 0.080563\n",
" Epoch :== 459 || TIME(sec):== 0.9074 || TrainError:== 0.052833 || TestError:== 0.080591\n",
" Epoch :== 460 || TIME(sec):== 0.8557 || TrainError:== 0.052995 || TestError:== 0.080226\n",
" Epoch :== 461 || TIME(sec):== 0.8553 || TrainError:== 0.052896 || TestError:== 0.080279\n",
" Epoch :== 462 || TIME(sec):== 0.8551 || TrainError:== 0.05312 || TestError:== 0.079779\n",
" Epoch :== 463 || TIME(sec):== 0.8551 || TrainError:== 0.053298 || TestError:== 0.080805\n",
" Epoch :== 464 || TIME(sec):== 0.8547 || TrainError:== 0.05369 || TestError:== 0.081121\n",
" Epoch :== 465 || TIME(sec):== 0.8562 || TrainError:== 0.053241 || TestError:== 0.079794\n",
" Epoch :== 466 || TIME(sec):== 0.8558 || TrainError:== 0.052981 || TestError:== 0.080111\n",
" Epoch :== 467 || TIME(sec):== 0.8337 || TrainError:== 0.052551 || TestError:== 0.080027\n",
" Epoch :== 468 || TIME(sec):== 0.832 || TrainError:== 0.052819 || TestError:== 0.080288\n",
" Epoch :== 469 || TIME(sec):== 0.8327 || TrainError:== 0.052534 || TestError:== 0.080273\n",
" Epoch :== 470 || TIME(sec):== 0.827 || TrainError:== 0.053172 || TestError:== 0.080319\n",
" Epoch :== 471 || TIME(sec):== 0.8282 || TrainError:== 0.053493 || TestError:== 0.081408\n",
" Epoch :== 472 || TIME(sec):== 0.8308 || TrainError:== 0.053396 || TestError:== 0.080365\n",
" Epoch :== 473 || TIME(sec):== 0.8253 || TrainError:== 0.05298 || TestError:== 0.080581\n",
" Epoch :== 474 || TIME(sec):== 0.8157 || TrainError:== 0.052988 || TestError:== 0.080758\n",
" Epoch :== 475 || TIME(sec):== 0.8308 || TrainError:== 0.052635 || TestError:== 0.080091\n",
" Epoch :== 476 || TIME(sec):== 0.8182 || TrainError:== 0.052368 || TestError:== 0.08029\n",
" Epoch :== 477 || TIME(sec):== 0.8348 || TrainError:== 0.053096 || TestError:== 0.08065\n",
" Epoch :== 478 || TIME(sec):== 0.8272 || TrainError:== 0.053199 || TestError:== 0.080684\n",
" Epoch :== 479 || TIME(sec):== 0.8305 || TrainError:== 0.052701 || TestError:== 0.080196\n",
" Epoch :== 480 || TIME(sec):== 0.8124 || TrainError:== 0.052521 || TestError:== 0.080217\n",
" Epoch :== 481 || TIME(sec):== 0.8309 || TrainError:== 0.051442 || TestError:== 0.079565\n",
" Epoch :== 482 || TIME(sec):== 0.8247 || TrainError:== 0.05124 || TestError:== 0.079272\n",
" Epoch :== 483 || TIME(sec):== 0.8287 || TrainError:== 0.050959 || TestError:== 0.07914\n",
" Epoch :== 484 || TIME(sec):== 0.8177 || TrainError:== 0.050969 || TestError:== 0.079129\n",
" Epoch :== 485 || TIME(sec):== 0.8194 || TrainError:== 0.051123 || TestError:== 0.079252\n",
" Epoch :== 486 || TIME(sec):== 0.8226 || TrainError:== 0.051013 || TestError:== 0.078976\n",
" Epoch :== 487 || TIME(sec):== 0.8365 || TrainError:== 0.050984 || TestError:== 0.079269\n",
" Epoch :== 488 || TIME(sec):== 0.82 || TrainError:== 0.051165 || TestError:== 0.079301\n",
" Epoch :== 489 || TIME(sec):== 0.8144 || TrainError:== 0.050977 || TestError:== 0.079756\n",
" Epoch :== 490 || TIME(sec):== 0.8208 || TrainError:== 0.051377 || TestError:== 0.079797\n",
" Epoch :== 491 || TIME(sec):== 0.8159 || TrainError:== 0.051466 || TestError:== 0.079426\n",
" Epoch :== 492 || TIME(sec):== 0.8241 || TrainError:== 0.051172 || TestError:== 0.079219\n",
" Epoch :== 493 || TIME(sec):== 0.8239 || TrainError:== 0.051018 || TestError:== 0.079809\n",
" Epoch :== 494 || TIME(sec):== 0.8285 || TrainError:== 0.051051 || TestError:== 0.079081\n",
" Epoch :== 495 || TIME(sec):== 0.8225 || TrainError:== 0.051047 || TestError:== 0.079642\n",
" Epoch :== 496 || TIME(sec):== 0.8215 || TrainError:== 0.05149 || TestError:== 0.07956\n",
" Epoch :== 497 || TIME(sec):== 0.8256 || TrainError:== 0.051214 || TestError:== 0.079628\n",
" Epoch :== 498 || TIME(sec):== 0.8134 || TrainError:== 0.051362 || TestError:== 0.07928\n",
" Epoch :== 499 || TIME(sec):== 0.8144 || TrainError:== 0.051469 || TestError:== 0.079853\n",
" Epoch :== 500 || TIME(sec):== 0.8215 || TrainError:== 0.051685 || TestError:== 0.079307\n"
]
}
],
"source": [
"\n",
"## ep wise error\n",
"trainerror=[] \n",
"testerror=[]\n",
"\n",
"##Train Test Loop\n",
"for ep in range(epochs):\n",
" model.train()\n",
" t1 = default_timer()\n",
" train_l2 = 0\n",
" for x, y in train_loader:\n",
" x, y = x.cuda(), y.cuda()\n",
" \n",
" optimizer.zero_grad()\n",
"\n",
" out = model(x).reshape(batch_size, dim, dim,strain_channels)\n",
" out = y_normalizer.decode(out)\n",
" y = y_normalizer.decode(y)\n",
"\n",
" loss = lossfunc(y.view(batch_size,-1),out.view(batch_size,-1))\n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
" train_l2 += loss.item()\n",
"\n",
" scheduler.step()\n",
" model.eval()\n",
" test_l2 = 0.0\n",
" with torch.no_grad():\n",
" for x, y in test_loader:\n",
" x, y = x.cuda(), y.cuda()\n",
" out = model(x).reshape(batch_size, dim, dim,strain_channels)\n",
" out = y_normalizer.decode(out)\n",
"\n",
" test_l2 += lossfunc(y.view(batch_size,-1),out.view(batch_size,-1)).item()\n",
"\n",
" train_l2/= ntrain\n",
" test_l2 /= ntest\n",
" trainerror += [train_l2]\n",
" testerror += [test_l2]\n",
" t2 = default_timer()\n",
" print(f' Epoch :== {ep+1} || TIME(sec):== {np.round((t2-t1),4)} '\n",
" f'|| TrainError:== {np.round(train_l2,6)} || TestError:== {np.round(test_l2,6)}')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "dcf3c283-a124-4f4a-bdd0-9d0eb7ce1823",
"metadata": {},
"outputs": [],
"source": [
"# trainn = np.array(trainerror)\n",
"# testnn = np.array(testerror)\n",
"# torch.save(model.state_dict(),'FNO_STRAIN_N1200_ep500.pt')\n",
"# np.save('trainFNO_STRAIN', trainn)\n",
"# np.save('testFNO_STRAIN', testnn)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "540a17d5-27fe-4906-933f-1479b33cbb41",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 700x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(7,6))\n",
"plt.plot(range(epochs),trainerror,linewidth = 4,label =\"TRAIN\")\n",
"plt.plot(range(epochs),testerror, linewidth = 4,label =\"TEST\")\n",
"plt.xlabel('Epoch', fontsize =20)\n",
"plt.ylabel('Loss', fontsize =20)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "09bb74c6-9f65-4418-b2af-77789d5f20ed",
"metadata": {},
"source": [
"## Testing "
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "85c08be1-a913-488d-8fc3-ece79ce2da09",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FNO2d(\n",
" (fc0): Linear(in_features=3, out_features=32, bias=True)\n",
" (conv0): SpectralConv2d()\n",
" (conv1): SpectralConv2d()\n",
" (conv2): SpectralConv2d()\n",
" (conv3): SpectralConv2d()\n",
" (w0): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))\n",
" (w1): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))\n",
" (w2): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))\n",
" (w3): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))\n",
" (fc1): Linear(in_features=32, out_features=128, bias=True)\n",
" (fc2): Linear(in_features=128, out_features=3, bias=True)\n",
")\n"
]
}
],
"source": [
"# path = 'FNO_STRAIN_N1200_ep500.pt'\n",
"# model.load_state_dict(torch.load(path))\n",
"# print(model)\n"
]
},
{
"cell_type": "markdown",
"id": "2562b485-c50d-42aa-99be-b3ad2d1768bf",
"metadata": {},
"source": [
"#### Testing--Strains"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "5448bab3-1f85-4d66-8dfa-50d9d4fd8204",
"metadata": {},
"outputs": [],
"source": [
"prediction = torch.zeros(y_test_strain.shape)\n",
"c = 0\n",
"model.eval()\n",
"with torch.no_grad():\n",
" for x,y in test_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" out = model(x).reshape(batch_size, dim, dim,strain_channels)\n",
" out = y_normalizer.decode(out)\n",
" prediction[c*batch_size :c*batch_size+batch_size] = out\n",
" c+=1\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "522c3a86-3d3d-479b-997e-1ef1582f6fe9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([200, 48, 48]) torch.Size([200, 48, 48, 3]) torch.Size([200, 48, 48, 3])\n"
]
}
],
"source": [
"print(x_test_plot.shape, y_test_strain.shape, prediction.shape)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "3e38bf63-c930-421f-9831-95150744d395",
"metadata": {},
"outputs": [],
"source": [
"strain_act = y_test_strain.reshape(ntest, -1)\n",
"strain_pred = prediction.reshape(ntest,-1)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "ae775d8e-d1d6-4eb1-9ac8-37ab7d2dfe65",
"metadata": {},
"outputs": [],
"source": [
"r2_strain =[] ## R2 for every sample [1,48,48,3\n",
"for i in range(strain_act.shape[0]):\n",
" act = strain_act[i]\n",
" pred = strain_pred[i]\n",
" r2 = r2_score(act,pred)\n",
" r2_strain += [r2]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "2f587e4c-2f75-489f-8087-c569aa116727",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9887908570324003 0.0300387711493383\n"
]
}
],
"source": [
"r2_avg_strain = np.average(r2_strain)\n",
"r2_std_strain = np.std(r2_strain)\n",
"\n",
"print(r2_avg_strain,r2_std_strain)"
]
},
{
"cell_type": "markdown",
"id": "2b3d6844-789c-421e-8cc6-953453e169a4",
"metadata": {},
"source": [
"### METRIC Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "fd29c4bc-58c1-4341-a216-ddda813ee9a9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.99\n",
"0.03\n"
]
}
],
"source": [
"print(np.round((r2_avg_strain),2))\n",
"print(np.round((r2_std_strain),2))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "ed026abe-fa30-425a-893b-42bc5d5dc4a7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(0.0793)\n"
]
}
],
"source": [
"### Total loss on test set\n",
"\n",
"loss = lossfunc(y_test_strain, prediction)\n",
"print(loss/ntest)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f19c6c0-37d9-4d99-929b-d6a34acef8d0",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "d29d406c-5254-4e29-b8d4-c77e4966c0e7",
"metadata": {
"tags": []
},
"source": [
"# STRESSES\n"
]
},
{
"cell_type": "markdown",
"id": "98eb22e4-b979-427f-8e5a-712c805a9cea",
"metadata": {},
"source": [
"#### Model"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4cb807e5-3cb8-4962-874a-67f6f7dc5f5d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d3ad6adc-887b-44ef-9f6c-8dfd915d91d5",
"metadata": {},
"outputs": [],
"source": [
"######################################################\n",
"### Data Loading & Hyperparameters\n",
"######################################################\n",
"\n",
"\n",
"X_Data = '/DATA1/meer/data/material-v3-3350.mat'\n",
"Y_Data = '/DATA1/meer/data/stress-v3-3350.mat'\n",
"\n",
"\n",
"ntrain =1200\n",
"ntest = 200\n",
"batch_size = 20\n",
"dim = 48\n",
"stress_channels = 3\n",
"modes = 12\n",
"width = 32\n",
"learning_rate = 0.001\n",
"epochs = 500\n",
"step_size = 80\n",
"gamma = 0.75\n",
"\n",
"\n",
"################################################################\n",
"# load data and data normalization\n",
"################################################################\n",
"reader = MatReader(X_Data) \n",
"x_train = reader.read_field('Emat')[:ntrain]\n",
"x_test = reader.read_field('Emat')[-ntest:]\n",
"x_test_plot = reader.read_field('Emat')[-ntest:]\n",
"\n",
"reader.load_file(Y_Data) \n",
"y_train = reader.read_field('stress')[:ntrain]# \n",
"y_test_stress = reader.read_field('stress')[-ntest:] \n",
"\n",
"\n",
"######################################################\n",
"### Normalization\n",
"######################################################\n",
"\n",
"x_normalizer = UnitGaussianNormalizer(x_train)\n",
"x_train = x_normalizer.encode(x_train)\n",
"x_test = x_normalizer.encode(x_test)\n",
"\n",
"y_normalizer = UnitGaussianNormalizer(y_train)\n",
"y_train = y_normalizer.encode(y_train)\n",
"\n",
"x_train = x_train.reshape(ntrain,dim,dim,1)\n",
"x_test = x_test.reshape(ntest,dim,dim,1)\n",
"\n",
"train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_train, y_train), batch_size=batch_size, shuffle=True)\n",
"test_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_test, y_test_stress), batch_size=batch_size, shuffle=False)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cb79cb44-fbb9-42c2-a2de-44c12b0a632d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" x_train Shape := torch.Size([1200, 48, 48, 1])\n",
" y_train Shape := torch.Size([1200, 48, 48, 3])\n",
" x_test Shape := torch.Size([200, 48, 48, 1])\n",
" y_test_stress Shape := torch.Size([200, 48, 48, 3])\n"
]
}
],
"source": [
"print(f' x_train Shape := {x_train.shape}')\n",
"print(f' y_train Shape := {y_train.shape}')\n",
"print(f' x_test Shape := {x_test.shape}')\n",
"print(f' y_test_stress Shape := {y_test_stress.shape}')\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b5285237-5e31-4bec-b926-65dbd76776e8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1188611\n"
]
}
],
"source": [
"\n",
"# ################################################################\n",
"## Model\n",
"# ################################################################\n",
"model = FNO2d(modes, modes, width).to(device)\n",
"print(count_params(model))\n",
"\n",
"optimizer = Adam(model.parameters(), lr=learning_rate, weight_decay=1e-3)\n",
"scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma)\n",
"\n",
"\n",
"if device=='cpu':\n",
" y_normalizer.cpu()\n",
"else:\n",
" y_normalizer.cuda()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "492da933-ebab-4c3c-969f-8b08402da108",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Epoch :== 1 || TIME(sec):== 3.5075 || TrainError:== 0.412682 || TestError:== 0.32846\n",
" Epoch :== 2 || TIME(sec):== 1.0269 || TrainError:== 0.284131 || TestError:== 0.255965\n",
" Epoch :== 3 || TIME(sec):== 0.9869 || TrainError:== 0.235074 || TestError:== 0.221014\n",
" Epoch :== 4 || TIME(sec):== 0.9851 || TrainError:== 0.210187 || TestError:== 0.204152\n",
" Epoch :== 5 || TIME(sec):== 0.9883 || TrainError:== 0.193079 || TestError:== 0.192052\n",
" Epoch :== 6 || TIME(sec):== 0.9756 || TrainError:== 0.181704 || TestError:== 0.182381\n",
" Epoch :== 7 || TIME(sec):== 0.8604 || TrainError:== 0.17328 || TestError:== 0.176395\n",
" Epoch :== 8 || TIME(sec):== 0.8599 || TrainError:== 0.164582 || TestError:== 0.169278\n",
" Epoch :== 9 || TIME(sec):== 0.8991 || TrainError:== 0.157321 || TestError:== 0.163621\n",
" Epoch :== 10 || TIME(sec):== 0.9028 || TrainError:== 0.151607 || TestError:== 0.161623\n",
" Epoch :== 11 || TIME(sec):== 0.9024 || TrainError:== 0.148934 || TestError:== 0.157802\n",
" Epoch :== 12 || TIME(sec):== 0.9574 || TrainError:== 0.144411 || TestError:== 0.153291\n",
" Epoch :== 13 || TIME(sec):== 0.93 || TrainError:== 0.139479 || TestError:== 0.152824\n",
" Epoch :== 14 || TIME(sec):== 0.899 || TrainError:== 0.13783 || TestError:== 0.149121\n",
" Epoch :== 15 || TIME(sec):== 0.8993 || TrainError:== 0.135174 || TestError:== 0.150724\n",
" Epoch :== 16 || TIME(sec):== 0.8973 || TrainError:== 0.132068 || TestError:== 0.145048\n",
" Epoch :== 17 || TIME(sec):== 0.9036 || TrainError:== 0.12951 || TestError:== 0.143882\n",
" Epoch :== 18 || TIME(sec):== 0.9007 || TrainError:== 0.128906 || TestError:== 0.145863\n",
" Epoch :== 19 || TIME(sec):== 0.9022 || TrainError:== 0.12504 || TestError:== 0.143443\n",
" Epoch :== 20 || TIME(sec):== 0.9032 || TrainError:== 0.123444 || TestError:== 0.139629\n",
" Epoch :== 21 || TIME(sec):== 0.9067 || TrainError:== 0.121781 || TestError:== 0.13908\n",
" Epoch :== 22 || TIME(sec):== 0.9018 || TrainError:== 0.120284 || TestError:== 0.137155\n",
" Epoch :== 23 || TIME(sec):== 0.8806 || TrainError:== 0.118713 || TestError:== 0.136362\n",
" Epoch :== 24 || TIME(sec):== 0.8224 || TrainError:== 0.117415 || TestError:== 0.13569\n",
" Epoch :== 25 || TIME(sec):== 0.8174 || TrainError:== 0.115417 || TestError:== 0.133403\n",
" Epoch :== 26 || TIME(sec):== 0.8196 || TrainError:== 0.115502 || TestError:== 0.133561\n",
" Epoch :== 27 || TIME(sec):== 0.8169 || TrainError:== 0.114329 || TestError:== 0.133226\n",
" Epoch :== 28 || TIME(sec):== 0.8141 || TrainError:== 0.114525 || TestError:== 0.134033\n",
" Epoch :== 29 || TIME(sec):== 0.8174 || TrainError:== 0.112679 || TestError:== 0.132404\n",
" Epoch :== 30 || TIME(sec):== 0.8281 || TrainError:== 0.11154 || TestError:== 0.133544\n",
" Epoch :== 31 || TIME(sec):== 0.8234 || TrainError:== 0.111942 || TestError:== 0.131121\n",
" Epoch :== 32 || TIME(sec):== 0.8158 || TrainError:== 0.109743 || TestError:== 0.127497\n",
" Epoch :== 33 || TIME(sec):== 0.8224 || TrainError:== 0.108039 || TestError:== 0.129045\n",
" Epoch :== 34 || TIME(sec):== 0.8233 || TrainError:== 0.106556 || TestError:== 0.128432\n",
" Epoch :== 35 || TIME(sec):== 0.8153 || TrainError:== 0.106231 || TestError:== 0.127391\n",
" Epoch :== 36 || TIME(sec):== 0.8259 || TrainError:== 0.105856 || TestError:== 0.127317\n",
" Epoch :== 37 || TIME(sec):== 0.8133 || TrainError:== 0.107067 || TestError:== 0.129096\n",
" Epoch :== 38 || TIME(sec):== 0.8227 || TrainError:== 0.104512 || TestError:== 0.12587\n",
" Epoch :== 39 || TIME(sec):== 0.8217 || TrainError:== 0.10426 || TestError:== 0.126206\n",
" Epoch :== 40 || TIME(sec):== 0.8302 || TrainError:== 0.103436 || TestError:== 0.123173\n",
" Epoch :== 41 || TIME(sec):== 0.8142 || TrainError:== 0.102399 || TestError:== 0.123078\n",
" Epoch :== 42 || TIME(sec):== 0.8129 || TrainError:== 0.100862 || TestError:== 0.120196\n",
" Epoch :== 43 || TIME(sec):== 0.8238 || TrainError:== 0.101387 || TestError:== 0.124468\n",
" Epoch :== 44 || TIME(sec):== 0.8186 || TrainError:== 0.102527 || TestError:== 0.125188\n",
" Epoch :== 45 || TIME(sec):== 0.8144 || TrainError:== 0.101275 || TestError:== 0.121156\n",
" Epoch :== 46 || TIME(sec):== 0.8299 || TrainError:== 0.100431 || TestError:== 0.125085\n",
" Epoch :== 47 || TIME(sec):== 0.818 || TrainError:== 0.100643 || TestError:== 0.122476\n",
" Epoch :== 48 || TIME(sec):== 0.8623 || TrainError:== 0.09951 || TestError:== 0.122392\n",
" Epoch :== 49 || TIME(sec):== 1.0622 || TrainError:== 0.098782 || TestError:== 0.11952\n",
" Epoch :== 50 || TIME(sec):== 1.0148 || TrainError:== 0.096655 || TestError:== 0.120346\n",
" Epoch :== 51 || TIME(sec):== 1.0178 || TrainError:== 0.096295 || TestError:== 0.119157\n",
" Epoch :== 52 || TIME(sec):== 1.0279 || TrainError:== 0.097131 || TestError:== 0.122992\n",
" Epoch :== 53 || TIME(sec):== 1.0124 || TrainError:== 0.097714 || TestError:== 0.120672\n",
" Epoch :== 54 || TIME(sec):== 1.029 || TrainError:== 0.097505 || TestError:== 0.119855\n",
" Epoch :== 55 || TIME(sec):== 1.0243 || TrainError:== 0.097108 || TestError:== 0.118772\n",
" Epoch :== 56 || TIME(sec):== 1.0258 || TrainError:== 0.095417 || TestError:== 0.116714\n",
" Epoch :== 57 || TIME(sec):== 1.0253 || TrainError:== 0.093361 || TestError:== 0.117776\n",
" Epoch :== 58 || TIME(sec):== 1.0392 || TrainError:== 0.095203 || TestError:== 0.118084\n",
" Epoch :== 59 || TIME(sec):== 1.0246 || TrainError:== 0.094301 || TestError:== 0.116851\n",
" Epoch :== 60 || TIME(sec):== 1.0357 || TrainError:== 0.092927 || TestError:== 0.118825\n",
" Epoch :== 61 || TIME(sec):== 1.0117 || TrainError:== 0.092849 || TestError:== 0.118124\n",
" Epoch :== 62 || TIME(sec):== 1.011 || TrainError:== 0.093329 || TestError:== 0.117532\n",
" Epoch :== 63 || TIME(sec):== 1.0263 || TrainError:== 0.093628 || TestError:== 0.116951\n",
" Epoch :== 64 || TIME(sec):== 1.0266 || TrainError:== 0.093969 || TestError:== 0.11753\n",
" Epoch :== 65 || TIME(sec):== 1.0247 || TrainError:== 0.093078 || TestError:== 0.118762\n",
" Epoch :== 66 || TIME(sec):== 1.0098 || TrainError:== 0.091979 || TestError:== 0.114999\n",
" Epoch :== 67 || TIME(sec):== 1.014 || TrainError:== 0.091187 || TestError:== 0.11496\n",
" Epoch :== 68 || TIME(sec):== 1.0262 || TrainError:== 0.091773 || TestError:== 0.116572\n",
" Epoch :== 69 || TIME(sec):== 1.017 || TrainError:== 0.091807 || TestError:== 0.114692\n",
" Epoch :== 70 || TIME(sec):== 1.0423 || TrainError:== 0.090695 || TestError:== 0.113464\n",
" Epoch :== 71 || TIME(sec):== 1.0186 || TrainError:== 0.089467 || TestError:== 0.113123\n",
" Epoch :== 72 || TIME(sec):== 1.036 || TrainError:== 0.089238 || TestError:== 0.114301\n",
" Epoch :== 73 || TIME(sec):== 1.027 || TrainError:== 0.089436 || TestError:== 0.113942\n",
" Epoch :== 74 || TIME(sec):== 1.0317 || TrainError:== 0.090179 || TestError:== 0.114992\n",
" Epoch :== 75 || TIME(sec):== 1.0106 || TrainError:== 0.088991 || TestError:== 0.114269\n",
" Epoch :== 76 || TIME(sec):== 1.0218 || TrainError:== 0.089304 || TestError:== 0.113371\n",
" Epoch :== 77 || TIME(sec):== 1.0271 || TrainError:== 0.087973 || TestError:== 0.112639\n",
" Epoch :== 78 || TIME(sec):== 1.0215 || TrainError:== 0.087618 || TestError:== 0.114952\n",
" Epoch :== 79 || TIME(sec):== 1.0183 || TrainError:== 0.08908 || TestError:== 0.111965\n",
" Epoch :== 80 || TIME(sec):== 1.0246 || TrainError:== 0.089 || TestError:== 0.113596\n",
" Epoch :== 81 || TIME(sec):== 1.0317 || TrainError:== 0.085309 || TestError:== 0.111821\n",
" Epoch :== 82 || TIME(sec):== 1.019 || TrainError:== 0.0831 || TestError:== 0.108168\n",
" Epoch :== 83 || TIME(sec):== 1.0252 || TrainError:== 0.082788 || TestError:== 0.109543\n",
" Epoch :== 84 || TIME(sec):== 1.0272 || TrainError:== 0.082748 || TestError:== 0.110722\n",
" Epoch :== 85 || TIME(sec):== 1.0274 || TrainError:== 0.082874 || TestError:== 0.109839\n",
" Epoch :== 86 || TIME(sec):== 1.0185 || TrainError:== 0.082619 || TestError:== 0.109505\n",
" Epoch :== 87 || TIME(sec):== 1.0288 || TrainError:== 0.083803 || TestError:== 0.108672\n",
" Epoch :== 88 || TIME(sec):== 1.0182 || TrainError:== 0.082053 || TestError:== 0.108748\n",
" Epoch :== 89 || TIME(sec):== 1.0128 || TrainError:== 0.082194 || TestError:== 0.111042\n",
" Epoch :== 90 || TIME(sec):== 1.0216 || TrainError:== 0.082736 || TestError:== 0.108551\n",
" Epoch :== 91 || TIME(sec):== 1.0179 || TrainError:== 0.08337 || TestError:== 0.110272\n",
" Epoch :== 92 || TIME(sec):== 1.0245 || TrainError:== 0.08225 || TestError:== 0.109079\n",
" Epoch :== 93 || TIME(sec):== 1.0215 || TrainError:== 0.081311 || TestError:== 0.108734\n",
" Epoch :== 94 || TIME(sec):== 1.0145 || TrainError:== 0.082725 || TestError:== 0.108918\n",
" Epoch :== 95 || TIME(sec):== 1.0262 || TrainError:== 0.08126 || TestError:== 0.110072\n",
" Epoch :== 96 || TIME(sec):== 1.0298 || TrainError:== 0.081647 || TestError:== 0.108664\n",
" Epoch :== 97 || TIME(sec):== 1.0279 || TrainError:== 0.080483 || TestError:== 0.106971\n",
" Epoch :== 98 || TIME(sec):== 1.0234 || TrainError:== 0.081174 || TestError:== 0.109269\n",
" Epoch :== 99 || TIME(sec):== 1.0274 || TrainError:== 0.080415 || TestError:== 0.107719\n",
" Epoch :== 100 || TIME(sec):== 1.0197 || TrainError:== 0.079586 || TestError:== 0.108145\n",
" Epoch :== 101 || TIME(sec):== 1.0271 || TrainError:== 0.080792 || TestError:== 0.106602\n",
" Epoch :== 102 || TIME(sec):== 1.0159 || TrainError:== 0.079882 || TestError:== 0.108822\n",
" Epoch :== 103 || TIME(sec):== 1.0284 || TrainError:== 0.07954 || TestError:== 0.107972\n",
" Epoch :== 104 || TIME(sec):== 1.0213 || TrainError:== 0.08053 || TestError:== 0.109334\n",
" Epoch :== 105 || TIME(sec):== 1.0179 || TrainError:== 0.08058 || TestError:== 0.108143\n",
" Epoch :== 106 || TIME(sec):== 1.0201 || TrainError:== 0.080863 || TestError:== 0.10613\n",
" Epoch :== 107 || TIME(sec):== 1.0218 || TrainError:== 0.079959 || TestError:== 0.10697\n",
" Epoch :== 108 || TIME(sec):== 1.0179 || TrainError:== 0.079138 || TestError:== 0.108125\n",
" Epoch :== 109 || TIME(sec):== 1.0293 || TrainError:== 0.079008 || TestError:== 0.10741\n",
" Epoch :== 110 || TIME(sec):== 1.0338 || TrainError:== 0.078149 || TestError:== 0.104808\n",
" Epoch :== 111 || TIME(sec):== 1.0192 || TrainError:== 0.079395 || TestError:== 0.107371\n",
" Epoch :== 112 || TIME(sec):== 1.0156 || TrainError:== 0.078208 || TestError:== 0.107694\n",
" Epoch :== 113 || TIME(sec):== 1.0234 || TrainError:== 0.078775 || TestError:== 0.107075\n",
" Epoch :== 114 || TIME(sec):== 1.0302 || TrainError:== 0.078694 || TestError:== 0.107361\n",
" Epoch :== 115 || TIME(sec):== 1.0306 || TrainError:== 0.078437 || TestError:== 0.106881\n",
" Epoch :== 116 || TIME(sec):== 1.0368 || TrainError:== 0.079107 || TestError:== 0.1086\n",
" Epoch :== 117 || TIME(sec):== 1.0227 || TrainError:== 0.078608 || TestError:== 0.105338\n",
" Epoch :== 118 || TIME(sec):== 1.0504 || TrainError:== 0.076947 || TestError:== 0.106765\n",
" Epoch :== 119 || TIME(sec):== 1.0336 || TrainError:== 0.077941 || TestError:== 0.10722\n",
" Epoch :== 120 || TIME(sec):== 1.0317 || TrainError:== 0.07772 || TestError:== 0.105459\n",
" Epoch :== 121 || TIME(sec):== 1.0309 || TrainError:== 0.078206 || TestError:== 0.104361\n",
" Epoch :== 122 || TIME(sec):== 1.033 || TrainError:== 0.076286 || TestError:== 0.104934\n",
" Epoch :== 123 || TIME(sec):== 1.0146 || TrainError:== 0.076009 || TestError:== 0.105521\n",
" Epoch :== 124 || TIME(sec):== 1.0167 || TrainError:== 0.076687 || TestError:== 0.107409\n",
" Epoch :== 125 || TIME(sec):== 1.0265 || TrainError:== 0.077756 || TestError:== 0.106749\n",
" Epoch :== 126 || TIME(sec):== 0.9833 || TrainError:== 0.076582 || TestError:== 0.105842\n",
" Epoch :== 127 || TIME(sec):== 0.9781 || TrainError:== 0.077363 || TestError:== 0.109826\n",
" Epoch :== 128 || TIME(sec):== 0.9787 || TrainError:== 0.076912 || TestError:== 0.103403\n",
" Epoch :== 129 || TIME(sec):== 0.9689 || TrainError:== 0.07651 || TestError:== 0.104899\n",
" Epoch :== 130 || TIME(sec):== 0.9802 || TrainError:== 0.075795 || TestError:== 0.103158\n",
" Epoch :== 131 || TIME(sec):== 0.9806 || TrainError:== 0.077412 || TestError:== 0.106143\n",
" Epoch :== 132 || TIME(sec):== 0.9726 || TrainError:== 0.076314 || TestError:== 0.103799\n",
" Epoch :== 133 || TIME(sec):== 0.9798 || TrainError:== 0.076352 || TestError:== 0.102932\n",
" Epoch :== 134 || TIME(sec):== 0.9748 || TrainError:== 0.074752 || TestError:== 0.104287\n",
" Epoch :== 135 || TIME(sec):== 0.983 || TrainError:== 0.074384 || TestError:== 0.104498\n",
" Epoch :== 136 || TIME(sec):== 0.9694 || TrainError:== 0.074306 || TestError:== 0.1037\n",
" Epoch :== 137 || TIME(sec):== 1.006 || TrainError:== 0.07483 || TestError:== 0.105169\n",
" Epoch :== 138 || TIME(sec):== 1.0219 || TrainError:== 0.076139 || TestError:== 0.103431\n",
" Epoch :== 139 || TIME(sec):== 1.0316 || TrainError:== 0.075218 || TestError:== 0.105537\n",
" Epoch :== 140 || TIME(sec):== 1.0162 || TrainError:== 0.075126 || TestError:== 0.103156\n",
" Epoch :== 141 || TIME(sec):== 1.0249 || TrainError:== 0.074891 || TestError:== 0.103712\n",
" Epoch :== 142 || TIME(sec):== 1.0351 || TrainError:== 0.07366 || TestError:== 0.102872\n",
" Epoch :== 143 || TIME(sec):== 1.0279 || TrainError:== 0.073821 || TestError:== 0.104687\n",
" Epoch :== 144 || TIME(sec):== 1.0246 || TrainError:== 0.073214 || TestError:== 0.101419\n",
" Epoch :== 145 || TIME(sec):== 1.021 || TrainError:== 0.074652 || TestError:== 0.105084\n",
" Epoch :== 146 || TIME(sec):== 1.021 || TrainError:== 0.074001 || TestError:== 0.102052\n",
" Epoch :== 147 || TIME(sec):== 1.017 || TrainError:== 0.074239 || TestError:== 0.102634\n",
" Epoch :== 148 || TIME(sec):== 1.0251 || TrainError:== 0.074358 || TestError:== 0.104646\n",
" Epoch :== 149 || TIME(sec):== 1.022 || TrainError:== 0.074769 || TestError:== 0.10341\n",
" Epoch :== 150 || TIME(sec):== 1.0203 || TrainError:== 0.073476 || TestError:== 0.102692\n",
" Epoch :== 151 || TIME(sec):== 1.0243 || TrainError:== 0.073067 || TestError:== 0.103083\n",
" Epoch :== 152 || TIME(sec):== 1.0548 || TrainError:== 0.072848 || TestError:== 0.102642\n",
" Epoch :== 153 || TIME(sec):== 0.9715 || TrainError:== 0.072838 || TestError:== 0.104467\n",
" Epoch :== 154 || TIME(sec):== 1.0228 || TrainError:== 0.072649 || TestError:== 0.102763\n",
" Epoch :== 155 || TIME(sec):== 0.9846 || TrainError:== 0.07277 || TestError:== 0.104142\n",
" Epoch :== 156 || TIME(sec):== 0.8307 || TrainError:== 0.076235 || TestError:== 0.105104\n",
" Epoch :== 157 || TIME(sec):== 0.831 || TrainError:== 0.073951 || TestError:== 0.102041\n",
" Epoch :== 158 || TIME(sec):== 0.828 || TrainError:== 0.073181 || TestError:== 0.103593\n",
" Epoch :== 159 || TIME(sec):== 0.8236 || TrainError:== 0.07192 || TestError:== 0.100735\n",
" Epoch :== 160 || TIME(sec):== 0.8215 || TrainError:== 0.070758 || TestError:== 0.10172\n",
" Epoch :== 161 || TIME(sec):== 0.8128 || TrainError:== 0.070089 || TestError:== 0.099987\n",
" Epoch :== 162 || TIME(sec):== 0.8253 || TrainError:== 0.069013 || TestError:== 0.099865\n",
" Epoch :== 163 || TIME(sec):== 0.8319 || TrainError:== 0.068486 || TestError:== 0.099142\n",
" Epoch :== 164 || TIME(sec):== 0.83 || TrainError:== 0.068041 || TestError:== 0.1003\n",
" Epoch :== 165 || TIME(sec):== 0.9354 || TrainError:== 0.068541 || TestError:== 0.100654\n",
" Epoch :== 166 || TIME(sec):== 0.9388 || TrainError:== 0.06837 || TestError:== 0.099206\n",
" Epoch :== 167 || TIME(sec):== 0.9848 || TrainError:== 0.068275 || TestError:== 0.100426\n",
" Epoch :== 168 || TIME(sec):== 1.0273 || TrainError:== 0.068252 || TestError:== 0.09904\n",
" Epoch :== 169 || TIME(sec):== 1.0247 || TrainError:== 0.068782 || TestError:== 0.100545\n",
" Epoch :== 170 || TIME(sec):== 1.0121 || TrainError:== 0.069543 || TestError:== 0.099706\n",
" Epoch :== 171 || TIME(sec):== 1.0212 || TrainError:== 0.06883 || TestError:== 0.099364\n",
" Epoch :== 172 || TIME(sec):== 1.0145 || TrainError:== 0.06845 || TestError:== 0.099852\n",
" Epoch :== 173 || TIME(sec):== 1.0185 || TrainError:== 0.068952 || TestError:== 0.100172\n",
" Epoch :== 174 || TIME(sec):== 1.0189 || TrainError:== 0.068285 || TestError:== 0.100828\n",
" Epoch :== 175 || TIME(sec):== 1.0219 || TrainError:== 0.068683 || TestError:== 0.09787\n",
" Epoch :== 176 || TIME(sec):== 1.0256 || TrainError:== 0.067982 || TestError:== 0.099711\n",
" Epoch :== 177 || TIME(sec):== 1.0292 || TrainError:== 0.067678 || TestError:== 0.099711\n",
" Epoch :== 178 || TIME(sec):== 1.0221 || TrainError:== 0.068217 || TestError:== 0.102014\n",
" Epoch :== 179 || TIME(sec):== 1.03 || TrainError:== 0.068962 || TestError:== 0.100027\n",
" Epoch :== 180 || TIME(sec):== 1.0178 || TrainError:== 0.068047 || TestError:== 0.099624\n",
" Epoch :== 181 || TIME(sec):== 1.0124 || TrainError:== 0.068683 || TestError:== 0.101874\n",
" Epoch :== 182 || TIME(sec):== 1.0209 || TrainError:== 0.069141 || TestError:== 0.099184\n",
" Epoch :== 183 || TIME(sec):== 1.0153 || TrainError:== 0.068475 || TestError:== 0.103013\n",
" Epoch :== 184 || TIME(sec):== 1.0161 || TrainError:== 0.068459 || TestError:== 0.098151\n",
" Epoch :== 185 || TIME(sec):== 1.0158 || TrainError:== 0.067974 || TestError:== 0.097667\n",
" Epoch :== 186 || TIME(sec):== 1.027 || TrainError:== 0.067101 || TestError:== 0.098603\n",
" Epoch :== 187 || TIME(sec):== 1.0198 || TrainError:== 0.067127 || TestError:== 0.099851\n",
" Epoch :== 188 || TIME(sec):== 1.0193 || TrainError:== 0.067626 || TestError:== 0.099209\n",
" Epoch :== 189 || TIME(sec):== 1.024 || TrainError:== 0.067764 || TestError:== 0.09911\n",
" Epoch :== 190 || TIME(sec):== 1.0283 || TrainError:== 0.067708 || TestError:== 0.10051\n",
" Epoch :== 191 || TIME(sec):== 1.0195 || TrainError:== 0.067208 || TestError:== 0.099805\n",
" Epoch :== 192 || TIME(sec):== 1.038 || TrainError:== 0.067556 || TestError:== 0.09818\n",
" Epoch :== 193 || TIME(sec):== 1.0185 || TrainError:== 0.066206 || TestError:== 0.098947\n",
" Epoch :== 194 || TIME(sec):== 1.0271 || TrainError:== 0.067167 || TestError:== 0.099938\n",
" Epoch :== 195 || TIME(sec):== 1.034 || TrainError:== 0.066754 || TestError:== 0.098745\n",
" Epoch :== 196 || TIME(sec):== 1.0293 || TrainError:== 0.066104 || TestError:== 0.100371\n",
" Epoch :== 197 || TIME(sec):== 1.0327 || TrainError:== 0.068122 || TestError:== 0.098695\n",
" Epoch :== 198 || TIME(sec):== 1.031 || TrainError:== 0.067371 || TestError:== 0.099434\n",
" Epoch :== 199 || TIME(sec):== 1.0312 || TrainError:== 0.066474 || TestError:== 0.098063\n",
" Epoch :== 200 || TIME(sec):== 1.0304 || TrainError:== 0.066716 || TestError:== 0.098952\n",
" Epoch :== 201 || TIME(sec):== 1.0359 || TrainError:== 0.067439 || TestError:== 0.098446\n",
" Epoch :== 202 || TIME(sec):== 1.0298 || TrainError:== 0.066172 || TestError:== 0.098779\n",
" Epoch :== 203 || TIME(sec):== 1.0171 || TrainError:== 0.066854 || TestError:== 0.09788\n",
" Epoch :== 204 || TIME(sec):== 1.0266 || TrainError:== 0.06682 || TestError:== 0.099902\n",
" Epoch :== 205 || TIME(sec):== 1.0311 || TrainError:== 0.06588 || TestError:== 0.098636\n",
" Epoch :== 206 || TIME(sec):== 1.0253 || TrainError:== 0.066129 || TestError:== 0.097731\n",
" Epoch :== 207 || TIME(sec):== 1.0279 || TrainError:== 0.065788 || TestError:== 0.098511\n",
" Epoch :== 208 || TIME(sec):== 1.0312 || TrainError:== 0.065293 || TestError:== 0.097454\n",
" Epoch :== 209 || TIME(sec):== 1.0257 || TrainError:== 0.065659 || TestError:== 0.098128\n",
" Epoch :== 210 || TIME(sec):== 1.0132 || TrainError:== 0.065647 || TestError:== 0.098153\n",
" Epoch :== 211 || TIME(sec):== 1.0304 || TrainError:== 0.066021 || TestError:== 0.098751\n",
" Epoch :== 212 || TIME(sec):== 1.0373 || TrainError:== 0.066127 || TestError:== 0.097257\n",
" Epoch :== 213 || TIME(sec):== 1.0396 || TrainError:== 0.06576 || TestError:== 0.097516\n",
" Epoch :== 214 || TIME(sec):== 1.0277 || TrainError:== 0.065597 || TestError:== 0.098405\n",
" Epoch :== 215 || TIME(sec):== 1.0194 || TrainError:== 0.066355 || TestError:== 0.098878\n",
" Epoch :== 216 || TIME(sec):== 1.0257 || TrainError:== 0.065235 || TestError:== 0.09751\n",
" Epoch :== 217 || TIME(sec):== 1.0175 || TrainError:== 0.065787 || TestError:== 0.099037\n",
" Epoch :== 218 || TIME(sec):== 1.0224 || TrainError:== 0.066254 || TestError:== 0.097999\n",
" Epoch :== 219 || TIME(sec):== 1.0296 || TrainError:== 0.066297 || TestError:== 0.098896\n",
" Epoch :== 220 || TIME(sec):== 1.0111 || TrainError:== 0.065085 || TestError:== 0.097411\n",
" Epoch :== 221 || TIME(sec):== 1.0214 || TrainError:== 0.0652 || TestError:== 0.09792\n",
" Epoch :== 222 || TIME(sec):== 1.0161 || TrainError:== 0.06528 || TestError:== 0.097344\n",
" Epoch :== 223 || TIME(sec):== 1.0273 || TrainError:== 0.065123 || TestError:== 0.096743\n",
" Epoch :== 224 || TIME(sec):== 1.0278 || TrainError:== 0.064396 || TestError:== 0.095241\n",
" Epoch :== 225 || TIME(sec):== 1.0354 || TrainError:== 0.064298 || TestError:== 0.097139\n",
" Epoch :== 226 || TIME(sec):== 1.0162 || TrainError:== 0.064441 || TestError:== 0.096569\n",
" Epoch :== 227 || TIME(sec):== 1.0249 || TrainError:== 0.064467 || TestError:== 0.097107\n",
" Epoch :== 228 || TIME(sec):== 1.0227 || TrainError:== 0.064433 || TestError:== 0.09649\n",
" Epoch :== 229 || TIME(sec):== 1.026 || TrainError:== 0.063846 || TestError:== 0.097265\n",
" Epoch :== 230 || TIME(sec):== 1.0139 || TrainError:== 0.064873 || TestError:== 0.098598\n",
" Epoch :== 231 || TIME(sec):== 1.0191 || TrainError:== 0.0657 || TestError:== 0.095864\n",
" Epoch :== 232 || TIME(sec):== 1.0216 || TrainError:== 0.064981 || TestError:== 0.099756\n",
" Epoch :== 233 || TIME(sec):== 1.0194 || TrainError:== 0.065363 || TestError:== 0.098905\n",
" Epoch :== 234 || TIME(sec):== 1.0193 || TrainError:== 0.064407 || TestError:== 0.095484\n",
" Epoch :== 235 || TIME(sec):== 1.0668 || TrainError:== 0.064077 || TestError:== 0.096018\n",
" Epoch :== 236 || TIME(sec):== 0.9664 || TrainError:== 0.063604 || TestError:== 0.097077\n",
" Epoch :== 237 || TIME(sec):== 0.9521 || TrainError:== 0.064859 || TestError:== 0.097878\n",
" Epoch :== 238 || TIME(sec):== 0.9782 || TrainError:== 0.065961 || TestError:== 0.097696\n",
" Epoch :== 239 || TIME(sec):== 1.041 || TrainError:== 0.065586 || TestError:== 0.09785\n",
" Epoch :== 240 || TIME(sec):== 1.0267 || TrainError:== 0.064729 || TestError:== 0.095306\n",
" Epoch :== 241 || TIME(sec):== 0.9886 || TrainError:== 0.062335 || TestError:== 0.096013\n",
" Epoch :== 242 || TIME(sec):== 1.0671 || TrainError:== 0.060887 || TestError:== 0.094411\n",
" Epoch :== 243 || TIME(sec):== 1.0759 || TrainError:== 0.060345 || TestError:== 0.094826\n",
" Epoch :== 244 || TIME(sec):== 1.0472 || TrainError:== 0.060245 || TestError:== 0.094728\n",
" Epoch :== 245 || TIME(sec):== 1.0598 || TrainError:== 0.06057 || TestError:== 0.094853\n",
" Epoch :== 246 || TIME(sec):== 0.9989 || TrainError:== 0.061222 || TestError:== 0.095839\n",
" Epoch :== 247 || TIME(sec):== 1.0224 || TrainError:== 0.061195 || TestError:== 0.095567\n",
" Epoch :== 248 || TIME(sec):== 1.0258 || TrainError:== 0.061064 || TestError:== 0.094814\n",
" Epoch :== 249 || TIME(sec):== 1.0335 || TrainError:== 0.060946 || TestError:== 0.093361\n",
" Epoch :== 250 || TIME(sec):== 1.0246 || TrainError:== 0.061133 || TestError:== 0.094669\n",
" Epoch :== 251 || TIME(sec):== 1.0206 || TrainError:== 0.061372 || TestError:== 0.095082\n",
" Epoch :== 252 || TIME(sec):== 1.0117 || TrainError:== 0.061002 || TestError:== 0.094441\n",
" Epoch :== 253 || TIME(sec):== 1.0127 || TrainError:== 0.060909 || TestError:== 0.094409\n",
" Epoch :== 254 || TIME(sec):== 1.0135 || TrainError:== 0.062498 || TestError:== 0.096231\n",
" Epoch :== 255 || TIME(sec):== 1.0205 || TrainError:== 0.062416 || TestError:== 0.096328\n",
" Epoch :== 256 || TIME(sec):== 1.0405 || TrainError:== 0.061326 || TestError:== 0.09464\n",
" Epoch :== 257 || TIME(sec):== 1.0188 || TrainError:== 0.060486 || TestError:== 0.095674\n",
" Epoch :== 258 || TIME(sec):== 1.0243 || TrainError:== 0.060804 || TestError:== 0.095404\n",
" Epoch :== 259 || TIME(sec):== 1.0124 || TrainError:== 0.06043 || TestError:== 0.094276\n",
" Epoch :== 260 || TIME(sec):== 1.0312 || TrainError:== 0.060106 || TestError:== 0.095852\n",
" Epoch :== 261 || TIME(sec):== 1.0186 || TrainError:== 0.060659 || TestError:== 0.094843\n",
" Epoch :== 262 || TIME(sec):== 1.0167 || TrainError:== 0.061568 || TestError:== 0.09569\n",
" Epoch :== 263 || TIME(sec):== 1.0231 || TrainError:== 0.062187 || TestError:== 0.094171\n",
" Epoch :== 264 || TIME(sec):== 1.0213 || TrainError:== 0.060903 || TestError:== 0.095836\n",
" Epoch :== 265 || TIME(sec):== 1.0153 || TrainError:== 0.060255 || TestError:== 0.094647\n",
" Epoch :== 266 || TIME(sec):== 1.0168 || TrainError:== 0.06095 || TestError:== 0.095063\n",
" Epoch :== 267 || TIME(sec):== 1.0143 || TrainError:== 0.061302 || TestError:== 0.09435\n",
" Epoch :== 268 || TIME(sec):== 1.0448 || TrainError:== 0.061217 || TestError:== 0.095386\n",
" Epoch :== 269 || TIME(sec):== 1.0225 || TrainError:== 0.060535 || TestError:== 0.094482\n",
" Epoch :== 270 || TIME(sec):== 1.02 || TrainError:== 0.060975 || TestError:== 0.095038\n",
" Epoch :== 271 || TIME(sec):== 1.0173 || TrainError:== 0.060642 || TestError:== 0.094908\n",
" Epoch :== 272 || TIME(sec):== 1.0355 || TrainError:== 0.06027 || TestError:== 0.094621\n",
" Epoch :== 273 || TIME(sec):== 1.0113 || TrainError:== 0.060057 || TestError:== 0.094833\n",
" Epoch :== 274 || TIME(sec):== 1.0156 || TrainError:== 0.059701 || TestError:== 0.094552\n",
" Epoch :== 275 || TIME(sec):== 1.017 || TrainError:== 0.059535 || TestError:== 0.094237\n",
" Epoch :== 276 || TIME(sec):== 1.0321 || TrainError:== 0.059802 || TestError:== 0.095053\n",
" Epoch :== 277 || TIME(sec):== 1.018 || TrainError:== 0.060097 || TestError:== 0.095428\n",
" Epoch :== 278 || TIME(sec):== 1.0264 || TrainError:== 0.059911 || TestError:== 0.095671\n",
" Epoch :== 279 || TIME(sec):== 1.028 || TrainError:== 0.060289 || TestError:== 0.096631\n",
" Epoch :== 280 || TIME(sec):== 1.0176 || TrainError:== 0.060167 || TestError:== 0.094675\n",
" Epoch :== 281 || TIME(sec):== 1.0195 || TrainError:== 0.059599 || TestError:== 0.094551\n",
" Epoch :== 282 || TIME(sec):== 1.0274 || TrainError:== 0.060481 || TestError:== 0.095112\n",
" Epoch :== 283 || TIME(sec):== 1.0273 || TrainError:== 0.060083 || TestError:== 0.09523\n",
" Epoch :== 284 || TIME(sec):== 1.0209 || TrainError:== 0.059824 || TestError:== 0.093888\n",
" Epoch :== 285 || TIME(sec):== 1.0308 || TrainError:== 0.05959 || TestError:== 0.094046\n",
" Epoch :== 286 || TIME(sec):== 1.0228 || TrainError:== 0.060039 || TestError:== 0.094701\n",
" Epoch :== 287 || TIME(sec):== 1.0265 || TrainError:== 0.060136 || TestError:== 0.094989\n",
" Epoch :== 288 || TIME(sec):== 1.0291 || TrainError:== 0.060077 || TestError:== 0.094674\n",
" Epoch :== 289 || TIME(sec):== 1.0295 || TrainError:== 0.059799 || TestError:== 0.094292\n",
" Epoch :== 290 || TIME(sec):== 1.0119 || TrainError:== 0.060121 || TestError:== 0.09356\n",
" Epoch :== 291 || TIME(sec):== 1.0286 || TrainError:== 0.059595 || TestError:== 0.094148\n",
" Epoch :== 292 || TIME(sec):== 1.0179 || TrainError:== 0.060736 || TestError:== 0.094086\n",
" Epoch :== 293 || TIME(sec):== 1.0105 || TrainError:== 0.060662 || TestError:== 0.094954\n",
" Epoch :== 294 || TIME(sec):== 0.9939 || TrainError:== 0.060196 || TestError:== 0.095212\n",
" Epoch :== 295 || TIME(sec):== 0.8175 || TrainError:== 0.059879 || TestError:== 0.094716\n",
" Epoch :== 296 || TIME(sec):== 0.8216 || TrainError:== 0.059288 || TestError:== 0.095406\n",
" Epoch :== 297 || TIME(sec):== 0.8323 || TrainError:== 0.058997 || TestError:== 0.093651\n",
" Epoch :== 298 || TIME(sec):== 0.8408 || TrainError:== 0.059001 || TestError:== 0.095864\n",
" Epoch :== 299 || TIME(sec):== 0.8249 || TrainError:== 0.059383 || TestError:== 0.09534\n",
" Epoch :== 300 || TIME(sec):== 0.8221 || TrainError:== 0.060354 || TestError:== 0.09648\n",
" Epoch :== 301 || TIME(sec):== 0.8258 || TrainError:== 0.060578 || TestError:== 0.094255\n",
" Epoch :== 302 || TIME(sec):== 0.8228 || TrainError:== 0.059804 || TestError:== 0.093905\n",
" Epoch :== 303 || TIME(sec):== 0.8291 || TrainError:== 0.058546 || TestError:== 0.094458\n",
" Epoch :== 304 || TIME(sec):== 0.8191 || TrainError:== 0.059083 || TestError:== 0.092716\n",
" Epoch :== 305 || TIME(sec):== 0.8282 || TrainError:== 0.059083 || TestError:== 0.093703\n",
" Epoch :== 306 || TIME(sec):== 0.8295 || TrainError:== 0.058407 || TestError:== 0.092532\n",
" Epoch :== 307 || TIME(sec):== 0.8256 || TrainError:== 0.058178 || TestError:== 0.093668\n",
" Epoch :== 308 || TIME(sec):== 0.8162 || TrainError:== 0.059635 || TestError:== 0.094514\n",
" Epoch :== 309 || TIME(sec):== 0.8302 || TrainError:== 0.05888 || TestError:== 0.093219\n",
" Epoch :== 310 || TIME(sec):== 0.8342 || TrainError:== 0.058909 || TestError:== 0.094143\n",
" Epoch :== 311 || TIME(sec):== 0.8194 || TrainError:== 0.059214 || TestError:== 0.094476\n",
" Epoch :== 312 || TIME(sec):== 0.8266 || TrainError:== 0.059262 || TestError:== 0.09353\n",
" Epoch :== 313 || TIME(sec):== 0.8292 || TrainError:== 0.05967 || TestError:== 0.094506\n",
" Epoch :== 314 || TIME(sec):== 0.8155 || TrainError:== 0.058332 || TestError:== 0.093926\n",
" Epoch :== 315 || TIME(sec):== 0.8173 || TrainError:== 0.059469 || TestError:== 0.093767\n",
" Epoch :== 316 || TIME(sec):== 0.8209 || TrainError:== 0.059086 || TestError:== 0.094338\n",
" Epoch :== 317 || TIME(sec):== 0.8237 || TrainError:== 0.059006 || TestError:== 0.094193\n",
" Epoch :== 318 || TIME(sec):== 0.8486 || TrainError:== 0.058746 || TestError:== 0.092945\n",
" Epoch :== 319 || TIME(sec):== 0.9923 || TrainError:== 0.058453 || TestError:== 0.093316\n",
" Epoch :== 320 || TIME(sec):== 0.9714 || TrainError:== 0.058308 || TestError:== 0.094086\n",
" Epoch :== 321 || TIME(sec):== 1.002 || TrainError:== 0.057345 || TestError:== 0.093658\n",
" Epoch :== 322 || TIME(sec):== 0.9771 || TrainError:== 0.056377 || TestError:== 0.092561\n",
" Epoch :== 323 || TIME(sec):== 0.9792 || TrainError:== 0.05585 || TestError:== 0.092862\n",
" Epoch :== 324 || TIME(sec):== 0.9733 || TrainError:== 0.055563 || TestError:== 0.092511\n",
" Epoch :== 325 || TIME(sec):== 1.0249 || TrainError:== 0.055761 || TestError:== 0.091973\n",
" Epoch :== 326 || TIME(sec):== 1.0348 || TrainError:== 0.055785 || TestError:== 0.093015\n",
" Epoch :== 327 || TIME(sec):== 1.0237 || TrainError:== 0.056065 || TestError:== 0.092767\n",
" Epoch :== 328 || TIME(sec):== 1.0154 || TrainError:== 0.056208 || TestError:== 0.091748\n",
" Epoch :== 329 || TIME(sec):== 1.0322 || TrainError:== 0.05651 || TestError:== 0.092079\n",
" Epoch :== 330 || TIME(sec):== 1.0267 || TrainError:== 0.056562 || TestError:== 0.093053\n",
" Epoch :== 331 || TIME(sec):== 1.0277 || TrainError:== 0.056843 || TestError:== 0.092416\n",
" Epoch :== 332 || TIME(sec):== 1.0244 || TrainError:== 0.057058 || TestError:== 0.093357\n",
" Epoch :== 333 || TIME(sec):== 1.0311 || TrainError:== 0.056705 || TestError:== 0.092639\n",
" Epoch :== 334 || TIME(sec):== 1.017 || TrainError:== 0.056293 || TestError:== 0.092442\n",
" Epoch :== 335 || TIME(sec):== 1.0286 || TrainError:== 0.056137 || TestError:== 0.092041\n",
" Epoch :== 336 || TIME(sec):== 0.9749 || TrainError:== 0.056321 || TestError:== 0.092615\n",
" Epoch :== 337 || TIME(sec):== 1.0313 || TrainError:== 0.056103 || TestError:== 0.091516\n",
" Epoch :== 338 || TIME(sec):== 1.0475 || TrainError:== 0.055429 || TestError:== 0.092986\n",
" Epoch :== 339 || TIME(sec):== 1.0259 || TrainError:== 0.055713 || TestError:== 0.090867\n",
" Epoch :== 340 || TIME(sec):== 1.0069 || TrainError:== 0.055854 || TestError:== 0.09312\n",
" Epoch :== 341 || TIME(sec):== 1.0496 || TrainError:== 0.056149 || TestError:== 0.092539\n",
" Epoch :== 342 || TIME(sec):== 1.047 || TrainError:== 0.055842 || TestError:== 0.092966\n",
" Epoch :== 343 || TIME(sec):== 1.0708 || TrainError:== 0.055847 || TestError:== 0.092559\n",
" Epoch :== 344 || TIME(sec):== 1.0075 || TrainError:== 0.056001 || TestError:== 0.091847\n",
" Epoch :== 345 || TIME(sec):== 0.8238 || TrainError:== 0.0567 || TestError:== 0.092683\n",
" Epoch :== 346 || TIME(sec):== 0.8318 || TrainError:== 0.0562 || TestError:== 0.091907\n",
" Epoch :== 347 || TIME(sec):== 0.8335 || TrainError:== 0.05592 || TestError:== 0.093755\n",
" Epoch :== 348 || TIME(sec):== 0.8315 || TrainError:== 0.055913 || TestError:== 0.092492\n",
" Epoch :== 349 || TIME(sec):== 0.8249 || TrainError:== 0.056323 || TestError:== 0.093189\n",
" Epoch :== 350 || TIME(sec):== 0.8234 || TrainError:== 0.056208 || TestError:== 0.094143\n",
" Epoch :== 351 || TIME(sec):== 0.8233 || TrainError:== 0.057143 || TestError:== 0.092693\n",
" Epoch :== 352 || TIME(sec):== 0.996 || TrainError:== 0.056332 || TestError:== 0.09236\n",
" Epoch :== 353 || TIME(sec):== 1.0497 || TrainError:== 0.056191 || TestError:== 0.092719\n",
" Epoch :== 354 || TIME(sec):== 1.0055 || TrainError:== 0.056467 || TestError:== 0.092399\n",
" Epoch :== 355 || TIME(sec):== 0.9549 || TrainError:== 0.056463 || TestError:== 0.093172\n",
" Epoch :== 356 || TIME(sec):== 0.9627 || TrainError:== 0.056076 || TestError:== 0.093118\n",
" Epoch :== 357 || TIME(sec):== 0.9743 || TrainError:== 0.055822 || TestError:== 0.091978\n",
" Epoch :== 358 || TIME(sec):== 0.9723 || TrainError:== 0.055059 || TestError:== 0.092059\n",
" Epoch :== 359 || TIME(sec):== 0.9722 || TrainError:== 0.055281 || TestError:== 0.091909\n",
" Epoch :== 360 || TIME(sec):== 0.9717 || TrainError:== 0.05506 || TestError:== 0.092008\n",
" Epoch :== 361 || TIME(sec):== 1.0082 || TrainError:== 0.055264 || TestError:== 0.092327\n",
" Epoch :== 362 || TIME(sec):== 0.9669 || TrainError:== 0.054909 || TestError:== 0.092413\n",
" Epoch :== 363 || TIME(sec):== 0.8294 || TrainError:== 0.055421 || TestError:== 0.092389\n",
" Epoch :== 364 || TIME(sec):== 0.8316 || TrainError:== 0.055735 || TestError:== 0.09238\n",
" Epoch :== 365 || TIME(sec):== 0.8272 || TrainError:== 0.055896 || TestError:== 0.092593\n",
" Epoch :== 366 || TIME(sec):== 0.9013 || TrainError:== 0.055975 || TestError:== 0.092819\n",
" Epoch :== 367 || TIME(sec):== 0.9032 || TrainError:== 0.05588 || TestError:== 0.091735\n",
" Epoch :== 368 || TIME(sec):== 0.9047 || TrainError:== 0.055538 || TestError:== 0.091258\n",
" Epoch :== 369 || TIME(sec):== 0.9016 || TrainError:== 0.05517 || TestError:== 0.091656\n",
" Epoch :== 370 || TIME(sec):== 0.8986 || TrainError:== 0.055193 || TestError:== 0.092174\n",
" Epoch :== 371 || TIME(sec):== 0.8215 || TrainError:== 0.055112 || TestError:== 0.092297\n",
" Epoch :== 372 || TIME(sec):== 0.8264 || TrainError:== 0.054875 || TestError:== 0.092344\n",
" Epoch :== 373 || TIME(sec):== 0.8244 || TrainError:== 0.055569 || TestError:== 0.09184\n",
" Epoch :== 374 || TIME(sec):== 0.8286 || TrainError:== 0.055414 || TestError:== 0.092602\n",
" Epoch :== 375 || TIME(sec):== 0.8197 || TrainError:== 0.055667 || TestError:== 0.09303\n",
" Epoch :== 376 || TIME(sec):== 0.8309 || TrainError:== 0.055289 || TestError:== 0.091051\n",
" Epoch :== 377 || TIME(sec):== 0.8296 || TrainError:== 0.055112 || TestError:== 0.0931\n",
" Epoch :== 378 || TIME(sec):== 0.8419 || TrainError:== 0.055815 || TestError:== 0.093478\n",
" Epoch :== 379 || TIME(sec):== 0.8394 || TrainError:== 0.056005 || TestError:== 0.091288\n",
" Epoch :== 380 || TIME(sec):== 0.836 || TrainError:== 0.055051 || TestError:== 0.091566\n",
" Epoch :== 381 || TIME(sec):== 0.8339 || TrainError:== 0.054732 || TestError:== 0.092092\n",
" Epoch :== 382 || TIME(sec):== 0.8352 || TrainError:== 0.054547 || TestError:== 0.091352\n",
" Epoch :== 383 || TIME(sec):== 0.8365 || TrainError:== 0.055296 || TestError:== 0.09144\n",
" Epoch :== 384 || TIME(sec):== 0.836 || TrainError:== 0.055453 || TestError:== 0.091418\n",
" Epoch :== 385 || TIME(sec):== 0.8374 || TrainError:== 0.055141 || TestError:== 0.092313\n",
" Epoch :== 386 || TIME(sec):== 0.8415 || TrainError:== 0.054675 || TestError:== 0.090917\n",
" Epoch :== 387 || TIME(sec):== 0.8395 || TrainError:== 0.054446 || TestError:== 0.092169\n",
" Epoch :== 388 || TIME(sec):== 0.8442 || TrainError:== 0.055306 || TestError:== 0.091439\n",
" Epoch :== 389 || TIME(sec):== 0.8389 || TrainError:== 0.054984 || TestError:== 0.092525\n",
" Epoch :== 390 || TIME(sec):== 0.8412 || TrainError:== 0.054873 || TestError:== 0.092517\n",
" Epoch :== 391 || TIME(sec):== 0.8352 || TrainError:== 0.055344 || TestError:== 0.0925\n",
" Epoch :== 392 || TIME(sec):== 0.8443 || TrainError:== 0.055503 || TestError:== 0.091443\n",
" Epoch :== 393 || TIME(sec):== 0.8344 || TrainError:== 0.055275 || TestError:== 0.092443\n",
" Epoch :== 394 || TIME(sec):== 0.8357 || TrainError:== 0.055237 || TestError:== 0.09022\n",
" Epoch :== 395 || TIME(sec):== 0.8393 || TrainError:== 0.054913 || TestError:== 0.092279\n",
" Epoch :== 396 || TIME(sec):== 0.8384 || TrainError:== 0.055405 || TestError:== 0.092278\n",
" Epoch :== 397 || TIME(sec):== 0.836 || TrainError:== 0.055233 || TestError:== 0.092167\n",
" Epoch :== 398 || TIME(sec):== 0.8382 || TrainError:== 0.055146 || TestError:== 0.092172\n",
" Epoch :== 399 || TIME(sec):== 0.8353 || TrainError:== 0.054913 || TestError:== 0.092652\n",
" Epoch :== 400 || TIME(sec):== 0.8382 || TrainError:== 0.054652 || TestError:== 0.092355\n",
" Epoch :== 401 || TIME(sec):== 0.839 || TrainError:== 0.053786 || TestError:== 0.090679\n",
" Epoch :== 402 || TIME(sec):== 0.836 || TrainError:== 0.052888 || TestError:== 0.091682\n",
" Epoch :== 403 || TIME(sec):== 0.8387 || TrainError:== 0.05255 || TestError:== 0.090589\n",
" Epoch :== 404 || TIME(sec):== 0.8332 || TrainError:== 0.052456 || TestError:== 0.090711\n",
" Epoch :== 405 || TIME(sec):== 0.83 || TrainError:== 0.052204 || TestError:== 0.090842\n",
" Epoch :== 406 || TIME(sec):== 0.8272 || TrainError:== 0.052575 || TestError:== 0.090252\n",
" Epoch :== 407 || TIME(sec):== 0.8239 || TrainError:== 0.052568 || TestError:== 0.090951\n",
" Epoch :== 408 || TIME(sec):== 0.8682 || TrainError:== 0.052758 || TestError:== 0.091412\n",
" Epoch :== 409 || TIME(sec):== 1.0471 || TrainError:== 0.052976 || TestError:== 0.090378\n",
" Epoch :== 410 || TIME(sec):== 1.0012 || TrainError:== 0.05269 || TestError:== 0.091048\n",
" Epoch :== 411 || TIME(sec):== 0.8247 || TrainError:== 0.052726 || TestError:== 0.090473\n",
" Epoch :== 412 || TIME(sec):== 0.8382 || TrainError:== 0.052668 || TestError:== 0.091832\n",
" Epoch :== 413 || TIME(sec):== 0.8395 || TrainError:== 0.05299 || TestError:== 0.090534\n",
" Epoch :== 414 || TIME(sec):== 0.8347 || TrainError:== 0.052814 || TestError:== 0.090969\n",
" Epoch :== 415 || TIME(sec):== 0.8397 || TrainError:== 0.053103 || TestError:== 0.090855\n",
" Epoch :== 416 || TIME(sec):== 0.8344 || TrainError:== 0.052719 || TestError:== 0.091582\n",
" Epoch :== 417 || TIME(sec):== 0.8388 || TrainError:== 0.052584 || TestError:== 0.090158\n",
" Epoch :== 418 || TIME(sec):== 0.8355 || TrainError:== 0.052664 || TestError:== 0.091859\n",
" Epoch :== 419 || TIME(sec):== 0.8347 || TrainError:== 0.052833 || TestError:== 0.090718\n",
" Epoch :== 420 || TIME(sec):== 0.837 || TrainError:== 0.053339 || TestError:== 0.091145\n",
" Epoch :== 421 || TIME(sec):== 0.8412 || TrainError:== 0.052895 || TestError:== 0.091593\n",
" Epoch :== 422 || TIME(sec):== 0.8418 || TrainError:== 0.052576 || TestError:== 0.089809\n",
" Epoch :== 423 || TIME(sec):== 0.8411 || TrainError:== 0.052897 || TestError:== 0.09074\n",
" Epoch :== 424 || TIME(sec):== 0.8418 || TrainError:== 0.05258 || TestError:== 0.089921\n",
" Epoch :== 425 || TIME(sec):== 0.8394 || TrainError:== 0.052011 || TestError:== 0.090926\n",
" Epoch :== 426 || TIME(sec):== 0.8357 || TrainError:== 0.052496 || TestError:== 0.090899\n",
" Epoch :== 427 || TIME(sec):== 0.8383 || TrainError:== 0.052558 || TestError:== 0.089896\n",
" Epoch :== 428 || TIME(sec):== 0.8353 || TrainError:== 0.052681 || TestError:== 0.09003\n",
" Epoch :== 429 || TIME(sec):== 0.8375 || TrainError:== 0.053023 || TestError:== 0.090881\n",
" Epoch :== 430 || TIME(sec):== 0.8377 || TrainError:== 0.052823 || TestError:== 0.090764\n",
" Epoch :== 431 || TIME(sec):== 0.8345 || TrainError:== 0.052752 || TestError:== 0.089834\n",
" Epoch :== 432 || TIME(sec):== 0.8429 || TrainError:== 0.052434 || TestError:== 0.091082\n",
" Epoch :== 433 || TIME(sec):== 0.8355 || TrainError:== 0.052332 || TestError:== 0.090756\n",
" Epoch :== 434 || TIME(sec):== 0.8389 || TrainError:== 0.052239 || TestError:== 0.089899\n",
" Epoch :== 435 || TIME(sec):== 0.8346 || TrainError:== 0.052255 || TestError:== 0.09101\n",
" Epoch :== 436 || TIME(sec):== 0.8364 || TrainError:== 0.052946 || TestError:== 0.090418\n",
" Epoch :== 437 || TIME(sec):== 0.8172 || TrainError:== 0.052677 || TestError:== 0.090187\n",
" Epoch :== 438 || TIME(sec):== 0.8224 || TrainError:== 0.052702 || TestError:== 0.08988\n",
" Epoch :== 439 || TIME(sec):== 0.8266 || TrainError:== 0.052319 || TestError:== 0.090845\n",
" Epoch :== 440 || TIME(sec):== 0.8188 || TrainError:== 0.052235 || TestError:== 0.091781\n",
" Epoch :== 441 || TIME(sec):== 0.822 || TrainError:== 0.05235 || TestError:== 0.090871\n",
" Epoch :== 442 || TIME(sec):== 0.8339 || TrainError:== 0.052241 || TestError:== 0.090456\n",
" Epoch :== 443 || TIME(sec):== 0.8214 || TrainError:== 0.052185 || TestError:== 0.091512\n",
" Epoch :== 444 || TIME(sec):== 0.8244 || TrainError:== 0.052658 || TestError:== 0.090383\n",
" Epoch :== 445 || TIME(sec):== 0.8384 || TrainError:== 0.053135 || TestError:== 0.091495\n",
" Epoch :== 446 || TIME(sec):== 0.8326 || TrainError:== 0.052761 || TestError:== 0.090569\n",
" Epoch :== 447 || TIME(sec):== 0.8765 || TrainError:== 0.052575 || TestError:== 0.091585\n",
" Epoch :== 448 || TIME(sec):== 0.9097 || TrainError:== 0.052703 || TestError:== 0.091121\n",
" Epoch :== 449 || TIME(sec):== 0.9026 || TrainError:== 0.05251 || TestError:== 0.090707\n",
" Epoch :== 450 || TIME(sec):== 0.8623 || TrainError:== 0.052299 || TestError:== 0.090849\n",
" Epoch :== 451 || TIME(sec):== 0.8337 || TrainError:== 0.052644 || TestError:== 0.090733\n",
" Epoch :== 452 || TIME(sec):== 0.8251 || TrainError:== 0.052126 || TestError:== 0.091132\n",
" Epoch :== 453 || TIME(sec):== 0.821 || TrainError:== 0.051883 || TestError:== 0.090902\n",
" Epoch :== 454 || TIME(sec):== 0.8283 || TrainError:== 0.051985 || TestError:== 0.090408\n",
" Epoch :== 455 || TIME(sec):== 0.8228 || TrainError:== 0.052251 || TestError:== 0.089525\n",
" Epoch :== 456 || TIME(sec):== 0.8642 || TrainError:== 0.05266 || TestError:== 0.090115\n",
" Epoch :== 457 || TIME(sec):== 0.8162 || TrainError:== 0.052363 || TestError:== 0.089379\n",
" Epoch :== 458 || TIME(sec):== 0.8356 || TrainError:== 0.052309 || TestError:== 0.091608\n",
" Epoch :== 459 || TIME(sec):== 0.8248 || TrainError:== 0.052099 || TestError:== 0.089979\n",
" Epoch :== 460 || TIME(sec):== 0.8331 || TrainError:== 0.052473 || TestError:== 0.090881\n",
" Epoch :== 461 || TIME(sec):== 0.827 || TrainError:== 0.051938 || TestError:== 0.090268\n",
" Epoch :== 462 || TIME(sec):== 0.823 || TrainError:== 0.051896 || TestError:== 0.090991\n",
" Epoch :== 463 || TIME(sec):== 0.909 || TrainError:== 0.051421 || TestError:== 0.090353\n",
" Epoch :== 464 || TIME(sec):== 0.9318 || TrainError:== 0.051452 || TestError:== 0.090653\n",
" Epoch :== 465 || TIME(sec):== 0.9509 || TrainError:== 0.051791 || TestError:== 0.090358\n",
" Epoch :== 466 || TIME(sec):== 0.9399 || TrainError:== 0.05219 || TestError:== 0.090794\n",
" Epoch :== 467 || TIME(sec):== 0.9368 || TrainError:== 0.052401 || TestError:== 0.090424\n",
" Epoch :== 468 || TIME(sec):== 0.9538 || TrainError:== 0.051727 || TestError:== 0.090129\n",
" Epoch :== 469 || TIME(sec):== 0.9401 || TrainError:== 0.051594 || TestError:== 0.090883\n",
" Epoch :== 470 || TIME(sec):== 0.9461 || TrainError:== 0.052031 || TestError:== 0.090809\n",
" Epoch :== 471 || TIME(sec):== 0.9319 || TrainError:== 0.05194 || TestError:== 0.090836\n",
" Epoch :== 472 || TIME(sec):== 0.933 || TrainError:== 0.051991 || TestError:== 0.090594\n",
" Epoch :== 473 || TIME(sec):== 0.9384 || TrainError:== 0.05201 || TestError:== 0.090851\n",
" Epoch :== 474 || TIME(sec):== 0.9442 || TrainError:== 0.051923 || TestError:== 0.089673\n",
" Epoch :== 475 || TIME(sec):== 0.9758 || TrainError:== 0.05175 || TestError:== 0.09095\n",
" Epoch :== 476 || TIME(sec):== 0.9769 || TrainError:== 0.051594 || TestError:== 0.090029\n",
" Epoch :== 477 || TIME(sec):== 0.9685 || TrainError:== 0.051432 || TestError:== 0.090669\n",
" Epoch :== 478 || TIME(sec):== 0.893 || TrainError:== 0.051517 || TestError:== 0.089597\n",
" Epoch :== 479 || TIME(sec):== 0.8231 || TrainError:== 0.051735 || TestError:== 0.090206\n",
" Epoch :== 480 || TIME(sec):== 0.8269 || TrainError:== 0.051967 || TestError:== 0.09152\n",
" Epoch :== 481 || TIME(sec):== 0.8187 || TrainError:== 0.050981 || TestError:== 0.089843\n",
" Epoch :== 482 || TIME(sec):== 0.8294 || TrainError:== 0.050293 || TestError:== 0.090133\n",
" Epoch :== 483 || TIME(sec):== 0.8229 || TrainError:== 0.049952 || TestError:== 0.089781\n",
" Epoch :== 484 || TIME(sec):== 0.8224 || TrainError:== 0.049915 || TestError:== 0.089851\n",
" Epoch :== 485 || TIME(sec):== 0.8265 || TrainError:== 0.050005 || TestError:== 0.089747\n",
" Epoch :== 486 || TIME(sec):== 0.835 || TrainError:== 0.050047 || TestError:== 0.089877\n",
" Epoch :== 487 || TIME(sec):== 0.8268 || TrainError:== 0.050065 || TestError:== 0.089521\n",
" Epoch :== 488 || TIME(sec):== 0.8281 || TrainError:== 0.049802 || TestError:== 0.089852\n",
" Epoch :== 489 || TIME(sec):== 0.8304 || TrainError:== 0.049739 || TestError:== 0.089318\n",
" Epoch :== 490 || TIME(sec):== 0.8246 || TrainError:== 0.049977 || TestError:== 0.090834\n",
" Epoch :== 491 || TIME(sec):== 0.8283 || TrainError:== 0.050276 || TestError:== 0.090413\n",
" Epoch :== 492 || TIME(sec):== 0.8247 || TrainError:== 0.050492 || TestError:== 0.089531\n",
" Epoch :== 493 || TIME(sec):== 0.8265 || TrainError:== 0.050538 || TestError:== 0.089641\n",
" Epoch :== 494 || TIME(sec):== 0.8242 || TrainError:== 0.050471 || TestError:== 0.08985\n",
" Epoch :== 495 || TIME(sec):== 0.8268 || TrainError:== 0.050345 || TestError:== 0.090114\n",
" Epoch :== 496 || TIME(sec):== 0.8157 || TrainError:== 0.050629 || TestError:== 0.089659\n",
" Epoch :== 497 || TIME(sec):== 0.8197 || TrainError:== 0.050214 || TestError:== 0.089782\n",
" Epoch :== 498 || TIME(sec):== 0.825 || TrainError:== 0.050102 || TestError:== 0.089563\n",
" Epoch :== 499 || TIME(sec):== 0.8246 || TrainError:== 0.050183 || TestError:== 0.089871\n",
" Epoch :== 500 || TIME(sec):== 0.8226 || TrainError:== 0.04995 || TestError:== 0.089625\n"
]
}
],
"source": [
"\n",
"\n",
"## ep wise error\n",
"trainerror=[] \n",
"testerror=[]\n",
"\n",
"##Train Test Loop\n",
"for ep in range(epochs):\n",
" model.train()\n",
" t1 = default_timer()\n",
" train_l2 = 0\n",
" for x, y in train_loader:\n",
" x, y = x.cuda(), y.cuda()\n",
" \n",
" optimizer.zero_grad()\n",
"\n",
" out = model(x).reshape(batch_size, dim, dim,stress_channels)\n",
" out = y_normalizer.decode(out)\n",
" y = y_normalizer.decode(y)\n",
"\n",
" loss = lossfunc(y.view(batch_size,-1),out.view(batch_size,-1))\n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
" train_l2 += loss.item()\n",
"\n",
" scheduler.step()\n",
" model.eval()\n",
" test_l2 = 0.0\n",
" with torch.no_grad():\n",
" for x, y in test_loader:\n",
" x, y = x.cuda(), y.cuda()\n",
" out = model(x).reshape(batch_size, dim, dim,stress_channels)\n",
" out = y_normalizer.decode(out)\n",
"\n",
" test_l2 += lossfunc(y.view(batch_size,-1),out.view(batch_size,-1)).item()\n",
"\n",
" train_l2/= ntrain\n",
" test_l2 /= ntest\n",
" trainerror += [train_l2]\n",
" testerror += [test_l2]\n",
" t2 = default_timer()\n",
" print(f' Epoch :== {ep+1} || TIME(sec):== {np.round((t2-t1),4)} '\n",
" f'|| TrainError:== {np.round(train_l2,6)} || TestError:== {np.round(test_l2,6)}')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e77714c9-04df-488a-a2cf-7661ec0c7c5e",
"metadata": {},
"outputs": [],
"source": [
"# trainn = np.array(trainerror)\n",
"# testnn = np.array(testerror)\n",
"# torch.save(model.state_dict(),'FNO_STRESS_N1200_ep500.pt')\n",
"# np.save('trainFNOStress', trainn)\n",
"# np.save('testFNOStress', testnn)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "31e663db-3571-4004-93f5-68475e55eef9",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 700x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(7,6))\n",
"plt.plot(range(epochs),trainerror,linewidth = 4,label =\"TRAIN\")\n",
"plt.plot(range(epochs),testerror, linewidth = 4,label =\"TEST\")\n",
"plt.xlabel('Epoch', fontsize =20)\n",
"plt.ylabel('Loss', fontsize =20)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "9ac59ab6-0e66-48ac-ba85-22e12e12a9fe",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FNO2d(\n",
" (fc0): Linear(in_features=3, out_features=32, bias=True)\n",
" (conv0): SpectralConv2d()\n",
" (conv1): SpectralConv2d()\n",
" (conv2): SpectralConv2d()\n",
" (conv3): SpectralConv2d()\n",
" (w0): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))\n",
" (w1): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))\n",
" (w2): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))\n",
" (w3): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))\n",
" (fc1): Linear(in_features=32, out_features=128, bias=True)\n",
" (fc2): Linear(in_features=128, out_features=3, bias=True)\n",
")\n"
]
}
],
"source": [
"# path = 'FNO_STRESS_N1200_ep500.pt'\n",
"# model.load_state_dict(torch.load(path))\n",
"# print(model)"
]
},
{
"cell_type": "markdown",
"id": "a97ef830-7169-4be0-ba47-7355b7c9a6cf",
"metadata": {},
"source": [
"### Testing -- Stresses"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d19adf7c-a885-4ada-8e77-96066264b5de",
"metadata": {},
"outputs": [],
"source": [
"prediction = torch.zeros(y_test_stress.shape)\n",
"c = 0\n",
"with torch.no_grad():\n",
" for x,y in test_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" out = model(x).reshape(batch_size, dim, dim,stress_channels)\n",
" out = y_normalizer.decode(out)\n",
" prediction[c*batch_size :c*batch_size+batch_size] = out\n",
" c+=1\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "98b52383-f514-4129-939d-7b0b361184ef",
"metadata": {},
"outputs": [],
"source": [
"stress_act = y_test_stress.reshape(ntest, -1)\n",
"stress_pred = prediction.reshape(ntest,-1)\n",
"\n",
"r2_stress =[]\n",
"for i in range(stress_act.shape[0]):\n",
" act = stress_act[i]\n",
" pred = stress_pred[i]\n",
" r2 = r2_score(act,pred)\n",
" r2_stress += [r2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d090fd1e-8b5d-47f1-bf21-257d8202d2da",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c0b1ac98-8399-4407-b6ac-8e94bf1ed116",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9764350534419417 0.06692226983719535\n"
]
}
],
"source": [
"r2_avg_stress = np.average(r2_stress)\n",
"r2_std_stress = np.std(r2_stress)\n",
"\n",
"print(r2_avg_stress,r2_std_stress)"
]
},
{
"cell_type": "markdown",
"id": "722d5bb1-b3bc-4b4b-a1dc-0f3ff622ab83",
"metadata": {},
"source": [
"#### R2 mean and std\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "0e839adb-66ab-4cd7-841d-c4a1b45e745a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.98\n",
"0.07\n"
]
}
],
"source": [
"print(np.round((r2_avg_stress),2))\n",
"print(np.round((r2_std_stress),2))"
]
},
{
"cell_type": "markdown",
"id": "e3c05031-89b8-438f-8be2-467f7dd2985b",
"metadata": {},
"source": [
"#### Total TestLoss"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "e6ce3a1d-528a-426d-9ca2-369a0d0ced87",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(0.0896)\n"
]
}
],
"source": [
"loss = lossfunc(y_test_stress, prediction)\n",
"print(loss/ntest)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5322b9b0-a23a-40b8-a18f-877a0591be39",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
2D | M3RG-IITD/FNO-StressStrain | benchmarking/ResNet.ipynb | .ipynb | 198,774 | 1,972 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "21985619-70e2-45de-8d87-b5f1b8995033",
"metadata": {},
"outputs": [],
"source": [
"## @meermehran -- M3RG Lab -- Indian Institute of Technology, Delhi\n",
"## @date : 28Sept2022\n",
"\n",
"####################################--DESCRIPTION####################################################\n",
"## ##\n",
"## -----------------------------Benchmarking- FNO-v-ResNet-UNet----------------------------------- ##\n",
"## ## \n",
"#####################################################################################################"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c6e6c049-8ee1-45ff-87ca-038cb5b73bc8",
"metadata": {},
"outputs": [],
"source": [
"\n",
"import numpy as np\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"from torch.nn.parameter import Parameter\n",
"from Adam import Adam\n",
"from sklearn.metrics import r2_score\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import operator\n",
"from functools import reduce\n",
"from functools import partial\n",
"import scipy.io as sio\n",
"\n",
"from timeit import default_timer\n",
"from utility import *\n",
"\n",
"device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
"torch.manual_seed(0)\n",
"np.random.seed(0)\n"
]
},
{
"cell_type": "markdown",
"id": "8c77eaa0-9225-4fc8-b7b8-66f506b433a9",
"metadata": {},
"source": [
"## STRAIN"
]
},
{
"cell_type": "markdown",
"id": "b8434ac2-64c7-4b24-b8cb-fb1b1289074f",
"metadata": {},
"source": [
"#### MODEL-RESNET"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4c1d3158-9a0d-4d17-af37-3f0564d10a78",
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"\n",
"class Bottleneck(nn.Module):\n",
" expansion = 4\n",
" def __init__(self, in_channels, out_channels, i_downsample=None, stride=1):\n",
" super(Bottleneck, self).__init__()\n",
" \n",
" self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)\n",
" self.batch_norm1 = nn.BatchNorm2d(out_channels)\n",
" \n",
" self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1)\n",
" self.batch_norm2 = nn.BatchNorm2d(out_channels)\n",
" \n",
" self.conv3 = nn.Conv2d(out_channels, out_channels*self.expansion, kernel_size=1, stride=1, padding=0)\n",
" self.batch_norm3 = nn.BatchNorm2d(out_channels*self.expansion)\n",
" \n",
" self.i_downsample = i_downsample\n",
" self.stride = stride\n",
" self.relu = nn.ReLU()\n",
" \n",
" def forward(self, x):\n",
" identity = x.clone()\n",
" x = self.relu(self.batch_norm1(self.conv1(x)))\n",
" \n",
" x = self.relu(self.batch_norm2(self.conv2(x)))\n",
" \n",
" x = self.conv3(x)\n",
" x = self.batch_norm3(x)\n",
" \n",
" #downsample if needed\n",
" if self.i_downsample is not None:\n",
" identity = self.i_downsample(identity)\n",
" #add identity\n",
" x+=identity\n",
" x=self.relu(x)\n",
" \n",
" return x\n",
"\n",
"class Block(nn.Module):\n",
" expansion = 1\n",
" def __init__(self, in_channels, out_channels, i_downsample=None, stride=1):\n",
" super(Block, self).__init__()\n",
" \n",
"\n",
" self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, stride=stride, bias=False)\n",
" self.batch_norm1 = nn.BatchNorm2d(out_channels)\n",
" self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, stride=stride, bias=False)\n",
" self.batch_norm2 = nn.BatchNorm2d(out_channels)\n",
"\n",
" self.i_downsample = i_downsample\n",
" self.stride = stride\n",
" self.relu = nn.ReLU()\n",
"\n",
" def forward(self, x):\n",
" \n",
" \n",
" identity = x.clone()\n",
"\n",
" x = self.relu(self.batch_norm2(self.conv1(x)))\n",
" x = self.batch_norm2(self.conv2(x))\n",
"\n",
" if self.i_downsample is not None:\n",
" identity = self.i_downsample(identity)\n",
" print(x.shape)\n",
" print(identity.shape)\n",
" x += identity\n",
" x = self.relu(x)\n",
" return x\n",
"\n",
"\n",
" \n",
" \n",
"class ResNet(nn.Module):\n",
" def __init__(self, ResBlock, layer_list, num_classes, num_channels=3):\n",
" super(ResNet, self).__init__()\n",
" self.in_channels = 64\n",
" \n",
" self.conv1 = nn.Conv2d(num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)\n",
" self.batch_norm1 = nn.BatchNorm2d(64)\n",
" self.relu = nn.ReLU()\n",
" self.max_pool = nn.MaxPool2d(kernel_size = 3, stride=2, padding=1)\n",
" \n",
" self.layer1 = self._make_layer(ResBlock, layer_list[0], planes=64)\n",
" self.layer2 = self._make_layer(ResBlock, layer_list[1], planes=128, stride=2)\n",
" self.layer3 = self._make_layer(ResBlock, layer_list[2], planes=256, stride=2)\n",
" self.layer4 = self._make_layer(ResBlock, layer_list[3], planes=512, stride=2)\n",
" \n",
" self.avgpool = nn.AdaptiveAvgPool2d((1,1))\n",
" self.fc = nn.Linear(512*ResBlock.expansion, num_classes)\n",
" \n",
" def forward(self, x):\n",
" x = self.relu(self.batch_norm1(self.conv1(x)))\n",
" x = self.max_pool(x)\n",
"\n",
" x = self.layer1(x)\n",
" x = self.layer2(x)\n",
" x = self.layer3(x)\n",
" x = self.layer4(x)\n",
" \n",
" x = self.avgpool(x)\n",
" x = x.reshape(x.shape[0], -1)\n",
" x = self.fc(x)\n",
" \n",
" return x\n",
" \n",
" def _make_layer(self, ResBlock, blocks, planes, stride=1):\n",
" ii_downsample = None\n",
" layers = []\n",
" \n",
" if stride != 1 or self.in_channels != planes*ResBlock.expansion:\n",
" ii_downsample = nn.Sequential(\n",
" nn.Conv2d(self.in_channels, planes*ResBlock.expansion, kernel_size=1, stride=stride),\n",
" nn.BatchNorm2d(planes*ResBlock.expansion)\n",
" )\n",
" \n",
" layers.append(ResBlock(self.in_channels, planes, i_downsample=ii_downsample, stride=stride))\n",
" self.in_channels = planes*ResBlock.expansion\n",
" \n",
" for i in range(blocks-1):\n",
" layers.append(ResBlock(self.in_channels, planes))\n",
" \n",
" return nn.Sequential(*layers)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "576fd6b7-39a5-4e1a-9764-2836890717db",
"metadata": {},
"outputs": [],
"source": [
"def ResNet50(num_classes, channels=3):\n",
" return ResNet(Bottleneck, [3,4,6,3], num_classes, channels)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "de6f7867-fb5c-4033-84f2-0be7c0ee9492",
"metadata": {},
"outputs": [],
"source": [
"######################################################\n",
"### Data Loading\n",
"######################################################\n",
"\n",
"\n",
"X_Data = '/DATA1/meer/data/material-v3-3350.mat'\n",
"Y_Data = '/DATA1/meer/data/strain-v3-3350.mat'\n",
"\n",
"ntrain =1200\n",
"ntest = 200\n",
"batch_size =20\n",
"dim = 48\n",
"strain_channels = 3\n",
"\n",
"\n",
"################################################################\n",
"# load data and data normalization\n",
"################################################################\n",
"\n",
"reader = MatReader(X_Data) \n",
"x_train = reader.read_field('Emat')[:ntrain]\n",
"x_test = reader.read_field('Emat')[-ntest:]\n",
"x_test_plot = reader.read_field('Emat')[-ntest:]\n",
"\n",
"\n",
"reader.load_file(Y_Data) \n",
"y_train = reader.read_field('strain')[:ntrain]# \n",
"y_test_strain = reader.read_field('strain')[-ntest:] \n",
"\n",
"\n",
"######################################################\n",
"### Normalization\n",
"######################################################\n",
"\n",
"x_normalizer = UnitGaussianNormalizer(x_train)\n",
"x_train = x_normalizer.encode(x_train)\n",
"x_test = x_normalizer.encode(x_test)\n",
"\n",
"y_normalizer = UnitGaussianNormalizer(y_train)\n",
"y_train = y_normalizer.encode(y_train)\n",
"\n",
"\n",
"\n",
"train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_train, y_train), batch_size=batch_size, shuffle=True)\n",
"test_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_test, y_test_strain), batch_size=batch_size, shuffle=False)\n",
"\n",
"\n",
"\n",
"if device=='cpu':\n",
" y_normalizer.cpu()\n",
"else:\n",
" y_normalizer.cuda()\n"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "bf51c310-970e-4213-9cde-99c05eb35c57",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" x_train Shape := torch.Size([1200, 48, 48])\n",
" y_train Shape := torch.Size([1200, 48, 48, 3])\n",
" x_test Shape := torch.Size([200, 48, 48])\n",
" y_test_strain Shape := torch.Size([200, 48, 48, 3])\n"
]
}
],
"source": [
"print(f' x_train Shape := {x_train.shape}')\n",
"print(f' y_train Shape := {y_train.shape}')\n",
"print(f' x_test Shape := {x_test.shape}')\n",
"print(f' y_test_strain Shape := {y_test_strain.shape}')\n"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "656fe4ab-5d33-4c10-a4e5-62f0084e9720",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MODEL PARAMETERS. :== 37690944\n",
"\n",
"\n",
"Epochs:= 500--- LR:=0.001---BatchSize:= 20\n"
]
}
],
"source": [
"# ################################################################\n",
"## Model\n",
"# ################################################################\n",
"\n",
"model = ResNet50(48*48*3, 1).to(device)\n",
"print(f'MODEL PARAMETERS. :== {count_params(model)}')\n",
"learning_rate = 1e-3\n",
"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay= 1e-4)\n",
"scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.5) ### gamma =0.1\n",
"epochs = 500\n",
"print('\\n')\n",
" \n",
"print(f'Epochs:= {epochs}--- LR:={learning_rate}---BatchSize:= {batch_size}')"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "d4b01cb9-2197-4e43-99e4-43d11e16e316",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Epoch :== 1 || TIME(sec):== 3.4079 || TrainError:== 0.750091 || TestError:== 0.721206\n",
" Epoch :== 2 || TIME(sec):== 3.2972 || TrainError:== 0.707028 || TestError:== 0.704414\n",
" Epoch :== 3 || TIME(sec):== 3.2882 || TrainError:== 0.694609 || TestError:== 0.690228\n",
" Epoch :== 4 || TIME(sec):== 3.3245 || TrainError:== 0.683731 || TestError:== 0.685511\n",
" Epoch :== 5 || TIME(sec):== 3.3254 || TrainError:== 0.656809 || TestError:== 0.638899\n",
" Epoch :== 6 || TIME(sec):== 3.3021 || TrainError:== 0.616193 || TestError:== 0.585048\n",
" Epoch :== 7 || TIME(sec):== 3.3377 || TrainError:== 0.586579 || TestError:== 1.483794\n",
" Epoch :== 8 || TIME(sec):== 3.2883 || TrainError:== 0.560886 || TestError:== 0.59791\n",
" Epoch :== 9 || TIME(sec):== 3.3141 || TrainError:== 0.510134 || TestError:== 0.538751\n",
" Epoch :== 10 || TIME(sec):== 3.2909 || TrainError:== 0.478582 || TestError:== 0.465638\n",
" Epoch :== 11 || TIME(sec):== 3.3054 || TrainError:== 0.436024 || TestError:== 0.420341\n",
" Epoch :== 12 || TIME(sec):== 3.3107 || TrainError:== 0.431366 || TestError:== 0.494067\n",
" Epoch :== 13 || TIME(sec):== 3.304 || TrainError:== 0.391025 || TestError:== 0.369752\n",
" Epoch :== 14 || TIME(sec):== 3.3223 || TrainError:== 0.344455 || TestError:== 0.351686\n",
" Epoch :== 15 || TIME(sec):== 3.3118 || TrainError:== 0.320574 || TestError:== 0.315034\n",
" Epoch :== 16 || TIME(sec):== 3.3239 || TrainError:== 0.289574 || TestError:== 0.297926\n",
" Epoch :== 17 || TIME(sec):== 3.3086 || TrainError:== 0.276267 || TestError:== 0.293079\n",
" Epoch :== 18 || TIME(sec):== 3.3201 || TrainError:== 0.257144 || TestError:== 0.281085\n",
" Epoch :== 19 || TIME(sec):== 3.2945 || TrainError:== 0.244373 || TestError:== 0.276679\n",
" Epoch :== 20 || TIME(sec):== 3.2832 || TrainError:== 0.233223 || TestError:== 0.271542\n",
" Epoch :== 21 || TIME(sec):== 3.3093 || TrainError:== 0.231667 || TestError:== 0.266747\n",
" Epoch :== 22 || TIME(sec):== 3.3094 || TrainError:== 0.220877 || TestError:== 0.259906\n",
" Epoch :== 23 || TIME(sec):== 3.2934 || TrainError:== 0.213037 || TestError:== 0.262637\n",
" Epoch :== 24 || TIME(sec):== 3.2946 || TrainError:== 0.205315 || TestError:== 0.246093\n",
" Epoch :== 25 || TIME(sec):== 3.2966 || TrainError:== 0.197935 || TestError:== 0.25025\n",
" Epoch :== 26 || TIME(sec):== 3.2971 || TrainError:== 0.190875 || TestError:== 0.238335\n",
" Epoch :== 27 || TIME(sec):== 3.3135 || TrainError:== 0.18764 || TestError:== 0.243135\n",
" Epoch :== 28 || TIME(sec):== 3.2922 || TrainError:== 0.181861 || TestError:== 0.230413\n",
" Epoch :== 29 || TIME(sec):== 3.2932 || TrainError:== 0.175994 || TestError:== 0.253555\n",
" Epoch :== 30 || TIME(sec):== 3.2955 || TrainError:== 0.181136 || TestError:== 0.230081\n",
" Epoch :== 31 || TIME(sec):== 3.3184 || TrainError:== 0.171119 || TestError:== 0.227137\n",
" Epoch :== 32 || TIME(sec):== 3.2721 || TrainError:== 0.168106 || TestError:== 0.229138\n",
" Epoch :== 33 || TIME(sec):== 3.2924 || TrainError:== 0.164396 || TestError:== 0.226504\n",
" Epoch :== 34 || TIME(sec):== 3.3219 || TrainError:== 0.162596 || TestError:== 0.225913\n",
" Epoch :== 35 || TIME(sec):== 3.3136 || TrainError:== 0.157778 || TestError:== 0.222593\n",
" Epoch :== 36 || TIME(sec):== 3.3186 || TrainError:== 0.156341 || TestError:== 0.225323\n",
" Epoch :== 37 || TIME(sec):== 3.3066 || TrainError:== 0.152536 || TestError:== 0.219256\n",
" Epoch :== 38 || TIME(sec):== 3.3147 || TrainError:== 0.154572 || TestError:== 0.22636\n",
" Epoch :== 39 || TIME(sec):== 3.3087 || TrainError:== 0.15037 || TestError:== 0.217558\n",
" Epoch :== 40 || TIME(sec):== 3.2988 || TrainError:== 0.148976 || TestError:== 0.250067\n",
" Epoch :== 41 || TIME(sec):== 3.3215 || TrainError:== 0.151542 || TestError:== 0.226753\n",
" Epoch :== 42 || TIME(sec):== 3.3118 || TrainError:== 0.149726 || TestError:== 0.217465\n",
" Epoch :== 43 || TIME(sec):== 3.29 || TrainError:== 0.145231 || TestError:== 0.220521\n",
" Epoch :== 44 || TIME(sec):== 3.3076 || TrainError:== 0.139841 || TestError:== 0.213851\n",
" Epoch :== 45 || TIME(sec):== 3.2801 || TrainError:== 0.140159 || TestError:== 0.215583\n",
" Epoch :== 46 || TIME(sec):== 3.31 || TrainError:== 0.136456 || TestError:== 0.212006\n",
" Epoch :== 47 || TIME(sec):== 3.3491 || TrainError:== 0.138246 || TestError:== 0.211377\n",
" Epoch :== 48 || TIME(sec):== 3.3746 || TrainError:== 0.138353 || TestError:== 0.209766\n",
" Epoch :== 49 || TIME(sec):== 3.5024 || TrainError:== 0.132599 || TestError:== 0.211483\n",
" Epoch :== 50 || TIME(sec):== 3.4755 || TrainError:== 0.132849 || TestError:== 0.214772\n",
" Epoch :== 51 || TIME(sec):== 3.354 || TrainError:== 0.132674 || TestError:== 0.209479\n",
" Epoch :== 52 || TIME(sec):== 3.3546 || TrainError:== 0.131791 || TestError:== 0.206146\n",
" Epoch :== 53 || TIME(sec):== 3.3356 || TrainError:== 0.130063 || TestError:== 0.209381\n",
" Epoch :== 54 || TIME(sec):== 3.3395 || TrainError:== 0.130535 || TestError:== 0.211036\n",
" Epoch :== 55 || TIME(sec):== 3.3041 || TrainError:== 0.128097 || TestError:== 0.208426\n",
" Epoch :== 56 || TIME(sec):== 3.3213 || TrainError:== 0.126109 || TestError:== 0.209693\n",
" Epoch :== 57 || TIME(sec):== 3.2945 || TrainError:== 0.126809 || TestError:== 0.207196\n",
" Epoch :== 58 || TIME(sec):== 3.3079 || TrainError:== 0.128201 || TestError:== 0.209155\n",
" Epoch :== 59 || TIME(sec):== 3.3305 || TrainError:== 0.12695 || TestError:== 0.205014\n",
" Epoch :== 60 || TIME(sec):== 3.3254 || TrainError:== 0.124475 || TestError:== 0.207614\n",
" Epoch :== 61 || TIME(sec):== 3.3033 || TrainError:== 0.123252 || TestError:== 0.203354\n",
" Epoch :== 62 || TIME(sec):== 3.3005 || TrainError:== 0.121418 || TestError:== 0.208138\n",
" Epoch :== 63 || TIME(sec):== 3.3509 || TrainError:== 0.121581 || TestError:== 0.204786\n",
" Epoch :== 64 || TIME(sec):== 3.3255 || TrainError:== 0.121013 || TestError:== 0.216689\n",
" Epoch :== 65 || TIME(sec):== 3.3013 || TrainError:== 0.121504 || TestError:== 0.204984\n",
" Epoch :== 66 || TIME(sec):== 3.3465 || TrainError:== 0.120154 || TestError:== 0.230248\n",
" Epoch :== 67 || TIME(sec):== 3.3502 || TrainError:== 0.119815 || TestError:== 0.203879\n",
" Epoch :== 68 || TIME(sec):== 3.3488 || TrainError:== 0.118936 || TestError:== 0.201175\n",
" Epoch :== 69 || TIME(sec):== 3.3218 || TrainError:== 0.116067 || TestError:== 0.201739\n",
" Epoch :== 70 || TIME(sec):== 3.3283 || TrainError:== 0.116694 || TestError:== 0.201402\n",
" Epoch :== 71 || TIME(sec):== 3.3679 || TrainError:== 0.115704 || TestError:== 0.202622\n",
" Epoch :== 72 || TIME(sec):== 3.3389 || TrainError:== 0.1149 || TestError:== 0.200849\n",
" Epoch :== 73 || TIME(sec):== 3.3296 || TrainError:== 0.114891 || TestError:== 0.199607\n",
" Epoch :== 74 || TIME(sec):== 3.3315 || TrainError:== 0.115938 || TestError:== 0.19996\n",
" Epoch :== 75 || TIME(sec):== 3.3255 || TrainError:== 0.11975 || TestError:== 0.203829\n",
" Epoch :== 76 || TIME(sec):== 3.3525 || TrainError:== 0.11645 || TestError:== 0.200207\n",
" Epoch :== 77 || TIME(sec):== 3.3426 || TrainError:== 0.11225 || TestError:== 0.200858\n",
" Epoch :== 78 || TIME(sec):== 3.3786 || TrainError:== 0.113099 || TestError:== 0.199108\n",
" Epoch :== 79 || TIME(sec):== 3.3588 || TrainError:== 0.113597 || TestError:== 0.204963\n",
" Epoch :== 80 || TIME(sec):== 3.3594 || TrainError:== 0.116541 || TestError:== 0.199819\n",
" Epoch :== 81 || TIME(sec):== 3.3481 || TrainError:== 0.113998 || TestError:== 0.200925\n",
" Epoch :== 82 || TIME(sec):== 3.3418 || TrainError:== 0.110837 || TestError:== 0.197132\n",
" Epoch :== 83 || TIME(sec):== 3.3298 || TrainError:== 0.10956 || TestError:== 0.198499\n",
" Epoch :== 84 || TIME(sec):== 3.3198 || TrainError:== 0.110789 || TestError:== 0.196956\n",
" Epoch :== 85 || TIME(sec):== 3.3402 || TrainError:== 0.109431 || TestError:== 0.196962\n",
" Epoch :== 86 || TIME(sec):== 3.3419 || TrainError:== 0.108983 || TestError:== 0.197856\n",
" Epoch :== 87 || TIME(sec):== 3.3408 || TrainError:== 0.109882 || TestError:== 0.19567\n",
" Epoch :== 88 || TIME(sec):== 3.3352 || TrainError:== 0.108576 || TestError:== 0.19565\n",
" Epoch :== 89 || TIME(sec):== 3.3328 || TrainError:== 0.108421 || TestError:== 0.19617\n",
" Epoch :== 90 || TIME(sec):== 3.3485 || TrainError:== 0.107751 || TestError:== 0.194605\n",
" Epoch :== 91 || TIME(sec):== 3.3531 || TrainError:== 0.107721 || TestError:== 0.194914\n",
" Epoch :== 92 || TIME(sec):== 3.3248 || TrainError:== 0.107301 || TestError:== 0.195619\n",
" Epoch :== 93 || TIME(sec):== 3.3323 || TrainError:== 0.10654 || TestError:== 0.196048\n",
" Epoch :== 94 || TIME(sec):== 3.3362 || TrainError:== 0.107029 || TestError:== 0.194609\n",
" Epoch :== 95 || TIME(sec):== 3.338 || TrainError:== 0.108079 || TestError:== 0.194423\n",
" Epoch :== 96 || TIME(sec):== 3.3636 || TrainError:== 0.106121 || TestError:== 0.191672\n",
" Epoch :== 97 || TIME(sec):== 3.3321 || TrainError:== 0.105916 || TestError:== 0.19399\n",
" Epoch :== 98 || TIME(sec):== 3.3435 || TrainError:== 0.106264 || TestError:== 0.193506\n",
" Epoch :== 99 || TIME(sec):== 3.3454 || TrainError:== 0.105271 || TestError:== 0.193351\n",
" Epoch :== 100 || TIME(sec):== 3.3332 || TrainError:== 0.104391 || TestError:== 0.194071\n",
" Epoch :== 101 || TIME(sec):== 3.3464 || TrainError:== 0.089525 || TestError:== 0.182648\n",
" Epoch :== 102 || TIME(sec):== 3.3459 || TrainError:== 0.078552 || TestError:== 0.182607\n",
" Epoch :== 103 || TIME(sec):== 3.3493 || TrainError:== 0.075153 || TestError:== 0.1824\n",
" Epoch :== 104 || TIME(sec):== 3.3513 || TrainError:== 0.07471 || TestError:== 0.18235\n",
" Epoch :== 105 || TIME(sec):== 3.3372 || TrainError:== 0.074657 || TestError:== 0.183356\n",
" Epoch :== 106 || TIME(sec):== 3.3374 || TrainError:== 0.07405 || TestError:== 0.183944\n",
" Epoch :== 107 || TIME(sec):== 3.341 || TrainError:== 0.074395 || TestError:== 0.183212\n",
" Epoch :== 108 || TIME(sec):== 3.3533 || TrainError:== 0.074173 || TestError:== 0.183167\n",
" Epoch :== 109 || TIME(sec):== 3.3236 || TrainError:== 0.074584 || TestError:== 0.183003\n",
" Epoch :== 110 || TIME(sec):== 3.355 || TrainError:== 0.07451 || TestError:== 0.182876\n",
" Epoch :== 111 || TIME(sec):== 3.3407 || TrainError:== 0.073674 || TestError:== 0.184023\n",
" Epoch :== 112 || TIME(sec):== 3.3545 || TrainError:== 0.073952 || TestError:== 0.184041\n",
" Epoch :== 113 || TIME(sec):== 3.3292 || TrainError:== 0.073595 || TestError:== 0.184195\n",
" Epoch :== 114 || TIME(sec):== 3.33 || TrainError:== 0.073263 || TestError:== 0.183466\n",
" Epoch :== 115 || TIME(sec):== 3.3204 || TrainError:== 0.072897 || TestError:== 0.183615\n",
" Epoch :== 116 || TIME(sec):== 3.3385 || TrainError:== 0.074036 || TestError:== 0.184674\n",
" Epoch :== 117 || TIME(sec):== 3.3388 || TrainError:== 0.073946 || TestError:== 0.183469\n",
" Epoch :== 118 || TIME(sec):== 3.3515 || TrainError:== 0.073103 || TestError:== 0.183656\n",
" Epoch :== 119 || TIME(sec):== 3.3176 || TrainError:== 0.073646 || TestError:== 0.184569\n",
" Epoch :== 120 || TIME(sec):== 3.3322 || TrainError:== 0.073142 || TestError:== 0.182511\n",
" Epoch :== 121 || TIME(sec):== 3.335 || TrainError:== 0.073814 || TestError:== 0.184567\n",
" Epoch :== 122 || TIME(sec):== 3.3389 || TrainError:== 0.072379 || TestError:== 0.18338\n",
" Epoch :== 123 || TIME(sec):== 3.3381 || TrainError:== 0.072284 || TestError:== 0.183999\n",
" Epoch :== 124 || TIME(sec):== 3.3553 || TrainError:== 0.072798 || TestError:== 0.182594\n",
" Epoch :== 125 || TIME(sec):== 3.3496 || TrainError:== 0.072005 || TestError:== 0.183931\n",
" Epoch :== 126 || TIME(sec):== 3.3438 || TrainError:== 0.071848 || TestError:== 0.183733\n",
" Epoch :== 127 || TIME(sec):== 3.3253 || TrainError:== 0.073082 || TestError:== 0.182462\n",
" Epoch :== 128 || TIME(sec):== 3.3517 || TrainError:== 0.071255 || TestError:== 0.183656\n",
" Epoch :== 129 || TIME(sec):== 3.3264 || TrainError:== 0.071882 || TestError:== 0.184124\n",
" Epoch :== 130 || TIME(sec):== 3.3517 || TrainError:== 0.072275 || TestError:== 0.186053\n",
" Epoch :== 131 || TIME(sec):== 3.3379 || TrainError:== 0.071584 || TestError:== 0.18444\n",
" Epoch :== 132 || TIME(sec):== 3.3362 || TrainError:== 0.071988 || TestError:== 0.182918\n",
" Epoch :== 133 || TIME(sec):== 3.3515 || TrainError:== 0.071553 || TestError:== 0.185126\n",
" Epoch :== 134 || TIME(sec):== 3.3314 || TrainError:== 0.071574 || TestError:== 0.182762\n",
" Epoch :== 135 || TIME(sec):== 3.3442 || TrainError:== 0.071621 || TestError:== 0.184218\n",
" Epoch :== 136 || TIME(sec):== 3.3386 || TrainError:== 0.070827 || TestError:== 0.183004\n",
" Epoch :== 147 || TIME(sec):== 3.343 || TrainError:== 0.069589 || TestError:== 0.182792\n",
" Epoch :== 148 || TIME(sec):== 3.3484 || TrainError:== 0.070359 || TestError:== 0.18371\n",
" Epoch :== 149 || TIME(sec):== 3.3537 || TrainError:== 0.07018 || TestError:== 0.183768\n",
" Epoch :== 150 || TIME(sec):== 3.3249 || TrainError:== 0.070002 || TestError:== 0.183337\n",
" Epoch :== 151 || TIME(sec):== 3.3455 || TrainError:== 0.070155 || TestError:== 0.183971\n",
" Epoch :== 152 || TIME(sec):== 3.363 || TrainError:== 0.069965 || TestError:== 0.18334\n",
" Epoch :== 153 || TIME(sec):== 3.3272 || TrainError:== 0.069715 || TestError:== 0.182064\n",
" Epoch :== 154 || TIME(sec):== 3.3412 || TrainError:== 0.068825 || TestError:== 0.183736\n",
" Epoch :== 155 || TIME(sec):== 3.3784 || TrainError:== 0.068479 || TestError:== 0.182584\n",
" Epoch :== 156 || TIME(sec):== 3.3653 || TrainError:== 0.068883 || TestError:== 0.182658\n",
" Epoch :== 157 || TIME(sec):== 3.3424 || TrainError:== 0.069246 || TestError:== 0.183653\n",
" Epoch :== 158 || TIME(sec):== 3.3351 || TrainError:== 0.068448 || TestError:== 0.183243\n",
" Epoch :== 159 || TIME(sec):== 3.3438 || TrainError:== 0.068545 || TestError:== 0.181433\n",
" Epoch :== 160 || TIME(sec):== 3.3483 || TrainError:== 0.068722 || TestError:== 0.183434\n",
" Epoch :== 161 || TIME(sec):== 3.3692 || TrainError:== 0.069539 || TestError:== 0.183825\n",
" Epoch :== 162 || TIME(sec):== 3.3356 || TrainError:== 0.068666 || TestError:== 0.183195\n",
" Epoch :== 163 || TIME(sec):== 3.3201 || TrainError:== 0.06806 || TestError:== 0.182455\n",
" Epoch :== 164 || TIME(sec):== 3.3381 || TrainError:== 0.06811 || TestError:== 0.182657\n",
" Epoch :== 165 || TIME(sec):== 3.3694 || TrainError:== 0.067779 || TestError:== 0.183022\n",
" Epoch :== 166 || TIME(sec):== 3.34 || TrainError:== 0.068939 || TestError:== 0.183957\n",
" Epoch :== 167 || TIME(sec):== 3.328 || TrainError:== 0.068213 || TestError:== 0.182487\n",
" Epoch :== 168 || TIME(sec):== 3.325 || TrainError:== 0.068334 || TestError:== 0.183959\n",
" Epoch :== 169 || TIME(sec):== 3.3318 || TrainError:== 0.067428 || TestError:== 0.18311\n",
" Epoch :== 170 || TIME(sec):== 3.3382 || TrainError:== 0.068637 || TestError:== 0.18218\n",
" Epoch :== 171 || TIME(sec):== 3.3189 || TrainError:== 0.068039 || TestError:== 0.182536\n",
" Epoch :== 172 || TIME(sec):== 3.3287 || TrainError:== 0.068957 || TestError:== 0.18193\n",
" Epoch :== 173 || TIME(sec):== 3.3527 || TrainError:== 0.068418 || TestError:== 0.184053\n",
" Epoch :== 174 || TIME(sec):== 3.3199 || TrainError:== 0.068183 || TestError:== 0.182571\n",
" Epoch :== 175 || TIME(sec):== 3.343 || TrainError:== 0.067862 || TestError:== 0.182397\n",
" Epoch :== 176 || TIME(sec):== 3.3473 || TrainError:== 0.067313 || TestError:== 0.183614\n",
" Epoch :== 177 || TIME(sec):== 3.3491 || TrainError:== 0.067974 || TestError:== 0.182316\n",
" Epoch :== 178 || TIME(sec):== 3.3339 || TrainError:== 0.067405 || TestError:== 0.18182\n",
" Epoch :== 179 || TIME(sec):== 3.3344 || TrainError:== 0.067985 || TestError:== 0.182478\n",
" Epoch :== 180 || TIME(sec):== 3.3337 || TrainError:== 0.067236 || TestError:== 0.183132\n",
" Epoch :== 181 || TIME(sec):== 3.3646 || TrainError:== 0.067314 || TestError:== 0.182771\n",
" Epoch :== 182 || TIME(sec):== 3.3279 || TrainError:== 0.068147 || TestError:== 0.184689\n",
" Epoch :== 183 || TIME(sec):== 3.7053 || TrainError:== 0.067244 || TestError:== 0.182896\n",
" Epoch :== 184 || TIME(sec):== 4.1752 || TrainError:== 0.06744 || TestError:== 0.181761\n",
" Epoch :== 185 || TIME(sec):== 4.1411 || TrainError:== 0.066569 || TestError:== 0.183223\n",
" Epoch :== 186 || TIME(sec):== 4.1522 || TrainError:== 0.068019 || TestError:== 0.18196\n",
" Epoch :== 187 || TIME(sec):== 4.17 || TrainError:== 0.066951 || TestError:== 0.18212\n",
" Epoch :== 188 || TIME(sec):== 4.1736 || TrainError:== 0.066319 || TestError:== 0.181811\n",
" Epoch :== 189 || TIME(sec):== 4.1654 || TrainError:== 0.066453 || TestError:== 0.181886\n",
" Epoch :== 190 || TIME(sec):== 4.1501 || TrainError:== 0.066245 || TestError:== 0.181806\n",
" Epoch :== 191 || TIME(sec):== 4.1609 || TrainError:== 0.066219 || TestError:== 0.181961\n",
" Epoch :== 192 || TIME(sec):== 4.1606 || TrainError:== 0.066333 || TestError:== 0.181396\n",
" Epoch :== 193 || TIME(sec):== 4.1487 || TrainError:== 0.066148 || TestError:== 0.182686\n",
" Epoch :== 194 || TIME(sec):== 4.144 || TrainError:== 0.066692 || TestError:== 0.182386\n",
" Epoch :== 195 || TIME(sec):== 4.1521 || TrainError:== 0.066235 || TestError:== 0.181296\n",
" Epoch :== 196 || TIME(sec):== 4.1503 || TrainError:== 0.065979 || TestError:== 0.181862\n",
" Epoch :== 197 || TIME(sec):== 4.128 || TrainError:== 0.066226 || TestError:== 0.180794\n",
" Epoch :== 198 || TIME(sec):== 4.1985 || TrainError:== 0.065963 || TestError:== 0.181494\n",
" Epoch :== 199 || TIME(sec):== 4.1638 || TrainError:== 0.066325 || TestError:== 0.181003\n",
" Epoch :== 200 || TIME(sec):== 4.1802 || TrainError:== 0.066267 || TestError:== 0.18144\n",
" Epoch :== 201 || TIME(sec):== 4.1514 || TrainError:== 0.057685 || TestError:== 0.177365\n",
" Epoch :== 202 || TIME(sec):== 4.172 || TrainError:== 0.05109 || TestError:== 0.176899\n",
" Epoch :== 203 || TIME(sec):== 3.6178 || TrainError:== 0.049547 || TestError:== 0.178002\n",
" Epoch :== 204 || TIME(sec):== 3.3163 || TrainError:== 0.049167 || TestError:== 0.178073\n",
" Epoch :== 205 || TIME(sec):== 3.3388 || TrainError:== 0.048573 || TestError:== 0.178279\n",
" Epoch :== 206 || TIME(sec):== 3.3298 || TrainError:== 0.048726 || TestError:== 0.178989\n",
" Epoch :== 207 || TIME(sec):== 3.3209 || TrainError:== 0.048497 || TestError:== 0.178381\n",
" Epoch :== 208 || TIME(sec):== 3.3276 || TrainError:== 0.048732 || TestError:== 0.178758\n",
" Epoch :== 209 || TIME(sec):== 3.3496 || TrainError:== 0.047742 || TestError:== 0.179139\n",
" Epoch :== 210 || TIME(sec):== 3.3452 || TrainError:== 0.047604 || TestError:== 0.179133\n",
" Epoch :== 211 || TIME(sec):== 3.3419 || TrainError:== 0.047204 || TestError:== 0.178659\n",
" Epoch :== 212 || TIME(sec):== 3.3399 || TrainError:== 0.047543 || TestError:== 0.179144\n",
" Epoch :== 213 || TIME(sec):== 3.3384 || TrainError:== 0.047696 || TestError:== 0.178386\n",
" Epoch :== 214 || TIME(sec):== 3.3346 || TrainError:== 0.047017 || TestError:== 0.179982\n",
" Epoch :== 215 || TIME(sec):== 3.3469 || TrainError:== 0.047432 || TestError:== 0.17894\n",
" Epoch :== 216 || TIME(sec):== 3.3361 || TrainError:== 0.047176 || TestError:== 0.179438\n",
" Epoch :== 217 || TIME(sec):== 3.3326 || TrainError:== 0.04749 || TestError:== 0.17943\n",
" Epoch :== 218 || TIME(sec):== 3.3473 || TrainError:== 0.047634 || TestError:== 0.179286\n",
" Epoch :== 219 || TIME(sec):== 3.3436 || TrainError:== 0.047215 || TestError:== 0.179836\n",
" Epoch :== 220 || TIME(sec):== 3.3356 || TrainError:== 0.047729 || TestError:== 0.179271\n",
" Epoch :== 221 || TIME(sec):== 3.3311 || TrainError:== 0.047695 || TestError:== 0.179563\n",
" Epoch :== 222 || TIME(sec):== 3.3429 || TrainError:== 0.047102 || TestError:== 0.179542\n",
" Epoch :== 223 || TIME(sec):== 3.311 || TrainError:== 0.046978 || TestError:== 0.179451\n",
" Epoch :== 224 || TIME(sec):== 3.3301 || TrainError:== 0.047445 || TestError:== 0.179411\n",
" Epoch :== 225 || TIME(sec):== 3.3615 || TrainError:== 0.047192 || TestError:== 0.180289\n",
" Epoch :== 226 || TIME(sec):== 3.3365 || TrainError:== 0.047201 || TestError:== 0.179609\n",
" Epoch :== 227 || TIME(sec):== 3.3262 || TrainError:== 0.04721 || TestError:== 0.178896\n",
" Epoch :== 228 || TIME(sec):== 3.3345 || TrainError:== 0.047111 || TestError:== 0.17917\n",
" Epoch :== 229 || TIME(sec):== 3.3302 || TrainError:== 0.046918 || TestError:== 0.179002\n",
" Epoch :== 230 || TIME(sec):== 3.3378 || TrainError:== 0.046747 || TestError:== 0.179287\n",
" Epoch :== 231 || TIME(sec):== 3.3467 || TrainError:== 0.047571 || TestError:== 0.18075\n",
" Epoch :== 232 || TIME(sec):== 3.3246 || TrainError:== 0.047031 || TestError:== 0.179733\n",
" Epoch :== 233 || TIME(sec):== 3.3525 || TrainError:== 0.047017 || TestError:== 0.180022\n",
" Epoch :== 234 || TIME(sec):== 3.3443 || TrainError:== 0.046514 || TestError:== 0.179877\n",
" Epoch :== 235 || TIME(sec):== 3.3195 || TrainError:== 0.046415 || TestError:== 0.1803\n",
" Epoch :== 236 || TIME(sec):== 3.3387 || TrainError:== 0.046311 || TestError:== 0.179044\n",
" Epoch :== 237 || TIME(sec):== 3.3387 || TrainError:== 0.046832 || TestError:== 0.179957\n",
" Epoch :== 238 || TIME(sec):== 3.3254 || TrainError:== 0.04655 || TestError:== 0.180074\n",
" Epoch :== 239 || TIME(sec):== 3.3454 || TrainError:== 0.046752 || TestError:== 0.1799\n",
" Epoch :== 240 || TIME(sec):== 3.3493 || TrainError:== 0.046755 || TestError:== 0.180041\n",
" Epoch :== 241 || TIME(sec):== 3.3506 || TrainError:== 0.046368 || TestError:== 0.179362\n",
" Epoch :== 242 || TIME(sec):== 3.3425 || TrainError:== 0.046349 || TestError:== 0.179662\n",
" Epoch :== 243 || TIME(sec):== 3.3282 || TrainError:== 0.046358 || TestError:== 0.179519\n",
" Epoch :== 244 || TIME(sec):== 3.3585 || TrainError:== 0.046249 || TestError:== 0.179058\n",
" Epoch :== 245 || TIME(sec):== 3.3278 || TrainError:== 0.046326 || TestError:== 0.180082\n",
" Epoch :== 246 || TIME(sec):== 3.3455 || TrainError:== 0.046255 || TestError:== 0.179979\n",
" Epoch :== 247 || TIME(sec):== 3.3489 || TrainError:== 0.046659 || TestError:== 0.180228\n",
" Epoch :== 248 || TIME(sec):== 3.3417 || TrainError:== 0.045959 || TestError:== 0.180137\n",
" Epoch :== 249 || TIME(sec):== 3.3537 || TrainError:== 0.046267 || TestError:== 0.179478\n",
" Epoch :== 250 || TIME(sec):== 3.3407 || TrainError:== 0.045951 || TestError:== 0.180223\n",
" Epoch :== 251 || TIME(sec):== 3.329 || TrainError:== 0.045952 || TestError:== 0.179871\n",
" Epoch :== 252 || TIME(sec):== 3.3353 || TrainError:== 0.046089 || TestError:== 0.180165\n",
" Epoch :== 253 || TIME(sec):== 3.3363 || TrainError:== 0.04654 || TestError:== 0.180276\n",
" Epoch :== 254 || TIME(sec):== 3.3672 || TrainError:== 0.045616 || TestError:== 0.180775\n",
" Epoch :== 255 || TIME(sec):== 3.3391 || TrainError:== 0.046199 || TestError:== 0.180265\n",
" Epoch :== 256 || TIME(sec):== 3.3579 || TrainError:== 0.046693 || TestError:== 0.18085\n",
" Epoch :== 257 || TIME(sec):== 3.3472 || TrainError:== 0.046358 || TestError:== 0.180421\n",
" Epoch :== 258 || TIME(sec):== 3.3376 || TrainError:== 0.046212 || TestError:== 0.18013\n",
" Epoch :== 259 || TIME(sec):== 3.324 || TrainError:== 0.046167 || TestError:== 0.17959\n",
" Epoch :== 260 || TIME(sec):== 3.3479 || TrainError:== 0.046293 || TestError:== 0.179436\n",
" Epoch :== 261 || TIME(sec):== 3.3596 || TrainError:== 0.045993 || TestError:== 0.180023\n",
" Epoch :== 262 || TIME(sec):== 3.3336 || TrainError:== 0.046477 || TestError:== 0.180363\n",
" Epoch :== 263 || TIME(sec):== 3.3401 || TrainError:== 0.045983 || TestError:== 0.179939\n",
" Epoch :== 264 || TIME(sec):== 3.3403 || TrainError:== 0.045757 || TestError:== 0.179957\n",
" Epoch :== 265 || TIME(sec):== 3.3465 || TrainError:== 0.046164 || TestError:== 0.179946\n",
" Epoch :== 266 || TIME(sec):== 3.3414 || TrainError:== 0.045471 || TestError:== 0.180467\n",
" Epoch :== 267 || TIME(sec):== 3.3247 || TrainError:== 0.046209 || TestError:== 0.179582\n",
" Epoch :== 268 || TIME(sec):== 3.3397 || TrainError:== 0.04604 || TestError:== 0.180524\n",
" Epoch :== 269 || TIME(sec):== 3.3314 || TrainError:== 0.045661 || TestError:== 0.180969\n",
" Epoch :== 270 || TIME(sec):== 3.34 || TrainError:== 0.045441 || TestError:== 0.180602\n",
" Epoch :== 271 || TIME(sec):== 3.3512 || TrainError:== 0.045681 || TestError:== 0.180633\n",
" Epoch :== 272 || TIME(sec):== 3.3577 || TrainError:== 0.045647 || TestError:== 0.180692\n",
" Epoch :== 273 || TIME(sec):== 3.3463 || TrainError:== 0.045375 || TestError:== 0.180125\n",
" Epoch :== 274 || TIME(sec):== 3.341 || TrainError:== 0.045422 || TestError:== 0.18002\n",
" Epoch :== 275 || TIME(sec):== 3.1076 || TrainError:== 0.045679 || TestError:== 0.180677\n",
" Epoch :== 276 || TIME(sec):== 3.3214 || TrainError:== 0.045658 || TestError:== 0.180613\n",
" Epoch :== 277 || TIME(sec):== 3.3225 || TrainError:== 0.04518 || TestError:== 0.180485\n",
" Epoch :== 278 || TIME(sec):== 3.3159 || TrainError:== 0.045314 || TestError:== 0.181008\n",
" Epoch :== 279 || TIME(sec):== 3.3241 || TrainError:== 0.045975 || TestError:== 0.180586\n",
" Epoch :== 280 || TIME(sec):== 3.3255 || TrainError:== 0.045204 || TestError:== 0.179849\n",
" Epoch :== 281 || TIME(sec):== 3.3102 || TrainError:== 0.045536 || TestError:== 0.18072\n",
" Epoch :== 282 || TIME(sec):== 3.3391 || TrainError:== 0.045482 || TestError:== 0.180816\n",
" Epoch :== 283 || TIME(sec):== 3.3202 || TrainError:== 0.045643 || TestError:== 0.18034\n",
" Epoch :== 284 || TIME(sec):== 3.3258 || TrainError:== 0.044919 || TestError:== 0.180295\n",
" Epoch :== 285 || TIME(sec):== 3.3358 || TrainError:== 0.044956 || TestError:== 0.181225\n",
" Epoch :== 286 || TIME(sec):== 3.3461 || TrainError:== 0.045923 || TestError:== 0.1807\n",
" Epoch :== 287 || TIME(sec):== 3.3385 || TrainError:== 0.045915 || TestError:== 0.181285\n",
" Epoch :== 288 || TIME(sec):== 3.3235 || TrainError:== 0.045571 || TestError:== 0.180355\n",
" Epoch :== 289 || TIME(sec):== 3.3267 || TrainError:== 0.045135 || TestError:== 0.180972\n",
" Epoch :== 290 || TIME(sec):== 3.3453 || TrainError:== 0.045571 || TestError:== 0.181303\n",
" Epoch :== 291 || TIME(sec):== 3.3459 || TrainError:== 0.04564 || TestError:== 0.181825\n",
" Epoch :== 292 || TIME(sec):== 3.31 || TrainError:== 0.045625 || TestError:== 0.180441\n",
" Epoch :== 293 || TIME(sec):== 3.3476 || TrainError:== 0.045476 || TestError:== 0.180934\n",
" Epoch :== 294 || TIME(sec):== 3.3588 || TrainError:== 0.045036 || TestError:== 0.180526\n",
" Epoch :== 295 || TIME(sec):== 3.3702 || TrainError:== 0.044837 || TestError:== 0.180552\n",
" Epoch :== 296 || TIME(sec):== 3.3877 || TrainError:== 0.045188 || TestError:== 0.181332\n",
" Epoch :== 297 || TIME(sec):== 3.3557 || TrainError:== 0.045244 || TestError:== 0.180415\n",
" Epoch :== 298 || TIME(sec):== 3.3266 || TrainError:== 0.045031 || TestError:== 0.180346\n",
" Epoch :== 299 || TIME(sec):== 3.3463 || TrainError:== 0.044887 || TestError:== 0.181292\n",
" Epoch :== 300 || TIME(sec):== 3.3419 || TrainError:== 0.045475 || TestError:== 0.180651\n",
" Epoch :== 301 || TIME(sec):== 3.3389 || TrainError:== 0.040837 || TestError:== 0.179288\n",
" Epoch :== 302 || TIME(sec):== 3.3139 || TrainError:== 0.036861 || TestError:== 0.179687\n",
" Epoch :== 303 || TIME(sec):== 3.3135 || TrainError:== 0.036383 || TestError:== 0.179098\n",
" Epoch :== 304 || TIME(sec):== 3.3306 || TrainError:== 0.035657 || TestError:== 0.180097\n",
" Epoch :== 305 || TIME(sec):== 2.8051 || TrainError:== 0.035652 || TestError:== 0.179378\n",
" Epoch :== 306 || TIME(sec):== 2.8403 || TrainError:== 0.035824 || TestError:== 0.179777\n",
" Epoch :== 307 || TIME(sec):== 2.842 || TrainError:== 0.035235 || TestError:== 0.179647\n",
" Epoch :== 308 || TIME(sec):== 2.8438 || TrainError:== 0.03507 || TestError:== 0.179926\n",
" Epoch :== 309 || TIME(sec):== 2.847 || TrainError:== 0.034942 || TestError:== 0.179523\n",
" Epoch :== 310 || TIME(sec):== 2.8288 || TrainError:== 0.035094 || TestError:== 0.180103\n",
" Epoch :== 311 || TIME(sec):== 2.8397 || TrainError:== 0.03496 || TestError:== 0.180072\n",
" Epoch :== 312 || TIME(sec):== 2.8229 || TrainError:== 0.035296 || TestError:== 0.180248\n",
" Epoch :== 313 || TIME(sec):== 2.8373 || TrainError:== 0.035075 || TestError:== 0.179905\n",
" Epoch :== 314 || TIME(sec):== 2.8528 || TrainError:== 0.035258 || TestError:== 0.180182\n",
" Epoch :== 315 || TIME(sec):== 2.8554 || TrainError:== 0.034883 || TestError:== 0.179842\n",
" Epoch :== 316 || TIME(sec):== 2.8273 || TrainError:== 0.035037 || TestError:== 0.180579\n",
" Epoch :== 317 || TIME(sec):== 3.266 || TrainError:== 0.035195 || TestError:== 0.180489\n",
" Epoch :== 318 || TIME(sec):== 3.3313 || TrainError:== 0.034862 || TestError:== 0.180411\n",
" Epoch :== 319 || TIME(sec):== 3.3089 || TrainError:== 0.034974 || TestError:== 0.18075\n",
" Epoch :== 320 || TIME(sec):== 3.3306 || TrainError:== 0.034757 || TestError:== 0.180519\n",
" Epoch :== 321 || TIME(sec):== 3.3518 || TrainError:== 0.034597 || TestError:== 0.180664\n",
" Epoch :== 322 || TIME(sec):== 3.3419 || TrainError:== 0.034605 || TestError:== 0.180211\n",
" Epoch :== 323 || TIME(sec):== 3.3162 || TrainError:== 0.034499 || TestError:== 0.180951\n",
" Epoch :== 324 || TIME(sec):== 3.3315 || TrainError:== 0.034748 || TestError:== 0.180645\n",
" Epoch :== 325 || TIME(sec):== 3.2854 || TrainError:== 0.034624 || TestError:== 0.180531\n",
" Epoch :== 326 || TIME(sec):== 3.3341 || TrainError:== 0.034385 || TestError:== 0.18033\n",
" Epoch :== 327 || TIME(sec):== 3.3342 || TrainError:== 0.034615 || TestError:== 0.180801\n",
" Epoch :== 328 || TIME(sec):== 3.3332 || TrainError:== 0.034435 || TestError:== 0.180827\n",
" Epoch :== 329 || TIME(sec):== 3.3272 || TrainError:== 0.03477 || TestError:== 0.180578\n",
" Epoch :== 330 || TIME(sec):== 3.3209 || TrainError:== 0.034588 || TestError:== 0.180945\n",
" Epoch :== 331 || TIME(sec):== 3.3057 || TrainError:== 0.035066 || TestError:== 0.180618\n",
" Epoch :== 332 || TIME(sec):== 3.3134 || TrainError:== 0.034559 || TestError:== 0.180198\n",
" Epoch :== 333 || TIME(sec):== 3.3108 || TrainError:== 0.0347 || TestError:== 0.180791\n",
" Epoch :== 334 || TIME(sec):== 3.327 || TrainError:== 0.034592 || TestError:== 0.18102\n",
" Epoch :== 335 || TIME(sec):== 3.2947 || TrainError:== 0.034291 || TestError:== 0.180842\n",
" Epoch :== 336 || TIME(sec):== 3.3251 || TrainError:== 0.034393 || TestError:== 0.180692\n",
" Epoch :== 337 || TIME(sec):== 3.3068 || TrainError:== 0.034532 || TestError:== 0.180878\n",
" Epoch :== 338 || TIME(sec):== 3.324 || TrainError:== 0.034143 || TestError:== 0.180078\n",
" Epoch :== 339 || TIME(sec):== 3.3186 || TrainError:== 0.03453 || TestError:== 0.180746\n",
" Epoch :== 340 || TIME(sec):== 3.3287 || TrainError:== 0.034313 || TestError:== 0.181234\n",
" Epoch :== 341 || TIME(sec):== 3.3111 || TrainError:== 0.034476 || TestError:== 0.180586\n",
" Epoch :== 342 || TIME(sec):== 3.3081 || TrainError:== 0.03416 || TestError:== 0.180988\n",
" Epoch :== 343 || TIME(sec):== 3.2847 || TrainError:== 0.034334 || TestError:== 0.180914\n",
" Epoch :== 344 || TIME(sec):== 3.321 || TrainError:== 0.034564 || TestError:== 0.180482\n",
" Epoch :== 345 || TIME(sec):== 3.309 || TrainError:== 0.034421 || TestError:== 0.180575\n",
" Epoch :== 346 || TIME(sec):== 2.82 || TrainError:== 0.034267 || TestError:== 0.180666\n",
" Epoch :== 347 || TIME(sec):== 3.2892 || TrainError:== 0.034449 || TestError:== 0.181288\n",
" Epoch :== 348 || TIME(sec):== 3.315 || TrainError:== 0.03448 || TestError:== 0.181171\n",
" Epoch :== 349 || TIME(sec):== 3.3115 || TrainError:== 0.034061 || TestError:== 0.181566\n",
" Epoch :== 350 || TIME(sec):== 3.2872 || TrainError:== 0.034068 || TestError:== 0.181339\n",
" Epoch :== 351 || TIME(sec):== 3.3134 || TrainError:== 0.034303 || TestError:== 0.18114\n",
" Epoch :== 352 || TIME(sec):== 3.3445 || TrainError:== 0.03404 || TestError:== 0.181259\n",
" Epoch :== 353 || TIME(sec):== 3.3411 || TrainError:== 0.034197 || TestError:== 0.181914\n",
" Epoch :== 354 || TIME(sec):== 3.4233 || TrainError:== 0.033913 || TestError:== 0.181489\n",
" Epoch :== 355 || TIME(sec):== 3.5943 || TrainError:== 0.034105 || TestError:== 0.181259\n",
" Epoch :== 356 || TIME(sec):== 3.5889 || TrainError:== 0.0339 || TestError:== 0.180897\n",
" Epoch :== 357 || TIME(sec):== 3.601 || TrainError:== 0.033751 || TestError:== 0.180974\n",
" Epoch :== 358 || TIME(sec):== 3.5991 || TrainError:== 0.033772 || TestError:== 0.181247\n",
" Epoch :== 359 || TIME(sec):== 3.5727 || TrainError:== 0.033704 || TestError:== 0.181353\n",
" Epoch :== 360 || TIME(sec):== 3.5912 || TrainError:== 0.033457 || TestError:== 0.181293\n",
" Epoch :== 361 || TIME(sec):== 3.5938 || TrainError:== 0.033653 || TestError:== 0.181132\n",
" Epoch :== 362 || TIME(sec):== 3.5776 || TrainError:== 0.033751 || TestError:== 0.181688\n",
" Epoch :== 363 || TIME(sec):== 3.6015 || TrainError:== 0.033747 || TestError:== 0.181264\n",
" Epoch :== 364 || TIME(sec):== 3.5995 || TrainError:== 0.033813 || TestError:== 0.181504\n",
" Epoch :== 365 || TIME(sec):== 3.5961 || TrainError:== 0.033693 || TestError:== 0.181185\n",
" Epoch :== 366 || TIME(sec):== 3.605 || TrainError:== 0.033586 || TestError:== 0.181104\n",
" Epoch :== 367 || TIME(sec):== 3.594 || TrainError:== 0.033635 || TestError:== 0.181206\n",
" Epoch :== 368 || TIME(sec):== 3.5848 || TrainError:== 0.034052 || TestError:== 0.181358\n",
" Epoch :== 369 || TIME(sec):== 3.5889 || TrainError:== 0.034021 || TestError:== 0.181656\n",
" Epoch :== 370 || TIME(sec):== 3.5856 || TrainError:== 0.033881 || TestError:== 0.180914\n",
" Epoch :== 371 || TIME(sec):== 3.5825 || TrainError:== 0.033704 || TestError:== 0.181163\n",
" Epoch :== 372 || TIME(sec):== 3.5862 || TrainError:== 0.033678 || TestError:== 0.18118\n",
" Epoch :== 373 || TIME(sec):== 3.6095 || TrainError:== 0.03367 || TestError:== 0.181203\n",
" Epoch :== 374 || TIME(sec):== 3.558 || TrainError:== 0.033772 || TestError:== 0.181166\n",
" Epoch :== 375 || TIME(sec):== 3.5797 || TrainError:== 0.033897 || TestError:== 0.180836\n",
" Epoch :== 376 || TIME(sec):== 3.6005 || TrainError:== 0.033811 || TestError:== 0.181541\n",
" Epoch :== 377 || TIME(sec):== 3.6008 || TrainError:== 0.033618 || TestError:== 0.181504\n",
" Epoch :== 378 || TIME(sec):== 3.6123 || TrainError:== 0.033708 || TestError:== 0.181197\n",
" Epoch :== 379 || TIME(sec):== 3.6162 || TrainError:== 0.033814 || TestError:== 0.181689\n",
" Epoch :== 380 || TIME(sec):== 3.5818 || TrainError:== 0.033296 || TestError:== 0.181382\n",
" Epoch :== 381 || TIME(sec):== 3.5757 || TrainError:== 0.033693 || TestError:== 0.181508\n",
" Epoch :== 382 || TIME(sec):== 3.5619 || TrainError:== 0.033552 || TestError:== 0.181213\n",
" Epoch :== 383 || TIME(sec):== 3.5833 || TrainError:== 0.033896 || TestError:== 0.181758\n",
" Epoch :== 384 || TIME(sec):== 3.5948 || TrainError:== 0.033545 || TestError:== 0.181609\n",
" Epoch :== 385 || TIME(sec):== 3.5955 || TrainError:== 0.033163 || TestError:== 0.181272\n",
" Epoch :== 386 || TIME(sec):== 3.5907 || TrainError:== 0.033682 || TestError:== 0.18148\n",
" Epoch :== 387 || TIME(sec):== 3.6079 || TrainError:== 0.033296 || TestError:== 0.181254\n",
" Epoch :== 388 || TIME(sec):== 3.5903 || TrainError:== 0.033581 || TestError:== 0.181366\n",
" Epoch :== 389 || TIME(sec):== 3.5893 || TrainError:== 0.033282 || TestError:== 0.181423\n",
" Epoch :== 390 || TIME(sec):== 3.5948 || TrainError:== 0.033832 || TestError:== 0.181627\n",
" Epoch :== 391 || TIME(sec):== 3.6081 || TrainError:== 0.033731 || TestError:== 0.181153\n",
" Epoch :== 392 || TIME(sec):== 3.5878 || TrainError:== 0.03303 || TestError:== 0.181555\n",
" Epoch :== 393 || TIME(sec):== 3.6065 || TrainError:== 0.033052 || TestError:== 0.181666\n",
" Epoch :== 394 || TIME(sec):== 3.5841 || TrainError:== 0.033321 || TestError:== 0.181522\n",
" Epoch :== 395 || TIME(sec):== 3.6073 || TrainError:== 0.033514 || TestError:== 0.181461\n",
" Epoch :== 396 || TIME(sec):== 3.5997 || TrainError:== 0.033462 || TestError:== 0.182142\n",
" Epoch :== 397 || TIME(sec):== 3.5838 || TrainError:== 0.033337 || TestError:== 0.181234\n",
" Epoch :== 398 || TIME(sec):== 3.5923 || TrainError:== 0.033414 || TestError:== 0.18179\n",
" Epoch :== 399 || TIME(sec):== 3.5911 || TrainError:== 0.033352 || TestError:== 0.181222\n",
" Epoch :== 400 || TIME(sec):== 3.6072 || TrainError:== 0.033519 || TestError:== 0.181227\n",
" Epoch :== 401 || TIME(sec):== 3.5666 || TrainError:== 0.030795 || TestError:== 0.180984\n",
" Epoch :== 402 || TIME(sec):== 3.5848 || TrainError:== 0.029195 || TestError:== 0.180923\n",
" Epoch :== 403 || TIME(sec):== 3.5789 || TrainError:== 0.028688 || TestError:== 0.181045\n",
" Epoch :== 404 || TIME(sec):== 3.5876 || TrainError:== 0.028783 || TestError:== 0.181267\n",
" Epoch :== 405 || TIME(sec):== 3.5922 || TrainError:== 0.028733 || TestError:== 0.181313\n",
" Epoch :== 406 || TIME(sec):== 3.5685 || TrainError:== 0.028709 || TestError:== 0.181446\n",
" Epoch :== 407 || TIME(sec):== 3.5955 || TrainError:== 0.028594 || TestError:== 0.180979\n",
" Epoch :== 408 || TIME(sec):== 3.5825 || TrainError:== 0.028321 || TestError:== 0.181225\n",
" Epoch :== 409 || TIME(sec):== 3.5813 || TrainError:== 0.02827 || TestError:== 0.181498\n",
" Epoch :== 410 || TIME(sec):== 3.6028 || TrainError:== 0.028508 || TestError:== 0.181575\n",
" Epoch :== 411 || TIME(sec):== 3.5848 || TrainError:== 0.028299 || TestError:== 0.181402\n",
" Epoch :== 412 || TIME(sec):== 3.5773 || TrainError:== 0.028308 || TestError:== 0.181741\n",
" Epoch :== 413 || TIME(sec):== 3.5933 || TrainError:== 0.028502 || TestError:== 0.181514\n",
" Epoch :== 414 || TIME(sec):== 3.5954 || TrainError:== 0.028063 || TestError:== 0.181512\n",
" Epoch :== 415 || TIME(sec):== 3.587 || TrainError:== 0.028088 || TestError:== 0.181803\n",
" Epoch :== 416 || TIME(sec):== 3.5987 || TrainError:== 0.028114 || TestError:== 0.181984\n",
" Epoch :== 417 || TIME(sec):== 3.5804 || TrainError:== 0.027983 || TestError:== 0.182007\n",
" Epoch :== 418 || TIME(sec):== 3.5759 || TrainError:== 0.028275 || TestError:== 0.181888\n",
" Epoch :== 419 || TIME(sec):== 3.5917 || TrainError:== 0.028063 || TestError:== 0.181803\n",
" Epoch :== 420 || TIME(sec):== 3.5916 || TrainError:== 0.028208 || TestError:== 0.181754\n",
" Epoch :== 421 || TIME(sec):== 3.568 || TrainError:== 0.02824 || TestError:== 0.182209\n",
" Epoch :== 422 || TIME(sec):== 3.6165 || TrainError:== 0.028197 || TestError:== 0.181536\n",
" Epoch :== 423 || TIME(sec):== 3.5933 || TrainError:== 0.027851 || TestError:== 0.181963\n",
" Epoch :== 424 || TIME(sec):== 3.5944 || TrainError:== 0.027804 || TestError:== 0.182175\n",
" Epoch :== 425 || TIME(sec):== 3.5935 || TrainError:== 0.027993 || TestError:== 0.18182\n",
" Epoch :== 426 || TIME(sec):== 3.6005 || TrainError:== 0.027805 || TestError:== 0.181893\n",
" Epoch :== 427 || TIME(sec):== 3.6212 || TrainError:== 0.02785 || TestError:== 0.182119\n",
" Epoch :== 428 || TIME(sec):== 3.5651 || TrainError:== 0.027806 || TestError:== 0.181967\n",
" Epoch :== 429 || TIME(sec):== 3.5895 || TrainError:== 0.028015 || TestError:== 0.181954\n",
" Epoch :== 430 || TIME(sec):== 3.5916 || TrainError:== 0.027857 || TestError:== 0.182232\n",
" Epoch :== 431 || TIME(sec):== 3.5795 || TrainError:== 0.027942 || TestError:== 0.182053\n",
" Epoch :== 432 || TIME(sec):== 3.5784 || TrainError:== 0.027593 || TestError:== 0.182026\n",
" Epoch :== 433 || TIME(sec):== 3.5987 || TrainError:== 0.027694 || TestError:== 0.18186\n",
" Epoch :== 434 || TIME(sec):== 3.595 || TrainError:== 0.027505 || TestError:== 0.182077\n",
" Epoch :== 435 || TIME(sec):== 3.5963 || TrainError:== 0.027828 || TestError:== 0.182031\n",
" Epoch :== 436 || TIME(sec):== 3.6024 || TrainError:== 0.027468 || TestError:== 0.18231\n",
" Epoch :== 437 || TIME(sec):== 3.5661 || TrainError:== 0.02797 || TestError:== 0.18223\n",
" Epoch :== 438 || TIME(sec):== 3.5837 || TrainError:== 0.027834 || TestError:== 0.182295\n",
" Epoch :== 439 || TIME(sec):== 3.6089 || TrainError:== 0.027659 || TestError:== 0.182128\n",
" Epoch :== 440 || TIME(sec):== 3.5792 || TrainError:== 0.027703 || TestError:== 0.18229\n",
" Epoch :== 441 || TIME(sec):== 3.5813 || TrainError:== 0.027643 || TestError:== 0.182508\n",
" Epoch :== 442 || TIME(sec):== 3.5879 || TrainError:== 0.027599 || TestError:== 0.182202\n",
" Epoch :== 443 || TIME(sec):== 3.6005 || TrainError:== 0.027435 || TestError:== 0.18225\n",
" Epoch :== 444 || TIME(sec):== 3.6012 || TrainError:== 0.02758 || TestError:== 0.182365\n",
" Epoch :== 445 || TIME(sec):== 3.6022 || TrainError:== 0.027774 || TestError:== 0.18199\n",
" Epoch :== 446 || TIME(sec):== 3.5831 || TrainError:== 0.027804 || TestError:== 0.18249\n",
" Epoch :== 447 || TIME(sec):== 3.5839 || TrainError:== 0.027557 || TestError:== 0.182263\n",
" Epoch :== 448 || TIME(sec):== 3.5729 || TrainError:== 0.027616 || TestError:== 0.182307\n",
" Epoch :== 449 || TIME(sec):== 3.5598 || TrainError:== 0.027679 || TestError:== 0.182477\n",
" Epoch :== 450 || TIME(sec):== 3.5986 || TrainError:== 0.027508 || TestError:== 0.182109\n",
" Epoch :== 451 || TIME(sec):== 3.6005 || TrainError:== 0.027402 || TestError:== 0.182525\n",
" Epoch :== 452 || TIME(sec):== 3.5808 || TrainError:== 0.027768 || TestError:== 0.182072\n",
" Epoch :== 453 || TIME(sec):== 3.6115 || TrainError:== 0.027624 || TestError:== 0.182313\n",
" Epoch :== 454 || TIME(sec):== 3.5959 || TrainError:== 0.027258 || TestError:== 0.182457\n",
" Epoch :== 455 || TIME(sec):== 3.5863 || TrainError:== 0.027598 || TestError:== 0.182565\n",
" Epoch :== 456 || TIME(sec):== 3.5772 || TrainError:== 0.027708 || TestError:== 0.182243\n",
" Epoch :== 457 || TIME(sec):== 3.59 || TrainError:== 0.027577 || TestError:== 0.18196\n",
" Epoch :== 458 || TIME(sec):== 3.5917 || TrainError:== 0.027398 || TestError:== 0.182289\n",
" Epoch :== 459 || TIME(sec):== 3.5889 || TrainError:== 0.027488 || TestError:== 0.182524\n",
" Epoch :== 460 || TIME(sec):== 3.5912 || TrainError:== 0.027311 || TestError:== 0.182214\n",
" Epoch :== 461 || TIME(sec):== 3.6119 || TrainError:== 0.027496 || TestError:== 0.182582\n",
" Epoch :== 462 || TIME(sec):== 3.585 || TrainError:== 0.027281 || TestError:== 0.182576\n",
" Epoch :== 463 || TIME(sec):== 3.5901 || TrainError:== 0.027454 || TestError:== 0.182395\n",
" Epoch :== 464 || TIME(sec):== 3.5866 || TrainError:== 0.027177 || TestError:== 0.182535\n",
" Epoch :== 465 || TIME(sec):== 3.5659 || TrainError:== 0.027249 || TestError:== 0.182681\n",
" Epoch :== 466 || TIME(sec):== 3.5832 || TrainError:== 0.027304 || TestError:== 0.182504\n",
" Epoch :== 467 || TIME(sec):== 3.5969 || TrainError:== 0.027322 || TestError:== 0.182841\n",
" Epoch :== 468 || TIME(sec):== 3.5729 || TrainError:== 0.027494 || TestError:== 0.182395\n",
" Epoch :== 469 || TIME(sec):== 3.5822 || TrainError:== 0.027182 || TestError:== 0.182416\n",
" Epoch :== 470 || TIME(sec):== 3.5735 || TrainError:== 0.027066 || TestError:== 0.182548\n",
" Epoch :== 471 || TIME(sec):== 3.5862 || TrainError:== 0.027277 || TestError:== 0.182673\n",
" Epoch :== 472 || TIME(sec):== 3.5894 || TrainError:== 0.027447 || TestError:== 0.182248\n",
" Epoch :== 473 || TIME(sec):== 3.5823 || TrainError:== 0.027121 || TestError:== 0.182624\n",
" Epoch :== 474 || TIME(sec):== 3.5978 || TrainError:== 0.027278 || TestError:== 0.182586\n",
" Epoch :== 475 || TIME(sec):== 3.5644 || TrainError:== 0.027211 || TestError:== 0.18248\n",
" Epoch :== 476 || TIME(sec):== 3.5741 || TrainError:== 0.02716 || TestError:== 0.182717\n",
" Epoch :== 477 || TIME(sec):== 3.6099 || TrainError:== 0.027226 || TestError:== 0.182494\n",
" Epoch :== 478 || TIME(sec):== 3.9181 || TrainError:== 0.02709 || TestError:== 0.182678\n",
" Epoch :== 479 || TIME(sec):== 3.6083 || TrainError:== 0.027174 || TestError:== 0.182747\n",
" Epoch :== 480 || TIME(sec):== 3.5762 || TrainError:== 0.02715 || TestError:== 0.182504\n",
" Epoch :== 481 || TIME(sec):== 3.5783 || TrainError:== 0.026941 || TestError:== 0.182362\n",
" Epoch :== 482 || TIME(sec):== 3.5962 || TrainError:== 0.027316 || TestError:== 0.182597\n",
" Epoch :== 483 || TIME(sec):== 3.5923 || TrainError:== 0.026972 || TestError:== 0.182554\n",
" Epoch :== 484 || TIME(sec):== 3.5914 || TrainError:== 0.027014 || TestError:== 0.18268\n",
" Epoch :== 485 || TIME(sec):== 3.5895 || TrainError:== 0.027233 || TestError:== 0.182599\n",
" Epoch :== 486 || TIME(sec):== 3.6084 || TrainError:== 0.027036 || TestError:== 0.182734\n",
" Epoch :== 487 || TIME(sec):== 3.6501 || TrainError:== 0.027137 || TestError:== 0.182722\n",
" Epoch :== 488 || TIME(sec):== 3.6662 || TrainError:== 0.027367 || TestError:== 0.182885\n",
" Epoch :== 489 || TIME(sec):== 3.5023 || TrainError:== 0.027411 || TestError:== 0.182774\n",
" Epoch :== 490 || TIME(sec):== 3.5606 || TrainError:== 0.027044 || TestError:== 0.182462\n",
" Epoch :== 491 || TIME(sec):== 3.5851 || TrainError:== 0.027102 || TestError:== 0.182901\n",
" Epoch :== 492 || TIME(sec):== 3.5807 || TrainError:== 0.026859 || TestError:== 0.183167\n",
" Epoch :== 493 || TIME(sec):== 3.581 || TrainError:== 0.026988 || TestError:== 0.183127\n",
" Epoch :== 494 || TIME(sec):== 3.5853 || TrainError:== 0.027033 || TestError:== 0.182831\n",
" Epoch :== 495 || TIME(sec):== 3.6046 || TrainError:== 0.026958 || TestError:== 0.183032\n",
" Epoch :== 496 || TIME(sec):== 3.5821 || TrainError:== 0.027009 || TestError:== 0.182641\n",
" Epoch :== 497 || TIME(sec):== 3.6025 || TrainError:== 0.02708 || TestError:== 0.182798\n",
" Epoch :== 498 || TIME(sec):== 3.5865 || TrainError:== 0.027168 || TestError:== 0.183123\n",
" Epoch :== 499 || TIME(sec):== 3.5778 || TrainError:== 0.02674 || TestError:== 0.182787\n",
" Epoch :== 500 || TIME(sec):== 3.5922 || TrainError:== 0.026763 || TestError:== 0.183031\n"
]
}
],
"source": [
"\n",
"trainerror=[] \n",
"testerror=[]\n",
"\n",
"for ep in range(epochs):\n",
" model.train()\n",
" t1 = default_timer()\n",
" train_l2 = 0\n",
" for x, y in train_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" x =x.reshape(x.shape[0], 1, dim, dim)\n",
"\n",
" optimizer.zero_grad()\n",
"\n",
" out = model(x).reshape(batch_size, dim, dim,strain_channels)\n",
"\n",
" out = y_normalizer.decode(out)\n",
" y = y_normalizer.decode(y)\n",
" \n",
" \n",
"\n",
" loss = lossfunc(y.view(batch_size,-1),out.view(batch_size,-1))\n",
" \n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
" train_l2 += loss.item()\n",
"\n",
" scheduler.step()\n",
" model.eval()\n",
" test_l2 = 0.0\n",
" with torch.no_grad():\n",
" for x, y in test_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" x =x.reshape(x.shape[0], 1, dim, dim)\n",
" out = model(x).reshape(batch_size, dim, dim,strain_channels)\n",
" out = y_normalizer.decode(out)\n",
"\n",
" test_l2 += lossfunc(y.view(batch_size,-1),out.view(batch_size,-1)).item()\n",
"\n",
" train_l2/= ntrain\n",
" test_l2 /= ntest\n",
" trainerror += [train_l2]\n",
" testerror += [test_l2]\n",
" t2 = default_timer()\n",
" print(f' Epoch :== {ep+1} || TIME(sec):== {np.round((t2-t1),4)} '\n",
" f'|| TrainError:== {np.round(train_l2,6)} || TestError:== {np.round(test_l2,6)}')"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "71b261a7-4792-4b27-9f17-817cdd6ffef3",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 700x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(7,6))\n",
"plt.plot(range(epochs),trainerror,linewidth = 4,label =\"TRAIN\")\n",
"plt.plot(range(epochs),testerror, linewidth = 4,label =\"TEST\")\n",
"plt.xlabel('Epoch', fontsize =20)\n",
"plt.ylabel('Loss', fontsize =20)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "f7065053-a4cb-482e-b29c-f30372f0f116",
"metadata": {},
"outputs": [],
"source": [
"# trainn = np.array(trainerror)\n",
"# testnn = np.array(testerror)\n",
"# torch.save(model.state_dict(),'ResNet_STAIN_N1200_ep1200.pt')\n",
"# np.save('trainResNet_STRAIN', trainn)\n",
"# np.save('testResNet_STRAIN', testnn)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e6047d8f-fdd0-408b-9f27-02a92c3ca681",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 33,
"id": "d2b37bdd-5895-41b4-94ef-f3b40eab5814",
"metadata": {},
"outputs": [],
"source": [
"# path = 'ResNet_STRAIN_N1200_ep1200.pt'\n",
"# model.load_state_dict(torch.load(path))\n",
"# print(model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b574e1d-725a-4882-9d5d-7dab628830b8",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "1dfcff0f-8f62-4cec-af4f-9d44142cce58",
"metadata": {},
"source": [
"#### Testing-STRAINS"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "e5742cc9-83e2-4759-9a96-45ad3f0b440e",
"metadata": {},
"outputs": [],
"source": [
"prediction = torch.zeros(y_test_strain.shape)\n",
"c = 0\n",
"with torch.no_grad():\n",
" for x,y in test_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" x =x.reshape(x.shape[0], 1, dim, dim)\n",
" out = model(x).reshape(batch_size, dim, dim,3)\n",
" out = y_normalizer.decode(out)\n",
" prediction[c*batch_size :c*batch_size+batch_size] = out\n",
" c+=1\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "20743723-9d05-4647-ac75-eb7ee1648bd5",
"metadata": {},
"outputs": [],
"source": [
"strain_act = y_test_strain.reshape(ntest, -1)\n",
"strain_pred = prediction.reshape(ntest,-1)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "e124bca2-2807-4c2d-9b09-185a4485d9cf",
"metadata": {},
"outputs": [],
"source": [
"r2_strain =[]\n",
"for i in range(strain_act.shape[0]):\n",
" act = strain_act[i]\n",
" pred = strain_pred[i]\n",
"\n",
" r2 = r2_score(act,pred)\n",
"\n",
" r2_strain += [r2]"
]
},
{
"cell_type": "markdown",
"id": "c0d8a945-94d2-4b74-a9e7-9459e954fa74",
"metadata": {},
"source": [
"### METRIC Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "8588fafd-cbca-431e-b289-e65895b4ff62",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9590832453346982 0.03296087252346871\n"
]
}
],
"source": [
"r2_avg_strain = np.average(r2_strain)\n",
"r2_std_strain = np.std(r2_strain)\n",
"\n",
"print(r2_avg_strain,r2_std_strain)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "c81ebf91-5bf3-4559-9848-8456f9af3441",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.96\n",
"0.03\n"
]
}
],
"source": [
"## R2 mean and std\n",
"print(np.round((r2_avg_strain),2))\n",
"print(np.round((r2_std_strain),2))"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "5f296730-e4ba-464a-b742-7a90b385145d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(0.1830)\n"
]
}
],
"source": [
"## total test loss\n",
"\n",
"loss = lossfunc(y_test_strain, prediction)\n",
"print(loss/ntest)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "45ad10b4-4c2a-45ec-8ade-17802e15966b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "e37eab46-0522-4c2a-bf17-bf5e6721e3f6",
"metadata": {},
"source": [
"## STRESS"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "eba7e291-1575-465f-977a-9df0067c5965",
"metadata": {},
"outputs": [],
"source": [
"######################################################\n",
"### Data Loading & Hyperparameters\n",
"######################################################\n",
"\n",
"\n",
"X_Data = '/DATA1/meer/data/material-v3-3350.mat'\n",
"Y_Data = '/DATA1/meer/data/stress-v3-3350.mat'\n",
"\n",
"\n",
"ntrain =1200\n",
"ntest = 200\n",
"batch_size =20\n",
"\n",
"\n",
"################################################################\n",
"# load data and data normalization\n",
"################################################################\n",
"\n",
"reader = MatReader(X_Data) \n",
"x_train = reader.read_field('Emat')[:ntrain]\n",
"x_test = reader.read_field('Emat')[-ntest:]\n",
"x_test_plot = reader.read_field('Emat')[-ntest:]\n",
"\n",
"reader.load_file(Y_Data) \n",
"y_train = reader.read_field('stress')[:ntrain]# \n",
"y_test_stress = reader.read_field('stress')[-ntest:] \n",
"\n",
"######################################################\n",
"### Normalization\n",
"######################################################\n",
"\n",
"x_normalizer = UnitGaussianNormalizer(x_train)\n",
"x_train = x_normalizer.encode(x_train)\n",
"x_test = x_normalizer.encode(x_test)\n",
"\n",
"y_normalizer = UnitGaussianNormalizer(y_train)\n",
"y_train = y_normalizer.encode(y_train)\n",
"\n",
"\n",
"train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_train, y_train), batch_size=batch_size, shuffle=True)\n",
"test_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_test, y_test_stress), batch_size=batch_size, shuffle=False)\n",
"\n",
"\n",
"\n",
"if device=='cpu':\n",
" y_normalizer.cpu()\n",
"else:\n",
" y_normalizer.cuda()\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0b561316-23c1-404e-871d-63023b61351b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" x_train Shape := torch.Size([1200, 48, 48])\n",
" y_train Shape := torch.Size([1200, 48, 48, 3])\n",
" x_test Shape := torch.Size([200, 48, 48])\n",
" y_test_stress Shape := torch.Size([200, 48, 48, 3])\n"
]
}
],
"source": [
"print(f' x_train Shape := {x_train.shape}')\n",
"print(f' y_train Shape := {y_train.shape}')\n",
"print(f' x_test Shape := {x_test.shape}')\n",
"print(f' y_test_stress Shape := {y_test_stress.shape}')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "bbef06c0-be3d-49aa-a41f-38f5407866f5",
"metadata": {},
"outputs": [],
"source": [
"def ResNet50(num_classes, channels=3):\n",
" return ResNet(Bottleneck, [3,4,6,3], num_classes, channels)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "560d108a-ba93-4ac6-8702-1ae4ea727f68",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MODEL PARAMETERS. :== 37690944\n",
"\n",
"\n",
"Epochs:= 500--- LR:=0.001---BatchSize:= 20\n"
]
}
],
"source": [
"# ################################################################\n",
"## Model\n",
"# ################################################################\n",
"\n",
"model = ResNet50(48*48*3, 1).to(device)\n",
"print(f'MODEL PARAMETERS. :== {count_params(model)}')\n",
"learning_rate = 1e-3\n",
"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay= 1e-4)\n",
"scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.5) ### gamma =0.1\n",
"epochs = 500\n",
"print('\\n')\n",
" \n",
"print(f'Epochs:= {epochs}--- LR:={learning_rate}---BatchSize:= {batch_size}')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "bf96d045-c904-429f-878f-9b4348e474dc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Epoch :== 1 || TIME(sec):== 6.3711 || TrainError:== 0.563467 || TestError:== 0.531812\n",
" Epoch :== 2 || TIME(sec):== 3.1854 || TrainError:== 0.526763 || TestError:== 0.526905\n",
" Epoch :== 3 || TIME(sec):== 3.1606 || TrainError:== 0.522204 || TestError:== 0.527631\n",
" Epoch :== 4 || TIME(sec):== 3.1532 || TrainError:== 0.517538 || TestError:== 0.517654\n",
" Epoch :== 5 || TIME(sec):== 3.1433 || TrainError:== 0.507306 || TestError:== 0.499562\n",
" Epoch :== 6 || TIME(sec):== 3.1301 || TrainError:== 0.466186 || TestError:== 0.439761\n",
" Epoch :== 7 || TIME(sec):== 3.1752 || TrainError:== 0.428133 || TestError:== 0.477868\n",
" Epoch :== 8 || TIME(sec):== 3.173 || TrainError:== 0.382711 || TestError:== 0.432242\n",
" Epoch :== 9 || TIME(sec):== 3.1462 || TrainError:== 0.356736 || TestError:== 0.360367\n",
" Epoch :== 10 || TIME(sec):== 3.1365 || TrainError:== 0.328501 || TestError:== 0.322819\n",
" Epoch :== 11 || TIME(sec):== 3.2037 || TrainError:== 0.310847 || TestError:== 0.303705\n",
" Epoch :== 12 || TIME(sec):== 3.1824 || TrainError:== 0.30411 || TestError:== 0.63692\n",
" Epoch :== 13 || TIME(sec):== 3.4723 || TrainError:== 0.289023 || TestError:== 0.280737\n",
" Epoch :== 14 || TIME(sec):== 3.6103 || TrainError:== 0.26656 || TestError:== 0.309839\n",
" Epoch :== 15 || TIME(sec):== 3.5785 || TrainError:== 0.250864 || TestError:== 0.267787\n",
" Epoch :== 16 || TIME(sec):== 3.5295 || TrainError:== 0.238197 || TestError:== 0.256986\n",
" Epoch :== 17 || TIME(sec):== 3.346 || TrainError:== 0.228138 || TestError:== 0.265478\n",
" Epoch :== 18 || TIME(sec):== 3.3373 || TrainError:== 0.221835 || TestError:== 0.236413\n",
" Epoch :== 19 || TIME(sec):== 3.3372 || TrainError:== 0.203149 || TestError:== 0.233247\n",
" Epoch :== 20 || TIME(sec):== 3.3276 || TrainError:== 0.191841 || TestError:== 0.224557\n",
" Epoch :== 21 || TIME(sec):== 3.327 || TrainError:== 0.184932 || TestError:== 0.214279\n",
" Epoch :== 22 || TIME(sec):== 3.3507 || TrainError:== 0.176046 || TestError:== 0.209752\n",
" Epoch :== 23 || TIME(sec):== 3.3354 || TrainError:== 0.173459 || TestError:== 0.212412\n",
" Epoch :== 24 || TIME(sec):== 3.2971 || TrainError:== 0.17038 || TestError:== 0.203567\n",
" Epoch :== 25 || TIME(sec):== 3.3365 || TrainError:== 0.16267 || TestError:== 0.20101\n",
" Epoch :== 26 || TIME(sec):== 3.0915 || TrainError:== 0.156282 || TestError:== 0.198413\n",
" Epoch :== 27 || TIME(sec):== 3.479 || TrainError:== 0.154469 || TestError:== 0.196232\n",
" Epoch :== 28 || TIME(sec):== 3.7257 || TrainError:== 0.152548 || TestError:== 0.194459\n",
" Epoch :== 29 || TIME(sec):== 4.1665 || TrainError:== 0.14391 || TestError:== 0.186432\n",
" Epoch :== 30 || TIME(sec):== 4.1557 || TrainError:== 0.144041 || TestError:== 0.189381\n",
" Epoch :== 31 || TIME(sec):== 4.1512 || TrainError:== 0.13867 || TestError:== 0.186944\n",
" Epoch :== 32 || TIME(sec):== 4.1592 || TrainError:== 0.139335 || TestError:== 0.18438\n",
" Epoch :== 33 || TIME(sec):== 4.1497 || TrainError:== 0.138188 || TestError:== 0.181989\n",
" Epoch :== 34 || TIME(sec):== 4.155 || TrainError:== 0.135978 || TestError:== 0.182893\n",
" Epoch :== 35 || TIME(sec):== 4.1776 || TrainError:== 0.132957 || TestError:== 0.223208\n",
" Epoch :== 36 || TIME(sec):== 4.1361 || TrainError:== 0.140381 || TestError:== 0.1833\n",
" Epoch :== 37 || TIME(sec):== 4.1711 || TrainError:== 0.134038 || TestError:== 0.185242\n",
" Epoch :== 38 || TIME(sec):== 4.1463 || TrainError:== 0.13206 || TestError:== 0.175712\n",
" Epoch :== 39 || TIME(sec):== 4.1442 || TrainError:== 0.124581 || TestError:== 0.177886\n",
" Epoch :== 40 || TIME(sec):== 4.1559 || TrainError:== 0.125925 || TestError:== 0.177458\n",
" Epoch :== 41 || TIME(sec):== 4.1573 || TrainError:== 0.122945 || TestError:== 0.17627\n",
" Epoch :== 42 || TIME(sec):== 4.1666 || TrainError:== 0.121566 || TestError:== 0.171837\n",
" Epoch :== 43 || TIME(sec):== 4.127 || TrainError:== 0.122211 || TestError:== 0.179693\n",
" Epoch :== 44 || TIME(sec):== 4.0985 || TrainError:== 0.11928 || TestError:== 0.177279\n",
" Epoch :== 45 || TIME(sec):== 4.1732 || TrainError:== 0.120412 || TestError:== 0.173714\n",
" Epoch :== 46 || TIME(sec):== 3.6713 || TrainError:== 0.121136 || TestError:== 0.175353\n",
" Epoch :== 47 || TIME(sec):== 3.7231 || TrainError:== 0.12196 || TestError:== 0.179364\n",
" Epoch :== 48 || TIME(sec):== 3.7207 || TrainError:== 0.12613 || TestError:== 0.179659\n",
" Epoch :== 49 || TIME(sec):== 3.7194 || TrainError:== 0.121361 || TestError:== 0.173774\n",
" Epoch :== 50 || TIME(sec):== 2.9192 || TrainError:== 0.115818 || TestError:== 0.171871\n",
" Epoch :== 51 || TIME(sec):== 3.0065 || TrainError:== 0.114407 || TestError:== 0.176047\n",
" Epoch :== 52 || TIME(sec):== 3.0137 || TrainError:== 0.113115 || TestError:== 0.169084\n",
" Epoch :== 53 || TIME(sec):== 3.2801 || TrainError:== 0.111252 || TestError:== 0.172522\n",
" Epoch :== 54 || TIME(sec):== 3.5813 || TrainError:== 0.111269 || TestError:== 0.170582\n",
" Epoch :== 55 || TIME(sec):== 3.5987 || TrainError:== 0.109912 || TestError:== 0.167529\n",
" Epoch :== 56 || TIME(sec):== 3.5812 || TrainError:== 0.107824 || TestError:== 0.182088\n",
" Epoch :== 57 || TIME(sec):== 3.5787 || TrainError:== 0.111715 || TestError:== 0.168836\n",
" Epoch :== 58 || TIME(sec):== 3.5775 || TrainError:== 0.10934 || TestError:== 0.171691\n",
" Epoch :== 59 || TIME(sec):== 3.5878 || TrainError:== 0.108574 || TestError:== 0.168998\n",
" Epoch :== 60 || TIME(sec):== 3.56 || TrainError:== 0.107879 || TestError:== 0.169447\n",
" Epoch :== 61 || TIME(sec):== 3.5826 || TrainError:== 0.107522 || TestError:== 0.168592\n",
" Epoch :== 62 || TIME(sec):== 3.5959 || TrainError:== 0.111469 || TestError:== 0.174164\n",
" Epoch :== 63 || TIME(sec):== 3.595 || TrainError:== 0.113315 || TestError:== 0.169125\n",
" Epoch :== 64 || TIME(sec):== 3.5899 || TrainError:== 0.109323 || TestError:== 0.167561\n",
" Epoch :== 65 || TIME(sec):== 3.5897 || TrainError:== 0.104575 || TestError:== 0.169417\n",
" Epoch :== 66 || TIME(sec):== 3.5866 || TrainError:== 0.105358 || TestError:== 0.170042\n",
" Epoch :== 67 || TIME(sec):== 3.598 || TrainError:== 0.102443 || TestError:== 0.16555\n",
" Epoch :== 68 || TIME(sec):== 3.5766 || TrainError:== 0.102776 || TestError:== 0.165295\n",
" Epoch :== 69 || TIME(sec):== 3.6165 || TrainError:== 0.101011 || TestError:== 0.164506\n",
" Epoch :== 70 || TIME(sec):== 3.5858 || TrainError:== 0.099421 || TestError:== 0.163605\n",
" Epoch :== 71 || TIME(sec):== 3.596 || TrainError:== 0.10201 || TestError:== 0.166734\n",
" Epoch :== 72 || TIME(sec):== 3.59 || TrainError:== 0.102848 || TestError:== 0.166211\n",
" Epoch :== 73 || TIME(sec):== 3.5769 || TrainError:== 0.105346 || TestError:== 0.172074\n",
" Epoch :== 74 || TIME(sec):== 3.5808 || TrainError:== 0.10477 || TestError:== 0.172063\n",
" Epoch :== 75 || TIME(sec):== 3.5685 || TrainError:== 0.109351 || TestError:== 0.168828\n",
" Epoch :== 76 || TIME(sec):== 3.5835 || TrainError:== 0.105741 || TestError:== 0.166759\n",
" Epoch :== 77 || TIME(sec):== 3.6171 || TrainError:== 0.104673 || TestError:== 0.167984\n",
" Epoch :== 78 || TIME(sec):== 3.577 || TrainError:== 0.102362 || TestError:== 0.169834\n",
" Epoch :== 79 || TIME(sec):== 3.5878 || TrainError:== 0.102513 || TestError:== 0.168246\n",
" Epoch :== 80 || TIME(sec):== 3.5735 || TrainError:== 0.100428 || TestError:== 0.166586\n",
" Epoch :== 81 || TIME(sec):== 3.5893 || TrainError:== 0.099272 || TestError:== 0.169183\n",
" Epoch :== 82 || TIME(sec):== 3.5937 || TrainError:== 0.096893 || TestError:== 0.166263\n",
" Epoch :== 83 || TIME(sec):== 3.6048 || TrainError:== 0.096926 || TestError:== 0.168056\n",
" Epoch :== 84 || TIME(sec):== 3.5989 || TrainError:== 0.09493 || TestError:== 0.164387\n",
" Epoch :== 85 || TIME(sec):== 3.5762 || TrainError:== 0.095257 || TestError:== 0.160089\n",
" Epoch :== 86 || TIME(sec):== 3.5783 || TrainError:== 0.095577 || TestError:== 0.169588\n",
" Epoch :== 87 || TIME(sec):== 3.5744 || TrainError:== 0.096177 || TestError:== 0.161273\n",
" Epoch :== 88 || TIME(sec):== 3.6301 || TrainError:== 0.094902 || TestError:== 0.160383\n",
" Epoch :== 89 || TIME(sec):== 3.5912 || TrainError:== 0.093365 || TestError:== 0.161095\n",
" Epoch :== 90 || TIME(sec):== 3.6238 || TrainError:== 0.093318 || TestError:== 0.162228\n",
" Epoch :== 91 || TIME(sec):== 3.1884 || TrainError:== 0.094321 || TestError:== 0.161878\n",
" Epoch :== 92 || TIME(sec):== 3.2479 || TrainError:== 0.094864 || TestError:== 0.162322\n",
" Epoch :== 93 || TIME(sec):== 3.3439 || TrainError:== 0.093287 || TestError:== 0.162394\n",
" Epoch :== 94 || TIME(sec):== 3.5709 || TrainError:== 0.093307 || TestError:== 0.159852\n",
" Epoch :== 95 || TIME(sec):== 3.571 || TrainError:== 0.095176 || TestError:== 0.166428\n",
" Epoch :== 96 || TIME(sec):== 3.5824 || TrainError:== 0.101087 || TestError:== 0.174015\n",
" Epoch :== 97 || TIME(sec):== 3.565 || TrainError:== 0.105023 || TestError:== 0.166746\n",
" Epoch :== 98 || TIME(sec):== 3.5546 || TrainError:== 0.100052 || TestError:== 0.162814\n",
" Epoch :== 99 || TIME(sec):== 3.5783 || TrainError:== 0.09911 || TestError:== 0.164363\n",
" Epoch :== 100 || TIME(sec):== 3.5704 || TrainError:== 0.094551 || TestError:== 0.160741\n",
" Epoch :== 101 || TIME(sec):== 3.5794 || TrainError:== 0.080728 || TestError:== 0.149637\n",
" Epoch :== 102 || TIME(sec):== 3.5789 || TrainError:== 0.072024 || TestError:== 0.150382\n",
" Epoch :== 103 || TIME(sec):== 3.5774 || TrainError:== 0.068726 || TestError:== 0.149563\n",
" Epoch :== 104 || TIME(sec):== 3.5579 || TrainError:== 0.067872 || TestError:== 0.151297\n",
" Epoch :== 105 || TIME(sec):== 3.5657 || TrainError:== 0.067492 || TestError:== 0.148583\n",
" Epoch :== 106 || TIME(sec):== 3.5438 || TrainError:== 0.068948 || TestError:== 0.150232\n",
" Epoch :== 107 || TIME(sec):== 3.5637 || TrainError:== 0.067574 || TestError:== 0.150753\n",
" Epoch :== 108 || TIME(sec):== 3.5455 || TrainError:== 0.066161 || TestError:== 0.154857\n",
" Epoch :== 109 || TIME(sec):== 3.5771 || TrainError:== 0.067521 || TestError:== 0.149591\n",
" Epoch :== 110 || TIME(sec):== 3.6197 || TrainError:== 0.066943 || TestError:== 0.150109\n",
" Epoch :== 111 || TIME(sec):== 3.7571 || TrainError:== 0.066543 || TestError:== 0.153209\n",
" Epoch :== 112 || TIME(sec):== 3.2802 || TrainError:== 0.06644 || TestError:== 0.151195\n",
" Epoch :== 113 || TIME(sec):== 3.2295 || TrainError:== 0.06602 || TestError:== 0.149751\n",
" Epoch :== 114 || TIME(sec):== 3.15 || TrainError:== 0.065163 || TestError:== 0.150603\n",
" Epoch :== 115 || TIME(sec):== 3.2489 || TrainError:== 0.065564 || TestError:== 0.149069\n",
" Epoch :== 116 || TIME(sec):== 3.1655 || TrainError:== 0.066009 || TestError:== 0.151054\n",
" Epoch :== 117 || TIME(sec):== 3.1977 || TrainError:== 0.064961 || TestError:== 0.151376\n",
" Epoch :== 118 || TIME(sec):== 3.7605 || TrainError:== 0.065625 || TestError:== 0.148992\n",
" Epoch :== 119 || TIME(sec):== 3.8512 || TrainError:== 0.06549 || TestError:== 0.151571\n",
" Epoch :== 120 || TIME(sec):== 3.642 || TrainError:== 0.065245 || TestError:== 0.153441\n",
" Epoch :== 121 || TIME(sec):== 3.172 || TrainError:== 0.064895 || TestError:== 0.148755\n",
" Epoch :== 122 || TIME(sec):== 3.126 || TrainError:== 0.065258 || TestError:== 0.150316\n",
" Epoch :== 123 || TIME(sec):== 3.112 || TrainError:== 0.064085 || TestError:== 0.149507\n",
" Epoch :== 124 || TIME(sec):== 3.1682 || TrainError:== 0.064001 || TestError:== 0.149146\n",
" Epoch :== 125 || TIME(sec):== 3.1805 || TrainError:== 0.064521 || TestError:== 0.149988\n",
" Epoch :== 126 || TIME(sec):== 3.1504 || TrainError:== 0.064353 || TestError:== 0.150448\n",
" Epoch :== 127 || TIME(sec):== 3.1117 || TrainError:== 0.064745 || TestError:== 0.150335\n",
" Epoch :== 128 || TIME(sec):== 3.1333 || TrainError:== 0.063928 || TestError:== 0.151021\n",
" Epoch :== 129 || TIME(sec):== 3.1127 || TrainError:== 0.064085 || TestError:== 0.150948\n",
" Epoch :== 130 || TIME(sec):== 3.1347 || TrainError:== 0.064307 || TestError:== 0.154524\n",
" Epoch :== 131 || TIME(sec):== 3.1409 || TrainError:== 0.064082 || TestError:== 0.152391\n",
" Epoch :== 132 || TIME(sec):== 3.1742 || TrainError:== 0.06499 || TestError:== 0.149779\n",
" Epoch :== 133 || TIME(sec):== 3.1574 || TrainError:== 0.064678 || TestError:== 0.150773\n",
" Epoch :== 134 || TIME(sec):== 3.1301 || TrainError:== 0.064376 || TestError:== 0.152762\n",
" Epoch :== 135 || TIME(sec):== 3.1403 || TrainError:== 0.063855 || TestError:== 0.152594\n",
" Epoch :== 136 || TIME(sec):== 3.1361 || TrainError:== 0.064665 || TestError:== 0.149911\n",
" Epoch :== 137 || TIME(sec):== 3.5639 || TrainError:== 0.063965 || TestError:== 0.149131\n",
" Epoch :== 138 || TIME(sec):== 3.8975 || TrainError:== 0.064255 || TestError:== 0.149157\n",
" Epoch :== 139 || TIME(sec):== 3.7333 || TrainError:== 0.064213 || TestError:== 0.149783\n",
" Epoch :== 140 || TIME(sec):== 3.5964 || TrainError:== 0.064624 || TestError:== 0.151325\n",
" Epoch :== 141 || TIME(sec):== 3.5884 || TrainError:== 0.063344 || TestError:== 0.152203\n",
" Epoch :== 142 || TIME(sec):== 3.6009 || TrainError:== 0.063736 || TestError:== 0.149748\n",
" Epoch :== 143 || TIME(sec):== 3.5878 || TrainError:== 0.063275 || TestError:== 0.149923\n",
" Epoch :== 144 || TIME(sec):== 3.6187 || TrainError:== 0.063174 || TestError:== 0.151959\n",
" Epoch :== 145 || TIME(sec):== 3.6092 || TrainError:== 0.062954 || TestError:== 0.151055\n",
" Epoch :== 146 || TIME(sec):== 3.5985 || TrainError:== 0.062599 || TestError:== 0.152675\n",
" Epoch :== 147 || TIME(sec):== 3.6049 || TrainError:== 0.062473 || TestError:== 0.150112\n",
" Epoch :== 148 || TIME(sec):== 3.6005 || TrainError:== 0.062841 || TestError:== 0.150407\n",
" Epoch :== 149 || TIME(sec):== 3.5909 || TrainError:== 0.063961 || TestError:== 0.150434\n",
" Epoch :== 150 || TIME(sec):== 3.5934 || TrainError:== 0.063284 || TestError:== 0.153592\n",
" Epoch :== 151 || TIME(sec):== 3.5827 || TrainError:== 0.06361 || TestError:== 0.149559\n",
" Epoch :== 152 || TIME(sec):== 3.5776 || TrainError:== 0.063193 || TestError:== 0.150184\n",
" Epoch :== 153 || TIME(sec):== 3.5973 || TrainError:== 0.062573 || TestError:== 0.1514\n",
" Epoch :== 154 || TIME(sec):== 3.5876 || TrainError:== 0.061288 || TestError:== 0.149774\n",
" Epoch :== 155 || TIME(sec):== 3.5891 || TrainError:== 0.061445 || TestError:== 0.151274\n",
" Epoch :== 156 || TIME(sec):== 3.5873 || TrainError:== 0.060888 || TestError:== 0.149894\n",
" Epoch :== 157 || TIME(sec):== 3.5815 || TrainError:== 0.061907 || TestError:== 0.150281\n",
" Epoch :== 158 || TIME(sec):== 3.5856 || TrainError:== 0.062113 || TestError:== 0.150108\n",
" Epoch :== 159 || TIME(sec):== 3.4394 || TrainError:== 0.061431 || TestError:== 0.152389\n",
" Epoch :== 160 || TIME(sec):== 3.5884 || TrainError:== 0.060978 || TestError:== 0.150459\n",
" Epoch :== 161 || TIME(sec):== 3.573 || TrainError:== 0.063823 || TestError:== 0.160314\n",
" Epoch :== 162 || TIME(sec):== 3.6009 || TrainError:== 0.069277 || TestError:== 0.159309\n",
" Epoch :== 163 || TIME(sec):== 3.5894 || TrainError:== 0.072983 || TestError:== 0.162155\n",
" Epoch :== 164 || TIME(sec):== 3.5946 || TrainError:== 0.07 || TestError:== 0.157416\n",
" Epoch :== 165 || TIME(sec):== 3.6052 || TrainError:== 0.066564 || TestError:== 0.150301\n",
" Epoch :== 166 || TIME(sec):== 3.6041 || TrainError:== 0.062741 || TestError:== 0.149356\n",
" Epoch :== 167 || TIME(sec):== 3.5858 || TrainError:== 0.060618 || TestError:== 0.149412\n",
" Epoch :== 168 || TIME(sec):== 3.61 || TrainError:== 0.060815 || TestError:== 0.149106\n",
" Epoch :== 169 || TIME(sec):== 3.5872 || TrainError:== 0.060108 || TestError:== 0.151812\n",
" Epoch :== 170 || TIME(sec):== 3.5803 || TrainError:== 0.060018 || TestError:== 0.150989\n",
" Epoch :== 171 || TIME(sec):== 3.5944 || TrainError:== 0.060055 || TestError:== 0.150244\n",
" Epoch :== 172 || TIME(sec):== 3.5854 || TrainError:== 0.060544 || TestError:== 0.149742\n",
" Epoch :== 173 || TIME(sec):== 3.5846 || TrainError:== 0.059348 || TestError:== 0.148709\n",
" Epoch :== 174 || TIME(sec):== 3.5933 || TrainError:== 0.059849 || TestError:== 0.150401\n",
" Epoch :== 175 || TIME(sec):== 3.5948 || TrainError:== 0.059831 || TestError:== 0.149177\n",
" Epoch :== 176 || TIME(sec):== 3.5736 || TrainError:== 0.059748 || TestError:== 0.148853\n",
" Epoch :== 177 || TIME(sec):== 3.5934 || TrainError:== 0.059457 || TestError:== 0.151719\n",
" Epoch :== 178 || TIME(sec):== 3.6063 || TrainError:== 0.058813 || TestError:== 0.149868\n",
" Epoch :== 179 || TIME(sec):== 3.5955 || TrainError:== 0.0599 || TestError:== 0.151154\n",
" Epoch :== 180 || TIME(sec):== 3.5871 || TrainError:== 0.059349 || TestError:== 0.148884\n",
" Epoch :== 181 || TIME(sec):== 3.5846 || TrainError:== 0.059136 || TestError:== 0.149847\n",
" Epoch :== 182 || TIME(sec):== 3.6175 || TrainError:== 0.059445 || TestError:== 0.151782\n",
" Epoch :== 183 || TIME(sec):== 3.5969 || TrainError:== 0.059676 || TestError:== 0.151341\n",
" Epoch :== 184 || TIME(sec):== 3.5771 || TrainError:== 0.059742 || TestError:== 0.149499\n",
" Epoch :== 185 || TIME(sec):== 3.588 || TrainError:== 0.059112 || TestError:== 0.151547\n",
" Epoch :== 186 || TIME(sec):== 3.5953 || TrainError:== 0.060822 || TestError:== 0.15128\n",
" Epoch :== 187 || TIME(sec):== 3.5961 || TrainError:== 0.060499 || TestError:== 0.150303\n",
" Epoch :== 188 || TIME(sec):== 3.6073 || TrainError:== 0.059311 || TestError:== 0.150052\n",
" Epoch :== 189 || TIME(sec):== 3.5809 || TrainError:== 0.05909 || TestError:== 0.147911\n",
" Epoch :== 190 || TIME(sec):== 3.5999 || TrainError:== 0.058988 || TestError:== 0.148875\n",
" Epoch :== 191 || TIME(sec):== 3.5964 || TrainError:== 0.058683 || TestError:== 0.150056\n",
" Epoch :== 192 || TIME(sec):== 3.5725 || TrainError:== 0.05931 || TestError:== 0.151927\n",
" Epoch :== 193 || TIME(sec):== 3.587 || TrainError:== 0.058843 || TestError:== 0.148595\n",
" Epoch :== 194 || TIME(sec):== 3.5956 || TrainError:== 0.059368 || TestError:== 0.148181\n",
" Epoch :== 195 || TIME(sec):== 3.5805 || TrainError:== 0.05897 || TestError:== 0.15046\n",
" Epoch :== 196 || TIME(sec):== 3.6264 || TrainError:== 0.059615 || TestError:== 0.149369\n",
" Epoch :== 197 || TIME(sec):== 3.5864 || TrainError:== 0.059325 || TestError:== 0.151555\n",
" Epoch :== 198 || TIME(sec):== 3.6085 || TrainError:== 0.059525 || TestError:== 0.148797\n",
" Epoch :== 199 || TIME(sec):== 3.5717 || TrainError:== 0.05885 || TestError:== 0.150577\n",
" Epoch :== 200 || TIME(sec):== 3.5824 || TrainError:== 0.05852 || TestError:== 0.150661\n",
" Epoch :== 201 || TIME(sec):== 3.5929 || TrainError:== 0.05259 || TestError:== 0.147194\n",
" Epoch :== 202 || TIME(sec):== 3.6003 || TrainError:== 0.046432 || TestError:== 0.145107\n",
" Epoch :== 203 || TIME(sec):== 3.5847 || TrainError:== 0.044627 || TestError:== 0.147467\n",
" Epoch :== 204 || TIME(sec):== 3.5205 || TrainError:== 0.044279 || TestError:== 0.147023\n",
" Epoch :== 205 || TIME(sec):== 3.4375 || TrainError:== 0.043437 || TestError:== 0.146874\n",
" Epoch :== 206 || TIME(sec):== 3.4692 || TrainError:== 0.044264 || TestError:== 0.148217\n",
" Epoch :== 207 || TIME(sec):== 3.4744 || TrainError:== 0.044054 || TestError:== 0.147372\n",
" Epoch :== 208 || TIME(sec):== 3.4975 || TrainError:== 0.043705 || TestError:== 0.146368\n",
" Epoch :== 209 || TIME(sec):== 3.5612 || TrainError:== 0.04298 || TestError:== 0.147171\n",
" Epoch :== 210 || TIME(sec):== 3.5889 || TrainError:== 0.042885 || TestError:== 0.148781\n",
" Epoch :== 211 || TIME(sec):== 3.5972 || TrainError:== 0.04315 || TestError:== 0.147016\n",
" Epoch :== 212 || TIME(sec):== 3.6294 || TrainError:== 0.0431 || TestError:== 0.146706\n",
" Epoch :== 213 || TIME(sec):== 3.5912 || TrainError:== 0.043329 || TestError:== 0.146902\n",
" Epoch :== 214 || TIME(sec):== 3.5776 || TrainError:== 0.042908 || TestError:== 0.146573\n",
" Epoch :== 215 || TIME(sec):== 3.6006 || TrainError:== 0.042714 || TestError:== 0.147019\n",
" Epoch :== 216 || TIME(sec):== 3.6154 || TrainError:== 0.042914 || TestError:== 0.147544\n",
" Epoch :== 217 || TIME(sec):== 3.5808 || TrainError:== 0.042878 || TestError:== 0.148029\n",
" Epoch :== 218 || TIME(sec):== 3.6055 || TrainError:== 0.043307 || TestError:== 0.148363\n",
" Epoch :== 219 || TIME(sec):== 3.6118 || TrainError:== 0.043029 || TestError:== 0.147059\n",
" Epoch :== 220 || TIME(sec):== 3.5803 || TrainError:== 0.042416 || TestError:== 0.148256\n",
" Epoch :== 221 || TIME(sec):== 3.6069 || TrainError:== 0.042998 || TestError:== 0.14745\n",
" Epoch :== 222 || TIME(sec):== 3.6325 || TrainError:== 0.042724 || TestError:== 0.147252\n",
" Epoch :== 223 || TIME(sec):== 3.6104 || TrainError:== 0.042747 || TestError:== 0.147393\n",
" Epoch :== 224 || TIME(sec):== 3.6235 || TrainError:== 0.042812 || TestError:== 0.147491\n",
" Epoch :== 225 || TIME(sec):== 3.606 || TrainError:== 0.04245 || TestError:== 0.148543\n",
" Epoch :== 226 || TIME(sec):== 3.6134 || TrainError:== 0.043184 || TestError:== 0.147653\n",
" Epoch :== 227 || TIME(sec):== 3.5851 || TrainError:== 0.042593 || TestError:== 0.146925\n",
" Epoch :== 228 || TIME(sec):== 3.6215 || TrainError:== 0.042244 || TestError:== 0.146705\n",
" Epoch :== 229 || TIME(sec):== 3.6054 || TrainError:== 0.04258 || TestError:== 0.146317\n",
" Epoch :== 230 || TIME(sec):== 3.5843 || TrainError:== 0.041801 || TestError:== 0.147265\n",
" Epoch :== 231 || TIME(sec):== 3.584 || TrainError:== 0.042509 || TestError:== 0.147464\n",
" Epoch :== 232 || TIME(sec):== 3.5819 || TrainError:== 0.042465 || TestError:== 0.148311\n",
" Epoch :== 233 || TIME(sec):== 3.5919 || TrainError:== 0.042184 || TestError:== 0.147125\n",
" Epoch :== 234 || TIME(sec):== 3.5921 || TrainError:== 0.042004 || TestError:== 0.147459\n",
" Epoch :== 235 || TIME(sec):== 3.5735 || TrainError:== 0.041903 || TestError:== 0.146939\n",
" Epoch :== 236 || TIME(sec):== 3.594 || TrainError:== 0.042001 || TestError:== 0.148507\n",
" Epoch :== 237 || TIME(sec):== 3.5916 || TrainError:== 0.042277 || TestError:== 0.147797\n",
" Epoch :== 238 || TIME(sec):== 3.5916 || TrainError:== 0.042066 || TestError:== 0.148837\n",
" Epoch :== 239 || TIME(sec):== 3.5984 || TrainError:== 0.042194 || TestError:== 0.14696\n",
" Epoch :== 240 || TIME(sec):== 3.5942 || TrainError:== 0.041928 || TestError:== 0.148671\n",
" Epoch :== 241 || TIME(sec):== 3.5692 || TrainError:== 0.041906 || TestError:== 0.147573\n",
" Epoch :== 242 || TIME(sec):== 3.5992 || TrainError:== 0.041917 || TestError:== 0.148089\n",
" Epoch :== 243 || TIME(sec):== 3.6171 || TrainError:== 0.041859 || TestError:== 0.147221\n",
" Epoch :== 244 || TIME(sec):== 3.5964 || TrainError:== 0.042403 || TestError:== 0.148066\n",
" Epoch :== 245 || TIME(sec):== 3.5951 || TrainError:== 0.041846 || TestError:== 0.148535\n",
" Epoch :== 246 || TIME(sec):== 3.5666 || TrainError:== 0.041769 || TestError:== 0.148581\n",
" Epoch :== 247 || TIME(sec):== 3.6044 || TrainError:== 0.041875 || TestError:== 0.148253\n",
" Epoch :== 248 || TIME(sec):== 3.6025 || TrainError:== 0.041477 || TestError:== 0.148762\n",
" Epoch :== 249 || TIME(sec):== 3.5958 || TrainError:== 0.041903 || TestError:== 0.1477\n",
" Epoch :== 250 || TIME(sec):== 3.6043 || TrainError:== 0.041368 || TestError:== 0.148341\n",
" Epoch :== 251 || TIME(sec):== 3.5942 || TrainError:== 0.041743 || TestError:== 0.147225\n",
" Epoch :== 252 || TIME(sec):== 3.5991 || TrainError:== 0.041715 || TestError:== 0.146955\n",
" Epoch :== 253 || TIME(sec):== 3.5981 || TrainError:== 0.04196 || TestError:== 0.148949\n",
" Epoch :== 254 || TIME(sec):== 3.6063 || TrainError:== 0.040951 || TestError:== 0.148265\n",
" Epoch :== 255 || TIME(sec):== 3.5667 || TrainError:== 0.041253 || TestError:== 0.147641\n",
" Epoch :== 256 || TIME(sec):== 3.6122 || TrainError:== 0.041609 || TestError:== 0.148491\n",
" Epoch :== 257 || TIME(sec):== 3.6041 || TrainError:== 0.041921 || TestError:== 0.147905\n",
" Epoch :== 258 || TIME(sec):== 3.6475 || TrainError:== 0.041441 || TestError:== 0.14836\n",
" Epoch :== 259 || TIME(sec):== 3.6208 || TrainError:== 0.041647 || TestError:== 0.147224\n",
" Epoch :== 260 || TIME(sec):== 3.6001 || TrainError:== 0.041288 || TestError:== 0.147391\n",
" Epoch :== 261 || TIME(sec):== 3.569 || TrainError:== 0.041225 || TestError:== 0.147795\n",
" Epoch :== 262 || TIME(sec):== 3.5692 || TrainError:== 0.041499 || TestError:== 0.14765\n",
" Epoch :== 263 || TIME(sec):== 3.6035 || TrainError:== 0.041663 || TestError:== 0.148327\n",
" Epoch :== 264 || TIME(sec):== 3.5915 || TrainError:== 0.041128 || TestError:== 0.14806\n",
" Epoch :== 265 || TIME(sec):== 3.6106 || TrainError:== 0.041353 || TestError:== 0.149424\n",
" Epoch :== 266 || TIME(sec):== 3.6074 || TrainError:== 0.04094 || TestError:== 0.146975\n",
" Epoch :== 267 || TIME(sec):== 3.5994 || TrainError:== 0.041175 || TestError:== 0.148857\n",
" Epoch :== 268 || TIME(sec):== 3.5687 || TrainError:== 0.041075 || TestError:== 0.147573\n",
" Epoch :== 269 || TIME(sec):== 3.5798 || TrainError:== 0.040839 || TestError:== 0.149849\n",
" Epoch :== 270 || TIME(sec):== 3.5912 || TrainError:== 0.041743 || TestError:== 0.147905\n",
" Epoch :== 271 || TIME(sec):== 3.6076 || TrainError:== 0.041962 || TestError:== 0.148099\n",
" Epoch :== 272 || TIME(sec):== 3.6074 || TrainError:== 0.041246 || TestError:== 0.147376\n",
" Epoch :== 273 || TIME(sec):== 3.6166 || TrainError:== 0.041023 || TestError:== 0.147768\n",
" Epoch :== 274 || TIME(sec):== 3.5921 || TrainError:== 0.040666 || TestError:== 0.148409\n",
" Epoch :== 275 || TIME(sec):== 3.5711 || TrainError:== 0.041065 || TestError:== 0.148037\n",
" Epoch :== 276 || TIME(sec):== 3.5989 || TrainError:== 0.041682 || TestError:== 0.149172\n",
" Epoch :== 277 || TIME(sec):== 3.6163 || TrainError:== 0.040487 || TestError:== 0.147881\n",
" Epoch :== 278 || TIME(sec):== 3.5988 || TrainError:== 0.040375 || TestError:== 0.147954\n",
" Epoch :== 279 || TIME(sec):== 3.6138 || TrainError:== 0.040825 || TestError:== 0.149568\n",
" Epoch :== 280 || TIME(sec):== 3.5824 || TrainError:== 0.041033 || TestError:== 0.147582\n",
" Epoch :== 281 || TIME(sec):== 3.5926 || TrainError:== 0.041297 || TestError:== 0.147552\n",
" Epoch :== 282 || TIME(sec):== 3.6262 || TrainError:== 0.041181 || TestError:== 0.148288\n",
" Epoch :== 283 || TIME(sec):== 3.5787 || TrainError:== 0.04043 || TestError:== 0.147069\n",
" Epoch :== 284 || TIME(sec):== 3.5993 || TrainError:== 0.040448 || TestError:== 0.148238\n",
" Epoch :== 285 || TIME(sec):== 3.5864 || TrainError:== 0.040105 || TestError:== 0.148173\n",
" Epoch :== 286 || TIME(sec):== 3.5969 || TrainError:== 0.040528 || TestError:== 0.148137\n",
" Epoch :== 287 || TIME(sec):== 3.5906 || TrainError:== 0.040711 || TestError:== 0.14867\n",
" Epoch :== 288 || TIME(sec):== 3.5953 || TrainError:== 0.040258 || TestError:== 0.148937\n",
" Epoch :== 289 || TIME(sec):== 3.6007 || TrainError:== 0.040638 || TestError:== 0.147932\n",
" Epoch :== 290 || TIME(sec):== 3.6201 || TrainError:== 0.041113 || TestError:== 0.148215\n",
" Epoch :== 291 || TIME(sec):== 3.6107 || TrainError:== 0.041343 || TestError:== 0.148676\n",
" Epoch :== 292 || TIME(sec):== 3.5925 || TrainError:== 0.041001 || TestError:== 0.147278\n",
" Epoch :== 293 || TIME(sec):== 3.6157 || TrainError:== 0.040663 || TestError:== 0.148891\n",
" Epoch :== 294 || TIME(sec):== 3.5969 || TrainError:== 0.039974 || TestError:== 0.147876\n",
" Epoch :== 295 || TIME(sec):== 3.6174 || TrainError:== 0.040455 || TestError:== 0.147111\n",
" Epoch :== 296 || TIME(sec):== 3.5826 || TrainError:== 0.040492 || TestError:== 0.148316\n",
" Epoch :== 297 || TIME(sec):== 3.6001 || TrainError:== 0.040128 || TestError:== 0.147693\n",
" Epoch :== 298 || TIME(sec):== 3.6212 || TrainError:== 0.039855 || TestError:== 0.148511\n",
" Epoch :== 299 || TIME(sec):== 3.5897 || TrainError:== 0.040026 || TestError:== 0.146955\n",
" Epoch :== 300 || TIME(sec):== 3.5957 || TrainError:== 0.040984 || TestError:== 0.147577\n",
" Epoch :== 301 || TIME(sec):== 3.5564 || TrainError:== 0.037241 || TestError:== 0.147005\n",
" Epoch :== 302 || TIME(sec):== 3.5901 || TrainError:== 0.033503 || TestError:== 0.147432\n",
" Epoch :== 303 || TIME(sec):== 3.5889 || TrainError:== 0.03299 || TestError:== 0.146502\n",
" Epoch :== 304 || TIME(sec):== 3.6273 || TrainError:== 0.032563 || TestError:== 0.147589\n",
" Epoch :== 305 || TIME(sec):== 3.6094 || TrainError:== 0.032646 || TestError:== 0.146532\n",
" Epoch :== 306 || TIME(sec):== 3.5821 || TrainError:== 0.032334 || TestError:== 0.147158\n",
" Epoch :== 307 || TIME(sec):== 3.6165 || TrainError:== 0.032126 || TestError:== 0.147511\n",
" Epoch :== 308 || TIME(sec):== 3.5768 || TrainError:== 0.031935 || TestError:== 0.147189\n",
" Epoch :== 309 || TIME(sec):== 3.5909 || TrainError:== 0.031758 || TestError:== 0.147191\n",
" Epoch :== 310 || TIME(sec):== 3.5828 || TrainError:== 0.03169 || TestError:== 0.146611\n",
" Epoch :== 311 || TIME(sec):== 3.5988 || TrainError:== 0.03183 || TestError:== 0.147325\n",
" Epoch :== 312 || TIME(sec):== 3.5873 || TrainError:== 0.032074 || TestError:== 0.146513\n",
" Epoch :== 313 || TIME(sec):== 3.5926 || TrainError:== 0.031853 || TestError:== 0.147712\n",
" Epoch :== 314 || TIME(sec):== 3.6085 || TrainError:== 0.031637 || TestError:== 0.147556\n",
" Epoch :== 315 || TIME(sec):== 3.5655 || TrainError:== 0.031566 || TestError:== 0.147082\n",
" Epoch :== 316 || TIME(sec):== 3.6309 || TrainError:== 0.03166 || TestError:== 0.147427\n",
" Epoch :== 317 || TIME(sec):== 3.5821 || TrainError:== 0.031924 || TestError:== 0.146497\n",
" Epoch :== 318 || TIME(sec):== 3.5855 || TrainError:== 0.031711 || TestError:== 0.148231\n",
" Epoch :== 319 || TIME(sec):== 3.5808 || TrainError:== 0.031752 || TestError:== 0.148364\n",
" Epoch :== 320 || TIME(sec):== 3.5815 || TrainError:== 0.031439 || TestError:== 0.147311\n",
" Epoch :== 321 || TIME(sec):== 3.5908 || TrainError:== 0.03132 || TestError:== 0.147345\n",
" Epoch :== 322 || TIME(sec):== 3.5919 || TrainError:== 0.031183 || TestError:== 0.147754\n",
" Epoch :== 323 || TIME(sec):== 3.603 || TrainError:== 0.030981 || TestError:== 0.147217\n",
" Epoch :== 324 || TIME(sec):== 3.5757 || TrainError:== 0.03134 || TestError:== 0.147488\n",
" Epoch :== 325 || TIME(sec):== 3.5943 || TrainError:== 0.031137 || TestError:== 0.146557\n",
" Epoch :== 326 || TIME(sec):== 3.5722 || TrainError:== 0.031237 || TestError:== 0.146828\n",
" Epoch :== 327 || TIME(sec):== 3.5701 || TrainError:== 0.031497 || TestError:== 0.147339\n",
" Epoch :== 328 || TIME(sec):== 3.5748 || TrainError:== 0.031479 || TestError:== 0.147083\n",
" Epoch :== 329 || TIME(sec):== 3.5829 || TrainError:== 0.031416 || TestError:== 0.146898\n",
" Epoch :== 330 || TIME(sec):== 3.5944 || TrainError:== 0.031357 || TestError:== 0.147235\n",
" Epoch :== 331 || TIME(sec):== 3.6014 || TrainError:== 0.031698 || TestError:== 0.146918\n",
" Epoch :== 332 || TIME(sec):== 3.608 || TrainError:== 0.031383 || TestError:== 0.148016\n",
" Epoch :== 333 || TIME(sec):== 3.5835 || TrainError:== 0.031158 || TestError:== 0.148076\n",
" Epoch :== 334 || TIME(sec):== 3.6099 || TrainError:== 0.031142 || TestError:== 0.148405\n",
" Epoch :== 335 || TIME(sec):== 3.5865 || TrainError:== 0.031223 || TestError:== 0.14829\n",
" Epoch :== 336 || TIME(sec):== 3.6043 || TrainError:== 0.031248 || TestError:== 0.148091\n",
" Epoch :== 337 || TIME(sec):== 3.6003 || TrainError:== 0.030993 || TestError:== 0.147879\n",
" Epoch :== 338 || TIME(sec):== 3.5884 || TrainError:== 0.030946 || TestError:== 0.1474\n",
" Epoch :== 339 || TIME(sec):== 3.5824 || TrainError:== 0.030972 || TestError:== 0.14877\n",
" Epoch :== 340 || TIME(sec):== 3.5968 || TrainError:== 0.030975 || TestError:== 0.147978\n",
" Epoch :== 341 || TIME(sec):== 3.5803 || TrainError:== 0.031211 || TestError:== 0.147507\n",
" Epoch :== 342 || TIME(sec):== 3.6135 || TrainError:== 0.030967 || TestError:== 0.148404\n",
" Epoch :== 343 || TIME(sec):== 3.5866 || TrainError:== 0.030981 || TestError:== 0.147464\n",
" Epoch :== 344 || TIME(sec):== 3.5898 || TrainError:== 0.030854 || TestError:== 0.147578\n",
" Epoch :== 345 || TIME(sec):== 3.6063 || TrainError:== 0.031115 || TestError:== 0.147566\n",
" Epoch :== 346 || TIME(sec):== 3.5825 || TrainError:== 0.030946 || TestError:== 0.147778\n",
" Epoch :== 347 || TIME(sec):== 3.607 || TrainError:== 0.030506 || TestError:== 0.148109\n",
" Epoch :== 348 || TIME(sec):== 3.5887 || TrainError:== 0.030878 || TestError:== 0.147362\n",
" Epoch :== 349 || TIME(sec):== 3.6085 || TrainError:== 0.030953 || TestError:== 0.14779\n",
" Epoch :== 350 || TIME(sec):== 3.5815 || TrainError:== 0.030681 || TestError:== 0.14839\n",
" Epoch :== 351 || TIME(sec):== 3.5448 || TrainError:== 0.031085 || TestError:== 0.147301\n",
" Epoch :== 352 || TIME(sec):== 3.343 || TrainError:== 0.03075 || TestError:== 0.148206\n",
" Epoch :== 353 || TIME(sec):== 3.3358 || TrainError:== 0.030417 || TestError:== 0.147831\n",
" Epoch :== 354 || TIME(sec):== 3.357 || TrainError:== 0.030612 || TestError:== 0.148029\n",
" Epoch :== 355 || TIME(sec):== 3.3254 || TrainError:== 0.030482 || TestError:== 0.148737\n",
" Epoch :== 356 || TIME(sec):== 3.3538 || TrainError:== 0.030406 || TestError:== 0.148597\n",
" Epoch :== 357 || TIME(sec):== 3.3255 || TrainError:== 0.030181 || TestError:== 0.147985\n",
" Epoch :== 358 || TIME(sec):== 3.3407 || TrainError:== 0.030657 || TestError:== 0.148823\n",
" Epoch :== 359 || TIME(sec):== 3.361 || TrainError:== 0.030441 || TestError:== 0.147568\n",
" Epoch :== 360 || TIME(sec):== 3.327 || TrainError:== 0.030298 || TestError:== 0.148228\n",
" Epoch :== 361 || TIME(sec):== 3.3282 || TrainError:== 0.030681 || TestError:== 0.148025\n",
" Epoch :== 362 || TIME(sec):== 3.3483 || TrainError:== 0.030444 || TestError:== 0.14805\n",
" Epoch :== 363 || TIME(sec):== 3.3249 || TrainError:== 0.030247 || TestError:== 0.148755\n",
" Epoch :== 364 || TIME(sec):== 3.3376 || TrainError:== 0.030168 || TestError:== 0.147898\n",
" Epoch :== 365 || TIME(sec):== 3.3575 || TrainError:== 0.030415 || TestError:== 0.148208\n",
" Epoch :== 366 || TIME(sec):== 3.3239 || TrainError:== 0.03062 || TestError:== 0.147892\n",
" Epoch :== 367 || TIME(sec):== 3.3523 || TrainError:== 0.030325 || TestError:== 0.14727\n",
" Epoch :== 368 || TIME(sec):== 3.3208 || TrainError:== 0.03036 || TestError:== 0.148162\n",
" Epoch :== 369 || TIME(sec):== 3.362 || TrainError:== 0.030236 || TestError:== 0.148351\n",
" Epoch :== 370 || TIME(sec):== 3.371 || TrainError:== 0.030372 || TestError:== 0.147765\n",
" Epoch :== 371 || TIME(sec):== 3.3568 || TrainError:== 0.030162 || TestError:== 0.147928\n",
" Epoch :== 372 || TIME(sec):== 3.3623 || TrainError:== 0.030422 || TestError:== 0.14778\n",
" Epoch :== 373 || TIME(sec):== 3.3598 || TrainError:== 0.030237 || TestError:== 0.147829\n",
" Epoch :== 374 || TIME(sec):== 3.3351 || TrainError:== 0.030435 || TestError:== 0.14827\n",
" Epoch :== 375 || TIME(sec):== 3.3202 || TrainError:== 0.030402 || TestError:== 0.147536\n",
" Epoch :== 376 || TIME(sec):== 3.3408 || TrainError:== 0.030125 || TestError:== 0.148254\n",
" Epoch :== 377 || TIME(sec):== 3.3471 || TrainError:== 0.030334 || TestError:== 0.149119\n",
" Epoch :== 378 || TIME(sec):== 3.3314 || TrainError:== 0.030159 || TestError:== 0.148119\n",
" Epoch :== 379 || TIME(sec):== 3.3612 || TrainError:== 0.029998 || TestError:== 0.148122\n",
" Epoch :== 380 || TIME(sec):== 3.3487 || TrainError:== 0.029957 || TestError:== 0.148793\n",
" Epoch :== 381 || TIME(sec):== 3.3418 || TrainError:== 0.03035 || TestError:== 0.147909\n",
" Epoch :== 382 || TIME(sec):== 3.3471 || TrainError:== 0.030079 || TestError:== 0.148411\n",
" Epoch :== 383 || TIME(sec):== 3.3675 || TrainError:== 0.030081 || TestError:== 0.147934\n",
" Epoch :== 384 || TIME(sec):== 3.374 || TrainError:== 0.030149 || TestError:== 0.148024\n",
" Epoch :== 385 || TIME(sec):== 3.3194 || TrainError:== 0.030121 || TestError:== 0.148083\n",
" Epoch :== 386 || TIME(sec):== 2.8136 || TrainError:== 0.0303 || TestError:== 0.147593\n",
" Epoch :== 387 || TIME(sec):== 2.8177 || TrainError:== 0.030078 || TestError:== 0.147844\n",
" Epoch :== 388 || TIME(sec):== 2.8002 || TrainError:== 0.030267 || TestError:== 0.148635\n",
" Epoch :== 389 || TIME(sec):== 2.8262 || TrainError:== 0.029994 || TestError:== 0.14765\n",
" Epoch :== 390 || TIME(sec):== 2.7993 || TrainError:== 0.030581 || TestError:== 0.149043\n",
" Epoch :== 391 || TIME(sec):== 3.1599 || TrainError:== 0.02994 || TestError:== 0.148082\n",
" Epoch :== 392 || TIME(sec):== 3.3148 || TrainError:== 0.03038 || TestError:== 0.149013\n",
" Epoch :== 393 || TIME(sec):== 3.2997 || TrainError:== 0.029837 || TestError:== 0.148755\n",
" Epoch :== 394 || TIME(sec):== 3.299 || TrainError:== 0.02986 || TestError:== 0.148915\n",
" Epoch :== 395 || TIME(sec):== 3.3032 || TrainError:== 0.030049 || TestError:== 0.148226\n",
" Epoch :== 396 || TIME(sec):== 3.3099 || TrainError:== 0.030006 || TestError:== 0.148355\n",
" Epoch :== 397 || TIME(sec):== 3.3048 || TrainError:== 0.030051 || TestError:== 0.148553\n",
" Epoch :== 398 || TIME(sec):== 2.7916 || TrainError:== 0.029821 || TestError:== 0.149032\n",
" Epoch :== 399 || TIME(sec):== 2.8135 || TrainError:== 0.03012 || TestError:== 0.147886\n",
" Epoch :== 400 || TIME(sec):== 3.1532 || TrainError:== 0.030148 || TestError:== 0.1477\n",
" Epoch :== 401 || TIME(sec):== 3.3347 || TrainError:== 0.02814 || TestError:== 0.147107\n",
" Epoch :== 402 || TIME(sec):== 3.3417 || TrainError:== 0.026698 || TestError:== 0.148103\n",
" Epoch :== 403 || TIME(sec):== 3.3553 || TrainError:== 0.025989 || TestError:== 0.147506\n",
" Epoch :== 404 || TIME(sec):== 3.3323 || TrainError:== 0.026072 || TestError:== 0.147731\n",
" Epoch :== 405 || TIME(sec):== 3.3428 || TrainError:== 0.025891 || TestError:== 0.147511\n",
" Epoch :== 406 || TIME(sec):== 3.3503 || TrainError:== 0.02598 || TestError:== 0.14786\n",
" Epoch :== 407 || TIME(sec):== 3.3353 || TrainError:== 0.026045 || TestError:== 0.148286\n",
" Epoch :== 408 || TIME(sec):== 3.3152 || TrainError:== 0.025583 || TestError:== 0.148333\n",
" Epoch :== 409 || TIME(sec):== 3.3174 || TrainError:== 0.025733 || TestError:== 0.148278\n",
" Epoch :== 410 || TIME(sec):== 3.3349 || TrainError:== 0.025433 || TestError:== 0.148331\n",
" Epoch :== 411 || TIME(sec):== 3.3328 || TrainError:== 0.025539 || TestError:== 0.147816\n",
" Epoch :== 412 || TIME(sec):== 3.3279 || TrainError:== 0.02564 || TestError:== 0.148112\n",
" Epoch :== 413 || TIME(sec):== 3.3219 || TrainError:== 0.025732 || TestError:== 0.14809\n",
" Epoch :== 414 || TIME(sec):== 3.3451 || TrainError:== 0.025464 || TestError:== 0.148346\n",
" Epoch :== 415 || TIME(sec):== 3.3386 || TrainError:== 0.025295 || TestError:== 0.148214\n",
" Epoch :== 416 || TIME(sec):== 3.3324 || TrainError:== 0.025454 || TestError:== 0.147775\n",
" Epoch :== 417 || TIME(sec):== 3.3254 || TrainError:== 0.025085 || TestError:== 0.147916\n",
" Epoch :== 418 || TIME(sec):== 3.3463 || TrainError:== 0.025349 || TestError:== 0.148495\n",
" Epoch :== 419 || TIME(sec):== 3.3364 || TrainError:== 0.02551 || TestError:== 0.148783\n",
" Epoch :== 420 || TIME(sec):== 4.1259 || TrainError:== 0.025204 || TestError:== 0.148266\n",
" Epoch :== 421 || TIME(sec):== 4.1553 || TrainError:== 0.025641 || TestError:== 0.14792\n",
" Epoch :== 422 || TIME(sec):== 4.1641 || TrainError:== 0.025129 || TestError:== 0.148171\n",
" Epoch :== 423 || TIME(sec):== 4.1638 || TrainError:== 0.025 || TestError:== 0.148285\n",
" Epoch :== 424 || TIME(sec):== 4.1588 || TrainError:== 0.025222 || TestError:== 0.148808\n",
" Epoch :== 425 || TIME(sec):== 4.1668 || TrainError:== 0.025212 || TestError:== 0.148341\n",
" Epoch :== 426 || TIME(sec):== 4.16 || TrainError:== 0.02511 || TestError:== 0.148719\n",
" Epoch :== 427 || TIME(sec):== 4.1766 || TrainError:== 0.025131 || TestError:== 0.148107\n",
" Epoch :== 428 || TIME(sec):== 4.1395 || TrainError:== 0.025361 || TestError:== 0.148276\n",
" Epoch :== 429 || TIME(sec):== 4.1466 || TrainError:== 0.025172 || TestError:== 0.148515\n",
" Epoch :== 430 || TIME(sec):== 4.1733 || TrainError:== 0.024975 || TestError:== 0.148408\n",
" Epoch :== 431 || TIME(sec):== 4.1667 || TrainError:== 0.025204 || TestError:== 0.148622\n",
" Epoch :== 432 || TIME(sec):== 4.1601 || TrainError:== 0.025095 || TestError:== 0.148168\n",
" Epoch :== 433 || TIME(sec):== 4.1565 || TrainError:== 0.025124 || TestError:== 0.148696\n",
" Epoch :== 434 || TIME(sec):== 4.1312 || TrainError:== 0.025058 || TestError:== 0.14865\n",
" Epoch :== 435 || TIME(sec):== 2.8267 || TrainError:== 0.025174 || TestError:== 0.148056\n",
" Epoch :== 436 || TIME(sec):== 2.8455 || TrainError:== 0.025133 || TestError:== 0.148224\n",
" Epoch :== 437 || TIME(sec):== 2.8071 || TrainError:== 0.02541 || TestError:== 0.147935\n",
" Epoch :== 438 || TIME(sec):== 2.8318 || TrainError:== 0.025168 || TestError:== 0.148385\n",
" Epoch :== 439 || TIME(sec):== 2.8112 || TrainError:== 0.025094 || TestError:== 0.148497\n",
" Epoch :== 440 || TIME(sec):== 3.307 || TrainError:== 0.025123 || TestError:== 0.148153\n",
" Epoch :== 441 || TIME(sec):== 3.3349 || TrainError:== 0.025053 || TestError:== 0.148352\n",
" Epoch :== 442 || TIME(sec):== 3.3017 || TrainError:== 0.025042 || TestError:== 0.148311\n",
" Epoch :== 443 || TIME(sec):== 3.3034 || TrainError:== 0.024828 || TestError:== 0.148562\n",
" Epoch :== 444 || TIME(sec):== 3.3265 || TrainError:== 0.025044 || TestError:== 0.149117\n",
" Epoch :== 445 || TIME(sec):== 3.3113 || TrainError:== 0.025109 || TestError:== 0.148647\n",
" Epoch :== 446 || TIME(sec):== 3.3051 || TrainError:== 0.025303 || TestError:== 0.148272\n",
" Epoch :== 447 || TIME(sec):== 3.3352 || TrainError:== 0.024994 || TestError:== 0.148623\n",
" Epoch :== 448 || TIME(sec):== 3.2902 || TrainError:== 0.025064 || TestError:== 0.148219\n",
" Epoch :== 449 || TIME(sec):== 3.3074 || TrainError:== 0.025131 || TestError:== 0.149221\n",
" Epoch :== 450 || TIME(sec):== 3.3114 || TrainError:== 0.025005 || TestError:== 0.148852\n",
" Epoch :== 451 || TIME(sec):== 3.3382 || TrainError:== 0.024736 || TestError:== 0.14833\n",
" Epoch :== 452 || TIME(sec):== 3.3498 || TrainError:== 0.025142 || TestError:== 0.148942\n",
" Epoch :== 453 || TIME(sec):== 3.3163 || TrainError:== 0.024745 || TestError:== 0.148538\n",
" Epoch :== 454 || TIME(sec):== 3.3133 || TrainError:== 0.024589 || TestError:== 0.148476\n",
" Epoch :== 455 || TIME(sec):== 3.3226 || TrainError:== 0.024823 || TestError:== 0.14881\n",
" Epoch :== 456 || TIME(sec):== 3.3139 || TrainError:== 0.024924 || TestError:== 0.148774\n",
" Epoch :== 457 || TIME(sec):== 3.3053 || TrainError:== 0.024892 || TestError:== 0.149204\n",
" Epoch :== 458 || TIME(sec):== 3.2948 || TrainError:== 0.02488 || TestError:== 0.149091\n",
" Epoch :== 459 || TIME(sec):== 3.3214 || TrainError:== 0.024609 || TestError:== 0.149239\n",
" Epoch :== 460 || TIME(sec):== 3.3259 || TrainError:== 0.024671 || TestError:== 0.148853\n",
" Epoch :== 461 || TIME(sec):== 3.3382 || TrainError:== 0.024757 || TestError:== 0.149165\n",
" Epoch :== 462 || TIME(sec):== 3.2966 || TrainError:== 0.024722 || TestError:== 0.148979\n",
" Epoch :== 463 || TIME(sec):== 3.3014 || TrainError:== 0.024873 || TestError:== 0.14877\n",
" Epoch :== 464 || TIME(sec):== 3.2812 || TrainError:== 0.024809 || TestError:== 0.148982\n",
" Epoch :== 465 || TIME(sec):== 3.3074 || TrainError:== 0.024639 || TestError:== 0.148764\n",
" Epoch :== 466 || TIME(sec):== 3.3146 || TrainError:== 0.02473 || TestError:== 0.148875\n",
" Epoch :== 467 || TIME(sec):== 3.265 || TrainError:== 0.024552 || TestError:== 0.148788\n",
" Epoch :== 468 || TIME(sec):== 3.2899 || TrainError:== 0.024521 || TestError:== 0.148914\n",
" Epoch :== 469 || TIME(sec):== 3.3012 || TrainError:== 0.024597 || TestError:== 0.148535\n",
" Epoch :== 470 || TIME(sec):== 3.2901 || TrainError:== 0.024401 || TestError:== 0.148942\n",
" Epoch :== 471 || TIME(sec):== 3.3037 || TrainError:== 0.024548 || TestError:== 0.148685\n",
" Epoch :== 472 || TIME(sec):== 3.283 || TrainError:== 0.0248 || TestError:== 0.149243\n",
" Epoch :== 473 || TIME(sec):== 3.3008 || TrainError:== 0.024738 || TestError:== 0.148878\n",
" Epoch :== 474 || TIME(sec):== 3.3325 || TrainError:== 0.024646 || TestError:== 0.148932\n",
" Epoch :== 475 || TIME(sec):== 3.3139 || TrainError:== 0.024506 || TestError:== 0.149181\n",
" Epoch :== 476 || TIME(sec):== 3.2998 || TrainError:== 0.024372 || TestError:== 0.149118\n",
" Epoch :== 477 || TIME(sec):== 3.2821 || TrainError:== 0.024526 || TestError:== 0.148736\n",
" Epoch :== 478 || TIME(sec):== 3.2949 || TrainError:== 0.0246 || TestError:== 0.148887\n",
" Epoch :== 479 || TIME(sec):== 3.3017 || TrainError:== 0.024688 || TestError:== 0.148862\n",
" Epoch :== 480 || TIME(sec):== 3.2977 || TrainError:== 0.02458 || TestError:== 0.148986\n",
" Epoch :== 481 || TIME(sec):== 3.3104 || TrainError:== 0.024401 || TestError:== 0.148644\n",
" Epoch :== 482 || TIME(sec):== 3.3227 || TrainError:== 0.024472 || TestError:== 0.148982\n",
" Epoch :== 483 || TIME(sec):== 3.2988 || TrainError:== 0.024203 || TestError:== 0.14909\n",
" Epoch :== 484 || TIME(sec):== 3.2992 || TrainError:== 0.024317 || TestError:== 0.148787\n",
" Epoch :== 485 || TIME(sec):== 3.3047 || TrainError:== 0.024587 || TestError:== 0.148639\n",
" Epoch :== 486 || TIME(sec):== 3.3083 || TrainError:== 0.024582 || TestError:== 0.149236\n",
" Epoch :== 487 || TIME(sec):== 3.3134 || TrainError:== 0.024476 || TestError:== 0.148727\n",
" Epoch :== 488 || TIME(sec):== 3.31 || TrainError:== 0.02451 || TestError:== 0.14916\n",
" Epoch :== 489 || TIME(sec):== 3.3148 || TrainError:== 0.024565 || TestError:== 0.149279\n",
" Epoch :== 490 || TIME(sec):== 3.3141 || TrainError:== 0.024128 || TestError:== 0.149557\n",
" Epoch :== 491 || TIME(sec):== 3.2998 || TrainError:== 0.024201 || TestError:== 0.148404\n",
" Epoch :== 492 || TIME(sec):== 3.1389 || TrainError:== 0.024318 || TestError:== 0.148693\n",
" Epoch :== 493 || TIME(sec):== 2.7845 || TrainError:== 0.024246 || TestError:== 0.148777\n",
" Epoch :== 494 || TIME(sec):== 2.8718 || TrainError:== 0.024598 || TestError:== 0.149296\n",
" Epoch :== 495 || TIME(sec):== 3.3375 || TrainError:== 0.024346 || TestError:== 0.148986\n",
" Epoch :== 496 || TIME(sec):== 3.3104 || TrainError:== 0.024209 || TestError:== 0.149226\n",
" Epoch :== 497 || TIME(sec):== 3.3416 || TrainError:== 0.024373 || TestError:== 0.149346\n",
" Epoch :== 498 || TIME(sec):== 3.3165 || TrainError:== 0.024428 || TestError:== 0.149206\n",
" Epoch :== 499 || TIME(sec):== 3.3479 || TrainError:== 0.023992 || TestError:== 0.149344\n",
" Epoch :== 500 || TIME(sec):== 3.3252 || TrainError:== 0.024058 || TestError:== 0.149145\n"
]
}
],
"source": [
"\n",
"trainerror=[] \n",
"testerror=[]\n",
"dim =48\n",
"for ep in range(epochs):\n",
" model.train()\n",
" t1 = default_timer()\n",
" train_l2 = 0\n",
" for x, y in train_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" x =x.reshape(x.shape[0], 1, dim, dim)\n",
"\n",
" optimizer.zero_grad()\n",
"\n",
" out = model(x).reshape(batch_size, dim, dim,3)\n",
"\n",
" out = y_normalizer.decode(out)\n",
" y = y_normalizer.decode(y)\n",
" \n",
" \n",
"\n",
" loss = lossfunc(y.view(batch_size,-1),out.view(batch_size,-1))\n",
" \n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
" train_l2 += loss.item()\n",
"\n",
" scheduler.step()\n",
" model.eval()\n",
" test_l2 = 0.0\n",
" with torch.no_grad():\n",
" for x, y in test_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" x =x.reshape(x.shape[0], 1, dim, dim)\n",
" out = model(x).reshape(batch_size, dim, dim,3)\n",
" out = y_normalizer.decode(out)\n",
"\n",
" test_l2 += lossfunc(y.view(batch_size,-1),out.view(batch_size,-1)).item()\n",
"\n",
" train_l2/= ntrain\n",
" test_l2 /= ntest\n",
" trainerror += [train_l2]\n",
" testerror += [test_l2]\n",
" t2 = default_timer()\n",
" print(f' Epoch :== {ep+1} || TIME(sec):== {np.round((t2-t1),4)} '\n",
" f'|| TrainError:== {np.round(train_l2,6)} || TestError:== {np.round(test_l2,6)}')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8f89221d-2535-470a-affe-543dce64a1ae",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 700x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(7,6))\n",
"plt.plot(range(epochs),trainerror,linewidth = 4,label =\"TRAIN\")\n",
"plt.plot(range(epochs),testerror, linewidth = 4,label =\"TEST\")\n",
"plt.xlabel('Epoch', fontsize =20)\n",
"plt.ylabel('Loss', fontsize =20)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cc025eb8-f048-4ba3-ad22-d100d2eda9e1",
"metadata": {},
"outputs": [],
"source": [
"# trainn = np.array(trainerror)\n",
"# testnn = np.array(testerror)\n",
"# torch.save(model.state_dict(),'ResNet_STRESS_N1200_ep1200.pt')\n",
"# np.save('trainResNetStress', trainn)\n",
"# np.save('testResNetStress', testnn)"
]
},
{
"cell_type": "markdown",
"id": "91006356-a2ea-402d-b670-39ead4c139d0",
"metadata": {},
"source": [
"#### Testing-Stress"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "7efc0645-a250-4006-9dc0-7712b044f283",
"metadata": {},
"outputs": [],
"source": [
"prediction = torch.zeros(y_test_stress.shape)\n",
"c = 0\n",
"with torch.no_grad():\n",
" for x,y in test_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" x =x.reshape(x.shape[0], 1, dim, dim)\n",
" \n",
" out = model(x).reshape(batch_size, dim, dim,3)\n",
" out = y_normalizer.decode(out)\n",
" prediction[c*batch_size :c*batch_size+batch_size] = out\n",
" c+=1\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f2c49446-88fd-4bd2-be26-e1ee04ebe97d",
"metadata": {},
"outputs": [],
"source": [
"stress_act = y_test_stress.reshape(ntest, -1)\n",
"stress_pred = prediction.reshape(ntest,-1)\n",
"\n",
"r2_stress =[]\n",
"for i in range(stress_act.shape[0]):\n",
" act = stress_act[i]\n",
" pred = stress_pred[i]\n",
" r2 = r2_score(act,pred)\n",
" r2_stress += [r2]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "772574d8-8adb-4092-893f-bb9d4b1b34e8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9577617246632376 0.06755535441836907\n"
]
}
],
"source": [
"r2_avg_stress = np.average(r2_stress)\n",
"r2_std_stress = np.std(r2_stress)\n",
"\n",
"print(r2_avg_stress,r2_std_stress)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1c1aacd3-33d2-4f31-90ac-d1aec3e7e575",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 14,
"id": "e6f512fb-7942-4a39-9d6f-2753caf327f1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.96\n",
"0.07\n"
]
}
],
"source": [
"print(np.round((r2_avg_stress),2))\n",
"print(np.round((r2_std_stress),2))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "2f4e6a63-7087-4de9-b13e-a98aed430d87",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(0.1491)\n"
]
}
],
"source": [
"\n",
"## total test loss\n",
"loss = lossfunc(y_test_stress, prediction)\n",
"print(loss/ntest)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c720805b-7467-4911-9774-f64c81824f7f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
2D | M3RG-IITD/FNO-StressStrain | benchmarking/utility.py | .py | 3,537 | 139 | ### @meermehran || M3RG Lab || IIT Delhi
import torch
import numpy as np
import scipy.io
import h5py
import torch.nn as nn
import operator
from functools import reduce
from functools import partial
#################################################
#
# Utilities
#
#################################################
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
### MAT FILES ----->>>> Tensor Objects
class MatReader(object):
def __init__(self, file_path, to_torch=True, to_cuda=False, to_float=True):
super(MatReader, self).__init__()
self.to_torch = to_torch
self.to_cuda = to_cuda
self.to_float = to_float
self.file_path = file_path
self.data = None
self.old_mat = None
self._load_file()
def _load_file(self):
try:
self.data = scipy.io.loadmat(self.file_path)
self.old_mat = True
except:
self.data = h5py.File(self.file_path)
self.old_mat = False
def load_file(self, file_path):
self.file_path = file_path
self._load_file()
def read_field(self, field):
x = self.data[field]
if not self.old_mat:
x = x[()]
x = np.transpose(x, axes=range(len(x.shape) - 1, -1, -1))
if self.to_float:
x = x.astype(np.float32)
if self.to_torch:
x = torch.from_numpy(x)
if self.to_cuda:
x = x.cuda()
return x
def set_cuda(self, to_cuda):
self.to_cuda = to_cuda
def set_torch(self, to_torch):
self.to_torch = to_torch
def set_float(self, to_float):
self.to_float = to_float
# normalization, pointwise gaussian
class UnitGaussianNormalizer(object):
def __init__(self, x, eps=0.00001):
super(UnitGaussianNormalizer, self).__init__()
# x could be in shape of ntrain*n or ntrain*T*n or ntrain*n*T
self.mean = torch.mean(x, 0)
self.std = torch.std(x, 0)
self.eps = eps
def encode(self, x):
x = (x - self.mean) / (self.std + self.eps)
return x
def decode(self, x, sample_idx=None):
if sample_idx is None:
std = self.std + self.eps # n
mean = self.mean
else:
if len(self.mean.shape) == len(sample_idx[0].shape):
std = self.std[sample_idx] + self.eps # batch*n
mean = self.mean[sample_idx]
if len(self.mean.shape) > len(sample_idx[0].shape):
std = self.std[:,sample_idx]+ self.eps # T*batch*n
mean = self.mean[:,sample_idx]
# x is in shape of batch*n or T*batch*n
x = (x * std) + mean
return x
def cuda(self):
self.mean = self.mean.cuda()
self.std = self.std.cuda()
def cpu(self):
self.mean = self.mean.cpu()
self.std = self.std.cpu()
def toTensor(array):
'''Converts a numpy array to a torch tensor.'''
x = torch.from_numpy(array).type(torch.FloatTensor)
x.requires_grad = True
return x
## Loss Function
def lossfunc(ytrue, ypred):
batch = ytrue.size()[0]
diff = torch.norm(ypred.reshape(batch,-1) - ytrue.reshape(batch,-1), 2,1)
ynorm = torch.norm(ytrue.reshape(batch,-1), 2,1)
err= torch.sum(diff/ynorm)
return err
# print the number of parameters
def count_params(model):
c = 0
for p in list(model.parameters()):
c += reduce(operator.mul, list(p.size()))
return c
| Python |
2D | SMLM-Challenge/Challenge2016 | BaselineLocalization/BaselineLocalizationAbstract.java | .java | 18,905 | 553 | // =========================================================================================
//
// Single-Molecule Localization Microscopy Challenge 2016
// http://bigwww.epfl.ch/smlm/
//
// Author:
// Daniel Sage, http://bigwww.epfl.ch/sage/
// Biomedical Imaging Group (BIG)
// Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne,
// Switzerland
//
// Reference:
// D. Sage, H. Kirshner, T. Pengo, N. Stuurman, J. Min, S. Manley, M. Unser
// Quantitative Evaluation of Software Packages for Single-Molecule Localization
// Microscopy
// Nature Methods 12, August 2015.
//
// Conditions of use:
// You'll be free to use this software for research purposes, but you should not
// redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
// =========================================================================================
import ij.IJ;
import ij.ImagePlus;
import ij.Prefs;
import ij.WindowManager;
import ij.gui.GenericDialog;
import ij.gui.Overlay;
import ij.gui.Roi;
import ij.io.Opener;
import java.awt.Button;
import java.awt.Choice;
import java.awt.Color;
import java.awt.Dimension;
import java.awt.GridBagConstraints;
import java.awt.GridBagLayout;
import java.awt.Insets;
import java.awt.Label;
import java.awt.Panel;
import java.awt.TextField;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileWriter;
import java.util.ArrayList;
import java.util.Vector;
import javax.swing.BorderFactory;
import javax.swing.JFileChooser;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.JPanel;
import javax.swing.JScrollPane;
import javax.swing.JTextPane;
public abstract class BaselineLocalizationAbstract implements ActionListener {
protected enum Mode {
D2, DH, AS, BP
};
// All
protected double pixelsize = 250;
protected double fwhm = 250;
protected String logging = "Progression";
protected double minSignal = 10;
protected double minSNR = 1;
protected String path = System.getProperty("user.home");
// DH: Double-Helix
protected double DH_distMinPairing = 200;
protected double DH_distMaxPairing = 700;
protected double DH_A = 10;
protected double DH_B = 0;
// AS: Astigmatism
protected double AS_A = 100;
protected double AS_B = 0;
// BP: Biplan
protected double BP_distMaxPairing = 700;
protected double BP_A = 10;
protected double BP_B = 0;
protected String title = "Untitled";
protected int framesMax = 50000;
protected Mode mode = Mode.D2;
protected boolean mute = false;
protected boolean display = false;
protected boolean verbose = false;
protected float radiusPix = 1;
protected double chrono = 0;
// Calibration panel
private Button help = new Button("Help");
private Button calibrate = new Button("Calibrate");
private TextField txtCalZ = new TextField("1 > -750; 151 > 750");
private Choice cmbBeads = new Choice();
private Choice cmbSource = new Choice();
private GenericDialog dlg;
protected abstract double[] calibrate(ImagePlus imp, int fmin, int fmax, double zmin, double zmax, double zstep, Roi roi);
protected abstract ArrayList<BaselineLocalizationParticle> process(ImagePlus imp);
protected void doDialog(Mode mode, String title) {
this.mode = mode;
this.title = title;
ImagePlus imp = WindowManager.getCurrentImage();
if (imp == null) {
IJ.error("No open image.");
return;
}
if (imp.getStackSize() < 2) {
IJ.error("No open stack of images.");
return;
}
framesMax = imp.getStackSize();
getParameters();
dlg = new GenericDialog(title);
calibrate.addActionListener(this);
help.addActionListener(this);
addInfo(dlg);
Panel pnCalibration = createCalibrationPanel(txtCalZ, help, calibrate);
if (mode == Mode.DH || mode == Mode.AS || mode == Mode.BP) {
dlg.addPanel(pnCalibration, GridBagConstraints.NORTH, new Insets(10, 10, 10, 10));
}
addLine(dlg, "General parameters");
dlg.addNumericField("FWHM of PSF [nm]", fwhm, 1);
dlg.addNumericField("Pixelsize [nm]", pixelsize, 1);
dlg.addNumericField("Signal - Minimal value", minSignal, 1);
dlg.addNumericField("SNR - Minimal value", minSNR, 1);
if (mode == Mode.DH) {
addLine(dlg, "Specific parameters for double-helix");
dlg.addNumericField("A_dh z=A*angle+B", DH_A, 1);
dlg.addNumericField("B_dh z=A*angle+B", DH_B, 1);
dlg.addNumericField("Mindist pairing [nm]", DH_distMinPairing, 1);
dlg.addNumericField("Maxdist pairing [nm]", DH_distMaxPairing, 1);
}
if (mode == Mode.AS) {
addLine(dlg, "Specific parameters for astigmatism");
dlg.addNumericField("A_as z=A*(wx-wy)+B", AS_A, 1);
dlg.addNumericField("B_as z=A*(wx-wy)+B", AS_B, 1);
}
if (mode == Mode.BP) {
addLine(dlg, "Specific parameters for biplane");
dlg.addNumericField("A_bp z=A*(w1-w2)+B", BP_A, 1);
dlg.addNumericField("B_bp z=A*(w1-w2)+B", BP_B, 1);
dlg.addNumericField("Maxdist pairing [nm]", BP_distMaxPairing, 1);
}
addLine(dlg, "Runtime parameters");
dlg.addNumericField("Frames to process", framesMax, 0);
dlg.addStringField("Path to store output", path, 25);
dlg.addChoice("Logging message", new String[] { "Progression", "Display prefilter", "Verbose", "Mute" }, "Progression");
Panel pnSequence = createSequencePanel(imp);
dlg.addPanel(pnSequence, GridBagConstraints.NORTH, new Insets(10, 10, 10, 10));
dlg.showDialog();
if (dlg.wasCanceled())
return;
fwhm = dlg.getNextNumber();
pixelsize = dlg.getNextNumber();
minSignal = dlg.getNextNumber();
minSNR = dlg.getNextNumber();
if (mode == Mode.DH) {
DH_A = dlg.getNextNumber();
DH_B = dlg.getNextNumber();
DH_distMinPairing = dlg.getNextNumber();
DH_distMaxPairing = dlg.getNextNumber();
}
if (mode == Mode.AS) {
AS_A = dlg.getNextNumber();
AS_B = dlg.getNextNumber();
}
if (mode == Mode.BP) {
BP_A = dlg.getNextNumber();
BP_B = dlg.getNextNumber();
BP_distMaxPairing = dlg.getNextNumber();
}
framesMax = (int) dlg.getNextNumber();
path = dlg.getNextString();
logging = dlg.getNextChoice();
verbose = logging.equals("Verbose");
mute = logging.equals("Mute");
display = logging.equals("Display prefilter");
try {
(new File(path)).mkdir();
}
catch (Exception ex) {
}
imp.setSlice(1);
framesMax = Math.min(framesMax, imp.getStackSize());
runProcess();
}
@Override
public void actionPerformed(ActionEvent e) {
if (e.getSource() == calibrate)
runCalibration();
if (e.getSource() == help) {
JFrame frame = new JFrame("Calibration");
JTextPane text = new JTextPane();
text.setContentType("text/html");
text.setEditable(false);
text.setBorder(BorderFactory.createEtchedBorder());
text.setMargin(new Insets(4, 4 ,4, 4));
text.setText("<html><body style=\"font-family:arial\""+
"<b>Axial Calibration for 3D SMLM</b><br>"+
"<p>The calibration is performed on one specific bead. " +
"You have to select a z-stack images in the field <b>Stack of bead</b> and provide the parameters" +
" of localization <b>FWHM</b>, <b>Pixelsize</b>, <b>Mininal Signal</b>, and <b>Mininmal SNR</b>. " +
" Select also a rectangle (region-of-interest) around a 'good' bead (for the biplane case, only select a bead in left image)." +
"</p><p>"+
"<p>This simple calibration tool relies a linear relation between z and a shape factor of the bead. " +
"The goal of the calibration is to identify <i>A</i> and <i>B</i> in the equation: z = <i>A</i> s + <i>B</i>, " +
" where z is the axial position in nm, and s in a shape factor that depends of the 3D modalities:" +
"<br>• (as) Astigmatism: s = w<sub>x</sub> - w<sub>x</sub>, " +
" where w<sub>x</sub> is the estimated size of the bright spot in X" +
" where w<sub>y</sub> is the estimated size of the bright spot in Y." +
"<br>• (dh) Double-Helix: s = angle, " +
" where angle is the estimated angle formed by the closest bright spots." +
"<br>• (bp) Biplane: s = w<sub>1</sub> - w<sub>2</sub>, " +
" where w<sub>1</sub> and w<sub>2</sub> are the eestimated size, respectively in the left and the right image."+
"</p><p>"+
"<p>The calibration requires to knowledge of the correspondence between the slice number and the axial position (z) in nm." +
"The syntax is the following: " +
"<br><i>start_slice > axial_postion_nm; end_slice > axial_postion_nm;</i>" +
"<br>For example if the slice 25 corresponds to -500nm and the slice 125 corresponds to +500nm " +
"you have to enter 25 > -500; 125 > 500; in the <b>Calibration</b> field."
);
JScrollPane scroll = new JScrollPane(text);
scroll.setPreferredSize(new Dimension(550, 550));
frame.add(scroll);
frame.pack();
frame.setVisible(true);
}
}
public void runProcess() {
ImagePlus imp = WindowManager.getImage(cmbSource.getSelectedItem());
if (imp == null) {
IJ.error("Invalid image source");
return;
}
radiusPix = (float) (fwhm / pixelsize);
if (!mute)
IJ.log("Starting to process " + framesMax + " frames in " + title + "(fwhm=" + radiusPix + " pix)");
chrono = System.nanoTime();
ArrayList<BaselineLocalizationParticle> particles = process(imp);
int nt = imp.getStackSize();
Overlay overlay = new Overlay();
double zmax = -Double.MAX_VALUE;
double zmin = Double.MAX_VALUE;
for(BaselineLocalizationParticle particle : particles) {
zmax = Math.max(zmax, particle.z);
zmin = Math.min(zmin, particle.z);
}
for(BaselineLocalizationParticle particle : particles) {
int frame = particle.frame;
if (frame >= 1 && frame <= nt) {
imp.setSlice(frame);
double h = (particle.z - zmin)/(zmax-zmin);
Color zc = Color.getHSBColor((float)(h*0.5), 1f, 1f);
Color c = new Color(zc.getRed(), zc.getGreen(), zc.getBlue(), 150);
particle.draw(overlay, c, pixelsize);
}
}
imp.setOverlay(overlay);
String filename = path + File.separator + imp.getTitle() + "-" + framesMax + "-" + minSNR + ".csv";
if (!mute) IJ.log("Store into " + filename);
File file = new File(filename);
int count = 0;
try {
BufferedWriter buffer = new BufferedWriter(new FileWriter(file));
buffer.write(title + ", frame, xnano, ynano, znano, intensity, snr, width_x, width_y\n");
for (BaselineLocalizationParticle particle : particles)
buffer.write("" + (++count) + ", " + particle.toString() + "\n");
buffer.close();
}
catch (Exception ex) {
IJ.log(ex.toString());
}
if (!mute)
IJ.log("End of processing " + framesMax + " frames, number of particles: " + count);
imp.setSlice(1);
setParameters();
}
private void runCalibration() {
ImagePlus imp = WindowManager.getImage(cmbBeads.getSelectedItem());
if (imp == null)
return;
String items[] = txtCalZ.getText().split("[>;]");
if (items.length < 4) {
IJ.error("Syntax error: enter something like '10 > -600; 100 > 600");
return;
}
try {
Vector numericFields = dlg.getNumericFields();
Object txt0 = numericFields.get(0);
if (txt0 instanceof TextField)
fwhm = Double.parseDouble(((TextField)txt0).getText());
Object txt1 = numericFields.get(1);
if (txt1 instanceof TextField)
pixelsize = Double.parseDouble(((TextField)txt1).getText());
Object txt2 = numericFields.get(2);
if (txt2 instanceof TextField)
minSignal = Double.parseDouble(((TextField)txt2).getText());
Object txt3 = numericFields.get(3);
if (txt3 instanceof TextField)
minSNR = Double.parseDouble(((TextField)txt3).getText());
int fmin = Integer.parseInt(items[0].trim());
double zmin = Double.parseDouble(items[1].trim());
int fmax = Integer.parseInt(items[2].trim());
double zmax = Double.parseDouble(items[3].trim());
double zstep = (zmax-zmin) / (fmax-fmin);
Roi roi = imp.getRoi();
if (roi == null) {
IJ.error("Select a Roi around a bead");
return;
}
radiusPix = (float) (fwhm / pixelsize);
if (!mute)
IJ.log("Starting to process " + framesMax + " frames in " + title + "(fwhm=" + radiusPix + " pix)");
chrono = System.nanoTime();
double[] params = calibrate(imp, fmin, fmax, zmin, zmax, zstep, roi);
if (mode == Mode.AS) {
AS_A = params[0];
AS_B = params[1];
}
if (mode == Mode.DH) {
DH_A = params[0];
DH_B = params[1];
}
if (mode == Mode.BP) {
BP_A = params[0];
BP_B = params[1];
}
Object txtA = numericFields.get(4);
if (txtA instanceof TextField)
((TextField)txtA).setText(""+params[0]);
Object txtB = numericFields.get(5);
if (txtB instanceof TextField)
((TextField)txtB).setText(""+params[1]);
}
catch(Exception ex) {
IJ.error("Syntax error: e.g '10 > -600; 100 > 600");
return;
}
}
private ArrayList<String> findOpenImages() {
int id[] = WindowManager.getIDList();
ArrayList<String> list = new ArrayList<String>();
for(int i=0; i<id.length; i++) {
ImagePlus imp = WindowManager.getImage(id[i]);
if (imp.getStackSize() > 1)
list.add(imp.getTitle());
}
return list;
}
private Panel createSequencePanel(ImagePlus imp) {
Panel pnSequence = new Panel();
cmbSource.setPreferredSize(new Dimension(200, 20));
ArrayList<String> list = findOpenImages();
for(String name : list)
cmbSource.addItem(name);
pnSequence.add(cmbSource);
cmbSource.select(imp.getTitle());
return pnSequence;
}
private Panel createCalibrationPanel(TextField txtCalZ, Button help, Button calibrate) {
Panel pnCalibration = new Panel();
cmbBeads.setPreferredSize(new Dimension(200, 20));
ArrayList<String> list = findOpenImages();
for(String name : list)
cmbBeads.addItem(name);
JPanel pn = new JPanel();
pn.setLayout(new GridBagLayout());
GridBagConstraints cbs = new GridBagConstraints();
pn.setBorder(BorderFactory.createTitledBorder("Axial calibration on a bead (Z)"));
cbs.anchor = GridBagConstraints.NORTHWEST;
cbs.insets = new Insets(1, 1, 1, 1);
cbs.fill = GridBagConstraints.HORIZONTAL;
cbs.gridx = 0; cbs.gridy = 0; pn.add(new Label("Stack of bead (Roi)"), cbs);
cbs.gridx = 1; cbs.gridy = 0; pn.add(cmbBeads, cbs);
cbs.gridx = 0; cbs.gridy = 1; pn.add(new Label("Calibration"), cbs);
cbs.gridx = 1; cbs.gridy = 1; pn.add(txtCalZ, cbs);
cbs.gridx = 0; cbs.gridy = 2; pn.add(help, cbs);
cbs.gridx = 1; cbs.gridy = 2; pn.add(calibrate, cbs);
pnCalibration.add(pn);
return pnCalibration;
}
private void addLine(GenericDialog dlg, String line) {
JLabel label = new JLabel("<html><b>" + line + "</b></html>");
label.setAlignmentX(JLabel.CENTER_ALIGNMENT);
label.setPreferredSize(new Dimension(400, 20));
label.setBorder(BorderFactory.createEtchedBorder());
Panel panel = new Panel();
panel.add(label);
dlg.addPanel(panel);
}
private void addInfo(GenericDialog dlg) {
JTextPane text = new JTextPane();
text.setContentType("text/html");
text.setEditable(false);
text.setBorder(BorderFactory.createEtchedBorder());
text.setMargin(new Insets(4, 4 ,4, 4));
text.setText("<html><body style=\"font-family:arial\""+
"<b>Single-Molecule Localization Microscopy (SMLM)</b><br>"+
"This localization software is only designed to establish the performance baseline for the SMLM challenge."+
"It is very fast, very inaccurate, and it only relies on two threshold parameters.<br>"+
"<i>© 2016 SMLM Challenge. Biomedical Imaging Group, EPFL.</i>"
);
JScrollPane scroll = new JScrollPane(text);
scroll.setPreferredSize(new Dimension(400, 120));
Panel panel = new Panel();
panel.add(scroll);
dlg.addPanel(panel);
}
protected void log(int frame, ArrayList<int[]> candidates, ArrayList<BaselineLocalizationParticle> particles) {
if (mute)
return;
String time = IJ.d2s(((System.nanoTime() - chrono)*1e-6), 2) + "ms ";
IJ.log(time + "Frame:" + frame + " Candidates:" + candidates.size() + " Particles:" + particles.size());
}
private void getParameters() {
minSignal = Prefs.get(title + ".minSignal", 10);
minSNR = Prefs.get(title + ".minSNR", 10);
fwhm = Prefs.get(title + ".fwhm", 250);
pixelsize = Prefs.get(title + ".pixelsize", 100);
path = Prefs.get(title + ".path", System.getProperty("user.home"));
framesMax = (int) Prefs.get(title + ".framesMax", 50000);
logging = Prefs.get(title + ".logging", "Progression");
if (mode == Mode.DH) {
DH_distMinPairing = Prefs.get(title + ".distMinPairing", 200);
DH_distMaxPairing = Prefs.get(title + ".distMaxPairing", 700);
DH_A = Prefs.get(title + ".A", 10);
DH_B = Prefs.get(title + ".B", 0);
}
if (mode == Mode.AS) {
AS_A = Prefs.get(title + ".A", 10);
AS_B = Prefs.get(title + ".B", 0);
}
if (mode == Mode.BP) {
BP_distMaxPairing = Prefs.get(title + ".distMaxPairing", 700);
BP_A = Prefs.get(title + ".A", 10);
BP_B = Prefs.get(title + ".B", 0);
}
txtCalZ.setText(Prefs.get(title + ".calibration", "1 > -750; 151 > 750"));
}
protected void setParameters() {
Prefs.set(title + ".minSignal", minSignal);
Prefs.set(title + ".minSNR", minSNR);
Prefs.set(title + ".fwhm", fwhm);
Prefs.set(title + ".pixelsize", pixelsize);
Prefs.set(title + ".path", path);
Prefs.set(title + ".framesMax", framesMax);
Prefs.set(title + ".logging", logging);
if (mode == Mode.DH) {
Prefs.set(title + ".distMinPairing", DH_distMinPairing);
Prefs.set(title + ".distMaxPairing", DH_distMaxPairing);
Prefs.set(title + ".A", DH_A);
Prefs.set(title + ".B", DH_B);
}
if (mode == Mode.AS) {
Prefs.set(title + ".A", DH_A);
Prefs.set(title + ".B", DH_B);
}
if (mode == Mode.BP) {
Prefs.set(title + ".distMaxPairing", BP_distMaxPairing);
Prefs.set(title + ".A", BP_A);
Prefs.set(title + ".B", BP_B);
}
Prefs.set(title + ".calibration", txtCalZ.getText());
}
protected double[] linearRegression(double x[], double[] y) {
int n = x.length;
double sx = 0.0;
double sy = 0.0;
double sxy = 0.0;
double sxx = 0.0;
for(int i=0; i<n; i++) {
sx += x[i];
sy += y[i];
sxy += x[i]*y[i];
sxx += x[i]*x[i];
}
sx /= n;
sy /= n;
sxy /= n;
sxx /= n;
double a = (sxy - sx*sy) / (sxx - sx*sx);
double b = sy - a * sx;
return new double[] {a, b};
}
protected static void chooseImage() {
JFileChooser fc = new JFileChooser();
fc.setFileSelectionMode(JFileChooser.FILES_ONLY);
int ret = fc.showOpenDialog(null);
if (ret == JFileChooser.APPROVE_OPTION) {
Opener opener = new Opener();
String filename = fc.getSelectedFile().getAbsolutePath();
System.out.println(filename);
ImagePlus imp = opener.openImage(filename);
if (imp == null)
IJ.error("Unable to open " + fc.getSelectedFile().getAbsolutePath());
else
imp.show();
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | BaselineLocalization/BaselineLocalization_3D_Biplane.java | .java | 7,137 | 188 | // =========================================================================================
//
// Single-Molecule Localization Microscopy Challenge 2016
// http://bigwww.epfl.ch/smlm/
//
// Author:
// Daniel Sage, http://bigwww.epfl.ch/sage/
// Biomedical Imaging Group (BIG)
// Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne,
// Switzerland
//
// Reference:
// D. Sage, H. Kirshner, T. Pengo, N. Stuurman, J. Min, S. Manley, M. Unser
// Quantitative Evaluation of Software Packages for Single-Molecule Localization
// Microscopy
// Nature Methods 12, August 2015.
//
// Conditions of use:
// You'll be free to use this software for research purposes, but you should not
// redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
// =========================================================================================
import ij.ImagePlus;
import ij.gui.Plot;
import ij.gui.Roi;
import ij.plugin.PlugIn;
import ij.process.FloatProcessor;
import java.util.ArrayList;
import java.util.Collections;
public class BaselineLocalization_3D_Biplane extends BaselineLocalizationAbstract implements PlugIn {
public static void main(String args[]) {
chooseImage();
new BaselineLocalization_3D_Biplane().run("");
}
@Override
public void run(String arg) {
doDialog(Mode.BP, "Baseline Localization 3D Biplane");
}
@Override
public ArrayList<BaselineLocalizationParticle> process(ImagePlus imp) {
ArrayList<BaselineLocalizationParticle> all = new ArrayList<BaselineLocalizationParticle>();
BaselineLocalizationOperators op = new BaselineLocalizationOperators(radiusPix, minSignal, minSNR, pixelsize, verbose, display);
for (int frame = 1; frame <= framesMax; frame++) {
FloatProcessor ip[] = split(imp.getStack().getProcessor(frame).convertToFloatProcessor());
imp.setSlice(frame);
ArrayList<int[]> candidates0 = op.detect(ip[0], op.filterDoG(ip[0], frame), false);
ArrayList<int[]> candidates1 = op.detect(ip[1], op.filterDoG(ip[1], frame), false);
ArrayList<BaselineLocalizationParticle> particles0 = op.localize(candidates0, ip[0], frame);
ArrayList<BaselineLocalizationParticle> particles1 = op.localize(candidates1, ip[1], frame);
ArrayList<BaselineLocalizationParticle> particles = pairingBiplane(particles0, particles1, imp.getWidth());
all.addAll(particles);
candidates0.addAll(candidates1);
log(frame, candidates0, particles);
}
return all;
}
@Override
public double[] calibrate(ImagePlus imp, int fmin, int fmax, double zmin, double zmax, double zstep, Roi roi) {
double param[] = new double[fmax-fmin+1];
double axial[] = new double[fmax-fmin+1];
BaselineLocalizationOperators op = new BaselineLocalizationOperators(radiusPix, minSignal, minSNR, pixelsize, verbose, display);
for (int z = 0; z <param.length; z++) {
FloatProcessor ip[] = split(imp.getStack().getProcessor(z+fmin).convertToFloatProcessor());
imp.setSlice(z+fmin);
ip[0].setRoi(roi);
ip[0] = (FloatProcessor)ip[0].crop();
ip[1].setRoi(roi);
ip[1] = (FloatProcessor)ip[1].crop();
imp.setSlice(z+1);
FloatProcessor fip0 = op.filterDoG(ip[0], z+fmin);
FloatProcessor fip1 = op.filterDoG(ip[1], z+fmin);
ArrayList<int[]> candidates0 = op.detect(ip[0], fip0, true);
ArrayList<int[]> candidates1 = op.detect(ip[1], fip1, true);
ArrayList<BaselineLocalizationParticle> particles0 = op.localize(candidates0, ip[0], z+fmin);
ArrayList<BaselineLocalizationParticle> particles1 = op.localize(candidates1, ip[1], z+fmin);
if (particles0.size() >= 1 && particles1.size() >= 1) {
BaselineLocalizationParticle p0 = particles0.get(0);
BaselineLocalizationParticle p1 = particles1.get(0);
double w0 = (p0.sigmaX + p0.sigmaY) * 0.5;
double w1 = (p1.sigmaX + p1.sigmaY) * 0.5;
param[z] = w0 - w1;
axial[z] = z*zstep + zmin;
}
}
new Plot("Calibration Biplane", "angle", "Z in nm", param, axial).show();
return linearRegression(param, axial);
}
private ArrayList<BaselineLocalizationParticle> pairingBiplane(ArrayList<BaselineLocalizationParticle> particles0, ArrayList<BaselineLocalizationParticle> particles1, int nx) {
int n0 = particles0.size();
int n1 = particles1.size();
ArrayList<Pair> pairs = new ArrayList<Pair>();
for (int i = 0; i < n0; i++) {
BaselineLocalizationParticle p0 = particles0.get(i);
for (int j = 0; j < n1; j++) {
BaselineLocalizationParticle p1 = particles1.get(j);
double dist = Math.sqrt((p0.x - p1.x) * (p0.x - p1.x) + (p0.y - p1.y) * (p0.y - p1.y));
if (dist <= BP_distMaxPairing) {
double x = p0.x;
double y = p0.y;
double w0 = (p0.sigmaX + p0.sigmaY) * 0.5;
double w1 = (p1.sigmaX + p1.sigmaY) * 0.5;
double z = BP_A * (w0-w1) + BP_B;
double signal = p0.signal + p1.signal;
double snr = (p0.snr + p1.snr) * 0.5;
pairs.add(new Pair(p0.frame, i, j, x, y, z, signal, snr, dist));
}
}
}
Collections.sort(pairs);
boolean taken0[] = new boolean[n0];
for (int i = 0; i < n0; i++)
taken0[i] = false;
boolean taken1[] = new boolean[n1];
for (int i = 0; i < n1; i++)
taken1[i] = false;
ArrayList<BaselineLocalizationParticle> particles = new ArrayList<BaselineLocalizationParticle>();
for (int i = 0; i < pairs.size(); i++) {
Pair pair = pairs.get(i);
if (taken0[pair.k1] == false && taken1[pair.k2] == false) {
taken0[pair.k1] = true;
taken1[pair.k2] = true;
double[] mxy = new double[] {pair.x, pair.y, 0.0, 0.0};
BaselineLocalizationParticle particle = new BaselineLocalizationParticle(pair.frame, mxy, pair.signal, pair.snr);
particle.z = pair.z;
BaselineLocalizationParticle p1 = particles0.get(pair.k1);
BaselineLocalizationParticle p2 = particles1.get(pair.k2);
particle.setBiSpot(new double[] {p1.x, p1.y}, new double[] {p2.x + nx/2*pixelsize, p2.y});
particles.add(particle);
}
}
return particles;
}
private FloatProcessor[] split(FloatProcessor source) {
int nx = source.getWidth();
int ny = source.getHeight();
FloatProcessor ipLeft = new FloatProcessor(nx/2, ny);
for(int i=0; i<nx/2; i++)
for(int j=0; j<ny; j++)
ipLeft.putPixelValue(i, j, source.getPixelValue(i, j));
FloatProcessor ipRight = new FloatProcessor(nx/2, ny);
for(int i=0; i<nx/2; i++)
for(int j=0; j<ny; j++)
ipRight.putPixelValue(i, j, source.getPixelValue(i+nx/2, j));
return new FloatProcessor[] {ipLeft , ipRight};
}
private class Pair implements Comparable<Pair> {
public int frame;
public double x;
public double y;
public double z;
public int k1;
public int k2;
public double signal;
public double dist;
public double snr;
public Pair(int frame, int k1, int k2, double x, double y, double z, double signal, double snr, double dist) {
this.frame = frame;
this.k1 = k1;
this.k2 = k2;
this.x = x;
this.y = y;
this.z = z;
this.signal = signal;
this.snr = snr;
this.dist = dist;
}
public int compareTo(Pair pair) {
return dist > pair.dist ? 1 : -1;
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | BaselineLocalization/BaselineLocalization_2D.java | .java | 2,341 | 64 | //=========================================================================================
//
// Single-Molecule Localization Microscopy Challenge 2016
// http://bigwww.epfl.ch/smlm/
//
// Author:
// Daniel Sage, http://bigwww.epfl.ch/sage/
// Biomedical Imaging Group (BIG)
// Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
//
// Reference:
// D. Sage, H. Kirshner, T. Pengo, N. Stuurman, J. Min, S. Manley, M. Unser
// Quantitative Evaluation of Software Packages for Single-Molecule Localization Microscopy
// Nature Methods 12, August 2015.
//
// Conditions of use:
// You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
import java.util.ArrayList;
import ij.ImagePlus;
import ij.gui.Roi;
import ij.plugin.PlugIn;
import ij.process.FloatProcessor;
public class BaselineLocalization_2D extends BaselineLocalizationAbstract implements PlugIn {
public static void main(String args[]) {
chooseImage();
new BaselineLocalization_2D().run("");
}
@Override
public void run(String arg) {
doDialog(Mode.D2, "Baseline Localization 2D");
}
@Override
public ArrayList<BaselineLocalizationParticle> process(ImagePlus imp) {
ArrayList<BaselineLocalizationParticle> all = new ArrayList<BaselineLocalizationParticle>();
BaselineLocalizationOperators op = new BaselineLocalizationOperators(radiusPix, minSignal, minSNR, pixelsize, verbose, display);
for (int frame = 1; frame <= framesMax; frame++) {
FloatProcessor ip = imp.getStack().getProcessor(frame).convertToFloatProcessor();
imp.setSlice(frame);
FloatProcessor fip = op.filterDoG(ip, frame);
ArrayList<int[]> candidates = op.detect(ip, fip, false);
ArrayList<BaselineLocalizationParticle> particles = op.localize(candidates, ip, frame);
all.addAll(particles);
log(frame, candidates, particles);
}
return all;
}
@Override
public double[] calibrate(ImagePlus imp, int fmin, int fmax, double zmin, double zmax, double zstep, Roi roi) {
// no calibration for the 2D case
return new double[] {1.0, 0.0};
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | BaselineLocalization/BaselineLocalization_3D_Astigmatism.java | .java | 3,388 | 85 | //=========================================================================================
//
// Single-Molecule Localization Microscopy Challenge 2016
// http://bigwww.epfl.ch/smlm/
//
// Author:
// Daniel Sage, http://bigwww.epfl.ch/sage/
// Biomedical Imaging Group (BIG)
// Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
//
// Reference:
// D. Sage, H. Kirshner, T. Pengo, N. Stuurman, J. Min, S. Manley, M. Unser
// Quantitative Evaluation of Software Packages for Single-Molecule Localization Microscopy
// Nature Methods 12, August 2015.
//
// Conditions of use:
// You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
import java.util.ArrayList;
import ij.IJ;
import ij.ImagePlus;
import ij.gui.Plot;
import ij.gui.Roi;
import ij.plugin.PlugIn;
import ij.process.FloatProcessor;
public class BaselineLocalization_3D_Astigmatism extends BaselineLocalizationAbstract implements PlugIn {
public static void main(String args[]) {
chooseImage();
new BaselineLocalization_3D_Astigmatism().run("");
}
@Override
public void run(String arg) {
doDialog(Mode.AS, "Baseline Localization 3D Astigmatism");
}
@Override
public ArrayList<BaselineLocalizationParticle> process(ImagePlus imp) {
ArrayList<BaselineLocalizationParticle> all = new ArrayList<BaselineLocalizationParticle>();
BaselineLocalizationOperators op = new BaselineLocalizationOperators(radiusPix, minSignal, minSNR, pixelsize, verbose, display);
for (int frame = 1; frame <= framesMax; frame++) {
FloatProcessor ip = imp.getStack().getProcessor(frame).convertToFloatProcessor();
imp.setSlice(frame);
FloatProcessor fip = op.filterDoG(ip, frame);
ArrayList<int[]> candidates = op.detect(ip, fip, false);
ArrayList<BaselineLocalizationParticle> particles = op.localize(candidates, ip, frame);
for (BaselineLocalizationParticle particle : particles)
particle.z = AS_A * (particle.sigmaX-particle.sigmaY) + AS_B;
all.addAll(particles);
log(frame, candidates, particles);
}
return all;
}
@Override
public double[] calibrate(ImagePlus imp, int fmin, int fmax, double zmin, double zmax, double zstep, Roi roi) {
double param[] = new double[fmax-fmin+1];
double axial[] = new double[fmax-fmin+1];
BaselineLocalizationOperators op = new BaselineLocalizationOperators(radiusPix, minSignal, minSNR, pixelsize, verbose, display);
for (int z = 0; z <param.length; z++) {
FloatProcessor ip = imp.getStack().getProcessor(z+fmin).convertToFloatProcessor();
imp.setSlice(z+fmin);
ip.setRoi(roi);
ip = (FloatProcessor)ip.crop();
FloatProcessor fip = op.filterDoG(ip, z+fmin);
ArrayList<int[]> candidates = op.detect(ip, fip, true);
ArrayList<BaselineLocalizationParticle> particles = op.localize(candidates, ip, z+fmin);
if (particles.size() == 1) {
BaselineLocalizationParticle p = particles.get(0);
param[z] = p.sigmaX - p.sigmaY;
axial[z] = z*zstep + zmin;
}
}
new Plot("Calibration Astimagtism", "sigmaX - sigmaY", "Z in nm", param, axial).show();
return linearRegression(param, axial);
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | BaselineLocalization/BaselineLocalizationOperators.java | .java | 10,923 | 334 | //=========================================================================================
//
// Single-Molecule Localization Microscopy Challenge 2016
// http://bigwww.epfl.ch/smlm/
//
// Author:
// Daniel Sage, http://bigwww.epfl.ch/sage/
// Biomedical Imaging Group (BIG)
// Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
//
// Reference:
// D. Sage, H. Kirshner, T. Pengo, N. Stuurman, J. Min, S. Manley, M. Unser
// Quantitative Evaluation of Software Packages for Single-Molecule Localization Microscopy
// Nature Methods 12, August 2015.
//
// Conditions of use:
// You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
import ij.IJ;
import ij.ImagePlus;
import ij.process.FloatProcessor;
import java.util.ArrayList;
public class BaselineLocalizationOperators {
private float radiusPix;
private double minSignal;
private boolean display;
private double minSNR;
private double pixelsize;
private boolean verbose;
public BaselineLocalizationOperators(float radiusPix, double minSignal, double minSNR, double pixelsize, boolean verbose, boolean display) {
this.radiusPix = radiusPix;
this.minSignal = minSignal;
this.minSNR = minSNR;
this.pixelsize = pixelsize;
this.verbose = verbose;
this.display = display;
}
public FloatProcessor filterDoG(FloatProcessor source, int frame) {
int nx = source.getWidth();
int ny = source.getHeight();
float signal[] = (float[]) source.getPixels();
int n = signal.length;
float signal1[] = new float[n];
float signal2[] = new float[n];
float dog[] = new float[n];
System.arraycopy(signal, 0, signal1, 0, n);
System.arraycopy(signal, 0, signal2, 0, n);
new FastGaussianSmoothing(signal1, radiusPix * 2f, nx, ny).run();
new FastGaussianSmoothing(signal2, radiusPix * 0.5f, nx, ny).run();
for (int k = 0; k < signal1.length; k++)
dog[k] = (signal2[k] > signal1[k] ? signal2[k] - signal1[k] : 0f);
FloatProcessor fp = new FloatProcessor(nx, ny, dog, null);
if (display && frame == 1)
new ImagePlus("Prefilter frame 1", fp).show();
return fp;
}
public ArrayList<int[]> detect(FloatProcessor source, FloatProcessor prefiltered, boolean calibration) {
int nx = prefiltered.getWidth();
int ny = prefiltered.getHeight();
int h = (int) Math.ceil(radiusPix/2);
ArrayList<int[]> lmall = new ArrayList<int[]>();
double max = -Double.MAX_VALUE;
int imax = nx/2;
int jmax = ny/2;
// Find the local max
for (int i = h; i < nx - h; i++) {
for (int j = h; j < ny - h; j++) {
if (source.getf(i, j) > minSignal) {
float v = prefiltered.getf(i, j);
if (v > prefiltered.getf(i - 1, j))
if (v > prefiltered.getf(i + 1, j))
if (v > prefiltered.getf(i - 1, j - 1))
if (v > prefiltered.getf(i, j - 1))
if (v > prefiltered.getf(i + 1, j - 1))
if (v > prefiltered.getf(i - 1, j + 1))
if (v > prefiltered.getf(i, j + 1))
if (v > prefiltered.getf(i + 1, j + 1)) {
if (v > max ) {
imax = i;
jmax = j;
max = v;
}
lmall.add(new int[] { i, j });
}
}
}
}
// Only 1 local max for calibration, all otherwise
ArrayList<int[]> lmcal = new ArrayList<int[]>();
lmcal.add(new int[] {imax, jmax});
return (calibration ? lmcal : lmall);
}
protected ArrayList<BaselineLocalizationParticle> localize(ArrayList<int[]> candidates, FloatProcessor source, int frame) {
ArrayList<BaselineLocalizationParticle> spots = new ArrayList<BaselineLocalizationParticle>();
for (int[] candidate : candidates) {
double snr = computeSNR(candidate, source, radiusPix);
if (snr > minSNR) {
double[] mxy = computeMoments(candidate, source, radiusPix);
double signal = source.getPixelValue(candidate[0], candidate[1]);
double x = mxy[0] * pixelsize;
double y = mxy[1] * pixelsize;
String pos = " (" + IJ.d2s(x, 5) + " " + IJ.d2s(y, 5) + ")";
if (verbose)
IJ.log(" " + pos + " signal:" + IJ.d2s(signal, 5) + " snr:" + IJ.d2s(snr,5) + " sigma:" + IJ.d2s(mxy[2], 5) + " " + IJ.d2s(mxy[3], 5));
double mxynm[] = new double[] {mxy[0] * pixelsize, mxy[1] * pixelsize, mxy[2], mxy[3]};
BaselineLocalizationParticle particle = new BaselineLocalizationParticle(frame, mxynm, signal, snr);
spots.add(particle);
}
}
return spots;
}
protected double[] computeMoments(int candidate[], FloatProcessor fp, float fwhmPixel) {
int h = (int) Math.ceil(fwhmPixel);
int i = candidate[0];
int j = candidate[1];
float v = 0.0f;
double sum = 0.0;
double x1 = 0.0;
double y1 = 0.0;
for (int x = i - h; x <= i + h; x++)
for (int y = j - h; y <= j + h; y++) {
v = fp.getPixelValue(x, y);
x1 += x * v;
y1 += y * v;
sum += v;
}
if (sum > 0) {
x1 /= sum;
y1 /= sum;
}
double x2 = 0.0;
double y2 = 0.0;
for (int x = i - h; x <= i + h; x++)
for (int y = j - h; y <= j + h; y++) {
v = fp.getPixelValue(x, y);
x2 += (x - x1) * (x - x1) * v;
y2 += (y - y1) * (y - y1) * v;
}
if (sum > 0) {
x2 = Math.sqrt(x2 / sum);
y2 = Math.sqrt(y2 / sum);
}
return new double[] { x1 + 0.5, y1 + 0.5, x2, y2 };
}
protected double computeSNR(int[] candidate, FloatProcessor source, float fwhmPixel) {
int h = (int) Math.ceil(fwhmPixel);
int nx = source.getWidth();
int ny = source.getHeight();
int i = candidate[0];
int j = candidate[1];
if (i - h < 0) return -Double.MAX_VALUE;
if (j - h < 0) return -Double.MAX_VALUE;
if (j + h > nx - 1) return -Double.MAX_VALUE;
if (j + h > ny - 1) return -Double.MAX_VALUE;
float[] pix = (float[]) source.getPixels();
// Signal
double meanSignal = 0.0;
double signal = -Double.MAX_VALUE;
int countSignal = 0;
for (int x = i - h; x <= i + h; x++)
for (int y = j - h; y <= j + h; y++) {
meanSignal += pix[x + j * nx];
countSignal++;
if (pix[x + j * nx] > signal) signal = pix[x + j * nx];
}
meanSignal = meanSignal / countSignal;
double noiseSignal = 0;
for (int x = i - h; x <= i + h; x++)
for (int y = j - h; y <= j + h; y++) {
noiseSignal += (pix[x + j * nx] - meanSignal) * (pix[x + j * nx] - meanSignal);
}
noiseSignal = Math.sqrt(noiseSignal / countSignal);
// background
double meanBackground = 0.0;
int countBackground = 0;
int n = 2 * h + 1;
for (int x = i - n; x < i - h; x++)
for (int y = j - h; y <= j + h; y++) {
countBackground++;
meanBackground += pix[x + j * nx];
}
for (int x = i + h + 1; x <= i + n; x++)
for (int y = j - h; y <= j + h; y++) {
countBackground++;
meanBackground += pix[x + j * nx];
}
for (int x = i - h; x <= i + h; x++)
for (int y = j - n; y < j - h; y++) {
countBackground++;
meanBackground += pix[x + j * nx];
}
for (int x = i - h; x <= i + h; x++)
for (int y = j + h + 1; y <= j + n; y++) {
countBackground++;
meanBackground += pix[x + j * nx];
}
meanBackground /= countBackground;
double noiseBackground = 0;
for (int x = i - n; x < i - h; x++)
for (int y = j - h; y <= j + h; y++) {
noiseBackground += (pix[x + j * nx] - meanBackground) * (pix[x + j * nx] - meanBackground);
}
for (int x = i + h + 1; x <= i + n; x++)
for (int y = j - h; y <= j + h; y++) {
noiseBackground += (pix[x + j * nx] - meanBackground) * (pix[x + j * nx] - meanBackground);
}
for (int x = i - h; x <= i + h; x++)
for (int y = j - n; y < j - h; y++) {
noiseBackground += (pix[x + j * nx] - meanBackground) * (pix[x + j * nx] - meanBackground);
}
for (int x = i - h; x <= i + h; x++)
for (int y = j + h + 1; y <= j + n; y++) {
noiseBackground += (pix[x + j * nx] - meanBackground) * (pix[x + j * nx] - meanBackground);
}
noiseBackground = Math.sqrt(noiseBackground / countBackground);
double snr = (signal - meanBackground) / noiseBackground;
return snr;
}
/**
* Gaussian filter class. Implementation of the Gaussian filter as a cascade
* of 3 exponential filters. The boundary conditions are mirroring. Threaded
* or directly by calling the run()
*/
protected class FastGaussianSmoothing implements Runnable {
private float signal[];
private float sigma;
private int nx;
private int ny;
public FastGaussianSmoothing(float signal[], float sigma, int nx, int ny) {
this.signal = signal;
this.sigma = sigma;
this.nx = nx;
this.ny = ny;
}
public void run() {
if (nx > 1 & sigma > 0) {
float row[] = new float[nx];
float s2 = sigma * sigma;
float pole = 1.0f + (3.0f / s2) - (float) (Math.sqrt(9.0 + 6.0 * s2) / s2);
for (int y = 0; y < ny * nx; y += nx) {
System.arraycopy(signal, y, row, 0, nx);
row = convolveIIR_TriplePole(row, pole);
System.arraycopy(row, 0, signal, y, nx);
}
}
if (ny > 1 & sigma > 0) {
float liney[] = new float[ny];
float s2 = sigma * sigma;
float pole = 1.0f + (3.0f / s2) - (float) (Math.sqrt(9.0 + 6.0 * s2) / s2);
for (int x = 0; x < nx; x++) {
for (int y = 0; y < ny; y++)
liney[y] = signal[y * nx + x];
liney = convolveIIR_TriplePole(liney, pole);
for (int y = 0; y < ny; y++)
signal[y * nx + x] = liney[y];
}
}
}
private float[] convolveIIR_TriplePole(float[] signal, float pole) {
int l = signal.length;
float lambda = 1.0f;
float[] output = new float[l];
for (int k = 0; k < 3; k++) {
lambda = lambda * (1.0f - pole) * (1.0f - 1.0f / pole);
}
for (int n = 0; n < l; n++) {
output[n] = signal[n] * lambda;
}
for (int k = 0; k < 3; k++) {
output[0] = getInitialCausalCoefficientMirror(output, pole);
for (int n = 1; n < l; n++) {
output[n] = output[n] + pole * output[n - 1];
}
output[l - 1] = getInitialAntiCausalCoefficientMirror(output, pole);
for (int n = l - 2; 0 <= n; n--) {
output[n] = pole * (output[n + 1] - output[n]);
}
}
return output;
}
private float getInitialAntiCausalCoefficientMirror(float[] c, float z) {
return ((z * c[c.length - 2] + c[c.length - 1]) * z / (z * z - 1.0f));
}
private float getInitialCausalCoefficientMirror(float[] c, float z) {
float tolerance = 10e-6f;
float z1 = z;
float zn = (float) Math.pow(z, c.length - 1);
float sum = c[0] + zn * c[c.length - 1];
int horizon = c.length;
if (tolerance > 0.0f) {
horizon = 2 + (int) (Math.log(tolerance) / Math.log(Math.abs(z)));
horizon = (horizon < c.length) ? (horizon) : (c.length);
}
zn = zn * zn;
for (int n = 1; n < horizon - 1; n++) {
zn = zn / z;
sum = sum + (z1 + zn) * c[n];
z1 = z1 * z;
}
return (sum / (1.0f - (float) Math.pow(z, 2 * c.length - 2)));
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | BaselineLocalization/BaselineLocalization_3D_DoubleHelix.java | .java | 7,292 | 203 | //=========================================================================================
//
// Single-Molecule Localization Microscopy Challenge 2016
// http://bigwww.epfl.ch/smlm/
//
// Author:
// Daniel Sage, http://bigwww.epfl.ch/sage/
// Biomedical Imaging Group (BIG)
// Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
//
// Reference:
// D. Sage, H. Kirshner, T. Pengo, N. Stuurman, J. Min, S. Manley, M. Unser
// Quantitative Evaluation of Software Packages for Single-Molecule Localization Microscopy
// Nature Methods 12, August 2015.
//
// Conditions of use:
// You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
import java.util.ArrayList;
import java.util.Collections;
import ij.ImagePlus;
import ij.gui.Plot;
import ij.gui.Roi;
import ij.plugin.PlugIn;
import ij.process.FloatProcessor;
public class BaselineLocalization_3D_DoubleHelix extends BaselineLocalizationAbstract implements PlugIn {
public static void main(String args[]) {
chooseImage();
new BaselineLocalization_3D_DoubleHelix().run("");
}
@Override
public void run(String arg) {
doDialog(Mode.DH, "Baseline Localization 3D DoubleHelix");
}
@Override
public ArrayList<BaselineLocalizationParticle> process(ImagePlus imp) {
ArrayList<BaselineLocalizationParticle> all = new ArrayList<BaselineLocalizationParticle>();
BaselineLocalizationOperators op = new BaselineLocalizationOperators(radiusPix, minSignal, minSNR, pixelsize, verbose, display);
for (int frame = 1; frame <= framesMax; frame++) {
FloatProcessor ip = imp.getStack().getProcessor(frame).convertToFloatProcessor();
imp.setSlice(frame);
FloatProcessor fip = op.filterDoG(ip, frame);
ArrayList<int[]> candidates = op.detect(ip, fip, false);
ArrayList<BaselineLocalizationParticle> spots = op.localize(candidates, ip, frame);
ArrayList<BaselineLocalizationParticle> particles = pairingDoubleHelix(spots);
all.addAll(particles);
log(frame, candidates, particles);
}
return all;
}
@Override
public double[] calibrate(ImagePlus imp, int fmin, int fmax, double zmin, double zmax, double zstep, Roi roi) {
double param[] = new double[fmax-fmin+1];
double axial[] = new double[fmax-fmin+1];
BaselineLocalizationOperators op = new BaselineLocalizationOperators(radiusPix, minSignal, minSNR, pixelsize, verbose, display);
for (int z = 0; z <param.length; z++) {
FloatProcessor ip = imp.getStack().getProcessor(z+fmin).convertToFloatProcessor();
ip.setRoi(roi);
ip = (FloatProcessor)ip.crop();
imp.setSlice(z+fmin);
FloatProcessor fip = op.filterDoG(ip, z+fmin);
ArrayList<int[]> candidates = op.detect(ip, fip, true);
ArrayList<BaselineLocalizationParticle> particles = op.localize(candidates, ip, z+fmin);
if (particles.size() >= 1) {
BaselineLocalizationParticle p = particles.get(0);
ArrayList<int[]> candq = searchCloseLocalMax(fip, p, z, pixelsize);
ArrayList<BaselineLocalizationParticle> particles2 = op.localize(candq, ip, z+1);
if (particles2.size() >= 1) {
BaselineLocalizationParticle q = particles2.get(0);
double angle = Math.toDegrees(Math.atan((p.x-q.x)/(p.y-q.y)));
param[z] = angle;
axial[z] = z*zstep + zmin;
}
}
}
new Plot("Calibration Double-Helix", "dx", "Z in nm", param, axial).show();
return linearRegression(param, axial);
}
private ArrayList<int[]> searchCloseLocalMax(FloatProcessor fip, BaselineLocalizationParticle p, int z, double pixelsize) {
int nx = fip.getWidth();
int ny = fip.getHeight();
double max = -Double.MAX_VALUE;
int imax = nx/2;
int jmax = ny/2;
int h = 2;
// Find the local max
for (int i = h; i < nx - h; i++)
for (int j = h; j < ny - h; j++) {
double x = i*pixelsize;
double y = j*pixelsize;
double d = Math.sqrt((p.x-x)*(p.x-x) + (p.y-y)*(p.y-y));
if (d > 200 && d < 2000) {
float v = fip.getf(i, j);
if (v > fip.getf(i - 1, j))
if (v > fip.getf(i + 1, j))
if (v > fip.getf(i - 1, j - 1))
if (v > fip.getf(i, j - 1))
if (v > fip.getf(i + 1, j - 1))
if (v > fip.getf(i - 1, j + 1))
if (v > fip.getf(i, j + 1))
if (v > fip.getf(i + 1, j + 1))
if (v > max ) {
imax = i;
jmax = j;
max = v;
}
}
}
ArrayList<int[]> cand = new ArrayList<int[]>();
cand.add(new int[] {imax, jmax});
return cand;
}
private ArrayList<BaselineLocalizationParticle> pairingDoubleHelix(ArrayList<BaselineLocalizationParticle> spots) {
int n = spots.size();
ArrayList<Pair> pairs = new ArrayList<Pair>();
for(int i=0; i<n; i++) {
BaselineLocalizationParticle p = spots.get(i);
for(int j=0; j<n; j++) {
BaselineLocalizationParticle q = spots.get(j);
double dist = Math.sqrt((p.x-q.x)*(p.x-q.x) + (p.y-q.y)*(p.y-q.y));
if (dist >= DH_distMinPairing && dist <= DH_distMaxPairing) {
double diffSignal = Math.abs(p.signal-q.signal);
double x = (p.x + q.x)*0.5;
double y = (p.y + q.y)*0.5;
double angle = Math.toDegrees(Math.atan((p.x-q.x)/(p.y-q.y)));
double z = DH_A * angle + DH_B;
double signal = (p.signal+q.signal)*0.5;
double snr = (p.snr+q.snr)*0.5;
pairs.add(new Pair(p.frame, i, j, x, y, z, signal, snr, p.sigmaX, p.sigmaY, diffSignal, angle));
}
}
}
Collections.sort(pairs);
boolean taken[] = new boolean[n];
for(int i=0; i<n; i++)
taken[i] = false;
ArrayList<BaselineLocalizationParticle> particles = new ArrayList<BaselineLocalizationParticle>();
for(int i=0; i<pairs.size(); i++) {
Pair pair = pairs.get(i);
if (taken[pair.k1] == false && taken[pair.k2] == false) {
taken[pair.k1] = true;
taken[pair.k2] = true;
double[] mxy = new double[] {pair.x, pair.y, pair.sigmax, pair.sigmay};
BaselineLocalizationParticle particle = new BaselineLocalizationParticle(pair.frame, mxy, pair.signal, pair.snr);
particle.z = pair.z;
particle.angleDH = pair.angle;
BaselineLocalizationParticle p1 = spots.get(pair.k1);
BaselineLocalizationParticle p2 = spots.get(pair.k2);
particle.setBiSpot(new double[] {p1.x, p1.y}, new double[] {p2.x, p2.y});
particles.add(particle);
}
}
return particles;
}
private class Pair implements Comparable<Pair> {
public int frame;
public double x;
public double y;
public double z;
public int k1;
public int k2;
public double signal;
public double diffSignal;
public double snr;
public double sigmax;
public double sigmay;
public double angle;
public Pair(int frame, int k1, int k2, double x, double y, double z, double signal, double snr, double sigmax, double sigmay, double diffSignal, double angle) {
this.frame = frame;
this.k1 = k1;
this.k2 = k2;
this.x = x;
this.y = y;
this.z = z;
this.signal = signal;
this.snr = snr;
this.diffSignal = diffSignal;
this.sigmax = sigmax;
this.sigmay = sigmay;
this.angle = angle;
}
public int compareTo(Pair pair) {
return diffSignal > pair.diffSignal ? 1 : -1;
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | BaselineLocalization/BaselineLocalizationParticle.java | .java | 2,963 | 98 | //=========================================================================================
//
// Single-Molecule Localization Microscopy Challenge 2016
// http://bigwww.epfl.ch/smlm/
//
// Author:
// Daniel Sage, http://bigwww.epfl.ch/sage/
// Biomedical Imaging Group (BIG)
// Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
//
// Reference:
// D. Sage, H. Kirshner, T. Pengo, N. Stuurman, J. Min, S. Manley, M. Unser
// Quantitative Evaluation of Software Packages for Single-Molecule Localization Microscopy
// Nature Methods 12, August 2015.
//
// Conditions of use:
// You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
import java.awt.Color;
import ij.IJ;
import ij.gui.Line;
import ij.gui.OvalRoi;
import ij.gui.Overlay;
public class BaselineLocalizationParticle {
public int frame;
public double x;
public double y;
public double z;
public double signal;
public double snr;
public double sigmaX;
public double sigmaY;
public double angleDH;
public boolean bispot = false;
public double xy1[] = new double[2];
public double xy2[] = new double[2];;
public BaselineLocalizationParticle(int frame, double mxy[], double signal, double snr) {
this.frame = frame;
this.x = mxy[0];
this.y = mxy[1];
this.sigmaX = mxy[2];
this.sigmaY = mxy[3];
this.signal = signal;
this.snr = snr;
}
public void setBiSpot(double xy1[], double xy2[]) {
this.xy1 = xy1;
this.xy2 = xy2;
this.bispot = true;
}
public void draw(Overlay overlay, Color c, double pixelsize) {
double sx = Math.max(1, sigmaX);
double sy = Math.max(1, sigmaY);
if (bispot) {
OvalRoi roi1 = new OvalRoi(xy1[0]/pixelsize - sx, xy1[1]/pixelsize - sy, sx*2, sy*2);
roi1.setPosition(frame);
roi1.setStrokeColor(c);
roi1.setFillColor(c);
overlay.add(roi1);
OvalRoi roi2 = new OvalRoi(xy2[0]/pixelsize - sx, xy2[1]/pixelsize - sy, sx*2, sy*2);
roi2.setPosition(frame);
roi2.setStrokeColor(c);
roi2.setFillColor(c);
overlay.add(roi2);
Line line = new Line(xy1[0]/pixelsize, xy1[1]/pixelsize, xy2[0]/pixelsize, xy2[1]/pixelsize);
line.setPosition(frame);
line.setStrokeColor(c);
line.setFillColor(c);
overlay.add(line);
}
else {
double pi = x/pixelsize - sx;
double pj = y/pixelsize - sy;
OvalRoi roi = new OvalRoi(pi, pj, sx*2, sy*2);
roi.setPosition(frame);
roi.setStrokeColor(c);
roi.setFillColor(c);
overlay.add(roi);
}
}
public String toString() {
return "" + frame + ", " + IJ.d2s(x, 2) + ", " + IJ.d2s(y, 2) + ", " + IJ.d2s(z, 2) + ", " +
IJ.d2s(signal, 5) + ", " + IJ.d2s(snr, 5) + ", " + IJ.d2s(sigmaX, 5) + ", " + IJ.d2s(sigmaY, 5);
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/psf_generation/generate_psf_lut4.m | .m | 13,029 | 470 | function imPsfAvg= generate_psf_lut4(fname,pixSzIn, pixSzOut, nMol,frz0,saveName,varargin)
%averages and interpolates PSF in XY
% assumes we dont have to interpolate in Z
nargin= numel(varargin);
MAXSHIFT = 300;%Max PSF shift (nm) when recentring anything larger generates an error
ctrdWindowSzNm = 1500;
boxSzNm =5000;
framePerZPos = 1;
interpZFactor=1;
ii=1;
while ii<= nargin
if strcmp(varargin{ii},'MaxShift')
MAXSHIFT = varargin{ii+1};
ii=ii+2;
elseif strcmp(varargin{ii},'CentroidWindowSz')
ctrdWindowSzNm= varargin{ii+1};
ii=ii+2;
elseif strcmp(varargin{ii},'BoxSzNm')
boxSzNm= varargin{ii+1};
ii=ii+2;
elseif strcmp(varargin{ii},'FramePerZPos')
framePerZPos= varargin{ii+1};
ii=ii+2;
elseif strcmp(varargin{ii},'InterpZFactor')
interpZFactor= varargin{ii+1};
ii=ii+2;
else
ii=ii+1;
end
end
boxSz = round(boxSzNm/pixSzIn);
HLFBOX = round(boxSz/2);
%load, preproc - use imreadstack
imstack = double(imreadstack(fname));
%if multiple frames per Z, average to increase SNR
if framePerZPos>1
imstack=zFrameAverage(imstack,framePerZPos);
frz0 = round(frz0/framePerZPos);
end
imz0 = imstack(:,:,frz0);
imLow = imstack(:,:,1);
imHigh = imstack(:,:,end);
%select psf areas
[posList] = getbeadpos(imz0,imLow,imHigh,nMol,HLFBOX);
%find the centroid
searchLimIn = round(ctrdWindowSzNm/pixSzIn);
posListCtrd = zeros(size(posList));
for ii = 1:nMol
[posListCtrd(ii,:)]= getPeakPosCentroid(imz0, posList(ii,:), searchLimIn);
end
%interpolate and crop
cropRatio = pixSzIn/pixSzOut;
imPsfInFocus = cropim(imz0,posListCtrd, HLFBOX, cropRatio);
%coalign the centres by cross correlating each PSF to the 1st one
searchLimOut = round(ctrdWindowSzNm/pixSzOut);
posListCorr = crossCorrPsfPos(imPsfInFocus,posListCtrd,cropRatio,searchLimOut);
%crop upscaled in focus im using updated positions
imPsfInFocus2 = cropim(imz0,posListCorr, HLFBOX, cropRatio);
%find the baseline using the in focus frame - need to subtract
for ii = 1:nMol
im = imPsfInFocus2(:,:,ii);
bg(ii) = median(im(:));
end
%find the brightness scale factor - again with in focus frame
% brightness scale factor is mean of all pixels 3sigma above background
% accounts for variations in brightness psf to psf but less noisy than just maximum
imStd = std(double(imz0(:)));
for ii = 1:nMol
im = imPsfInFocus2(:,:,ii);
th = bg(ii)+ imStd*3;
iBright = im(find(im>=th));
bAvg(ii) = mean(iBright);
end
%crop upscaled stack in xy using updated positions
imPsfStack = cropstack(imstack,posListCorr,HLFBOX,cropRatio);
%<DEBUG
%iw = cast(imPsfStack{2},'uint16');
%imwrite(iw(:,:,2),'testPsf_2.tif');
%for ii=2:nFr
% imwrite(iw(:,:,ii),'testPsf_2.tif','WriteMode','append');
%end
%keyboard
%DEBUG>
%normalize each PSF
for ii = 1:nMol
imPsfStackNorm{ii} = (imPsfStack{ii}-bg(ii))/(bAvg(ii)-bg(ii));
end
%average
nFr = size(imstack,3);
imsz=size(imPsfStack{1},1);
imPsfAvg = zeros(imsz,imsz,nFr);
for ii = 1:nFr
for jj =1:nMol
imPsfAvg(:,:,ii) = imPsfAvg(:,:,ii) + imPsfStackNorm{jj}(:,:,ii);
end
end
%normalize
iMin = min(imPsfAvg(:));
iMax = max(imPsfAvg(:));
imPsfAvg= (imPsfAvg-iMin)/(iMax-iMin);
%shift everything so that the in-focus frame CoM is at the centre pixel
psfCtrZ0 = getPsfCtr(imPsfAvg(:,:,frz0),searchLimOut);
imPsfAvg = recentre(imPsfAvg,psfCtrZ0,pixSzOut, MAXSHIFT);
%interpolate along Z if required
if interpZFactor>1
imPsfAvgSave = interpZ(imPsfAvg, interpZFactor);
nFrSave = size(imPsfAvgSave,3);
else
imPsfAvgSave = imPsfAvg;
nFrSave = nFr;
end
iw = im2uint16(imPsfAvgSave);
imwrite(iw(:,:,1),saveName);
for ii=2:nFrSave
imwrite(iw(:,:,ii),saveName,'WriteMode','append');
end
%plot the wobble curve
psfCtr = getPsfCtr(imPsfAvg,searchLimOut);
szY = size(imPsfAvg,1);
ctrY = floor(szY/2)+1;
imCtr = repmat([ctrY,ctrY],nFr,1);
psfShift = psfCtr-imCtr;
psfShiftNm = psfShift*pixSzOut;
d=sqrt(sum(psfShift.^2,2));
d=d*pixSzOut;
figure;plot(d);
xlabel('z frame');ylabel('PSF CoM dist from centre pix nm');
figure;
hold all;
plot(psfShiftNm(:,1),psfShiftNm(:,2));
plot(psfShiftNm(frz0,1),psfShiftNm(frz0,2),'rx');
xlabel('z frame');ylabel('PSF shift from centre pix nm');
%-----------------------------------
function [posList]= getbeadpos(imz0,imLow,imHigh,nMol,HLFBOX)
imsz = size(imz0);
h1=figure;
subplot(1,3,1);
imagesc(imLow);
axis equal;
axis([0 imsz(2) 0 imsz(1)]);
hold all;
subplot(1,3,2);
imagesc(imz0);
axis equal;
axis([0 imsz(2) 0 imsz(1)]);
hold all;
subplot(1,3,3);
imagesc(imHigh);
axis equal;
axis([0 imsz(2) 0 imsz(1)]);
hold all;
h2=figure;
hold all;
imagesc(imz0);
axis equal;
axis([0 imsz(2) 0 imsz(1)]);
set(gca,'YDir','reverse');
for ii = 1:nMol
figure(h2)
[x,y] = ginput(1);
posList(ii,:)=[x,y];
box{ii} = [x-HLFBOX,y-HLFBOX;...
x+HLFBOX,y-HLFBOX;...
x+HLFBOX,y+HLFBOX;...
x-HLFBOX,y+HLFBOX;...
x-HLFBOX,y-HLFBOX];
plot(x,y,'kx');
plot(box{ii}(:,1),box{ii}(:,2),'k-');
fprintf('%d of %d\n',ii,nMol);
end
for ii = 1:nMol
for jj=1:3
figure(h1)
subplot(1,3,jj);
plot(x,y,'kx');
plot(box{ii}(:,1),box{ii}(:,2),'k-');
end
end
%--------------------------------------------------------
function [pos amplitude]= getPeakPosCentroid(im, posGuess, windowRadius)
% crop small subimage around each im
% get centroid of each sub image
[windowLim] = getSubWindow(im,posGuess,windowRadius);
xLim = windowLim(1:2);
yLim = windowLim(3:4);
subIm = im(yLim(1):yLim(2),xLim(1):xLim(2));
posGuessSubIm = [posGuess(1) - xLim(1), posGuess(2)-yLim(1)];
[posSubIm, amplitude] = getCentroid(subIm);
pos = posSubIm + [xLim(1),yLim(1)]-[1,1];
%hold off;
%imagesc(subIm);
%hold all;
%plot(posGuessSubIm(:,1),posGuessSubIm(:,2),'kx');
%plot(posSubIm(:,1),posSubIm(:,2),'mx');
%pause
%
%hold off;
%imagesc(im);
%hold all;
%plot(posGuess(:,1),posGuess(:,2),'kx');
%plot(pos(:,1),pos(:,2),'gx');
%keyboard
%--------------------------------------------------
function [windowLim posGuessSubIm] = getSubWindow(im,posGuess,windowRadius);
[sizey sizex] = size(im);
X0=posGuess(1);
Y0=posGuess(2);
%round X0, Y0 to use as matrix locations
X0_int = round(X0);
Y0_int = round(Y0);
windowRadius = round(windowRadius); %radius should already be an integer anyway
% setup the limits of the cropped image
xstart = X0_int-windowRadius;
xfinish = X0_int+windowRadius;
ystart = Y0_int-windowRadius;
yfinish = Y0_int+windowRadius;
% check if any of the limits are out of bounds - if so, skip that point
if (xstart<1)
xstart=1;
end
%if (xstart > sizex)
%if (xfinish<1)
if (xfinish > sizex)
xfinish=sizex;
end
if (ystart<1)
ystart=1;
end
%if (ystart > sizey)
%if (yfinish<1)
if (yfinish > sizey)
yfinish = sizey;
end
windowLim = [xstart, xfinish,ystart,yfinish];
%---------------------------------------------
function [pos, amplitude] = getCentroid(im);
im = im-min(im(:));%offset the image to avoid baseline "zero" values contributing to the avg
[sizey sizex] = size(im);
totalIntensity = sum(im(:));
posx = sum(sum(im,1).*[1:sizex]) ./ totalIntensity;
posy = sum(sum(im,2).*[1:sizey]') ./ totalIntensity;
pos = [posx,posy];
amplitude = im(round(posy),round(posx));
%----------------------------------------------------
function imPsf = cropim(im,posList, HLFBOX, cropRatio)
%interpolate the image
imBig = imresize(im,cropRatio);
posListBig = posList*cropRatio;
hlfBoxBig = round(HLFBOX*cropRatio);
boxSz = hlfBoxBig*2+1;
nPsf = size(posList,1);
imPsf = zeros(boxSz, boxSz,nPsf);
for ii=1:nPsf
x = round(posListBig(ii,1));
y = round(posListBig(ii,2));
imPsf(:,:,ii) = imBig(y-hlfBoxBig:y+hlfBoxBig, x-hlfBoxBig:x+hlfBoxBig);
end
%----------------------------------------------------------------------------------------------
function imPsfStack = cropstack(imstack,posList,HLFBOX,cropRatio);
nFr = size(imstack,3);
nPsf = size(posList,1);
%for each frame crop around the particle centre
for ii = 1:nFr
iStackTmp{ii} = cropim(imstack(:,:,ii),posList, HLFBOX, cropRatio);
end
%rearrange so that z axis is actual z axis and cell # is particle #
imsz=size(iStackTmp{1},1);
IMz = zeros(imsz,imsz,nFr);
for ii = 1:nPsf
imPsfStack{ii} = IMz;%preallocate
for jj = 1:nFr
imPsfStack{ii}(:,:,jj) = iStackTmp{jj}(:,:,ii);
end
end
%--------------------------------------------
function [drift] = getImDrift(templateIm,featureIm,windowSize)
% spatial cross correlation function = SCCF
if ~exist('windowSize','var')
windowSize= 10; %seems like a pretty reasonable number
end
%calculate the correlation function
% C is the image of the correlation function.
%zeroCoord is the [i,j] coordinate corresponding to zero displacement in
% the correlation function
[C,zeroCoord] = corrfunc(templateIm,featureIm);
% careful with (i,j) vs (x,y)!!
[corrMaxPos corAmplitude] = getPeakPosCentroid(C,zeroCoord,windowSize);
drift = zeroCoord - corrMaxPos;
%-------------------------------------------------------------
function [G,zeroCoord] = corrfunc(template,feature)
% July 9, 2003
% David Kolin
% 18/03/11
% Seamus Holden
% 1)Minor modification 2011 SH to output zeroCoordinate in ICS image
% 2) 110318 Now a very heavily modified version of original function. Does cross not auto correlation
% NB:template and feature should be same size
% zeroCoord is (x,y) coordinates, not (i,j)!
template=double(template);
feature=double(feature);
% Calculates 2D correlation functions for each z slice of a given 3D matrix (imgser)
% Output as a 3D matrix with same dimensions as imgser
G = zeros(size(template)); % Preallocates matrix
% Calculates corr func
%autocorrelation:
%%G = (fftshift(real(ifft2(fft2(template).*conj(fft2(template)))))) ...
%% /(mean(template(:))^2*size(template,1)*size(template,2) ) ...
%% -1;
%cross correlation
G = (fftshift(real(ifft2(fft2(template).*conj(fft2(feature))))))/...
( (mean(template(:))*mean(feature(:))) * size(template,1)*size(template,2) ) ...
- 1;
% SH mod
% make sure that you know where the zero coordinate is from the DFT
% so that we can calculate absolute drift
imsize = size(template);
zeroCoordX = (floor(imsize(2)/2)+1);
zeroCoordY = (floor(imsize(1)/2)+1);
zeroCoord = [ zeroCoordX,zeroCoordY];
%----------------------------------------------
function psfCtr = getPsfCtr(imPsf,windowSz)
imsz = size(imPsf,1);
imctrX= floor(imsz/2)+1;
imctr=[imctrX,imctrX];
nFr = size(imPsf,3);
for ii=1:nFr
[psfCtr(ii,:)]= getPeakPosCentroid(imPsf(:,:,ii), imctr, windowSz);
end
%---------------------------------------------------
function posListCorr = crossCorrPsfPos(imPsfInFocus,posListCtrd,cropRatio,windowSize)
%cross correlate in focus PSFs to 1st psf to find updated centre pos
%windowSize = round(HLFBOX*cropRatio/2);%for the centroid finding in th CCF image. Ie PSF to PSF shift can be big but not huge
im1 = imPsfInFocus(:,:,1);
nMol= size(posListCtrd,1);
for ii= 1:nMol
imN = imPsfInFocus(:,:,ii);
[psfShift(ii,:)] = getImDrift(im1,imN, windowSize);
posListCorr(ii,:) = posListCtrd(ii,:)+psfShift(ii,:)/cropRatio;
end
%---------------------------------------------------------
function imPsfCtrd= recentre(imPsf,psfCtr,pixSzOut, MAXSHIFT);
%shift each frame so CoM is at middle of image (ie floor(sizeX/2)+1)
nFr = size(psfCtr,1);
szY = size(imPsf,1);
ctrY = floor(szY/2)+1;
%d=sqrt(sum((psfCtr-imCtr).^2,2));
%dNm=d*pixSzOut;
imCtr=[ctrY,ctrY];
d = psfCtr-imCtr;
biggestShift = ceil(max(abs(d)));
if biggestShift*pixSzOut>=MAXSHIFT
error('Shift in PSF centroid position too large for realignment. Reduce Z-range, increase MAXSHIFT, or increase quality of data.');
else
cropLim = floor(szY/2-biggestShift);
x = round(psfCtr(1));
y = round(psfCtr(2));
imPsfCtrd= imPsf(y-cropLim:y+cropLim, x-cropLim:x+cropLim,:);
end
%---------------------------------------------------------
function imstackAvg=zFrameAverage(imstack,framePerZPos)
imstack = double(imstack);
sz = size(imstack);
nFr = sz(3);
if mod(nFr,framePerZPos)~=0
error('Total number of frames not divisible by number per Z-position');
else
%average the frames
nFr2 = nFr/framePerZPos;
imstackAvg = zeros(sz(1),sz(2),nFr2);
imAvg = zeros(sz(1),sz(2));
for ii = 1:nFr2
imAvg=imAvg*0;
for jj=1:framePerZPos
curFrame = (ii-1)*framePerZPos+jj;
imAvg = imAvg+imstack(:,:,curFrame)/framePerZPos;
end
imstackAvg(:,:,ii)=imAvg;
end
end
%---------------------------------------------------------
function imstack2= interpZ(imstack, interpZFactor);
%assumes imstack of type double
%interpolated z
nFr = size(imstack,3);
nY= size(imstack,1);
nX= size(imstack,2);
z1 = 1:nFr;
z2 = 1:(1/interpZFactor):nFr;
nFr2 = numel(z2);
imstack2 = zeros(nY,nX,nFr2);
%interpolate each pixel
for ii = 1:nY
for jj = 1:nX
i1 = squeeze(imstack(ii,jj,:));
i2 = interp1(z1,i1,z2,'pchip');
imstack2(ii,jj,:) = reshape(i2,1,1,nFr2);
end
end
| MATLAB |
2D | SMLM-Challenge/Challenge2016 | Simulator/psf_generation/imreadstack.m | .m | 633 | 36 | function imstack = imreadstack( path1)
% imstack = imreadstack( path1)
%
% Description:
% Automatically load all the images in a stack as a
% 3D matrix
%
% Inputs:
% path
%
% Outputs:
% imstack
%
% History:
% 130208 - First alpha complete (SH)
% 180208 - Function headers and help standardised (SH)
%
% Author:
% Seamus Holden
%
% Notes:
% AK group internal use only
%number of frames in image and size of image
numFrames = numel(imfinfo(path1));
I = imread( path1, 1);
%preallocate array
imstack = zeros( [size(I) numFrames], class(I));
imstack(:,:,1) = I;
for p = 2:numFrames
imstack(:,:,p) = imread(path1, p);
end
| MATLAB |
2D | SMLM-Challenge/Challenge2016 | Simulator/psf_generation/psfGeneration160517.m | .m | 1,059 | 35 | %---------------
%astig PSF
fname = 'experimental-PSFs/AS-ZStack-6Beads-3um-10nmStep-AstigCylLens-5xAverage.tif';
saveName = 'interpolated-PSFs/astig-PSF-JBS-10nm-voxel.tif';
frz0 = 156;
pixSzIn = 160;
pixSzOut = [10];
nMol = 4;
generate_psf_lut4(fname,pixSzIn, pixSzOut, nMol,frz0,saveName);
%---------------
%DH PSF
fname = 'experimental-PSFs/DH-beads100nm_1.49NA100x_3um_20nm_2ImStep100ms_Pix16um_647.tif';
saveName = 'interpolated-PSFs/DH-PSF-10nm-voxel.tif';
frz0 = 130;
pixSzIn = 160;
pixSzOut = [10];
nMol = 3;
generate_psf_lut4(fname,pixSzIn, pixSzOut, nMol,frz0,saveName,'FramePerZPos',2,'InterpZFactor',2,'BoxSzNm',6000);
%---------------
%2D PSF
% Note the 2D PSF is also used to generate the semisynthetic biplane PSF
fname = 'experimental-PSFs/2D-160601 bead 0.1um nstorm 642nm 10nmZstep 42.7nmPix002.tif';
saveName = 'interpolated-PSFs/2D-PSF-newcastle-10nm-voxel.tif';
frz0 = 139;
pixSzIn = 42.7;
pixSzOut = [10];
nMol = 3;
generate_psf_lut4(fname,pixSzIn, pixSzOut, nMol,frz0,saveName,'FramePerZPos',1,'InterpZFactor',1,'BoxSzNm',6000);
| MATLAB |
2D | SMLM-Challenge/Challenge2016 | Simulator/photophysics/brightness_palmSubframe.m | .m | 3,230 | 124 | function [photons]=brightness_palmSubframe(Ion,Ton,Tdark,Tbl,frames)
% INPUTS
% Ion: mean photon emission during on state
% frames: # of frames
% Rates defined here:
% %
%for 4 state system:
%
% UNIFORM 1/Tbl
% OFF ------> ON ------> BLEACH
% |/\
% | |
% 1/Ton | | 1/Tdark
% | |
% | |
% \/ |
% DARK
%
% NB: the OFF to ON is not poisson distributed it is uniform random
% To reflect that in normal experimental conditions constant imaging density is
% maintained
%
% All switching is calculated at sub-frame resolution and then integrated o er each frame.
%
% Note actual mean lifetime in On state is 1/(1/Ton + 1/Tbleach) due to two decay paths
% ARGUMENTS
% OUTPUTS
% photons: photon count for each frame (no noise)
% Note, molecules alway starts in off state
%
%nArg = numel(varargin);
%ii=1;
%while ii<=nArg
% ii=ii+1;
%end
%calculate the frame where the molecule turns on
tToOn = max(1,frames*rand);%Uniform probability of on switching within the movie
tSwitch=[];
if tToOn<frames
tSwitch(1) = tToOn;
isBleach = false;
%while not bleached
while ~isBleach && tSwitch(end)<=frames
% calculate the frame where the molecule goes dark or bleaches
tToBl = -Tbl*log(rand);
tToDark = -Ton*log(rand);
% if bleaches before goes dark -> break
if tToBl<tToDark
isBleach = true;
tSwitch= [tSwitch,tSwitch(end)+tToBl];
% else calculate when the molecule goes back on
else
tSwitch= [tSwitch,tSwitch(end)+tToDark];
%switch back from dark to on
tDarkToOn = -Tdark*log(rand);
tSwitch= [tSwitch,tSwitch(end)+tDarkToOn];
end
end
end
isOn = zeros(1,frames);
isOnCur =0;
tSwitchInt = floor(tSwitch);
for ii = 1:frames
if ~(any(tSwitchInt==ii))
isOn(ii)=isOnCur;%just propogate current state
else
subFrSwitchTime = tSwitch(find(tSwitchInt==ii)) - ii;
totOnTimeFr = 0;
tCurFr = 0;
%add all the sub frame on states together
for jj = 1:numel(subFrSwitchTime)
tState = subFrSwitchTime(jj)-tCurFr;
if isOnCur
totOnTimeFr = totOnTimeFr+tState;
end
isOnCur = ~isOnCur;
tCurFr = subFrSwitchTime(jj);
end
%add the on time from the end of the frame if its still in the on state
if isOnCur
tState = 1-tCurFr;
totOnTimeFr = totOnTimeFr+tState;
end
isOn(ii) = totOnTimeFr;
end
end
photons=Ion*isOn;
%%DEBUG
%tSwitch
%switchLen = diff(tSwitch)
%tSwitch-tSwitchInt
%%on time check
%onTimeIntegrated = sum(isOn)
%onTimeTSwitch = sum(switchLen(1:2:end)) %wont get it right if switch is at the end but otherwise good
%hold off;
%plot(tSwitch,0*tSwitch+0.5,'x');
%hold all;
%stairs(1:frames,isOn);
%legend('Switch time', 'Integrated intensity');
%ylim([0 1]);
%pause
%-----------------------------------------------------
function stateVector = getStateVector(tSwitch);
%helper function for debugging
nSwitch = numel(tSwitch);
stateVector = zeros(size(tSwitch));
if nSwitch>0
stateVector(1:2:end) = 1;
end
| MATLAB |
2D | SMLM-Challenge/Challenge2016 | Simulator/photophysics/testSimCore2_160518.m | .m | 3,705 | 136 | % Test simulation - PALM like photokinetics
% tomas.lukes@epfl.ch
nfluo = 1000;
Ion = 5000;
frames = 200;
%FP-like param
Ton = 3;
Tdark = 2.5;
Tbl=1.5;
% giving mean on time per blink 1 frame
% Note "mean on time per blink" calculation below is an overestimate for small on-times due to quantization error
%% Generate fluorophore time trace
emitter_brightness = zeros(nfluo,frames);
onTime=[];
totOnTime = [];
for k=1:nfluo
[photons]=brightness_palmSubframe(Ion,Ton,Tdark,Tbl,frames);
disp(k);
emitter_brightness(k,:)=photons;
isOn = photons>0;
tSwitch = find(diff(isOn));
tState = diff(tSwitch);
onTimeCur = tState(1:2:end);%the on times are every second "state" time from the switch vector
onTime = [onTime, onTimeCur];
totPhot(k) = sum(photons);
end
for f=1:frames
noFl(f) = numel(find(emitter_brightness(:,f)>0.1*Ion));
end
% figure;
subplot(141);histogram(noFl);xlim([0 20]);
xlabel('Fluorophores per frame');
ylabel('Number of occurences');
disp(['Mean no of fluorophores per frame = ', num2str(mean(noFl))]);
for k=1:nfluo
totOn(k) = numel(find(emitter_brightness(k,:)>0.1*Ion));
end
%nblinks(nblinks==0)=[];
%% figure;
%subplot(132);histogram(nblinks);
%xlabel('Number of blinks in one burst ');
%ylabel('Number of occurences');
%% title(['Average blinks per fluorophore ',num2str(sum(nblinks)/nfluo)]);
%text(4,1050,['Average blinks per fluorophore ',num2str(sum(nblinks)/nfluo)],...
% 'HorizontalAlignment','left');
%disp(['Average time of a burst with 8 blinks = ',num2str(8*(Ton+Toff)/50)]);
phot_on = emitter_brightness(:);
phot_on = phot_on(phot_on>0);
subplot(142);histogram(phot_on(:));
xlabel('Number of photons per frame');
ylabel('Freq.');
p = mean(phot_on);
disp(['Mean number of photons ',num2str(p)]);
subplot(143); histogram(onTime);
xlabel('On time per blink');
ylabel('Freq.');
o=mean(onTime);
disp(['Mean on time per blink ', num2str(o)]);
subplot(144); histogram(totPhot);
xlabel('Number of photons per molecule');
ylabel('Freq.');
o=mean(totPhot);
disp(['Mean photons per molecule ', num2str(o)]);
%individual photon count plots
figure;
histogram(phot_on(:));
xlabel('Number of photons per frame');
ylabel('Freq.');
p = mean(phot_on);
disp(['Mean number of photons ',num2str(p)]);
title('Sub frame switching model, no noise');
saveas(gcf,['photon count subframe switch no poisson nphot', num2str(Ion),'.png']);
%add poisson noise
figure
photPois=poissrnd(phot_on);
histogram(photPois);
xlabel('Number of photons per frame');
ylabel('Freq.');
p = mean(photPois);
disp(['Mean number of photons ',num2str(p)]);
title('Sub frame switching model, with poisson noise');
saveas(gcf,['photon count sub frame swich with poisson nphot', num2str(Ion),'.png']);
%% Plot time traces
%figure('Position',[200, 200, 2000, 700]),
figure;
nMolPlot=5;
for ii = 1:nMolPlot
subplot(nMolPlot,2,2*ii-1);plot(emitter_brightness(ii,:));
if ii==1; title('Whole time trace (1-10)');end;
set(gca,'XTickLabel','')
ylim([0 Ion]);
if ii==nMolPlot;
set(gca,'Xtick',[0:2000:frames],'XTickLabel',[0:2000:frames])
xlabel('Frames');
ylabel('Photons');
end
idx = find(emitter_brightness(ii,:)>0);
MINTRACE=nMolPlot;
t= min(idx)-3:min(max([max(idx)+3, min(idx)-3+MINTRACE]),frames);
ttrace = emitter_brightness(ii,t);
subplot(nMolPlot,2,2*ii);plot(ttrace);
set(gca,'YTickLabel','')
ylim([0 Ion]);
if ii==1; title('Zoom on the burst');end;
%set(gca,'Xtick',[0:length(ttrace)/10:length(ttrace)],...
% 'XTickLabel',[max(idx-15*(Ton+Toff),1):2*(Ton+Toff):min(idx+15*(Ton+Toff)-1,frames)]);
if ii==nMolPlot;
xlabel('Frames');
end
end
| MATLAB |
2D | SMLM-Challenge/Challenge2016 | Simulator/photophysics/activation_4states.m | .m | 13,728 | 383 | function rateEffectiveActivations = activation_4states(Ton, Tdark, Tbleaching, nframes, framerate, fov, pixelsize, dye, power_laser, path)
% ACTIVATION_4STATES - Activate a list of fluorophores with a 4-states model
%
% Description
%
% UNIFORM 1/Tbl
% OFF ------> ON ------> BLEACH
% | /\
% | |
% 1/Ton | | 1/Tdark
% | |
% \/ |
% DARK
%
% The input is a list of position (X, Y, Z) stored in a CSV file.
% The filename is hardcoded 'positions.csv', this input file is
% required.
% The output is a list of activation (count, frame, X, Y, Z, number of photons) stored in a CSV file.
% The filename is harcoded 'activations.csv.'
%
% Project
% Simulator for the challenge SMLMS 2016
% Benchmarking of single-molecule localization software
%
% Reference
% Citation: submitted
% Website: http://bigwww.epfl.ch/smlm/
%
% Authors
% Seamus Holden,
% Tomas Lukes,
% Daniel Sage, daniel.sage@epfl.ch, Biomedical Imaging Group, EPFL
%
% Date
% May 2016
%
% Input parameters
% Ton On (recommended value 3)
% Tdark Dark (recommended value 2.5
% Tbleaching: Bleaching (recommended value 1.5
% nframes: number of frames
% framerate: frame rate in Hz (typical value 10)
% fov: field of view in nm (typical value 6400)
% pixelsize: size of camera pixel in nm
% path: directory to store the results
% dye: 'Alexa647' or 'Dendra2'
% power_laser: power of the laser in W / cm^2 (typical value 300)
%
% Output parameters
% rateEffectiveActivations: rate of activations per frame
%
% Required
% Other m-files required: none
% MAT-files required: none
% A CSV position file: required
%
% Examples of usage
% >> rate = activation_4states(3, 2.5, 1.5, 100, 10, 6400, 100, 'Alexa647', 200, pwd)
% >> rate = activation_4states(3, 2.5, 1.5, 100, 10, 6400, 100, 'Dendra2', 200, pwd)
%
% Conditions of usage
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
%
%
%% Other Fixed parameters
fstart = 20; % number of removed first frames
Q = -1;
%% Emission parameters Alexa647
if (strcmpi(dye, 'Alexa647') == 1)
Q = 0.33; % Quantum yeild of the dye
lambda = 647e-9; % Wavelength in m
EC = 239000; % Absorbtivity, Molar extinction coefficient in cm^2 / mol
end
%% Emission parameters Dendra2
if (strcmpi(dye, 'Dendra2') == 1)
Q = 0.55; % Quantum yeild of the dye
lambda = 573e-9; % Wavelength in m
EC = 4000; % Absorbtivity, Molar extinction coefficient in cm^2 / mol
end
if Q <= 0
fprintf('This dye (%s) is unkown \n', dye);
return;
end
rng(123); % Initialize the seed of the random generator
%% Read the positisons file
cd(path);
positions = csvread([path filesep 'positions.csv']);
path = [path filesep 'activations-' num2str(nframes) '-' num2str(fov) filesep];
if ~exist(path, 'dir')
mkdir(path);
end
cd(path);
fprintf('Lifetime Ton=%4.3f Tdark=%4.3f Tbleach=%4.3f\n', Ton, Tdark, Tbleaching);
fprintf('Activation of the positions %s \n', path);
nfluos = size(positions,1);
fprintf('Number of fluorophore positions: %6.0f on %6.0f frames \n', nfluos, nframes);
xmin = min(positions(:,1));
xmax = max(positions(:,1));
ymin = min(positions(:,2));
ymax = max(positions(:,2));
fprintf('Range in X: %6.3f ... %6.3f nm (fov %4.1f) \n', xmin, xmax, fov);
fprintf('Range in Y: %6.3f ... %6.3f nm (fov %4.1f) \n', ymin, ymax, fov);
fprintf('Depth in Z: %6.3f ... %6.3f nm \n', min(positions(:,3)), max(positions(:,3)));
h = 6.626E-34; % Planck constant in mol^-1
Na = 6.022e23; % Number of Avogadro
c = 3e8; % Celerity in m/s
e = h * c / lambda; % Energy of a Photon in J
s = 1000 * log(10)*EC / Na; % Absorption cross section in cm^2
%% Non uniform power illumination (Radial sigmoid function)
xcenter = fov*0.5 + 0.2*fov*(0.5-rand()); % simulate misalignment
ycenter = fov*0.5 + 0.2*fov*(0.5-rand()); % simulate misalignment
DistanceAtHalfMax = fov*0.55; % Distance form the center, half maximum, in nm
DistanceAtQuarterMax = fov*0.7; % Distance form the center, quarter maximum in nm
slope = log(3)/(DistanceAtQuarterMax-DistanceAtHalfMax);
%% Display the power illumination
fovPixel = int16(fov / pixelsize);
power = zeros(fovPixel, fovPixel);
for x=1 : fovPixel
for y=1 : fovPixel
power(x, y) = powerIllumination(double(x)*pixelsize, double(y)*pixelsize, xcenter, ycenter, slope, DistanceAtHalfMax);
end
end
average_power = mean(mean(power));
fprintf('Average of power: %6.6f \n', average_power);
%% Flux of photons
flux = Q * s * power_laser / e;% Flux of photons per second
fprintf('Flux of photons per seconds: %6.6f \n', flux);
flux = flux / framerate; % Flux of photons per frame
fprintf('Flux of photons per frame: %6.6f \n', flux);
epsilon = 0.01 * flux;
%% Generate fluorophore time trace
count = 0;
onTime=[];
stats = zeros(nframes, 2);
activate = zeros(nfluos,1);
fid = fopen([path filesep 'activations.csv'], 'w');
fif = fopen([path filesep 'stats_activation_fluos.csv'], 'w');
fprintf(fid, 'Ground-truth,frame,xnano,ynano,znano,intensity \n');
fprintf(fif, 'fluos,f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13,f14,f15,f16,f17,f18,f19,f20,f21,f22,f23,f24,f25 \n');
totalPhotons = 0;
for k=1:nfluos
pos = positions(k,:);
attenuation = powerIllumination(pos(1), pos(2), xcenter, ycenter, slope, DistanceAtHalfMax) / average_power;
[photons] = brightness_palmSubframe(attenuation*flux,Ton,Tdark,Tbleaching,nframes+fstart);
progression('Activate ', k);
fprintf(fif, '%5.0f ', k);
for f=1:nframes
if photons(f+fstart) > epsilon
stats(f, 1) = stats(f, 1) + 1;
stats(f, 2) = stats(f, 2) + photons(f+fstart);
activate(k) = activate(k)+1;
count = count + 1;
totalPhotons = totalPhotons + photons(f+fstart);
fprintf(fid, '%5.0f, %5.0f, %4.3f, %4.3f, %4.3f, %4.3f \n', count, f, positions(k,1), positions(k,2), positions(k,3), photons(f+fstart));
fprintf(fif, ', %5.0f', f);
end
end
fprintf(fif, '\n');
end
fclose(fid);
fclose(fif);
disp(strcat('End of storage on ', path, 'activations.csv'));
rateEffectiveActivations = count / nfluos;
%% Plot stats
figure('Position',[100, 100, 900, 900]),
axes('Position', [0.05 0.05 .44 .27]);
plot(stats(:,1));
xlabel('Frames');
ylabel('Act. Fluorophore per frame');
legend(sprintf('total: %4.0f activations', sum(stats(:,1))));
axes('Position', [0.05 0.38 .44 .27]);
plot(stats(:,2));
xlabel('Frames');
ylabel('Photons per frame');
legend(sprintf('total: %4.1f photons', sum(stats(:,2))));
axes('Position', [0.05 0.72 .44 .27]);
histogram(stats(:,1));
xlabel('Nb of act. fluorophore per frame');
ylabel('Number of frames');
legend(sprintf('mean: %4.2f fluos/frame', mean(stats(:,1))));
axes('Position', [0.55 0.72 .44 .27]);
histogram(stats(:,2));
xlabel('Number of photons per fluo.');
ylabel('Freq.');
legend(sprintf('mean: %4.1f photons/frame', mean(stats(:,2))));
axes('Position', [0.55 0.38 .44 .27]);
histogram(activate);
xlabel('Total ON time per fluo.');
ylabel('Freq.');
print('stats', '-dpng', '-r300');
axes('Position', [0.55 0.05 .44 .27]);
histogram(onTime);
xlabel('On time per blink');
ylabel('Freq.');
legend(sprintf('Mean on time per blink %4.1f', mean(onTime)));
%% Save stats frame
fid = fopen(strcat(path, 'stats_activation_frames.csv'), 'w');
fprintf(fid, 'frame,nbfluos,nbphotons \n');
for f=1:nframes
fprintf(fid, '%5.0f, %5.0f, %6.6f \n', f, stats(f,1), stats(f,2));
end
fclose(fid);
%% Save stats frame
fprintf('Number of activations, %6.0f\n', count);
fprintf('Average number of photons / activation, %6.6f\n', totalPhotons / count);
fid = fopen(strcat(path, 'stats_activation_sumary.csv'), 'w');
fprintf(fid, 'feature,value \n');
fprintf(fid, 'Number of fluos, %6.0f\n', nfluos);
fprintf(fid, 'Number of frames, %6.0f\n', nframes);
fprintf(fid, 'Field of view (nm), %6.2f\n', fov);
fprintf(fid, 'Number of activations, %6.0f\n', count);
fprintf(fid, 'Average number of photons / activation, %6.6f\n', totalPhotons / count);
fprintf(fid, 'Density - number of fluorophores per frame, %6.6f\n', count / nframes);
fprintf(fid, 'Density - number of photons per frame, %6.6f\n', totalPhotons / nframes);
fprintf(fid, 'Center of power illumination X (nm), %6.3f\n', xcenter);
fprintf(fid, 'Center of power illumination X (nm), %6.3f\n', ycenter);
fprintf(fid, 'Distance at half maximum illumination (nm), %6.3f\n', DistanceAtHalfMax);
fprintf(fid, 'Distance at quarter maximum illumination (nm), %6.3f\n', DistanceAtQuarterMax);
fprintf(fid, 'Power illumination (W), %6.3f\n', power_laser);
fprintf(fid, 'Framerate, %6.3f\n', framerate);
fprintf(fid, 'Maximum flux of photons per frame, %6.3f\n', flux);
fprintf(fid, 'Threshold for activation (min photons), %6.3f\n', epsilon);
fprintf(fid, 'Ton, %6.3f\n', Ton);
fprintf(fid, 'Tdark, %6.3f\n', Tdark);
fprintf(fid, 'Tbleaching, %6.3f\n', Tbleaching);
fclose(fid);
cd(path);
end
% Progression
function progression(message, k)
if mod(k, 100) == 0
fprintf('%5.0f: %s \n', k, message);
end
end
% Spatial non-uniform illumation
function P=powerIllumination(x, y, xcenter, ycenter, slope, DistanceAtHalfMax)
dm = DistanceAtHalfMax + DistanceAtHalfMax*0.05*(0.5-rand());
d = sqrt((x - xcenter)^2 + (y - ycenter)^2);
P = 1 - 1 / (1+exp(-slope*(d-dm)));
end
function [photons]=brightness_palmSubframe(Ion,Ton,Tdark,Tbl,frames)
% INPUTS
% Ion: mean photon emission during on state
% frames: # of frames
% Rates defined here:
% %
%for 4 state system:
%
% UNIFORM 1/Tbl
% OFF ------> ON ------> BLEACH
% |/\
% | |
% 1/Ton | | 1/Tdark
% | |
% | |
% \/ |
% DARK
%
% NB: the OFF to ON is not poisson distributed it is uniform random
% To reflect that in normal experimental conditions constant imaging density is
% maintained
%
% Switching events are integrated over each individual frame (ie half-frame blink gives
% half frame intensity
%
% Note actual mean lifetime in On state is 1/(1/Ton + 1/Tbleach) due to two decay paths
% ARGUMENTS
% OUTPUTS
% photons: photon count for each frame (no noise)
% Note, molecules alway starts in off state
%
%calculate the frame where the molecule turns on
tToOn = max(1,frames*rand);%Uniform probability of on switching within the movie
tSwitch=[];
if tToOn<frames
tSwitch(1) = tToOn;
isBleach = false;
%while not bleached
while ~isBleach && tSwitch(end)<=frames
% calculate the frame where the molecule goes dark or bleaches
tToBl = -Tbl*log(rand);
tToDark = -Ton*log(rand);
% if bleaches before goes dark -> break
if tToBl<tToDark
isBleach = true;
tSwitch= [tSwitch,tSwitch(end)+tToBl];
% else calculate when the molecule goes back on
else
tSwitch= [tSwitch,tSwitch(end)+tToDark];
%switch back from dark to on
tDarkToOn = -Tdark*log(rand);
tSwitch= [tSwitch,tSwitch(end)+tDarkToOn];
end
end
end
%integrate the switching to single frame resolution
isOn = zeros(1,frames);
isOnCur =0;
tSwitchInt = floor(tSwitch);
for ii = 1:frames
if ~(any(tSwitchInt==ii))
isOn(ii)=isOnCur;%just propogate current state
else
subFrSwitchTime = tSwitch(find(tSwitchInt==ii)) - ii;
totOnTimeFr = 0;
tCurFr = 0;
%add all the sub frame on states together
for jj = 1:numel(subFrSwitchTime)
tState = subFrSwitchTime(jj)-tCurFr;
if isOnCur
totOnTimeFr = totOnTimeFr+tState;
end
isOnCur = ~isOnCur;
tCurFr = subFrSwitchTime(jj);
end
%add the on time from the end of the frame if its still in the on state
if isOnCur
tState = 1-tCurFr;
totOnTimeFr = totOnTimeFr+tState;
end
isOn(ii) = totOnTimeFr;
end
photons=Ion*isOn;
end
%-----------------------------------------------------
function stateVector = getStateVector(tSwitch);
%helper function for debugging
nSwitch = numel(tSwitch);
stateVector = zeros(size(tSwitch));
if nSwitch>0
stateVector(1:2:end) = 1;
end
end
end
| MATLAB |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/Test_EMCCD.java | .java | 3,908 | 116 |
import org.apache.commons.math3.distribution.GammaDistribution;
import org.apache.commons.math3.distribution.PoissonDistribution;
import smlms.tools.PsRandom;
public class Test_EMCCD {
private PsRandom psrand = new PsRandom(1234);
public static void main(String[] arg) {
new Test_EMCCD();
}
public Test_EMCCD() {
for(double i=1; i<10000; i*=Math.sqrt(2)) {
double[] v = test(i);
System.out.println("" + v[0] + "\t" + v[1] + "\t stdev=" + v[2]);
}
}
private double[] test(double x) {
int N = 1000;
double[] values = new double[N];
for(int i=0; i<N; i++) {
int xp = poisson(x);
values[i] = value(xp);
}
double mean = 0.0;
for(int i=0; i<N; i++)
mean += values[i];
mean = mean / N;
double stdev = 0.0;
for(int i=0; i<N; i++)
stdev += (values[i]-mean)*(values[i]-mean);
stdev = Math.sqrt(stdev / N);
return new double[] {x, mean, stdev};
}
private double value(double photons_on_pixel) {
// Pseudo-code noise model for LM challenge 2016.
// This assumes all input light is fluorescence (background or signal)
// and thus follows poisson statistics
// And that the camera is an EMCCD, Photometrics Evolve Delta 512 sitting in my lab (Seamus)
//for each pixel, n input electrons n_ie input
// *** gives total system gain G=0.9*300/30 =9
//model quantization noise not an issue as noted by lots of astronomers and e.g. [1] Hirsch et al, PLoS One 2015
// poisson noise including shot noise and spurious charge plus binomial quantum efficiency conversion is just a poisson distribution as per [1]
// ADC conversion has arbitary value within similar magnitude to typical manufacturer values
// EMCCD model, shape param k=n_ie, scale param theta=EMgain after Basden et al Mon Not R Astron Soc 2003
double QE=0.9; // Evolve quantum efficiency @700 nm
double readout = 74.4;//maunfacturer measured rms electrons for my Evolve
double c=0.002; //manufacturer quoted spurious charge (CIC only, dark counts negligible) for my Evolve
double e_per_edu = 0.01;
double baseline = 100;
double EMgain = 300;
double n_ie = poisson(QE*photons_on_pixel + c);
double n_oe = gamma(n_ie, EMgain);
n_oe = n_oe + gaussian(baseline, readout);
double ADU_out = (int)(n_oe * e_per_edu) + baseline;
double saturation = Math.pow(2, 16);
int dn = (int)Math.floor(Math.min(saturation, Math.max(0, ADU_out)));
return dn;
}
private double value_old(double photons_on_pixel) {
double darknoise = 10;
double background = 100.0;
double EMgain = 1.41;
double QE = 0.921;
double baseline = 100;
double readout = 5;
double saturation = Math.pow(2, 16); // Quantization on 16 bits
double gainADC = 1.0;
double offsetADC = 0.0;
int photons_back = (int)photons_on_pixel + poisson(background);
double e = binomial(QE, photons_back);
double e_dark = e + poisson(darknoise);
double e_em = gamma(e_dark, EMgain);
double e_read = e_em + gaussian(baseline, readout);
double adc = e_read * gainADC + offsetADC;
int dn = (int)Math.floor(Math.min(saturation, Math.max(0, adc)));
return dn;
}
private double gamma(double shape, double scale) {
shape = Math.max(1E-6, shape);
return new GammaDistribution(shape, scale).sample();
}
private int poisson(double lambda) {
lambda = Math.max(1E-6, lambda);
return new PoissonDistribution(lambda).sample();
}
private double binomial(double p, int ntrials) {
return psrand.nextBinomial(p, ntrials);
}
private double gaussian(double mean, double stdev) {
return psrand.nextGaussian(mean, stdev);
}
/*
System.out.println("Photons on a pixel: " + photons_on_pixel);
System.out.println("Photons and dark noise: " + photons_back);
System.out.println("Conversion photon to e: " + e);
System.out.println("e + dark: " + e_dark);
System.out.println("e * em: " + e_em);
System.out.println("e + read: " + e_read);
System.out.println("Digital Number: " + dn);
*/
} | Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/bpalm/model/Bessel.java | .java | 3,740 | 97 | //=====================================================================================
// Project: PSF Generator
//
// Organization: Biomedical Imaging Group (BIG)
// Ecole Polytechnique Federale de Lausanne (EPFL)
// Lausanne, Switzerland
//
// Information: http://bigwww.epfl.ch/deconvolution/
//
// Reference:
// Alessandra Griffa, Nathalie Garin, Daniel Sage,
// Comparison of Deconvolution Software in 3D Microscopy: A User Point of View
// G.I.T. Imaging & Microscopy, vol. 12, no. 1, pp. 43-45, March 2010.
// Available: http://bigwww.epfl.ch/publications/griffa1001.html
//
// Conditions of use:
// You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we
// expect you to include a citation or acknowledgment whenever
// you present or publish results that are based on it.
//=====================================================================================
package bpalm.model;
/**
* This class evaluates the Bessel function J0(x).
* It uses the polynomial approximations on p. 369-70 of Abramowitz & Stegun.
* The error in J0 is supposed to be less than or equal to 5 x 10^-8.
* The error in J1 is supposed to be less than or equal to 4 x 10^-8, relative to the value of x.
* The error in J2 depends on the error of J0 and J1, as J2 = 2*J1/x + J0.
*
* @author Hagai Kirshner, Biomedical Imaging Group, Ecole Polytechnique Federale de Lausanne (EPFL).
*/
public class Bessel {
/** Constants for Bessel function approximation according Abramowitz & Stegun */
private static double[] tJ0 = {1.0, -2.2499997, 1.2656208, -0.3163866, 0.0444479, -0.0039444, 0.0002100};
private static double[] pJ0 = {-.78539816, -.04166397, -.00003954, 0.00262573, -.00054125, -.00029333, .00013558};
private static double[] fJ0 = {.79788456, -0.00000077, -.00552740, -.00009512, 0.00137237, -0.00072805, 0.00014476};
private static double[] tJ1 = {0.5, -0.56249985, 0.21093573, -0.03954289, 0.0443319, -0.00031761, 0.0001109};
private static double[] pJ1 = {-2.35619449, 0.12499612, 0.00005650, -0.00637879, 0.00074348, 0.00079824, -0.00029166};
private static double[] fJ1 = {0.79788456, 0.00000156, 0.01689667, 0.00017105, -0.00249511, 0.00113653, -0.00020033};
/**
* Returns the value of the Bessel function of the first kind of order zero (J0) at x.
*/
public static double J0(double x) {
if (x < 0.0)
x *= -1.0;
if (x <= 3.0) {
double y = x*x/9.0;
return tJ0[0] + y*(tJ0[1] + y*(tJ0[2] + y*(tJ0[3] + y*(tJ0[4] + y*(tJ0[5] + y*tJ0[6])))));
}
double y = 3.0/x;
double theta0 = x + pJ0[0] + y*(pJ0[1] + y*(pJ0[2] + y*(pJ0[3] + y*(pJ0[4] + y*(pJ0[5] + y*pJ0[6])))));
double f0 = fJ0[0] + y*(fJ0[1] + y*(fJ0[2] + y*(fJ0[3] + y*(fJ0[4] + y*(fJ0[5] + y*fJ0[6])))));
return Math.sqrt(1.0/x)*f0*Math.cos(theta0);
}
/**
* Returns the value of the Bessel function of the first kind of order one (J1) at x.
*/
public static double J1(double x) {
int sign=1;
if (x < 0.0) {
x *= -1.0;
sign = -1;
}
if (x <= 3.0) {
double y = x*x/9.0;
return sign*x*(tJ1[0] + y*(tJ1[1] + y*(tJ1[2] + y*(tJ1[3] + y*(tJ1[4] + y*(tJ1[5] + y*tJ1[6]))))));
}
double y = 3.0/x;
double theta1 = x + pJ1[0] + y*(pJ1[1] + y*(pJ1[2] + y*(pJ1[3] + y*(pJ1[4] + y*(pJ1[5] + y*pJ1[6])))));
double f1 = fJ1[0] + y*(fJ1[1] + y*(fJ1[2] + y*(fJ1[3] + y*(fJ1[4] + y*(fJ1[5] + y*fJ1[6])))));
return sign*Math.sqrt(1.0/x)*f1*Math.cos(theta1);
}
/**
* Returns the value of the Bessel function of the first kind of order two (J2) at x.
*/
public static double J2(double x) {
double value0 = J0(x);
double value1 = J1(x);
if (x==0.0) return 0.0;
else return 2.0*value1/x + value0;
}
} | Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/bpalm/model/GibsonLanniParameters.java | .java | 2,776 | 104 | package bpalm.model;
import ij.IJ;
import java.io.Serializable;
/**
* This class manages all parameters of the Gibson-Lanni model.
*
* @author Hagai Kirshner, Biomedical Imaging Group, Ecole Polytechnique Federale de Lausanne (EPFL)
*/
public class GibsonLanniParameters implements Serializable {
/** Working distance of the objective (design value). This is also the width of the immersion layer.*/
public double ti0;
/** Working distance of the objective (experimental value). influenced by the stage displacement.*/
public double ti;
/** Immersion medium refractive index (experimental value).*/
public double ni;
/** Sample refractive index.*/
public double ns;
/** Distance between imaging planes <em>in terms of stage displacement</em> [nm]*/
public double delta_z;
/** Additional stage offset [nm]*/
public double depth;
/** Emission wavelength of the fluorophoes.*/
public double lambda;
/** Numerical aperture (normalized by ni0) */
public double NA;
/** Magnification of the objective */
public double M;
/** Effective size of a single pixels (physical size divided by the magnification).*/
public double pixelSize;
/** Effective size of a single stage displacement.*/
public double axialResolution;
/** Aperture of the objective, projected onto the tube length (Gibson Lanni 1992, page 156) */
public double a;
/** Tube length */
public double zd_star;
/** Distance of the defocused plane relative to the back focal plane */
public double zd;
/** Axial position of the particle */
public double ts;
/** Constants for fast computations */
public double k0; // wave number
public double k_a_over_zd;
public double const2;
public double ns_ts;
public double NA_over_ni_squared;
public double NA_over_ns_squared;
//public double ni_over_ns_squared;
public GibsonLanniParameters() {}
public GibsonLanniParameters(GibsonLanniParameters p) {
this.ti0 = p.ti0;
this.ti = p.ti;
this.ni = p.ni;
this.ns = p.ns;
this.delta_z = p.delta_z;
this.depth = p.depth;
this.lambda = p.lambda;
this.NA = p.NA;
this.M = p.M;
this.pixelSize = p.pixelSize;
this.axialResolution = p.axialResolution;
this.a = p.a;
this.zd_star = p.zd_star;
this.zd = p.zd;
this.ts = p.ts;
calculateConstants();
}
public void calculateConstants() {
k0 = 2*Math.PI/lambda;
a = zd_star*NA/Math.sqrt(M*M-NA*NA); // From equation page 51 Gibson thesis, bottom
zd = (zd_star*a*a*ni)/(delta_z*zd_star*NA*NA+a*a*ni); // From equation page 70 Gibson thesis, bottom
k_a_over_zd = k0*a/zd;
const2 = ((zd_star-zd)*a*a)/(2*zd*zd_star);
ns_ts = ns*ts;
NA_over_ni_squared = (NA/ni)*(NA/ni);
NA_over_ns_squared = (NA/ns)*(NA/ns);
IJ.log(" GLP ns_ts " + ns_ts);
}
} | Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/bpalm/model/KirchhoffDiffractionSimpson.java | .java | 5,321 | 175 | package bpalm.model;
/**
* Kirchhoff Diffraction integral formula for the Gibson and Lanni PSF model
*
* @author Hagai Kirshner, Biomedical Imaging Group, Ecole Polytechnique Federale de Lausanne (EPFL)
*/
public class KirchhoffDiffractionSimpson{
// Gibson & Lanni parameters of the acquisition
private GibsonLanniParameters p;
// Stopping conditions:
// Difference between consecutive Riemann approximations.
double TOL = 1E-1;
// The number of consecutive approximations that meet the TOL criterion
int K;
// Constructor
public KirchhoffDiffractionSimpson(GibsonLanniParameters p, int accuracy) {
this.p = new GibsonLanniParameters(p);
this.p.calculateConstants();
if (accuracy == 0) K = 5;
else if (accuracy == 1) K = 7;
else if (accuracy == 2) K = 9;
else K = 3;
}
// calculate()
// Simpson approximation for the Kirchhoff diffraction integral
// 'r' is the radial distance of the detector relative to the optical axis.
public double calculate(double r) {
// Lower and upper limits of the integral
double a = 0.0;
double b=Math.min(1, p.ns/p.NA); //1.0
int N; // number of sub-intervals
int k; // number of consecutive successful approximations
double del; // integration interval
int iteration; // number of iterations.
double curDifference; // Stopping criterion
double realSum, imagSum, rho;
double[] sumOddIndex = new double[2], sumEvenIndex = new double[2];
double[] valueX0 = new double[2], valueXn = new double[2];
double[] value = new double[2];
double curI = 0.0, prevI = 0.0;
// Initialization of the Simpson sum (first iteration)
N=2;
del=(b-a)/2.0;
k=0;
iteration = 1;
rho = (b-a)/2.0;
sumOddIndex = this.integrand(rho,r);
sumEvenIndex[0] = 0.0; sumEvenIndex[1] = 0.0;
valueX0 = this.integrand(a,r);
valueXn = this.integrand(b,r);
realSum = valueX0[0] + 2.0*sumEvenIndex[0] + 4.0*sumOddIndex[0] + valueXn[0];
imagSum = valueX0[1] + 2.0*sumEvenIndex[1] + 4.0*sumOddIndex[1] + valueXn[1];
curI = (realSum*realSum+imagSum*imagSum)*del*del;
prevI=curI;
curDifference = TOL;
boolean convergingErrorGiven = false;
// Finer sampling grid until we meet the TOL value with the specified number of repetitions, K
while(k<K) {
iteration++;
N *= 2;
del = del * 0.5;
sumEvenIndex[0] = sumEvenIndex[0] + sumOddIndex[0];
sumEvenIndex[1] = sumEvenIndex[1] + sumOddIndex[1];
sumOddIndex[0] = 0.0;
sumOddIndex[1] = 0.0;
for(int n=1; n<N; n=n+2) {
rho = n*del;
value = this.integrand(rho,r);
sumOddIndex[0] += value[0];
sumOddIndex[1] += value[1];
}
realSum = valueX0[0] + 2.0*sumEvenIndex[0] + 4.0*sumOddIndex[0] + valueXn[0];
imagSum = valueX0[1] + 2.0*sumEvenIndex[1] + 4.0*sumOddIndex[1] + valueXn[1];
curI = (realSum*realSum+imagSum*imagSum)*del*del;
// Relative error between consecutive approximations
if (prevI==0.0)
curDifference = Math.abs((prevI-curI)/1E-5);
else
curDifference = Math.abs((prevI-curI)/curI);
if (curDifference<=TOL)
k++;
else
k = 0;
prevI=curI;
if (!convergingErrorGiven && iteration>15) {
System.err.println("Integral not converging at r="+r+" after "+iteration+"iterations. Optical parameters?");
convergingErrorGiven = true;
}
}
return curI;
}
// integrand()
double[] integrand(double rho, double r) {
// 'rho' is the integration parameter.
// 'r' is the radial distance of the detector relative to the optical axis in the DETECTOR.
// The return value is a complex number that is described by an array
// The relevant equations in the paper are are (4) and (5)
double BesselValueRho = Bessel.J0(p.k_a_over_zd*r*rho)*rho;
//double BesselValueRho = Bessel.J0(p.k0*p.NA*r*rho/p.M)*rho;
double OPD_real, OPD_imag, OPD1_real, OPD1_imag, OPD2_real, OPD2_imag, OPD3; // Optical path differences
double[] I = new double[2];
// Phase term due to immersion layer thickness
double X = 1-p.NA_over_ni_squared*rho*rho;
if (X>=0) {
OPD1_real = p.ni*(p.ti-p.ti0)*Math.sqrt(X);
OPD1_imag = 0;
} else {
OPD1_real = 0;
OPD1_imag = p.ni*(p.ti-p.ti0)*Math.sqrt(-X);
}
// Phase term due to point source depth
X = 1-p.NA_over_ns_squared*rho*rho;
if (X>0) {
OPD2_real = p.ns_ts*Math.sqrt(X);
OPD2_imag = 0;
} else {
OPD2_real = 0;
OPD2_imag = p.ns_ts*Math.sqrt(-X);
}
// Defocus in image plane due to imaging plane displacement
OPD3 = p.const2*rho*rho;
// See equation page 50, bottom, Gibson thesis and defocus equation page 70, top
OPD_real = OPD1_real+OPD2_real+OPD3;
OPD_imag = OPD1_imag+OPD2_imag;
//double Dz = (p.ti-p.ti0) + p.ni*p.ts/p.ns;
//OPD_real = -p.NA*p.NA*Dz*rho*rho/(2.0*p.ni)-p.NA*p.NA*p.NA*p.NA*Dz*rho*rho*rho*rho/(8.0*p.ni*p.ni*p.ni);
//OPD_real = -p.NA*p.NA*rho*rho*(((p.ti-p.ti0)/p.ni)+p.ts/p.ns)/2.0-p.NA*p.NA*p.NA*p.NA*rho*rho*rho*rho*p.ti/(8.0*p.ni*p.ni*p.ni)-p.NA*p.NA*p.NA*p.NA*rho*rho*rho*rho*p.ts/(8.0*p.ns*p.ns*p.ns);
//OPD_imag = 0.0;
double W = p.k0*OPD_real;
// The real part
I[0] = BesselValueRho*Math.cos(W)*Math.exp(-p.k0*OPD_imag); // See eq 4.9, pag 53, Gibson thesis
// The imaginary part
I[1] = BesselValueRho*Math.sin(W)*Math.exp(-p.k0*OPD_imag);
return I;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/bpalm/simulator/BiplaneAlgorithm.java | .java | 4,507 | 145 | package bpalm.simulator;
import ij.IJ;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import smlms.simulation.PSFModule;
import bpalm.model.KirchhoffDiffractionSimpson;
/**
* This class manages all aspects of the algorithm for generating the data.
*
* @author Hagai Kirshner, Biomedical Imaging Group, Ecole Polytechnique Federale de Lausanne (EPFL)
*/
public class BiplaneAlgorithm {
public BPALMParameters p;
public double[][][] zStack;
public double effRadius;
static int OVER_SAMPLING = 2;
static boolean verbose = false;
SliceOfPSF[] psfSlice;
private boolean columnwise;
public BiplaneAlgorithm(PSFModule module) {
columnwise = module.bpalm.orientation.equals("Columns");
this.p = new BPALMParameters(module.bpalm); // Making a copy of the Parameters
}
public int getSliceIndex(Particle particle, BPALMParameters p) {
int i = (int)(Math.ceil(particle.t*p.noFrames)-1);
return (i<0)?0:(i<=p.noFrames)?i:p.noFrames-1;
}
public void renderSequence(Particle[] particle, float image[][]) {
p.calculateConstants();
int nx = image.length;
int ny = image[0].length;
// Determine the effective radius by the most out of focus particle.
effRadius = getEffectiveRadius(nx, ny);
IJ.log(" DEBUG renderSequence line 56 " + nx + " " + ny + " nframes " + p.noFrames + " " + p.doSequence + effRadius);
// generate the psf pattern of the various particles
psfSlice = new SliceOfPSF[particle.length*(p.doSplit?2:1)];
double tmp1 = p.delta_z;
double tmp2 = p.delta_z2;
if (p.doSplit) {
Particle[] particle2 = generateSecondPlaneParticles(particle, nx, ny);
p.delta_z = tmp1;
p.calculateConstants();
for (int n=0;n<particle2.length;n++)
psfSlice[particle.length+n] = new SliceOfPSF(image, p, getSliceIndex(particle2[n],p), particle2[n], 3.0*effRadius);
p.delta_z = tmp2;
p.calculateConstants();
for (int n=0;n<particle.length;n++)
psfSlice[n] = new SliceOfPSF(image, p, getSliceIndex(particle[n],p), particle[n], 3.0*effRadius);
}
else {
for (int n=0;n<particle.length;n++)
psfSlice[n] = new SliceOfPSF(image, p, getSliceIndex(particle[n],p), particle[n],3.0*effRadius);
}
// Threading the slices
boolean multithread = true;
if (multithread) {
ExecutorService executor = Executors.newFixedThreadPool(
Runtime.getRuntime().availableProcessors());
for(SliceOfPSF s : psfSlice)
executor.execute(s);
executor.shutdown();
while (!executor.isTerminated()) {}
}
else
for(SliceOfPSF s : psfSlice)
s.run();
// Making sure the pixels are within the required range
//normalize(p.maxValue);
// Scaling the pixel values
//intensityScale();
}
public Particle[] generateSecondPlaneParticles(Particle[] particle, int nx, int ny) {
Particle[] particle2 = new Particle[particle.length];
for (int i=0; i<particle.length; i++)
particle2[i] = new Particle(particle[i]);
double rotRadians = p.rotation/180.0*Math.PI;
double cosa = Math.cos(rotRadians) * p.scale ;
double sina = Math.cos(rotRadians) * p.scale;
if (columnwise) { // COLUMNWISE
for (int n=0;n<particle.length; n++) {
double dx = p.dx/p.pixelSize + nx/2.0;
double dy = p.dy/p.pixelSize;
particle[n].x = particle[n].x/2.0;
particle2[n].x = particle[n].x*cosa + particle[n].y*sina + dx;
particle2[n].y = -particle[n].x*sina + particle[n].y*cosa + dy;
}
}
else { // ROWISE
for (int n=0;n<particle.length; n++) {
double dx = p.dx/p.pixelSize;
double dy = p.dy/p.pixelSize + ny/2.0;
particle[n].y = particle[n].y/2.0;
particle2[n].x = particle[n].x*cosa + particle[n].y*sina + dx;
particle2[n].y = -particle[n].x*sina + particle[n].y*cosa + dy;
}
}
return particle2;
}
double getEffectiveRadius(int nx, int ny) {
// The return value is in units of [pixels]
double x0 = (nx-1)/2, y0 = (ny-1)/2;
int maxRadius = ((int) Math.round(Math.sqrt((nx-x0)*(nx-x0)+(ny-y0)*(ny-y0))))+1;
double[] r = new double[maxRadius*OVER_SAMPLING];
double[] h = new double[r.length];
double sum=0.0, sigma2=0.0;
p.ti = p.ti0;
p.ts = p.thick;
p.delta_z = 0;
p.calculateConstants();
KirchhoffDiffractionSimpson I = new KirchhoffDiffractionSimpson(p,p.accuracy);
for (int n=0; n<r.length; n++) {
r[n] = ((double)n)/((double)OVER_SAMPLING);
h[n] = I.calculate(r[n]*p.pixelSize*p.M);
sum += h[n];
sigma2 += h[n]*r[n]*r[n];
}
return Math.sqrt(sigma2/sum);
}
} | Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/bpalm/simulator/PoissonRandomNumber.java | .java | 733 | 34 | package bpalm.simulator;
import java.util.Random;
public class PoissonRandomNumber extends Random {
public PoissonRandomNumber() {
super();
}
public double nextPoisson(double lambda) {
if (lambda==0) return 0.0;
else if (lambda<100) {
// Knuth algorithm
double L = Math.exp(-lambda);
int k = 0;
double p = 1;
do {
k++;
p *= nextDouble();
} while (p >= L);
//IJ.log("(lambda,k)=("+lambda + ", " + (k-1) + ")");
return (double)(k - 1);
} else {
// Gussian distribution which approximates the Poisson one for large lambda values
double value = (nextGaussian()*Math.sqrt(lambda))+lambda;
//IJ.log("(lambda,value)=("+ lambda + ", " + value + ")");
return value;
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/bpalm/simulator/SliceOfPSF.java | .java | 3,121 | 81 | package bpalm.simulator;
import ij.IJ;
import bpalm.model.KirchhoffDiffractionSimpson;
// This class generates a slice of single source according to the the Gibson-Lanni PSF model.
// @author Hagai Kirshner, Biomedical Imaging Group, Ecole Polytechnique Federale de Lausanne (EPFL)
/**
*
* @author Hagai Kirshner, Biomedical Imaging Group, Ecole Polytechnique Federale de Lausanne (EPFL)
* @author Thomas Pengo, Laboratory of Experimental Biophysics, Ecole Polytechnique Federale de Lausanne (EPFL)
*
*/
public class SliceOfPSF implements Runnable{
static int OVER_SAMPLING = 1; //5
float[][] zStack;
private BPALMParameters p;
int z; // slice index
Particle cur;
double radius; // the range of 'r' values for which we calculate the psf values.
int nx, ny;
double progress = 0;
// Constructor.
public SliceOfPSF(float[][] zStack, BPALMParameters p, int z, Particle cur, double radius) {
this.zStack = zStack;
nx = zStack.length;
ny = zStack[0].length;
this.z = z;
this.cur = cur;
this.radius = radius;
// making a new copy of the parameters is required, as different threads use different parameters.
this.p = new BPALMParameters(p);
}
public void run() {
// Initializing the Gibson-Lanni psf model
p.ts = cur.z;
p.ti = p.ti0 - z*p.axialResolution - p.depth; // The smaller the immersion layer is, the farther is the focal plane of the objective
//IJ.log("zp="+cur.z + " [nm]");
p.calculateConstants();
KirchhoffDiffractionSimpson I = new KirchhoffDiffractionSimpson(p, p.accuracy);
// PSF values at radial locations
// We consider a larger radius than the radius parameter because we evaluate on a rectangle
int nr = (int)Math.round(Math.sqrt(2.0)*(radius+1)*OVER_SAMPLING);
IJ.log(">> " + IJ.d2s(p.ti, 10) + " z " + z + " " + p.depth + " p.ts= " + cur.z + " nr " + nr);
double[] r = new double[nr];
double[] h = new double[nr];
for (int nn=0; nn<nr; nn++) {
r[nn] = ((double)nn)/((double)OVER_SAMPLING);
h[nn] = I.calculate(r[nn]*p.pixelSize*p.M);
progress = progress + 1.0/(r.length+1);
}
// Linear interpolation of the pixels values
// Interpolation is carried out in the neighborhood of (x,y) particle position
double xp = cur.x;
double yp = cur.y;
double rPixel, value;
int index;
int xLow = Math.max(0,(int)Math.ceil(cur.x-radius));
int xHigh = Math.min(nx-1,(int)Math.floor(cur.x+radius));
int yLow = Math.max(0,(int)Math.ceil(cur.y-radius));
int yHigh = Math.min(ny-1,(int)Math.floor(cur.y+radius));
for (int x=xLow; x<=xHigh; x++)
for (int y=yLow; y<=yHigh; y++) {
rPixel = Math.sqrt((x-xp)*(x-xp)+(y-yp)*(y-yp)); // radius of the current pixel in units of [pixels]
index = (int)(rPixel*OVER_SAMPLING); // Index of nearest coordinate from bellow
value = h[index] + (h[index+1]-h[index])*(rPixel-r[index])*OVER_SAMPLING ; // Interpolated value.
zStack[x][y] += value;
}
progress = progress + 1.0/(r.length+1); // progress = 1
}
public double getProgress() { return progress; }
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/bpalm/simulator/BPALMParameters.java | .java | 1,879 | 73 | package bpalm.simulator;
import bpalm.model.GibsonLanniParameters;
/**
* This class manages all parameters of the acquisition, output and sample tabs of the BiplaneDataGenerator plugin.
*
* @author Hagai Kirshner, Biomedical Imaging Group, Ecole Polytechnique Federale de Lausanne (EPFL)
* @author Thomas Pengo, Laboratory for Experimental Biophysics, Ecole Polytechnique Federale de Lausanne (EPFL)
*/
public class BPALMParameters extends GibsonLanniParameters {
public static enum StructureType {
UNIFORM ("Uniform"),
SINGLE ("One particle centered, at chosen max depth"),
SPIRAL_10_REVOLUTIONS ("Spiral 10 revolutions"),
SPIRAL_1_REVOLUTION ("Spiral 1 revolution");
String str;
private StructureType(String s) {str=s;};
public String toString() {return str;}
}
// Output parameters
public double maxValue;
public int bits, accuracy;
public String intensityScale, lut;
// Split parameters
public boolean doSplit;
public String orientation;
public double rotation;
public double scale;
public double dx;
public double dy;
public double border;
public double thick;
// Sequence
public boolean doSequence;
public int noFrames;
public boolean saveParticles;
public double delta_z2;
public BPALMParameters() {}
public BPALMParameters(BPALMParameters p) {
super(p);
this.thick = p.thick;
this.doSplit = p.doSplit;
this.rotation = p.rotation;
this.scale = p.scale;
this.dx = p.dx;
this.dy = p.dy;
this.border = p.border;
this.delta_z2 = p.delta_z2;
this.doSequence = true;
this.orientation = p.orientation;
this.noFrames = 1;
this.maxValue = 1.0;
this.bits = 32;
this.intensityScale = "Linear";
this.lut = "Fire";
this.accuracy = 0; // Good, fastest
this.saveParticles = false;
}
public void calculateConstants() {
super.calculateConstants();
}
} | Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/bpalm/simulator/Particle.java | .java | 406 | 27 | package bpalm.simulator;
public class Particle {
public double x,y,z,t;
public Particle() {
x=0; y=0; z=0; t=0;
}
public Particle(Particle p) {
x=p.x;
y=p.y;
z=p.z;
t=p.t;
}
public Particle scale(double fX, double fY, double fZ) {
x=x*fX; y=y*fY; z=z*fZ;
return this;
}
public Particle offset(double fX, double fY, double fZ) {
x=x+fX; y=y+fY; z=z+fZ;
return this;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/bpalm/simulator/common/generators/Function.java | .java | 167 | 8 | package bpalm.simulator.common.generators;
public interface Function {
public double getX(double t);
public double getY(double t);
public double getZ(double t);
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/bpalm/simulator/common/generators/UniformParticleGenerator.java | .java | 446 | 22 | package bpalm.simulator.common.generators;
import bpalm.simulator.Particle;
public class UniformParticleGenerator extends ParticleGenerator {
public UniformParticleGenerator() {}
@Override
public Particle nextParticle() {
Particle p = new Particle(template);
p.x = randomGenerator.nextDouble();
p.y = randomGenerator.nextDouble();
p.z = randomGenerator.nextDouble();
p.t = randomGenerator.nextDouble();
return p;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/bpalm/simulator/common/generators/FunctionParticleGenerator.java | .java | 628 | 31 | package bpalm.simulator.common.generators;
import bpalm.simulator.Particle;
public class FunctionParticleGenerator extends ParticleGenerator {
Function theFunction;
public FunctionParticleGenerator() {
this(new SpiralFunction());
}
public FunctionParticleGenerator(Function theFunction) {
this.theFunction = theFunction;
}
@Override
public Particle nextParticle() {
double rand = randomGenerator.nextDouble();
Particle p = new Particle(template);
p.x = theFunction.getX(rand);
p.y = theFunction.getY(rand);
p.z = theFunction.getZ(rand);
p.t = randomGenerator.nextDouble();
return p;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/bpalm/simulator/common/generators/ParticleGenerator.java | .java | 1,480 | 56 | package bpalm.simulator.common.generators;
import java.util.Random;
import bpalm.simulator.Particle;
/**
* A particle generator, as the name indicates, generates a particle according to a particular
* pattern. The particle is generated each time a "nextParticle" is called.
*
* This default implementation generates a new particle with random coordinates between 0 and 1, but
* other implementations are possible by calling the appropriate factory method.
*
* @author Thomas Pengo, Laboratory of Experimental Biophysics, EPFL
*
*/
abstract public class ParticleGenerator {
double minX, maxX, minY, maxY, minZ, maxZ;
Random randomGenerator = new Random();
Particle template = new Particle();
public Particle getTemplate() {
return template;
}
public void setTemplate(Particle template) {
this.template = template;
}
double scaleX=1, scaleY=1, scaleZ=1;
double offX=0, offY=0, offZ=0;
public static ParticleGenerator newRandomParticleGenerator() {
return new UniformParticleGenerator();
}
public void setRandomGenerator(Random generator) {
this.randomGenerator = generator;
}
public abstract Particle nextParticle();
public Particle nextTraslatedParticle() {
return nextParticle().scale(scaleX, scaleY, scaleZ).offset(offX, offY, offZ);
}
public void setScale(double d, double e, double f) {
scaleX = d; scaleY = e; scaleZ = f;
}
public void setOffset(double d, double e, double f) {
offX = d; offY = e; offZ = f;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/bpalm/simulator/common/generators/SpiralFunction.java | .java | 453 | 17 | package bpalm.simulator.common.generators;
public class SpiralFunction implements Function {
double turns;
public SpiralFunction() {
this(1);
}
public SpiralFunction(double turns) {
this.turns = turns;
}
@Override public double getX(double t) { return (t*Math.sin(t*2*Math.PI*turns)/2+.5); }
@Override public double getY(double t) { return (t*Math.cos(t*2*Math.PI*turns)/2+.5); }
@Override public double getZ(double t) { return t; }
} | Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/assessment/AssessmentTable.java | .java | 4,553 | 147 | package smlms.assessment;
import ij.IJ;
import java.awt.Color;
import java.awt.Component;
import java.awt.Dimension;
import java.awt.event.MouseEvent;
import java.awt.event.MouseMotionAdapter;
import java.util.ArrayList;
import java.util.HashMap;
import javax.swing.JFrame;
import javax.swing.JScrollPane;
import javax.swing.JTable;
import javax.swing.ListSelectionModel;
import javax.swing.table.DefaultTableCellRenderer;
import javax.swing.table.DefaultTableModel;
import javax.swing.table.JTableHeader;
import javax.swing.table.TableCellRenderer;
import javax.swing.table.TableColumn;
import javax.swing.table.TableColumnModel;
public class AssessmentTable extends JTable {
private Color colorOddRow = new Color(245, 245, 250);
private Color colorEvenRow = new Color(232, 232, 237);
private String name = "Untitled";
public AssessmentTable(String headers[]) {
super();
DefaultTableModel tableModel = new DefaultTableModel() {
@Override
public boolean isCellEditable(int row, int column) {
return false;
}
};
setModel(tableModel);
DefaultTableModel model = (DefaultTableModel) getModel();
model.setColumnIdentifiers(headers);
setSelectionMode(ListSelectionModel.MULTIPLE_INTERVAL_SELECTION);
setRowSelectionAllowed(true);
setAutoCreateRowSorter(true);
for (int i = 0; i < headers.length; i++)
getColumnModel().getColumn(i).setCellRenderer(new AlternatedRowRenderer());
}
@Override
public Component prepareRenderer(TableCellRenderer renderer, int row, int column) {
Component component = super.prepareRenderer(renderer, row, column);
TableColumn tableColumn = getColumnModel().getColumn(column);
if (column == 0)
tableColumn.setPreferredWidth(10);
return component;
}
public JScrollPane getPane(int width, int height) {
JScrollPane scrollpane = new JScrollPane(this);
scrollpane.setPreferredSize(new Dimension(width, height));
return scrollpane;
}
public void show(int width, int height) {
JScrollPane scrollpane = getPane(width, height);
JFrame frame = new JFrame(name);
frame.add(scrollpane);
frame.pack();
frame.setVisible(true);
}
public int getRow(int id) {
for (int row = 0; row < getRowCount(); row++) {
if (((Integer)getValueAt(row, 0)) == id)
return row;
}
return -1;
}
public void update(ArrayList<CompareResult> results) {
DefaultTableModel model = (DefaultTableModel) getModel();
model.getDataVector().removeAllElements();
int count = 1;
for(CompareResult result : results)
model.addRow(result.getResultsValues(count++));
}
public void update(CompareResult results[]) {
DefaultTableModel model = (DefaultTableModel) getModel();
model.getDataVector().removeAllElements();
for(int i=0; i<results.length; i++)
model.addRow(results[i].getResultsValues(i));
}
public void update(CompareResult results[], CompareResult result) {
DefaultTableModel model = (DefaultTableModel) getModel();
model.getDataVector().removeAllElements();
for(int i=0; i<results.length; i++)
model.addRow(results[i].getResultsValues(i));
name = "Frame-to-frame-" + result.dataset + "-" + result.software + "J:" +
IJ.d2s(result.jaccard, 4) + " XY:" + IJ.d2s(result.rmseLateral, 4) + " Z:" + IJ.d2s(result.rmseAxial, 4);
}
public class AlternatedRowRenderer extends DefaultTableCellRenderer {
@Override
public Component getTableCellRendererComponent(JTable table, Object value, boolean isSelected, boolean hasFocus, int row, int col) {
super.getTableCellRendererComponent(table, value, isSelected, hasFocus, row, col);
Component c = super.getTableCellRendererComponent(table, value, isSelected, hasFocus, row, col);
if (!isSelected)
c.setBackground(row % 2 == 0 ? colorEvenRow : colorOddRow);
return c;
}
}
class ColumnHeaderToolTips extends MouseMotionAdapter {
TableColumn curCol;
HashMap<TableColumn, String> tips = new HashMap<TableColumn, String>();
public void setToolTip(TableColumn col, String tooltip) {
if (tooltip == null)
tips.remove(col);
else
tips.put(col, tooltip);
}
@Override
public void mouseMoved(MouseEvent evt) {
JTableHeader header = (JTableHeader) evt.getSource();
JTable table = header.getTable();
TableColumnModel colModel = table.getColumnModel();
int vColIndex = colModel.getColumnIndexAtX(evt.getX());
TableColumn col = null;
if (vColIndex >= 0) {
col = colModel.getColumn(vColIndex);
}
if (col != curCol) {
header.setToolTipText((String) tips.get(col));
curCol = col;
}
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/assessment/FluorophorePair.java | .java | 1,378 | 43 | //=========================================================================================
//
// Project: Single-Molecule Localization Microscopy
// Benchmarking of Localization Microscopy Software for Super-resolution Imaging
//
// Author : Daniel Sage, Biomedical Imaging Group (BIG), http://bigwww.epfl.ch/sage/
//
// Organization: Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
//
// Reference: paper submitted, 2013
//
// Conditions of use: You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
package smlms.assessment;
import smlms.file.Fluorophore;
public class FluorophorePair implements Comparable<FluorophorePair> {
public Fluorophore ref;
public Fluorophore test;
public double cost = 0;
public FluorophorePair(Fluorophore ref, Fluorophore test) {
this.ref = ref;
this.test = test;
this.cost = ref.distance(test);
}
public int compareTo(FluorophorePair pair) {
return (cost > pair.cost ? 1 : 0);
}
public String toString() {
String a = ref.x + ", " + ref.y;
String b = test.x + ", " + test.y;
return a + ", " + b;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/assessment/Localization_Assessment_Batch.java | .java | 1,830 | 62 | package smlms.assessment;
import ij.IJ;
import java.io.File;
import java.util.ArrayList;
import javax.swing.JLabel;
import smlms.file.Description;
import smlms.file.Fluorophores;
import additionaluserinterface.WalkBar;
public class Localization_Assessment_Batch {
public static String groundtruth = "/Users/sage/Desktop/beads/MT1.0/activations.csv";
public static String path = "/Users/sage/Desktop/beads/MT /results/";
private WalkBar walk = new WalkBar("(c) 2016 EPFL, BIG", false, false, true);
public Localization_Assessment_Batch() {
String dataset = "MT0-6";
double pixelsize = 100;
double radius = 250;
int algo = 0;
String logging = "Talk";
File dir = new File(path);
String files[] = dir.list();
int n = files.length;
Fluorophores refsall = Fluorophores.load( groundtruth, new Description("activations"), new JLabel());
Fluorophores[] refs = refsall.reshapeInFrames();
ArrayList<CompareResult> results = new ArrayList<CompareResult>();
for(int i=0; i<n; i++) {
if (files[i].endsWith("csv") || files[i].endsWith("xls")) {
String filename = path + File.separator + files[i];
String software = files[i];
IJ.log(" \n\n" + files[i]);
String[] items = files[i].split("[.]");
IJ.log(" " + items.length);
if (items.length >= 2) {
Description desc = new Description(items[0]);
Fluorophores testsall = Fluorophores.load(filename, desc, new JLabel());
Fluorophores[] tests = testsall.reshapeInFrames();
CompareLocalization cl = new CompareLocalization(walk, dataset, software, logging, refs, tests, algo, radius, pixelsize);
cl.run();
results.add(cl.getResult());
}
}
}
AssessmentTable table = new AssessmentTable(CompareResult.getResultsHeader());
table.update(results);
table.show(1000, 100);
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/assessment/CompareLocalization.java | .java | 8,352 | 270 | //=========================================================================================
//
// Project: Single-Molecule Localization Microscopy
// Benchmarking of Localization Microscopy Software for Super-resolution Imaging
//
// Author : Daniel Sage, Biomedical Imaging Group (BIG), http://bigwww.epfl.ch/sage/
//
// Organization: Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
//
// Reference: paper submitted, 2013
//
// Conditions of use: You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
package smlms.assessment;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.Vector;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.TimeUnit;
import smlms.assessment.hungarian.MaximumWeightedMatching;
import smlms.assessment.hungarian.WeightedEdge;
import smlms.file.Fluorophore;
import smlms.file.Fluorophores;
import additionaluserinterface.WalkBar;
public class CompareLocalization implements Runnable {
public static final int ALGO_GLOBAL_SORT_NEAREST_NEIGHBORHOOR = 0;
public static final int ALGO_NEAREST_NEIGHBORHOOR = 1;
public static final int ALGO_HUNGARIAN = 2;
private Fluorophores[] refs;
private Fluorophores[] tests;
private double tolerance = Double.MAX_VALUE;
private CompareResult results[];
private CompareResult result;
private Vector<FluorophorePair> pairs[];
private int algo = ALGO_NEAREST_NEIGHBORHOOR;
private double pixelsize;
private WalkBar walk;
private int frameBegin = 0;
private int frameEnd = Integer.MAX_VALUE;
private String dataset;
private String software;
private String logging;
public CompareLocalization(WalkBar walk, String dataset, String software, String logging, Fluorophores[] refs, Fluorophores[] tests, int algo, double tolerance, double pixelsize) {
this.walk = walk;
this.dataset = dataset;
this.software = software;
this.refs = refs;
this.tests = tests;
this.algo = algo;
this.tolerance = tolerance;
this.pixelsize = pixelsize;
this.logging = logging;
}
public void setTolerance(double tolerance) {
this.tolerance = tolerance;
}
public void setFrameRange(int frameBegin, int frameEnd) {
this.frameBegin = frameBegin;
this.frameEnd = frameEnd;
}
public CompareResult getResult() {
return result;
}
public CompareResult[] getResults() {
return results;
}
public double[] getJaccard() {
double[] jaccard = new double[results.length];
for(int i=0; i<results.length; i++)
jaccard[i] = results[i].jaccard / 100.0;
return jaccard;
}
public double[] getPrecision() {
double[] precision = new double[results.length];
for(int i=0; i<results.length; i++)
precision[i] = results[i].precision;
return precision;
}
public double[] getRecall() {
double[] recall = new double[results.length];
for(int i=0; i<results.length; i++)
recall[i] = results[i].recall;
return recall;
}
public double[] getRMSE() {
double[] rmse = new double[results.length];
for(int i=0; i<results.length; i++)
rmse[i] = results[i].rmseLateral;
return rmse;
}
public double[] getFrames() {
double[] frames = new double[results.length];
for(int i=0; i<results.length; i++)
frames[i] = i+1;
return frames;
}
public Vector<FluorophorePair>[] getPairs() {
return pairs;
}
public void run() {
if (refs == null)
return;
if (tests == null)
return;
int nbframes = Math.min(refs.length, tests.length);
double timeTotal = System.nanoTime();
pairs = new Vector[nbframes];
int ntest = 0;
int nref = 0;
boolean talk = logging.equals("Talk");
boolean verbose = logging.equals("Verbose");
int f1 = Math.max(0, frameBegin);
int f2 = Math.min(nbframes, frameEnd);
results = new CompareResult[f2];
result = new CompareResult(dataset, software, algo, tolerance, pixelsize);
walk.reset();
for (int f = f1; f < f2; f++) {
double time = System.nanoTime();
Fluorophores ref = refs[f];
Fluorophores test = tests[f];
ntest += test.size();
nref += ref.size();
walk.progress("Frame " + f + "(" + ref.size() + ":" + test.size() + ")", (100 * f) / (f2 - f1 + 1));
//if (talk || verbose)
// IJ.log("Frame: " + f + " nref:" + ref.size() + " ntest:" + test.size());
for (int i = 0; i < ref.size(); i++)
ref.get(i).matching = false;
for (int i = 0; i < test.size(); i++)
test.get(i).matching = false;
switch (algo) {
case ALGO_GLOBAL_SORT_NEAREST_NEIGHBORHOOR:
pairs[f] = matchNNAlgo(ref, test, tolerance, true);
break;
case ALGO_NEAREST_NEIGHBORHOOR:
pairs[f] = matchNNAlgo(ref, test, tolerance, false);
break;
case ALGO_HUNGARIAN:
pairs[f] = matchHungarianAlgo(f, ref, test, tolerance);
break;
}
results[f] = new CompareResult("Dataset", "Software", algo, tolerance, pixelsize);
for (int i = 0; i < pairs[f].size(); i++) {
results[f].add(pairs[f].get(i));
result.add(pairs[f].get(i));
}
time = (System.nanoTime() - time) * 10e-9;
results[f].compute(ref.size(), test.size(), time);
}
timeTotal = (System.nanoTime() - timeTotal) * 10e-9;
result.compute(nref, ntest, timeTotal);
walk.finish("Comparison Localization");
}
/**
* Matching two sets of fluorophores using the Nearest-Neighborhood
* algorithm. The version with the global sorting of the candidates is much
* better.
*/
private Vector<FluorophorePair> matchNNAlgo(Fluorophores ref, Fluorophores test, double tolerance, boolean globalSort) {
Vector<FluorophorePair> candidates = new Vector<FluorophorePair>();
for (int i = 0; i < ref.size(); i++) {
Fluorophore a = ref.get(i);
for (int j = 0; j < test.size(); j++) {
if (a.distanceLateral(test.get(j)) <= tolerance)
candidates.add(new FluorophorePair(a, test.get(j)));
}
}
if (globalSort)
Collections.sort(candidates);
Vector<FluorophorePair> pairsFrame = new Vector<FluorophorePair>();
for (int i = 0; i < candidates.size(); i++) {
FluorophorePair pair = candidates.get(i);
if (pair.ref.matching == false)
if (pair.test.matching == false) {
pair.ref.matching = true;
pair.test.matching = true;
pairsFrame.add(pair);
}
}
return pairsFrame;
}
/**
* Matching two sets of fluorophores using the Hungarian algorithm.
*/
private Vector<FluorophorePair> matchHungarianAlgo(int frame, Fluorophores ref, Fluorophores test, double tolerance) {
int M = ref.size();
int N = test.size();
ArrayList<WeightedEdge> graph = new ArrayList<WeightedEdge>();
for (int i = 0; i < M; i++) {
Fluorophore a = ref.get(i);
for (int j = 0; j < N; j++) {
double d = a.distanceLateral(test.get(j));
if (d <= tolerance) {
graph.add(new WeightedEdge(i, M + j, -d));
}
}
}
Vector<FluorophorePair> pairsFrame = new Vector<FluorophorePair>();
MaximumWeightedMatching maximumWeightedMatching = new MaximumWeightedMatching(graph, M, N);
ExecutorService executor = Executors.newSingleThreadExecutor();
Collection<Callable<MaximumWeightedMatching>> arr = new ArrayList<Callable<MaximumWeightedMatching>>();
arr.add(maximumWeightedMatching);
try {
executor.invokeAll(arr, 10, TimeUnit.SECONDS);
if (maximumWeightedMatching.isTerminated()) {
ArrayList<WeightedEdge> matching = maximumWeightedMatching.getMaximumWeightMatching();
for (int i = 0; i < matching.size(); i++) {
Fluorophore r = ref.get(matching.get(i).source);
Fluorophore d = test.get(matching.get(i).destination - M);
FluorophorePair pair = new FluorophorePair(r, d);
pairsFrame.add(pair);
}
}
else {
System.out.println("frame: " + frame + " ERROR not terminated ");
}
}
catch (Exception e) {
System.out.println("Error " + e);
for (int i = 0; i < e.getStackTrace().length; i++)
System.out.println("Error " + e.getStackTrace()[i]);
}
return pairsFrame;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/assessment/Localization_Assessment.java | .java | 1,105 | 32 | package smlms.assessment;
//=========================================================================================
//
// Project:
// Single-Molecule Localization Microscopy Challenge 2016
// http://bigwww.epfl.ch/smlm/
//
// Author:
// Daniel Sage, http://bigwww.epfl.ch/sage/
// Biomedical Imaging Group (BIG)
// Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
//
// Reference:
// D. Sage, H. Kirshner, T. Pengo, N. Stuurman, J. Min, S. Manley, M. Unser
// Quantitative Evaluation of Software Packages for Single-Molecule Localization Microscopy
// Nature Methods 12, August 2015.
//
// Conditions of use:
// You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
public class Localization_Assessment {
public Localization_Assessment() {
new AssessmentDialog();
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/assessment/CompareResult.java | .java | 6,547 | 188 | //=========================================================================================
//
// Project: Single-Molecule Localization Microscopy
// Benchmarking of Localization Microscopy Software for Super-resolution Imaging
//
// Author : Daniel Sage, Biomedical Imaging Group (BIG), http://bigwww.epfl.ch/sage/
//
// Organization: Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
//
// Reference: paper submitted, 2013
//
// Conditions of use: You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
package smlms.assessment;
public class CompareResult {
private String algo;
private double tolerance;
private double time = 0;
public int intersection = 0;
public double rmseLateral = 0;
public double rmseAxial = 0;
public double deltaX = 0;
public double deltaY = 0;
public double deltaZ = 0;
public double mdIntensity = 0;
public double precision = 0;
public double recall = 0;
public double jaccard = 0;
public double fscore = 0;
public double snr = 0;
public double frc = 0; // Fourier Ring Correlation
private int ntest;
private double pixelsize = 100;
public String dataset;
public String software;
public CompareResult(String dataset, String software, int algo, double tolerance, double pixelsize) {
this.algo = (algo == 0 ? "GS-NN" : (algo == 1 ? "NN" : "Hungarian"));
this.dataset = dataset;
this.software = software;
this.tolerance = tolerance;
this.pixelsize = pixelsize;
}
public void add(FluorophorePair pair) {
intersection++;
deltaX += pair.ref.deltaX(pair.test);
deltaY += pair.ref.deltaY(pair.test);
deltaZ += pair.ref.deltaZ(pair.test);
mdIntensity += pair.ref.differenceIntensity(pair.test);
rmseLateral += pair.ref.distanceLateral(pair.test) * pair.ref.distanceLateral(pair.test);
rmseAxial += pair.ref.distanceAxial(pair.test) * pair.ref.distanceAxial(pair.test);
}
public void compute(int nref, int ntest, double time) {
this.ntest = ntest;
this.time = time;
int TP = intersection;
double FP = ntest - TP;
double FN = nref - TP;
precision = TP / (TP + FP);
recall = TP / (TP + FN);
double union = nref + ntest - intersection;
if (intersection > 0) {
deltaX /= intersection;
deltaY /= intersection;
deltaZ /= intersection;
mdIntensity /= intersection;
rmseLateral = Math.sqrt(rmseLateral / intersection);
rmseAxial = Math.sqrt(rmseAxial / intersection);
jaccard = (100.0 * intersection) / union;
fscore = 2.0 * (precision * recall) / (precision + recall);
}
}
public String[] getResultsValues(int num) {
String line[] = new String[20];
for (int i = 0; i < line.length; i++)
line[i] = "-";
line[ 0] = "" + num;
line[ 1] = dataset;
line[ 2] = software;
line[ 3] = algo;
line[ 4] = String.format("%1.3f", time);
line[ 5] = String.format("%3.1f", tolerance);
line[ 6] = String.format("%d", ntest);
line[ 7] = String.format("%d", intersection);
line[ 8] = String.format("%2.3f", jaccard);
line[ 9] = String.format("%1.3f", fscore);
line[10] = String.format("%1.3f", precision);
line[11] = String.format("%1.3f", recall);
line[12] = String.format("%3.3f", rmseLateral);
line[13] = String.format("%3.3f", rmseAxial);
line[14] = String.format("%3.3f", deltaX);
line[15] = String.format("%3.3f", deltaY);
line[16] = String.format("%3.3f", deltaZ);
line[17] = String.format("%5.3f", mdIntensity);
line[18] = String.format("%5.3f", snr);
line[19] = String.format("%5.3f", frc);
return line;
}
public static String[] getResultsHeader() {
return new String[] { "no", "Dataset", "Software", "Algo", "Time", "Tolerance", "#Fluos", "#Intersection", "Jaccard", "F-Score", "Precision",
"Recall", "RMSE Lateral", "RMSE Axial", "Delta X", "Delta Y", "Delta Z", "MD Intensity", "SNR", "Resolution FRC" };
}
public String[] getResultsUnit() {
return new String[] { "", "", "", "", "s.", "nm", "", "", "%", "", "", "", "nm", "nm", "nm", "nm", "nm", "", "dB", "nm" };
}
public String[] getSummaryValues(String dataset, String softname) {
String line[] = new String[5];
for (int i = 0; i < line.length; i++)
line[i] = "-";
line[0] = dataset;
line[1] = softname;
line[2] = String.format("%2.3f", jaccard);
line[3] = String.format("%3.3f", rmseLateral);
line[4] = String.format("%5.3f", snr);
return line;
}
public String[] getSummaryHeader() {
return new String[] { "Dataset", "Software", "Jaccard", "RMSE Lateral", "SNR" };
}
public String[] getHighlightTableHeader() {
return new String[] {"Feature", "Value", "Unit"};
}
public String[][] getHighlightTableValues() {
String table[][] = new String[4][3];
table[0] = new String[] {"Number of fluorophores", String.format("%d", ntest), ""};
table[1] = new String[] {"Jaccard", String.format("%3.2f", jaccard), "%"};
table[2] = new String[] {"Accuracy", String.format("%3.2f", rmseLateral), "nm"};
table[3] = new String[] {"Image-based SNR", String.format("%3.2f", snr), "dB"};
return table;
}
public String[] getCompleteTableHeader() {
return new String[] {"Protocol of assessment", "Value", "Localization of particles", "Results", "Accuracy of localization", "Results"};
}
public String[][] getCompleteTableValues() {
String table[][] = new String[4][6];
table[0] = new String[] {
"Radius of tolerance", String.format("%3.1f nm", tolerance),
"Jaccard", String.format("%3.2f", jaccard) + "",
"RMSE", String.format("%3.2f nm", rmseLateral)
};
table[1] = new String[] {
"Number of fluorophores", String.format("%d", ntest),
"F-Score", String.format("%3.2f", fscore),
"Delta in X axis", String.format("%3.3f nm", deltaX)
};
table[2] = new String[] {
"Number of matching", String.format("%d", intersection),
"Precision", String.format("%3.2f", precision),
"Delta in Y axis", String.format("%3.3f nm", deltaY)
};
table[3] = new String[] {
"Pixelsize", String.format("%3.0f nm", pixelsize),
"Recall", String.format("%3.3f", recall),
"Image-based SNR", String.format("%3.2f dB", snr)};
return table;
}
public String[] getNumberOfFluorophores() {
String table[] = new String[] {"Number of fluorophores", String.format("%d", ntest), ""};
return table;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/assessment/AssessmentDialog.java | .java | 4,551 | 142 | package smlms.assessment;
import ij.IJ;
import ij.gui.GUI;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import javax.swing.BorderFactory;
import javax.swing.JButton;
import javax.swing.JComboBox;
import javax.swing.JDialog;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.JTextField;
import javax.swing.event.ChangeEvent;
import javax.swing.event.ChangeListener;
import smlms.file.FluorophoreComponent;
import smlms.file.Fluorophores;
import additionaluserinterface.GridPanel;
import additionaluserinterface.Settings;
import additionaluserinterface.SpinnerDouble;
import additionaluserinterface.WalkBar;
public class AssessmentDialog extends JDialog implements ActionListener, ChangeListener, Runnable {
private WalkBar walk = new WalkBar("(c) 2016 EPFL, BIG", false, false, true);
private Settings settings = new Settings("localization-microscopy", IJ.getDirectory("plugins") + "smlm-2016.txt");
private JButton bnAssessment = new JButton("Assessment");
public JComboBox cmbAlgo = new JComboBox(new String[] {"Global Sorted NN", "N.N", "Hungarian"});
public JComboBox cmbLogging = new JComboBox(new String[] {"Talk", "Verbose", "Mute"});
public SpinnerDouble spnPixelsize = new SpinnerDouble(100, 0.01, 10000, 1);
public SpinnerDouble spnToleranceXY = new SpinnerDouble(250, 0.01, 10000, 1);
public SpinnerDouble spnToleranceZ = new SpinnerDouble(500, 0.01, 10000, 1);
private JLabel lblSize = new JLabel("-");
public JTextField txtSoftware = new JTextField("software", 10);
public JTextField txtDataset = new JTextField("dataset", 10);
private FluorophoreComponent channels[] = new FluorophoreComponent[2];
private Thread thread = null;
public static void main(String args[]) {
new AssessmentDialog();
}
public AssessmentDialog() {
super(new JFrame(), "Assessment");
settings.record("assessment-spnPixelsize", spnPixelsize, "100");
settings.record("assessment-spnToleranceXY", spnToleranceXY, "250");
settings.record("assessment-spnToleranceZ", spnToleranceZ, "250");
lblSize.setBorder(BorderFactory.createEtchedBorder());
channels[0] = new FluorophoreComponent(settings, "Ground-truth");
channels[1] = new FluorophoreComponent(settings, "Tested software");
settings.loadRecordedItems();
GridPanel pn1 = new GridPanel("Settings", 1);
pn1.place(0, 0, new JLabel("Pixelsize"));
pn1.place(0, 1, spnPixelsize);
pn1.place(1, 0, new JLabel("Tolerance XY"));
pn1.place(1, 1, spnToleranceXY);
pn1.place(2, 0, new JLabel("Tolerance Z"));
pn1.place(2, 1, spnToleranceZ);
pn1.place(3, 0, new JLabel("Matching"));
pn1.place(3, 1, cmbAlgo);
pn1.place(4, 0, txtSoftware);
pn1.place(4, 1, txtDataset);
pn1.place(7, 0, cmbLogging);
pn1.place(7, 1, bnAssessment);
GridPanel main = new GridPanel(false);
main.place(0, 0, channels[0]);
main.place(1, 0, channels[1]);
main.place(2, 0, pn1);
main.place(3, 0, walk);
add(main);
pack();
setModal(false);
GUI.center(this);
setVisible(true);
spnPixelsize.addChangeListener(this);
bnAssessment.addActionListener(this);
walk.getButtonClose().addActionListener(this);
updateInterface();
}
@Override
public void actionPerformed(ActionEvent e) {
if (e.getSource() == walk.getButtonClose()) {
settings.storeRecordedItems();
dispose();
}
else if (e.getSource() == bnAssessment) {
if (thread == null) {
thread = new Thread(this);
thread.setPriority(Thread.MIN_PRIORITY);
thread.start();
}
}
updateInterface();
}
public void run() {
walk.reset();
double pixelsize = spnPixelsize.get();
double toleranceXY = spnToleranceXY.get();
double toleranceZ = spnToleranceZ.get();
Fluorophores refs[] = channels[0].getFluorophoresPerFrames();
Fluorophores tests[] = channels[1].getFluorophoresPerFrames();
int algo = cmbAlgo.getSelectedIndex();
String logging = (String)cmbLogging.getSelectedItem();
String dataset = txtDataset.getText();
String software = txtDataset.getText();
CompareLocalization cl = new CompareLocalization(walk, dataset, software, logging, refs, tests, algo, toleranceXY, pixelsize);
cl.run();
AssessmentTable table = new AssessmentTable(CompareResult.getResultsHeader());
table.update(cl.getResults(), cl.getResult());
table.show(1000, 100);
walk.finish();
thread = null;
}
private void updateInterface() {
}
@Override
public void stateChanged(ChangeEvent e) {
updateInterface();
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/assessment/hungarian/Matching.java | .java | 1,101 | 31 | //=========================================================================================
//
// Project: Single-Molecule Localization Microscopy
// Benchmarking of Localization Microscopy Software for Super-resolution Imaging
//
// Author : Daniel Sage, Biomedical Imaging Group (BIG), http://bigwww.epfl.ch/sage/
//
// Organization: Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
//
// Reference: paper submitted, 2013
//
// Conditions of use: You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
package smlms.assessment.hungarian;
import java.util.ArrayList;
public class Matching {
public ArrayList<WeightedEdge> cTEdges;
public double weight;
public Matching(ArrayList<WeightedEdge> cTEdges, double weight) {
this.cTEdges = cTEdges;
this.weight = weight;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/assessment/hungarian/BellmanFord.java | .java | 2,077 | 68 | //=========================================================================================
//
// Project: Single-Molecule Localization Microscopy
// Benchmarking of Localization Microscopy Software for Super-resolution Imaging
//
// Author : Daniel Sage, Biomedical Imaging Group (BIG), http://bigwww.epfl.ch/sage/
//
// Organization: Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
//
// Reference: paper submitted, 2013
//
// Conditions of use: You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
package smlms.assessment.hungarian;
import java.util.ArrayList;
import java.util.Arrays;
public class BellmanFord {
public static double NOVAL = Double.POSITIVE_INFINITY;
private ArrayList<WeightedEdge> cTEdges = null;
private double[] distance = null;
private int[] predecessor = null;
private int numNodes = 0;
private int sourceNodeIndex = 0;
public BellmanFord(ArrayList<WeightedEdge> cTEdges, int Nnodes, int source) {
this.cTEdges = cTEdges;
this.numNodes = Nnodes;
this.sourceNodeIndex = source;
}
public void computeSpanningTree() {
distance = new double[numNodes];
predecessor = new int[numNodes];
Arrays.fill(distance, NOVAL);
Arrays.fill(predecessor, -1);
distance[sourceNodeIndex] = 0.0;
for (int i = 0; i < numNodes; i++) {
// relax every edge in 'edges'
for (WeightedEdge e : cTEdges) {
if (distance[e.source] == NOVAL) {
continue;
}
double newDistance = distance[e.source] + e.weight;
if (newDistance < distance[e.destination]) {
distance[e.destination] = newDistance;
predecessor[e.destination] = e.source;
}
}
}
}
public int[] getPredecessor() {
return predecessor;
}
public double[] getTotalWeight() {
return distance;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/assessment/hungarian/WeightedEdge.java | .java | 1,319 | 42 | //=========================================================================================
//
// Project: Single-Molecule Localization Microscopy
// Benchmarking of Localization Microscopy Software for Super-resolution Imaging
//
// Author : Daniel Sage, Biomedical Imaging Group (BIG), http://bigwww.epfl.ch/sage/
//
// Organization: Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
//
// Reference: paper submitted, 2013
//
// Conditions of use: You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
package smlms.assessment.hungarian;
public class WeightedEdge {
public int source;
public int destination;
public double weight;
boolean matched;
public WeightedEdge(int s, int d, double w) {
source = s;
destination = d;
weight = w;
}
public boolean equals(WeightedEdge cTEdge) {
return (cTEdge.source == source && cTEdge.destination == destination);
}
@Override
public String toString() {
return new String("s=" + source + ", d=" + destination + ", w=" + weight);
}
} | Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/assessment/hungarian/MaximumWeightedMatching.java | .java | 4,889 | 184 | //=========================================================================================
//
// Project: Single-Molecule Localization Microscopy
// Benchmarking of Localization Microscopy Software for Super-resolution Imaging
//
// Author : Daniel Sage, Biomedical Imaging Group (BIG), http://bigwww.epfl.ch/sage/
//
// Organization: Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
//
// Reference: paper submitted, 2013
//
// Conditions of use: You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
package smlms.assessment.hungarian;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.concurrent.Callable;
public class MaximumWeightedMatching implements Callable {
private int Unodes;
private int Nnodes;
private ArrayList<WeightedEdge> maximumWeightedMatching;
private ArrayList<WeightedEdge> G;
private boolean terminated = false;
public MaximumWeightedMatching(ArrayList<WeightedEdge> G, int Unodes, int Wnodes) {
this.Unodes = Unodes;
this.Nnodes = Unodes + Wnodes;
this.G = G;
}
public Boolean call() {
try {
ArrayList<Matching> Mset = new ArrayList<Matching>();
ArrayList<WeightedEdge> GM = new ArrayList<WeightedEdge>();
ArrayList<WeightedEdge> M = new ArrayList<WeightedEdge>();
ArrayList<WeightedEdge> path = new ArrayList<WeightedEdge>();
boolean[] matched = new boolean[Nnodes];
Arrays.fill(matched, false);
do {
for (WeightedEdge e : G) {
boolean found = false;
for (WeightedEdge e2 : M) {
if (e.equals(e2)) {
found = true;
matched[e.source] = true;
matched[e.destination] = true;
GM.add(new WeightedEdge(e.destination, e.source, e.weight));
}
if (found) {
break;
}
}
if (!found) {
GM.add(new WeightedEdge(e.source, e.destination, -e.weight));
}
}
path = findBestAugmentingPath(GM, matched);
GM.clear();
if (path.size() != 0) {
for (WeightedEdge e : path) {
WeightedEdge removableEdge = null;
for (WeightedEdge e2 : M) {
if (e.equals(e2)) {
removableEdge = e2;
break;
}
}
if (removableEdge != null) {
M.remove(removableEdge);
}
else {
M.add(new WeightedEdge(e.source, e.destination, e.weight));
matched[e.source] = true;
matched[e.destination] = true;
}
}
}
ArrayList<WeightedEdge> MClone = new ArrayList<WeightedEdge>(M.size());
for (WeightedEdge e : M) {
MClone.add(e);
}
Mset.add(new Matching(MClone, matchingWeight(M)));
}
while (path.size() != 0);
// identify best matching
Matching bestMatching = Mset.get(0);
for (Matching m : Mset) {
double currentWeight = m.weight;
if (currentWeight > bestMatching.weight) {
bestMatching = m;
}
}
maximumWeightedMatching = bestMatching.cTEdges;
terminated = true;
return true;
}
catch (Exception ex) {
return false;
}
}
public boolean isTerminated() {
return terminated;
}
public ArrayList<WeightedEdge> getMaximumWeightMatching() {
return maximumWeightedMatching;
}
private double matchingWeight(ArrayList<WeightedEdge> M) {
double w = 0;
for (WeightedEdge e : M) {
w += Math.abs(e.weight);
}
return w;
}
private ArrayList<WeightedEdge> findBestAugmentingPath(ArrayList<WeightedEdge> GM, boolean[] matched) {
// count unmatched
int numMatched = 0;
for (int i = 0; i < matched.length; i++) {
if (matched[i]) {
numMatched++;
}
}
// cloning GM
ArrayList<WeightedEdge> G = new ArrayList<WeightedEdge>(GM.size() + (matched.length - numMatched + 1));
for (WeightedEdge e : GM) {
G.add(e);
}
for (int i = 0; i < Nnodes; i++) {
if (!matched[i]) {
if (i < Unodes) {
G.add(new WeightedEdge(Nnodes, i, 0.0));
}
else {
G.add(new WeightedEdge(i, Nnodes + 1, 0.0));
}
}
}
BellmanFord trackerBellmanFord = new BellmanFord(G, Nnodes + 2, Nnodes);
trackerBellmanFord.computeSpanningTree();
int[] predecessor = trackerBellmanFord.getPredecessor();
double[] weights = trackerBellmanFord.getTotalWeight();
ArrayList<WeightedEdge> path = new ArrayList<WeightedEdge>();
int n1 = predecessor[Nnodes + 1];
while (n1 != Nnodes && n1 != -1) {
int n2 = predecessor[n1];
if (predecessor[n2] != -1) {
if (n1 < n2) {
path.add(new WeightedEdge(n1, n2, Math.abs(weights[n1] - weights[n2])));
}
else {
path.add(new WeightedEdge(n2, n1, Math.abs(weights[n1] - weights[n2])));
}
}
n1 = n2;
}
return path;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/rendering/RenderingDialog.java | .java | 12,047 | 359 | package smlms.rendering;
import ij.CompositeImage;
import ij.IJ;
import ij.ImagePlus;
import ij.ImageStack;
import ij.WindowManager;
import ij.gui.GUI;
import ij.gui.Roi;
import ij.process.ImageProcessor;
import ij.process.LUT;
import imageware.ImageWare;
import java.awt.Color;
import java.awt.Rectangle;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.util.ArrayList;
import javax.swing.BorderFactory;
import javax.swing.JButton;
import javax.swing.JComboBox;
import javax.swing.JDialog;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.JTabbedPane;
import javax.swing.event.ChangeEvent;
import javax.swing.event.ChangeListener;
import smlms.file.FluorophoreComponent;
import smlms.file.Fluorophores;
import smlms.file.Statistics;
import smlms.tools.Point3D;
import smlms.tools.PsRandom;
import smlms.tools.Volume;
import additionaluserinterface.GridPanel;
import additionaluserinterface.Settings;
import additionaluserinterface.SpinnerDouble;
import additionaluserinterface.SpinnerInteger;
import additionaluserinterface.WalkBar;
public class RenderingDialog extends JDialog implements ActionListener, ChangeListener, Runnable {
private WalkBar walk = new WalkBar("(c) 2016 EPFL, BIG", false, false, true);
private Settings settings = new Settings("localization-microscopy",
IJ.getDirectory("plugins") + "smlm-2016.txt");
private JButton bnRender3D = new JButton("Render 3D");
private JButton bnRenderProj = new JButton("Render 2D");
private JButton bnGetRoi = new JButton("Get ROI");
private JButton bnAutoRoi = new JButton("Full FOV");
private JComboBox cmbMethod = new JComboBox(
new String[] { "Gaussian", "Histogram", "Triangle" });
private SpinnerInteger spnTimeOut = new SpinnerInteger(10, 0, 9999999, 1);
private SpinnerDouble spnPixelsize = new SpinnerDouble(10, 0.01, 10000, 1);
private SpinnerDouble spnFWHM = new SpinnerDouble(10, 0.01, 10000, 1);
private JLabel lblSize = new JLabel("-");
public String names[] = new String[] { "X", "Y", "Z", "Frame", "Photons" };
public SpinnerDouble spnOrigin[] = new SpinnerDouble[5];
public SpinnerDouble spnEnd[] = new SpinnerDouble[5];
private FluorophoreComponent channels[] = new FluorophoreComponent[4];
private JComboBox cmbAmplitude[] = new JComboBox[channels.length];
private Thread thread = null;
private JButton job;
private PsRandom rand = new PsRandom(1234);
private ArrayList<RenderingParameters> params = new ArrayList<RenderingParameters>();
private RenderingTable table = new RenderingTable(params, this);
private JTabbedPane tab = new JTabbedPane();
public static void main(String args[]) {
new RenderingDialog();
}
public RenderingDialog() {
super(new JFrame(), "Rendering");
settings.record("rendering-spnTimeOut", spnTimeOut, "20");
settings.record("rendering-spnPixelsize", spnPixelsize, "10");
settings.record("rendering-spnFWHM", spnFWHM, "10");
settings.record("rendering-cmbMethod", cmbMethod, (String) cmbMethod.getSelectedItem());
lblSize.setBorder(BorderFactory.createEtchedBorder());
for(int nc=0; nc<channels.length; nc++) {
cmbAmplitude[nc] = new JComboBox(new String[] {"None", "Unit", "Photons", "Frame", "X", "Y", "Z"});
settings.record("rendering-cmbAmplitude"+nc, cmbAmplitude[nc], (String) cmbAmplitude[nc].getSelectedItem());
channels[nc] = new FluorophoreComponent(settings, "Channel" + (nc+1));
GridPanel pnF = new GridPanel(false, 1);
pnF.place(0, 0, channels[nc]);
pnF.place(1, 0, cmbAmplitude[nc]);
tab.addTab("Channel" + (nc+1), pnF);
}
settings.loadRecordedItems();
GridPanel pn1 = new GridPanel("Settings", 1);
pn1.place(0, 0, new JLabel("Pixelsize"));
pn1.place(0, 1, spnPixelsize);
pn1.place(0, 2, lblSize);
pn1.place(1, 0, new JLabel("FWHM"));
pn1.place(1, 1, spnFWHM);
pn1.place(1, 2, cmbMethod);
pn1.place(3, 0, new JLabel("TimeOut"));
pn1.place(3, 1, spnTimeOut);
pn1.place(7, 0, bnRender3D);
pn1.place(7, 1, bnRenderProj);
GridPanel pn2 = new GridPanel("Selection", 1);
for (int i = 0; i < names.length; i++) {
pn2.place(1 + i, 0, new JLabel(names[i]));
spnOrigin[i] = new SpinnerDouble(0, -999999, 999999, 100);
spnEnd[i] = new SpinnerDouble(0, -999999, 999999, 100);
pn2.place(1 + i, 1, spnOrigin[i]);
pn2.place(1 + i, 2, spnEnd[i]);
spnOrigin[i].addChangeListener(this);
spnEnd[i].addChangeListener(this);
}
pn2.place(7, 1, bnGetRoi);
pn2.place(7, 2, bnAutoRoi);
pn2.place(8, 0, 3, 1, table.getPane(400, 200));
GridPanel main = new GridPanel(false);
main.place(0, 0, tab);
main.place(2, 0, pn1);
main.place(1, 0, pn2);
main.place(3, 0, walk);
add(main);
pack();
setModal(false);
GUI.center(this);
setVisible(true);
spnPixelsize.addChangeListener(this);
bnGetRoi.addActionListener(this);
bnAutoRoi.addActionListener(this);
bnRender3D.addActionListener(this);
walk.getButtonClose().addActionListener(this);
bnRenderProj.addActionListener(this);
updateInterface();
}
@Override
public void actionPerformed(ActionEvent e) {
if (e.getSource() == walk.getButtonClose()) {
settings.storeRecordedItems();
dispose();
}
else if (e.getSource() == bnRender3D || e.getSource() == bnRenderProj) {
job = (JButton) e.getSource();
if (thread == null) {
thread = new Thread(this);
thread.setPriority(Thread.MIN_PRIORITY);
thread.start();
}
}
else if (e.getSource() == bnGetRoi) {
RenderingParameters params = table.getSelected();
ImagePlus imp = WindowManager.getImage(params.name);
if (imp == null) return;
setParameters(params);
double pixelsize = spnPixelsize.get();
Roi roi = imp.getRoi();
if (roi == null) {
IJ.error("Select a ROI");
return;
}
Rectangle rect = roi.getBounds();
double x1 = rect.x * pixelsize + spnOrigin[0].get();
double y1 = rect.y * pixelsize + spnOrigin[1].get();
double z1 = spnOrigin[2].get();
double x2 = x1 + rect.width * pixelsize;
double y2 = y1 + rect.height * pixelsize;
double z2 = spnEnd[2].get();
double dx = spnEnd[0].get() + spnOrigin[0].get();
double dy = spnEnd[1].get() + spnOrigin[1].get();
double dz = z2 - z1;
double diag1 = Math.sqrt(dx * dx + dy * dy);
double diag2 = Math.sqrt(rect.width * rect.width + rect.height * rect.height) * pixelsize;
spnPixelsize.set(round2(pixelsize * diag2 / diag1));
spnOrigin[0].set(x1);
spnOrigin[1].set(y1);
spnEnd[0].set(x2);
spnEnd[1].set(y2);
if (imp.getStackSize() > 1) {
int z = (int) Math.round(imp.getSlice() * pixelsize + z1);
spnOrigin[2].set(z - dz / 4);
spnEnd[2].set(z + dz / 4);
}
}
else if (e.getSource() == bnAutoRoi) {
for(int nc=0; nc<channels.length; nc++) {
if (tab.getSelectedIndex() == nc)
resetLimits(channels[nc].getFluorophores());
}
}
updateInterface();
}
public void run() {
walk.reset();
double pixelsize = spnPixelsize.get();
double fwhm = spnFWHM.get();
double timeout = spnTimeOut.get();
Rendering rendering = new Rendering(walk, pixelsize, timeout);
rendering.setLimitFrames(spnOrigin[3].get(), spnEnd[3].get());
rendering.setLimitPhotons(spnOrigin[4].get(), spnEnd[4].get());
Point3D origin = new Point3D(spnOrigin[0].get(), spnOrigin[1].get(), spnOrigin[2].get());
Point3D end = new Point3D(spnEnd[0].get(), spnEnd[1].get(), spnEnd[2].get());
Point3D dim = new Point3D(end.x - origin.x, end.y - origin.y, end.z - origin.z);
Volume vol = new Volume(origin, dim);
Rendering.Method method = Rendering.Method.values()[cmbMethod.getSelectedIndex()];
ArrayList<ImageWare> images = new ArrayList<ImageWare>();
if (job == this.bnRenderProj) {
for(int nc = 0; nc<channels.length; nc++) {
Fluorophores fluos = channels[nc].getFluorophores();
Rendering.Amplitude amp = Rendering.Amplitude.values()[cmbAmplitude[nc].getSelectedIndex()];
if (fluos != null && amp != Rendering.Amplitude.NONE)
images.add(rendering.projection(fluos, method, amp, vol, fwhm));
}
}
else {
for(int nc = 0; nc<channels.length; nc++) {
Fluorophores fluos = channels[nc].getFluorophores();
Rendering.Amplitude amp = Rendering.Amplitude.values()[cmbAmplitude[nc].getSelectedIndex()];
if (fluos != null && amp != Rendering.Amplitude.NONE)
images.add(rendering.render(fluos, method, amp, vol, fwhm));
}
}
if (images.size() == 0)
return;
int nchannel = images.size();
int nx = images.get(0).getSizeX();
int ny = images.get(0).getSizeY();
int nz = images.get(0).getSizeZ();
IJ.log("nchannel " + nchannel + " " + nz);
ImageStack stacks[] = new ImageStack[nchannel];
for (int i = 0; i < nchannel; i++)
stacks[i] = images.get(i).buildImageStack();
IJ.log("stacks " + stacks.length);
String name = "R-" + rand.nextInteger(1000) + "-" + pixelsize;
ImagePlus imp = createComposite(nx, ny, nz, stacks);
imp.setTitle(name);
imp.show();
params.add(getParameters(imp.getTitle()));
table.update();
walk.finish();
thread = null;
}
public void setParameters(RenderingParameters param) {
spnPixelsize.set(param.pixelsize);
spnFWHM.set(param.fwhm);
for (int k = 0; k < 5; k++) {
spnOrigin[k].set(param.min[k]);
spnEnd[k].set(param.max[k]);
}
}
public RenderingParameters getParameters(String name) {
double pixelsize = spnPixelsize.get();
double fwhm = spnFWHM.get();
return new RenderingParameters(name, (String) cmbMethod.getSelectedItem(), pixelsize, fwhm,
new double[] { spnOrigin[0].get(), spnOrigin[1].get(), spnOrigin[2].get(), spnOrigin[3]
.get(), spnOrigin[4].get() },
new double[] { spnEnd[0].get(), spnEnd[1].get(), spnEnd[2].get(), spnEnd[3].get(), spnEnd[4].get() });
}
public void resetLimits(Fluorophores fluos) {
IJ.log(" reset limits " + fluos.size());
if (fluos == null) return;
Statistics[] stats = fluos.getStats();
for (int i = 0; i < 5; i++) {
spnOrigin[i].set(stats[i + 1].min);
spnEnd[i].set(stats[i + 1].max);
}
}
private double complexity() {
double dx = spnEnd[0].get() - spnOrigin[0].get();
double dy = spnEnd[0].get() - spnOrigin[0].get();
double dz = spnEnd[0].get() - spnOrigin[0].get();
double px = spnPixelsize.get();
return (dx / px * dy / px * dz / px) * 10e-6;
}
private void updateInterface() {
lblSize.setText(IJ.d2s(complexity()) + " Mvoxels");
}
@Override
public void stateChanged(ChangeEvent e) {
updateInterface();
}
private double round2(double a) {
int e = (int) Math.log10(a);
double m = a / Math.pow(10, e);
int mi = ((int) Math.round(m * 100));
return mi * Math.pow(10, e - 2);
}
public ImagePlus createComposite(int w, int h, int d, ImageStack[] stacks) {
ImageStack composite = new ImageStack(w, h);
int n = stacks.length;
int[] index = new int[n];
int channels = 0;
boolean customColors = false;
for (int i = 0; i < n; i++) {
index[i] = 1;
if (stacks[i] != null) {
channels++;
if (i > 0 && stacks[i - 1] == null) customColors = true;
}
}
for (int i = 0; i < d; i++) {
for (int j = 0; j < n; j++) {
if (stacks[j] != null) {
ImageProcessor ip = stacks[j].getProcessor(index[j]);
composite.addSlice(null, ip);
if (stacks[j] != null) stacks[j].deleteSlice(1);
}
}
}
ImagePlus imp2 = new ImagePlus("Composite", composite);
imp2.setDimensions(channels, d, 1);
imp2 = new CompositeImage(imp2, CompositeImage.COMPOSITE);
if (customColors) {
Color[] colors = { Color.red, Color.green, Color.blue, Color.white };
CompositeImage ci = (CompositeImage) imp2;
int color = 0;
int c = 1;
for (int i = 0; i < n; i++) {
if (stacks[i] != null && c <= n) {
ci.setPosition(c, 1, 1);
LUT lut = ci.createLutFromColor(colors[color]);
ci.setChannelLut(lut);
c++;
}
color++;
}
ci.setPosition(1, 1, 1);
}
if (d > 1) imp2.setOpenAsHyperStack(true);
return imp2;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/rendering/RenderingTable.java | .java | 5,758 | 210 | package smlms.rendering;
import ij.IJ;
import ij.ImagePlus;
import ij.WindowManager;
import java.awt.Color;
import java.awt.Component;
import java.awt.Dimension;
import java.awt.event.MouseEvent;
import java.awt.event.MouseListener;
import java.awt.event.MouseMotionAdapter;
import java.util.ArrayList;
import java.util.HashMap;
import javax.swing.JFrame;
import javax.swing.JScrollPane;
import javax.swing.JTable;
import javax.swing.ListSelectionModel;
import javax.swing.table.DefaultTableCellRenderer;
import javax.swing.table.DefaultTableModel;
import javax.swing.table.JTableHeader;
import javax.swing.table.TableCellRenderer;
import javax.swing.table.TableColumn;
import javax.swing.table.TableColumnModel;
public class RenderingTable extends JTable implements MouseListener {
private Color colorOddRow = new Color(245, 245, 250);
private Color colorEvenRow = new Color(232, 232, 237);
private String[] headers = new String[] {
"Name", "Method", "Pixelsize", "FWHM", "X1", "X2", "Y1", "Y2", "Z1", "Z2", "Frame1", "Frame2", "Photons1", "Photons2" };
private ArrayList<RenderingParameters> params;
private RenderingDialog dialog;
public RenderingTable(ArrayList<RenderingParameters> params, RenderingDialog dialog) {
super();
this.params = params;
this.dialog = dialog;
DefaultTableModel
tableModel = new DefaultTableModel() {
@Override
public boolean isCellEditable(int row, int column) {
return false;
}
@Override
public Class<?> getColumnClass(int column) {
if (column <= 2)
return Integer.class;
else if (column <= 6)
return Double.class;
return super.getColumnClass(column);
}
};
setModel(tableModel);
DefaultTableModel model = (DefaultTableModel) getModel();
model.setColumnIdentifiers(headers);
setSelectionMode(ListSelectionModel.SINGLE_SELECTION);
setRowSelectionAllowed(true);
setAutoCreateRowSorter(true);
addMouseListener(this);
for (int i = 0; i < headers.length; i++) {
TableColumn tc = getColumnModel().getColumn(i);
tc.setCellRenderer(new AlternatedRowRenderer());
}
}
@Override
public Component prepareRenderer(TableCellRenderer renderer, int row, int column) {
Component component = super.prepareRenderer(renderer, row, column);
TableColumn tableColumn = getColumnModel().getColumn(column);
if (column == 0)
tableColumn.setPreferredWidth(200);
return component;
}
public JScrollPane getPane(int width, int height) {
JScrollPane scrollpane = new JScrollPane(this);
scrollpane.setPreferredSize(new Dimension(width, height));
return scrollpane;
}
public void show(int width, int height) {
JScrollPane scrollpane = getPane(width, height);
JFrame frame = new JFrame("History");
frame.add(scrollpane);
frame.pack();
frame.setVisible(true);
}
public int getRow(int id) {
for (int row = 0; row < getRowCount(); row++) {
if (((Integer)getValueAt(row, 0)) == id)
return row;
}
return -1;
}
public void update() {
DefaultTableModel model = (DefaultTableModel) getModel();
model.getDataVector().removeAllElements();
for (RenderingParameters param : params)
model.addRow(param.getObjectAsArray());
repaint();
}
public RenderingParameters getSelected() {
int row = getSelectedRow();
if (row < 0) {
setRowSelectionInterval(getRowCount()-1, getRowCount());
row = getSelectedRow();
if (row < 0)
return null;
}
String name = (String)this.getModel().getValueAt(row, 0);
ImagePlus imp = WindowManager.getImage(name);
imp.setActivated();
if (row >= 0)
if (row < params.size())
return params.get(row);
return params.get(0);
}
@Override
public void mouseClicked(MouseEvent e) {
if (e.getClickCount() == 2) {
JTable target = (JTable) e.getSource();
int row = target.getSelectedRow();
String name = (String)this.getModel().getValueAt(row, 0);
ImagePlus imp = WindowManager.getImage(name);
IJ.selectWindow(name);
imp.setActivated();
if (row >= 0)
if (row < params.size())
dialog.setParameters(params.get(row));
}
}
@Override
public void mouseEntered(MouseEvent arg0) {
// TODO Auto-generated method stub
}
@Override
public void mouseExited(MouseEvent arg0) {
// TODO Auto-generated method stub
}
@Override
public void mousePressed(MouseEvent arg0) {
// TODO Auto-generated method stub
}
@Override
public void mouseReleased(MouseEvent arg0) {
// TODO Auto-generated method stub
}
public class AlternatedRowRenderer extends DefaultTableCellRenderer {
@Override
public Component getTableCellRendererComponent(JTable table, Object value, boolean isSelected, boolean hasFocus, int row, int col) {
super.getTableCellRendererComponent(table, value, isSelected, hasFocus, row, col);
Component c = super.getTableCellRendererComponent(table, value, isSelected, hasFocus, row, col);
if (!isSelected)
c.setBackground(row % 2 == 0 ? colorEvenRow : colorOddRow);
return c;
}
}
class ColumnHeaderToolTips extends MouseMotionAdapter {
TableColumn curCol;
HashMap<TableColumn, String> tips = new HashMap<TableColumn, String>();
public void setToolTip(TableColumn col, String tooltip) {
if (tooltip == null)
tips.remove(col);
else
tips.put(col, tooltip);
}
@Override
public void mouseMoved(MouseEvent evt) {
JTableHeader header = (JTableHeader) evt.getSource();
JTable table = header.getTable();
TableColumnModel colModel = table.getColumnModel();
int vColIndex = colModel.getColumnIndexAtX(evt.getX());
TableColumn col = null;
if (vColIndex >= 0) {
col = colModel.getColumn(vColIndex);
}
if (col != curCol) {
header.setToolTipText((String) tips.get(col));
curCol = col;
}
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/rendering/Rendering_2016.java | .java | 188 | 14 | package smlms.rendering;
public class Rendering_2016 {
public Rendering_2016() {
new RenderingDialog();
}
public static void main(String args[]) {
new RenderingDialog();
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/rendering/RenderingParameters.java | .java | 765 | 39 | package smlms.rendering;
import ij.ImagePlus;
public class RenderingParameters {
public String name;
public ImagePlus imp;
public double pixelsize;
public double fwhm;
public String method;
public double min[]; // x, y, z, frame, photons
public double max[]; // x, y, z, frame, photons
public RenderingParameters(String name, String method, double pixelsize, double fwhm, double min[], double max[]) {
this.name = name;
this.method = method;
this.pixelsize = pixelsize;
this.fwhm = fwhm;
this.min = min;
this.max = max;
}
public Object[] getObjectAsArray() {
return new Object[] {
name, method, pixelsize, fwhm,
min[0], max[0],
min[1], max[1],
min[2], max[2],
min[3], max[3],
min[4], max[4] };
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/rendering/Rendering.java | .java | 8,041 | 264 | package smlms.rendering;
import ij.IJ;
import ij.ImagePlus;
import ij.ImageStack;
import ij.process.ColorProcessor;
import imageware.ImageWare;
import java.util.ArrayList;
import smlms.file.Fluorophore;
import smlms.tools.Point3D;
import smlms.tools.Volume;
import additionaluserinterface.WalkBar;
public class Rendering {
private WalkBar walk;
private double pixelsize;
private double timeout;
public enum Method {GAUSSIAN, HISTO, TRIANGLE};
public enum Amplitude {NONE, UNIT, PHOTONS, FRAME, X, Y, Z};
private double minFrame = -Double.MAX_VALUE;
private double maxFrame = -Double.MAX_VALUE;
private double minPhotons = -Double.MAX_VALUE;
private double maxPhotons = -Double.MAX_VALUE;
public Rendering(WalkBar walk, double pixelsize, double timeout) {
this.walk = walk;
this.pixelsize = pixelsize;
this.timeout = timeout;
}
public void setLimitFrames(double minFrame, double maxFrame) {
this.minFrame = minFrame;
this.maxFrame = maxFrame;
}
public void setLimitPhotons(double minPhotons, double maxPhotons) {
this.minPhotons = minPhotons;
this.maxPhotons = maxPhotons;
}
public ImageWare render(ArrayList<Fluorophore> fluos, Method method, Amplitude amplitude, Volume volume, double fwhm) {
ImageWare image = volume.allocate3D(pixelsize);
IJ.log("Rendering at pixelsize: " + pixelsize);
IJ.log("Fluos:" + fluos.size());
IJ.log("Method:" + method.name() + " Amplitude:" + amplitude.name() + " FWHM:" + fwhm);
IJ.log("Volume:" + volume.toString());
double r = 1.0/pixelsize;
int n = fluos.size();
walk.reset();
double count = 0;
double chrono = System.nanoTime();
double a = 1;
for(Fluorophore fluo : fluos) {
if (fluo.frame >= minFrame)
if (fluo.frame <= maxFrame)
if (fluo.photons >= minPhotons)
if (fluo.photons <= maxPhotons)
if (volume.contains(fluo)) {
count++;
double x = (fluo.x-volume.x1)*r;
double y = (fluo.y-volume.y1)*r;
double z = (fluo.z-volume.z1)*r;
if (count % 100 == 0)
walk.progress("Render " + count, 100.0*count/n);
if (amplitude == Amplitude.PHOTONS)
a = fluo.photons;
else if (amplitude == Amplitude.FRAME)
a = fluo.frame;
else if (amplitude == Amplitude.X)
a = fluo.x;
else if (amplitude == Amplitude.Y)
a = fluo.y;
else if (amplitude == Amplitude.Z)
a = fluo.z;
if (method == Method.GAUSSIAN)
addGaussian(image, fwhm/pixelsize, x, y, z, a);
else if (method == Method.TRIANGLE)
addTriangle(image, x, y, z, a);
else
image.putPixel((int)x, (int)y, (int)z, a + image.getPixel((int)x, (int)y, (int)z));
}
if ((System.nanoTime() - chrono) * 10e-9 > timeout) {
walk.progress("TIMEOUT " + count, 100.0*count/n);
break;
}
}
return image;
}
public ImageWare projection(ArrayList<Fluorophore> fluos, Method method, Amplitude amplitude, Volume volume, double fwmh) {
ImageWare image = volume.allocate2D(pixelsize);
double r = 1.0/pixelsize;
int n = fluos.size();
if (walk != null)
walk.reset();
double count = 0;
double chrono = System.nanoTime();
double a = 1;
for(Fluorophore fluo : fluos) {
if (volume.contains(fluo)) {
count++;
double x = (fluo.x-volume.x1)*r;
double y = (fluo.y-volume.y1)*r;
if (walk != null)
if (count % 100 == 0)
walk.progress("Projection " + count, count/n);
if (amplitude == Amplitude.PHOTONS)
a = fluo.photons;
else if (amplitude == Amplitude.FRAME)
a = fluo.frame;
else if (amplitude == Amplitude.X)
a = fluo.x;
else if (amplitude == Amplitude.Y)
a = fluo.y;
else if (amplitude == Amplitude.Z)
a = fluo.z;
a = Math.min(1, Math.max(0, a));
if (method == Method.GAUSSIAN)
addGaussian(image, fwmh/pixelsize, x, y, a);
else if (method == Method.TRIANGLE)
addTriangle(image, x, y, a);
else
image.putPixel((int)x, (int)y, 0, a + image.getPixel((int)x, (int)y, 0));
}
if ((System.nanoTime() - chrono) * 10e-9 > timeout) {
walk.progress("TIMEOUT " + count, 100.0*count/n);
break;
}
}
return image;
}
private void show(ImageWare image[], String name) {
if (image.length == 1) {
image[0].show(name);
}
else {
image[0].clip();
image[1].clip();
image[2].clip();
ImageWare rb = image[0].convert(ImageWare.BYTE);
ImageWare gb = image[1].convert(ImageWare.BYTE);
ImageWare bb = image[2].convert(ImageWare.BYTE);
int nx = image[0].getSizeX();
int ny = image[0].getSizeY();
int nz = image[0].getSizeZ();
ImageStack stack = new ImageStack(nx, ny);
for(int z=0; z<nz; z++) {
byte r[] = rb.getSliceByte(z);
byte g[] = gb.getSliceByte(z);
byte b[] = bb.getSliceByte(z);
ColorProcessor cp = new ColorProcessor(nx, ny);
cp.setRGB(r, g, b);
stack.addSlice(cp);
}
ImagePlus imp = new ImagePlus(name, stack);
imp.show();
}
}
public ImageWare renderVolHisto(ArrayList<Point3D> points, Volume volume, double pixelsize) {
ImageWare image = volume.allocate3D(pixelsize);
double r = 1.0/pixelsize;
int n = points.size();
walk.reset();
double count = 0;
for(Point3D point : points) {
if (volume.contains(point)) {
count++;
if (count % 100 == 0)
walk.progress("Render " + count, count/n);
int i = (int)Math.round((point.x-volume.x1)*r);
int j = (int)Math.round((point.y-volume.y1)*r);
int k = (int)Math.round((point.z-volume.z1)*r);
image.putPixel(i, j, k, 1 + image.getPixel(i, j, k));
}
}
walk.finish("Render " + n);
return image;
}
public ImageWare renderVolGaussian(ArrayList<Point3D> points, Volume volume, double pixelsize, double fwmh) {
ImageWare image = volume.allocate3D(pixelsize);
double r = 1.0/pixelsize;
int n = points.size();
walk.reset();
double count = 0;
for(Point3D point : points) {
if (volume.contains(point)) {
count++;
if (count % 100 == 0)
walk.progress("Render " + count, count/n);
addGaussian(image, fwmh/pixelsize, (point.x-volume.x1)*r, (point.y-volume.y1)*r, (point.z-volume.z1)*r, 100);
}
}
walk.finish("Render " + n);
return image;
}
private void addGaussian(ImageWare image, double sigma, double x, double y, double z, double photons) {
int xi = (int)Math.round(x);
int yi = (int)Math.round(y);
int zi = (int)Math.round(z);
int size = (int) Math.ceil(3 * sigma) + 1;
double coefXY = 1.0 / (sigma * sigma * 2.0);
for (int i = xi - size; i <= xi + size; i++)
for (int j = yi - size; j <= yi + size; j++)
for (int k = zi - size; k <= zi + size; k++) {
double v = photons * Math.exp(-coefXY * ((i-x) * (i-x) + (j-y) * (j-y) + (k-z) * (k-z)));
image.putPixel(i, j, k, v + image.getPixel(i, j, k));
}
}
private void addGaussian(ImageWare image, double sigma, double x, double y, double photons) {
int xi = (int)Math.round(x);
int yi = (int)Math.round(y);
int size = (int) Math.ceil(3 * sigma) + 1;
double coefXY = 1.0 / (sigma * sigma * 2.0);
for (int i = xi - size; i <= xi + size; i++)
for (int j = yi - size; j <= yi + size; j++) {
double v = photons * Math.exp(-coefXY * ((i-x) * (i-x) + (j-y) * (j-y) ));
image.putPixel(i, j, 0, v + image.getPixel(i, j, 0));
}
}
private void addTriangle(ImageWare image, double x, double y, double z, double photons) {
int xi = (int)Math.round(x);
int yi = (int)Math.round(y);
int zi = (int)Math.round(z);
for (int i = xi - 1; i <= xi + 1; i++)
for (int j = yi - 1; j <= yi + 1; j++)
for (int k = zi - 1; k <= zi + 1; k++) {
double v = photons * Math.sqrt((i-x) * (i-x) + (j-y) * (j-y) + (k-z) * (k-z));
image.putPixel(i, j, k, v + image.getPixel(i, j, k));
}
}
private void addTriangle(ImageWare image, double x, double y, double photons) {
int xi = (int)Math.round(x);
int yi = (int)Math.round(y);
for (int i = xi - 1; i <= xi + 1; i++)
for (int j = yi - 1; j <= yi + 1; j++) {
double v = photons * Math.sqrt((i-x) * (i-x) + (j-y) * (j-y) );
image.putPixel(i, j, 0, v + image.getPixel(i, j, 0));
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/tools/Chart.java | .java | 7,122 | 223 | package smlms.tools;
import ij.IJ;
import ij.ImagePlus;
import ij.io.FileSaver;
import ij.process.ColorProcessor;
import java.awt.BasicStroke;
import java.awt.Color;
import java.awt.Dimension;
import java.awt.Graphics2D;
import java.awt.Rectangle;
import java.awt.Stroke;
import java.awt.geom.Rectangle2D;
import java.awt.image.BufferedImage;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileWriter;
import java.io.IOException;
import javax.swing.JFrame;
import org.jfree.chart.ChartFactory;
import org.jfree.chart.ChartPanel;
import org.jfree.chart.JFreeChart;
import org.jfree.chart.axis.NumberAxis;
import org.jfree.chart.axis.ValueAxis;
import org.jfree.chart.block.BlockBorder;
import org.jfree.chart.plot.PlotOrientation;
import org.jfree.chart.plot.XYPlot;
import org.jfree.chart.renderer.xy.XYLineAndShapeRenderer;
import org.jfree.data.xy.XYSeries;
import org.jfree.data.xy.XYSeriesCollection;
public class Chart {
private XYSeriesCollection data;
private XYLineAndShapeRenderer render;
private int count = 0;
private String absisseName;
private String valueName;
private String title;
private double[] abscisseValue;
public int nbins;
public double bin;
private String legend = "";
public Chart(String title, String valueName, double abscisseValue[]) {
this.title = title;
this.valueName = valueName;
this.abscisseValue = abscisseValue;
data = new XYSeriesCollection();
render = new XYLineAndShapeRenderer();
}
public void setLegend(String legend) {
this.legend = legend;
}
public String getLegend() {
return legend;
}
public String getTitle() {
return title;
}
public void setBinningInformation(int nbins, double bin) {
this.nbins = nbins;
this.bin = bin;
}
public void add(String name, double[] function) {
XYSeries plot = new XYSeries(name);
for (int i = 0; i < Math.min(function.length, abscisseValue.length); i++)
plot.add(abscisseValue[i], function[i]);
count++;
data.addSeries(plot);
}
private JFreeChart plot() {
if (count == 1) {
render.setSeriesPaint(0, Color.black);
render.setSeriesShape(0, new Rectangle(new Dimension(0, 0)));
}
else if (count == 2) {
render.setSeriesPaint(0, new Color(255, 10, 10, 200));
render.setSeriesPaint(1, new Color(10, 255, 10, 200));
render.setSeriesShape(0, new Rectangle(new Dimension(0, 0)));
render.setSeriesShape(1, new Rectangle(new Dimension(0, 0)));
Stroke dashedStroke = new BasicStroke(1.0f, BasicStroke.CAP_ROUND, BasicStroke.JOIN_ROUND, 1.0f, new float[] { 10.0f, 6.0f }, 0.0f);
render.setSeriesOutlineStroke(0, dashedStroke);
}
else if (count < 9) {
Stroke dashedStroke = new BasicStroke(1.0f, BasicStroke.CAP_ROUND, BasicStroke.JOIN_ROUND, 1.0f, new float[] { 10.0f, 6.0f }, 0.0f);
render.setSeriesOutlineStroke(0, dashedStroke);
for (int i = 0; i < count; i++) {
Color color = Color.getHSBColor((float) (i) / count, 0.9f, 0.9f);
Color tc = new Color(color.getGreen(), color.getBlue(), color.getRed(), 150);
render.setSeriesPaint(i, tc);
render.setSeriesShape(i, new Rectangle(new Dimension(0, 0)));
}
}
else {
for (int i = 0; i < count; i++) {
float h = (float) ((int) (i / 3) * 3) / count;
Color color = Color.getHSBColor(h, 0.2f + (i % 4) * 0.2f, 0.1f + (i % 3) * 0.3f);
Stroke stroke = new BasicStroke(1.0f + (i % 2) * 1f, BasicStroke.CAP_ROUND, BasicStroke.JOIN_ROUND, 1.0f, new float[] { 2.0f, 2.0f }, (i % 3) * 10f);
render.setSeriesStroke(i, stroke);
Color tc = new Color(color.getGreen(), color.getBlue(), color.getRed(), 100);
render.setSeriesPaint(i, tc);
render.setSeriesShape(i, new Rectangle(new Dimension(0, 0)));
}
}
JFreeChart chart = ChartFactory.createXYLineChart(title, "", "", data, PlotOrientation.VERTICAL, true, true, true);
XYPlot plot = chart.getXYPlot();
plot.setRangeGridlinePaint(new Color(140, 140, 140));
plot.setDomainGridlinePaint(new Color(140, 140, 140));
plot.setRenderer(render);
plot.setBackgroundPaint(new Color(248, 248, 248));
ValueAxis range = new NumberAxis();
range.setAutoRange(true);
range.setLabel(valueName);
plot.setRangeAxis(range);
chart.getLegend().setFrame(new BlockBorder(new Color(220, 220, 220)));
return chart;
}
public void show(String name, int width, int height) {
JFreeChart chart = plot();
ChartPanel chartPanel = new ChartPanel(chart);
chartPanel.setSize(width, height);
chartPanel.setBackground(Color.RED);
JFrame frame = new JFrame(name);
frame.add(chartPanel);
frame.pack();
frame.setVisible(true);
}
public ColorProcessor createImage(int width, int height) {
JFreeChart chart = plot();
BufferedImage img = new BufferedImage(width, height, BufferedImage.TYPE_INT_RGB);
Graphics2D g2 = img.createGraphics();
chart.draw(g2, new Rectangle2D.Double(0, 0, width, height));
g2.dispose();
ImagePlus imp = new ImagePlus("", img);
return (ColorProcessor) imp.getProcessor();
}
public void savePNG(String filename, int width, int height) {
ImagePlus imp = new ImagePlus("", createImage(width, height));
(new FileSaver(imp)).saveAsPng(filename);
}
/*
public void savePDF(int width, int height, String filename) {
JFreeChart chart = plot();
try {
BufferedOutputStream out = new BufferedOutputStream(new FileOutputStream(filename));
Document document = new Document(PageSize.A4, 36, 36, 154, 54);
document.addAuthor("Daniel Sage");
// document.setPageSize(PageSize.A4);
PdfWriter writer = PdfWriter.getInstance(document, out);
document.open();
document.setPageSize(PageSize.A4);
PdfContentByte contentByte = writer.getDirectContent();
PdfTemplate template = contentByte.createTemplate(width, height);
Graphics2D g = template.createGraphics(width, height, new DefaultFontMapper());
Rectangle2D rectangle2d = new Rectangle2D.Double(0, 0, width, height);
chart.draw(g, rectangle2d);
g.dispose();
contentByte.addTemplate(template, 1f, 0, 0, 1f, 0, 0);
document.close();
out.close();
} catch (Exception e) {
e.printStackTrace();
}
}
*/
public void saveCVS(String filename, boolean saveHeader, boolean transpose) {
File file = new File(filename);
IJ.log("Save into " + filename);
if (data == null) {
IJ.log("No data to store " + filename);
return;
}
try {
BufferedWriter buffer = new BufferedWriter(new FileWriter(file));
String s = "";
int n = data.getSeriesCount();
int m = data.getSeries(0).getItemCount();
if (transpose) {
for (int j = 0; j < m; j++) {
s = "";
for (int i = 0; i < n; i++)
s += data.getSeries(i).getY(j).doubleValue() + ",";
buffer.write(s + "\n");
}
}
else {
for (int i = 0; i < n; i++) {
XYSeries series = data.getSeries(i);
m = series.getItemCount();
IJ.log(" >>>>>>>>>>>>>> series " + i + " " + n);
s = "";
for (int j = 0; j < m - 1; j++)
s += series.getY(j).doubleValue() + ",";
s += series.getY(m - 1).doubleValue();
buffer.write(s + "\n");
}
}
buffer.close();
} catch (IOException ex) {
System.out.println("" + ex);
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/tools/NormalizedVariable.java | .java | 4,845 | 147 | package smlms.tools;
import java.util.ArrayList;
public class NormalizedVariable {
public ArrayList<double[]> linears = new ArrayList<double[]>();
public ArrayList<double[]> cosines = new ArrayList<double[]>();
public ArrayList<double[]> sigmoids = new ArrayList<double[]>();
public double constant = 0.0;
private PsRandom rand;
public double[] uniform = { 0.0, 0.0, 0.0 };
public double[] gaussian = { 0.0, 0.0, 0.0 };
public double[] poisson = { 0.0, 0.0, 0.0 };
public double[] lorentz = { 0.0, 0.0, 0.0 };
public double[] rayleigh = { 0.0, 0.0, 0.0 };
public NormalizedVariable(double constant) {
this.constant = constant;
rand = new PsRandom(123);
}
public NormalizedVariable(double constant, double uniformNoise) {
this.constant = constant;
rand = new PsRandom(123);
uniform[0] = 1.0;
uniform[1] = constant-uniformNoise;
uniform[2] = constant+uniformNoise;
}
// x is value between 0 and 1
public double get(double x) {
double value = constant;
for(double[] param : linears)
value += param[0] * x + param[1];
for(double[] param : cosines)
value += param[0] * Math.cos(2*Math.PI*(x*param[1]-param[2]));
for(double[] param : sigmoids)
value += param[0] / (1.0 + Math.exp(param[2]*(x-param[1])));
if (uniform[0] > 0)
value += rand.nextDouble(uniform[1], uniform[2]);
if (gaussian[0] > 0)
value += rand.nextGaussian(gaussian[1], gaussian[2]);
if (poisson[0] > 0)
value += rand.nextPoissonian(poisson[1]);
if (lorentz[0] > 0)
value += rand.nextLorentzian(lorentz[1], lorentz[2]);
if (rayleigh[0] > 0)
value += rand.nextRayleigh(rayleigh[1]);
return value;
}
public void addUniformRandom(double bottom, double top) {
uniform[0] = 1;
uniform[1] = bottom;
uniform[2] = top;
}
public void addGaussianRandom(double mean, double sd) {
gaussian[0] = 1;
gaussian[1] = mean;
gaussian[2] = sd;
}
public void addPoissonRandom(double mean) {
poisson[0] = 1;
poisson[1] = mean;
poisson[2] = 0;
}
public void addLorentzRandom(double mu, double gamma) {
lorentz[0] = 1;
lorentz[1] = mu;
lorentz[2] = gamma;
}
public void addRayleighRandom(double sigma) {
rayleigh[0] = 1;
rayleigh[1] = sigma;
}
public void addLinear(double valueAtMin, double valueAtMax) {
linears.add(new double[] { valueAtMax - valueAtMin, valueAtMin, 0.0 });
}
public void addCosine(double amplitude, double nbCycle, double phase) {
cosines.add(new double[] { amplitude, nbCycle, phase });
}
public void addSigmoid(double amplitude, double position, double widthSlope) {
sigmoids.add(new double[] { amplitude, position, 6.0 / widthSlope });
}
public String write() {
String s = "" + constant + ", ";
for(double[] param : linears)
s += "linear, " + param[0] + ", " + param[1] + ", ";
for(double[] param : cosines)
s += "cosine, " + param[0] + ", " + param[1] + ", " + param[2] + ", ";
for(double[] param : sigmoids)
s += "sigmoid, " + param[0] + ", " + param[1] + ", " + param[2] + ", ";
if (uniform[0] > 0)
s += "uniform, " + uniform[1] + ", " + uniform[2] + ", ";
if (gaussian[0] > 0)
s += "gaussian, " + gaussian[1] + ", " + gaussian[2] + ", ";
if (poisson[0] > 0)
s += "poisson, " + poisson[1] + ", ";
if (lorentz[0] > 0)
s += "lorentz, " + lorentz[1] + ", " + lorentz[2] + ", ";
if (rayleigh[0] > 0)
s += "rayleigh, " + rayleigh[1] + ", ";
return s;
}
public static NormalizedVariable read(String line) {
String[] tokens = line.split("[,]");
if (tokens.length >= 0) {
NormalizedVariable var = new NormalizedVariable(Double.parseDouble(tokens[0]));
for(int i=1; i<tokens.length; i++) {
if (tokens[i].startsWith("uniform"))
var.addUniformRandom(Double.parseDouble(tokens[++i]), Double.parseDouble(tokens[++i]));
else if (tokens[i].startsWith("gaussian"))
var.addGaussianRandom(Double.parseDouble(tokens[++i]), Double.parseDouble(tokens[++i]));
else if (tokens[i].startsWith("poisson"))
var.addPoissonRandom(Double.parseDouble(tokens[++i]));
else if (tokens[i].startsWith("lorentz"))
var.addLorentzRandom(Double.parseDouble(tokens[++i]), Double.parseDouble(tokens[++i]));
else if (tokens[i].startsWith("rayleigh"))
var.addRayleighRandom(Double.parseDouble(tokens[++i]));
else if (tokens[i].startsWith("linear"))
var.addLinear(Double.parseDouble(tokens[++i]), Double.parseDouble(tokens[++i]));
else if (tokens[i].startsWith("cosine"))
var.addCosine(Double.parseDouble(tokens[++i]), Double.parseDouble(tokens[++i]), Double.parseDouble(tokens[++i]));
else if (tokens[i].startsWith("sigmoid"))
var.addSigmoid(Double.parseDouble(tokens[++i]), Double.parseDouble(tokens[++i]), Double.parseDouble(tokens[++i]));
}
return var;
}
else {
return new NormalizedVariable(0);
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/tools/Progress.java | .java | 724 | 29 | package smlms.tools;
import ij.IJ;
public class Progress {
private double chrono;
public Progress() {
chrono = System.nanoTime();
}
public void print(String message) {
double c = (System.nanoTime() - chrono);
if (c < 1e3)
IJ.log(String.format("%3.1f ns // ", c) + message);
else if (c < 1e6)
IJ.log(String.format("%3.1f us // ", (c*1e-3)) + message);
else if (c < 1e9)
IJ.log(String.format("%3.1f ms // ", (c*1e-6)) + message);
else if (c < 1e12)
IJ.log(String.format("%3.1f s // ", (c*1e-9)) + message);
else if (c < 1e14)
IJ.log(String.format("%3.1f mn // ", (c*1e-9)/60.0) + message);
else if (c < 1e16)
IJ.log(String.format("%3.1f h // ", (c*1e-9)/3600.0) + message);
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/tools/Drawing.java | .java | 3,544 | 122 | package smlms.tools;
import ij.ImagePlus;
import ij.gui.ImageCanvas;
import ij.gui.ImageWindow;
import ij.process.ByteProcessor;
import java.awt.BasicStroke;
import java.awt.Color;
import java.awt.Dimension;
import java.awt.Font;
import java.awt.Graphics;
import java.awt.Graphics2D;
import java.awt.Image;
import java.util.ArrayList;
public abstract class Drawing extends ImageCanvas {
private ImageCanvas canvasOriginal;
private Dimension dim;
private Image offscreen;
private Graphics2D bufferGraphics;
protected double pixelsize = 100;
abstract public void draw(Graphics2D g);
public Drawing(int nx, int ny, double pixelsize) {
super(new ImagePlus("", new ByteProcessor(nx, ny)));
this.pixelsize = pixelsize;
imp.show();
canvasOriginal = imp.getCanvas();
imp.setWindow(new ImageWindow(imp, this));
resetBuffer();
}
public Drawing(ImagePlus imp, double pixelsize) {
super(imp);
this.pixelsize = pixelsize;
canvasOriginal = imp.getCanvas();
imp.setWindow(new ImageWindow(imp, this));
resetBuffer();
}
public void resetBuffer() {
if (bufferGraphics != null) {
bufferGraphics.dispose();
bufferGraphics = null;
}
if (offscreen != null) {
offscreen.flush();
offscreen = null;
}
dim = getSize();
offscreen = createImage(dim.width, dim.height);
bufferGraphics = (Graphics2D) offscreen.getGraphics();
}
public void close() {
if (imp != null)
if (canvasOriginal != null)
imp.setWindow(new ImageWindow(imp, canvasOriginal));
}
@Override
public void paint(Graphics g) {
if (imp == null)
return;
if (bufferGraphics == null)
return;
if (dim.width != getSize().width || dim.height != getSize().height || bufferGraphics == null || offscreen == null)
resetBuffer();
super.paint(bufferGraphics);
draw(bufferGraphics);
g.drawImage(offscreen, 0, 0, this);
}
public void drawCross(double x, double y, double length, Color color, float stroke) {
double mag = this.getMagnification();
int rm = (int) Math.round(mag * length / pixelsize);
int xp = screenXD(x/pixelsize);
int yp = screenYD(y/pixelsize);
bufferGraphics.setColor(color);
bufferGraphics.setStroke(new BasicStroke(stroke));
bufferGraphics.drawLine(xp - rm, yp, xp + rm, yp);
bufferGraphics.drawLine(xp, yp - rm, xp, yp + rm);
}
public void drawCross(ArrayList<Point3D> points, double length, Color color, float stroke) {
for(Point3D point : points)
drawCross(point.x, point.y, length, color, stroke);
}
public void drawPolyline(ArrayList<Point3D> points, Color color, float stroke) {
bufferGraphics.setColor(color);
bufferGraphics.setStroke(new BasicStroke(stroke));
for(int i=0; i<points.size()-1; i++) {
Point3D p1 = points.get(i);
Point3D p2 = points.get(i+1);
int xp1 = screenXD(p1.x/pixelsize);
int yp1 = screenYD(p1.y/pixelsize);
int xp2 = screenXD(p2.x/pixelsize);
int yp2 = screenYD(p2.y/pixelsize);
bufferGraphics.drawLine(xp1, yp1, xp2, yp2);
}
}
public void print(int x, int y, String message, Color color, int fontsize) {
double distWhite = Math.abs(255 - color.getGreen()) + Math.abs(255 - color.getRed()) + Math.abs(255 - color.getBlue());
double distBlack = color.getGreen() + color.getRed() + color.getBlue();
bufferGraphics.setFont(new Font("Monospace", fontsize, Font.PLAIN));
bufferGraphics.setColor(distWhite < distBlack ? Color.WHITE : Color.BLACK);
bufferGraphics.drawString(message, x + 1, y + 1);
bufferGraphics.setColor(color);
bufferGraphics.drawString(message, x, y);
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/tools/TableDouble.java | .java | 4,084 | 152 | package smlms.tools;
import ij.IJ;
import ij.ImagePlus;
import ij.WindowManager;
import java.awt.Color;
import java.awt.Component;
import java.awt.Dimension;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileWriter;
import java.io.IOException;
import javax.swing.JFrame;
import javax.swing.JScrollPane;
import javax.swing.JTable;
import javax.swing.ListSelectionModel;
import javax.swing.table.DefaultTableCellRenderer;
import javax.swing.table.DefaultTableModel;
import javax.swing.table.TableColumn;
import javax.swing.table.TableModel;
public class TableDouble extends JTable {
private Color colorOddRow = new Color(245, 245, 250);
private Color colorEvenRow = new Color(232, 232, 237);
private double[][] data;
private String name;
private String[] firstcol;
private String[] headers;
public TableDouble(String name, String[] firstcol, String[] headers, double[][] data) {
super();
this.name = name;
this.data = data;
this.firstcol = firstcol;
this.headers = headers;
IJ.log(" firstcol " + firstcol.length);
IJ.log(" headers " + headers.length);
IJ.log(" data " + data.length);
IJ.log(" data " + data[0].length);
DefaultTableModel
tableModel = new DefaultTableModel() {
@Override
public boolean isCellEditable(int row, int column) {
return false;
}
};
setModel(tableModel);
DefaultTableModel model = (DefaultTableModel) getModel();
model.setColumnIdentifiers(headers);
setSelectionMode(ListSelectionModel.SINGLE_SELECTION);
setRowSelectionAllowed(true);
setAutoCreateRowSorter(true);
for (int i = 0; i < headers.length; i++) {
TableColumn tc = getColumnModel().getColumn(i);
tc.setCellRenderer(new AlternatedRowRenderer());
}
update();
}
public void show(int width, int height) {
JScrollPane scrollpane = new JScrollPane(this);
scrollpane.setPreferredSize(new Dimension(width, height));
JFrame frame = new JFrame(name);
frame.add(scrollpane);
frame.pack();
frame.setVisible(true);
}
public int getRow(int id) {
for (int row = 0; row < getRowCount(); row++) {
if (((Integer)getValueAt(row, 0)) == id)
return row;
}
return -1;
}
public void update() {
DefaultTableModel model = (DefaultTableModel) getModel();
model.getDataVector().removeAllElements();
if (data == null)
return;
int ncol = data[0].length;
int nrow = data.length;
for (int j=0; j<nrow; j++) {
Object o[] = new Object[ncol+1];
o[0] = firstcol[j];
for(int i=0; i<ncol; i++) {
double d = data[j][i];
o[i+1] = (Object)d;
}
model.addRow(o);
}
repaint();
}
public void saveCVS(String filename) {
File file = new File(filename);
try {
BufferedWriter buffer = new BufferedWriter(new FileWriter(file));
String s = "";
for (int i = 0; i < headers.length; i++)
s += headers[i] + ",";
buffer.write(s + "\n");
TableModel model = this.getModel();
int ncols = model.getColumnCount();
int nrows = model.getRowCount();
for (int row = 0; row < nrows; row++) {
s = "";
for (int col = 0; col < ncols - 1; col++)
s += model.getValueAt(row, col) + ",";
s += model.getValueAt(row, ncols - 1);
buffer.write(s + "\n");
}
buffer.close();
}
catch (IOException ex) {
System.out.println("" + ex);
}
}
public double[] getSelected() {
int row = getSelectedRow();
String name = (String)this.getModel().getValueAt(row, 0);
ImagePlus imp = WindowManager.getImage(name);
imp.setActivated();
if (row >= 0)
if (row < data.length)
return data[row];
return null;
}
public class AlternatedRowRenderer extends DefaultTableCellRenderer {
@Override
public Component getTableCellRendererComponent(JTable table, Object value, boolean isSelected, boolean hasFocus, int row, int col) {
super.getTableCellRendererComponent(table, value, isSelected, hasFocus, row, col);
Component c = super.getTableCellRendererComponent(table, value, isSelected, hasFocus, row, col);
if (!isSelected)
c.setBackground(row % 2 == 0 ? colorEvenRow : colorOddRow);
return c;
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/tools/Verbose.java | .java | 1,307 | 45 | //=========================================================================================
//
// Project: Localization Microscopy
//
// Author : Daniel Sage, Biomedical Imaging Group (BIG), http://bigwww.epfl.ch/sage/
//
// Organization: Ecole Polytechnique F�d�rale de Lausanne (EPFL), Lausanne, Switzerland
//
// Conditions of use: You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
package smlms.tools;
import ij.IJ;
public class Verbose {
static public String[] names = new String[] {"Mute", "Talk", "Prolix"};
private static int level = 1;
public static void setLevel(int verboseLevel) {
level = verboseLevel;
}
public static void talk(String msg) {
if (level >= 1)
IJ.log(msg);
}
public static void prolix(String msg) {
if (level >= 2)
IJ.log(msg);
}
public static void exception(Exception ex) {
StackTraceElement elements[] = ex.getStackTrace();
for(int i=0; i<elements.length; i++)
IJ.log("Trace Exception " + elements[i].toString());
IJ.log("" + ex);
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/tools/PsRandom.java | .java | 39,459 | 972 | /* Program PsRandom
*
* Class for obtaining a single decimal or binary pseudorandom number
* or a sequence of decimal or binary pseudorandom numbers
* Supplements the Java random class with the generation of
* of lorentzian, poissonian, logistic, student's t, pareto,
* exponential, gumbel, weibull, frechet, rayleigh,
* beta distribution, gamma distribution, erlang distribution,
* chi-square distribution, F-distribution, uniform,
* gaussian (normal) and correlated gaussian pseudo-random deviates
* and pseudorandom binary numbers (bits).
* Also offers a choice of Knuth or Park-Miller generation methods.
*
* Binary pseudorandom number calculations are adapted from
* the Numerical Recipes methods written in the C language
* based on the "primitive polynomials modulo 2" method:
* Numerical Recipes in C, The Art of Scientific Computing,
* W.H. Press, S.A. Teukolsky, W.T. Vetterling & B.P. Flannery,
* Cambridge University Press, 2nd Edition (1992) pp 296 - 300.
* (http://www.nr.com/).
*
* AUTHOR: Dr Michael Thomas Flanagan
* DATE: 22 April 2004
* UPDATE: 21 November 2006, 31 December 2006, 14 April 2007, 19 October 2007, 16-29 March 2008, 3 July 2008
* 19 September 2008, 28 September 2008, 13 and 18 October 2009, 12-24 July 2010, 8 July 2011, 7 March 2012
*
* DOCUMENTATION:
* See Michael Thomas Flanagan's Java library on-line web page:
* http://www.ee.ucl.ac.uk/~mflanaga/java/PsRandom.html
* http://www.ee.ucl.ac.uk/~mflanaga/java/
*
* Copyright (c) 2004 - 2009 Michael Thomas Flanagan
*
* PERMISSION TO COPY:
*
* Permission to use, copy and modify this software and its documentation for NON-COMMERCIAL purposes is granted, without fee,
* provided that an acknowledgement to the author, Dr Michael Thomas Flanagan at www.ee.ucl.ac.uk/~mflanaga, appears in all copies
* and associated documentation or publications.
*
* Redistributions of the source code of this source code, or parts of the source codes, must retain the above copyright notice, this list of conditions
* and the following disclaimer and requires written permission from the Michael Thomas Flanagan:
*
* Redistribution in binary form of all or parts of this class must reproduce the above copyright notice, this list of conditions and
* the following disclaimer in the documentation and/or other materials provided with the distribution and requires written permission from the Michael Thomas Flanagan:
*
* Dr Michael Thomas Flanagan makes no representations about the suitability or fitness of the software for any or for a particular purpose.
* Dr Michael Thomas Flanagan shall not be liable for any damages suffered as a result of using, modifying or distributing this software
* or its derivatives.
*
***************************************************************************************/
package smlms.tools;
import java.io.Serializable;
import java.util.Random;
public class PsRandom implements Serializable{
private static final long serialVersionUID = 1L; // serial version unique identifier
private long seed; // current seed value - updated after each generation of a pseudorandom bit
// initial seed supplied as either:
// i. as the current clock time (in milliseconds since 1970)
// (no argument given to the constructor)
// ii. by the user as the constructor argument
// method are available for resetting the value of the seed
private long initialSeed; // initial seed value
private int methodOptionDecimal = 1; // Method for calculating pseudorandom decimal numbers
// = 1 Method - Knuth - this class calls the Java Random class which
// implements this method
// See Numerical Recipes in C - W.H. Press et al. (Cambridge)
// 1st edition 1988 p. 212, 2nd edition 1992 p283 for details
// This is the default option used in all methods in this class,
// Pseudorandom, generating a decimal random number, i.e. all but
// the methods to generate pseudorandom binary numbers.
// = 2 Method - Park and Miller random number generator with Bays-Durham shuffle
// after ran1 (Numerical Recipes in C - W.H. Press et al. (Cambridge)
// 2nd edition 1992 p280.
private int methodOptionInteger = 1; // Method for calculating pseudorandom integer numbers
// = 1 Method - java Random nextInt(int(n)
// = 2 Method - next double scaled with rounding
// = 3 Method - next doubled scaled with flooring
private Random rr = null; // instance of java.util.Random if Knuth method (default method) used
private int methodOptionBinary = 1; // Method for calculating pseudorandom binary numbers
// = 1 Method - Primitive polynomials modulo 2 method - version 1
// This method is the more cumbersome one in terms of coding (see method 2 below)
// but more readily lends itself to a shift register hardware implementation
// = 2 Method - Primitive polynomials modulo 2 method - version 2
// This method is the more suited to software implementation (compare with method 1 above)
// but lends itself less readily to a shift register hardware implementation
// Method 1 is the default option
// Park and Miller constants and variables
private long ia = 16807L;
private long im = 2147483647L;
private double am = 1.0D/im;
private long iq = 127773L;
private long ir = 2836L;
private int ntab = 32;
private long ndiv = (1L + (im - 1L)/ntab);
private double eps = 1.2e-7;
private double rnmx = 1.0D - eps;
private long iy = 0L;
private long[] iv = new long[ntab];
// Box-Muller variables
private int iset = 0;
private double gset = 0.0D;
// Polynomial powers of 2 (used in calculation of psedorandom binary numbers)
// See header reference (Numerical Recipes) above for polynomials other than (18, 5, 2, 1, 0)
private long powTwo1 = 1;
private long powTwo2 = 2;
private long powTwo5 = 16;
private long powTwo18 = 131072;
private long mask = powTwo1 + powTwo2 + powTwo5;
// CONSTRUCTORS
// Seed taken from the clock
public PsRandom(){
this.seed = System.currentTimeMillis();
this.initialSeed = this.seed;
this.rr = new Random(this.seed);
}
// Seed supplied by user
public PsRandom(long seed){
this.seed = seed;
this.initialSeed = seed;
this.rr = new Random(this.seed);
}
// METHODS
// Resets the value of the seed
public void setSeed(long seed){
this.seed = seed;
if(this.methodOptionDecimal==1)rr = new Random(this.seed);
}
// Returns the initial value of the seed
public long getInitialSeed(){
return this.initialSeed;
}
// Returns the current value of the seed
public long getSeed(){
return this.seed;
}
// Resets the method of calculation of a pseudorandom decimal number
// argument = 1 -> Knuth; argument = 2 -> Parker-Miller
// Default option = 1
public void setMethodDecimal(int methodOpt){
if(methodOpt<1 || methodOpt>2)throw new IllegalArgumentException("Argument to PsRandom.setMethodDecimal must 1 or 2\nValue transferred was"+methodOpt);
this.methodOptionDecimal = methodOpt;
if(methodOpt==1)rr = new Random(this.seed);
}
// Return the pseudorandom decimal number method option; 1 = Method 1 (Knuth), 2= Method 2 (Parker-Miller)
public int getMethodDecimal(){
return this.methodOptionDecimal;
}
// Resets the method of calculation of a pseudorandom integer number
// argument = 1 -> Diego Moreira alternative 1; argument = 2 -> Diego Moreira alternative 2; 3 -> Java Random class method nextInt
// Default option = 1
public void setMethodInteger(int methodOpt){
if(methodOpt<1 || methodOpt>3)throw new IllegalArgumentException("Argument to PsRandom.setMethodInteger must 1, 2 or 3\nValue transferred was"+methodOpt);
this.methodOptionInteger = methodOpt;
}
// Return the pseudorandom integer number method option; 1 = Method 1 (Diego Moreira alternative 1), 2= Method 2 (Diego Moreira alternative 2), 2 = ; 3 -> Java Random class method nextInt
public int getMethodInteger(){
return this.methodOptionInteger;
}
// Resets the method of calculation of a pseudorandom binary number
// argument = 1 -> method 1; argument = 2 -> Method 2
// See above and Numerical Recipes reference (in program header) for method descriptions
// Default option = 1
public void setMethodBinary(int methodOpt){
if(methodOpt<1 || methodOpt>2)throw new IllegalArgumentException("Argument to PsRandom.setMethodBinary must 1 or 2\nValue transferred was"+methodOpt);
this.methodOptionBinary = methodOpt;
if(methodOpt==1)rr = new Random(this.seed);
}
// Return the binary pseudorandom number method option; 1 = Method 1, 2= Method 2
public int getMethodBinary(){
return this.methodOptionBinary;
}
// Returns a pseudorandom double between 0.0 and 1.0
public double nextDouble(){
if(this.methodOptionDecimal==1){
return this.rr.nextDouble();
}
else{
return this.parkMiller();
}
}
// Returns a pseudorandom double between 0.0 and top
public double nextDouble(double top){
return top*this.nextDouble();
}
// Returns a pseudorandom double between 0.0 and top
public double nextSquare(double top){
return top*Math.pow(nextDouble(), 2);
}
// Returns a pseudorandom double between bottom and top
public double nextDouble(double bottom, double top){
return (top - bottom)*this.nextDouble() + bottom;
}
// Returns an array, of length arrayLength, of pseudorandom doubles between 0.0 and 1.0
public double[] doubleArray(int arrayLength){
double[] array = new double[arrayLength];
for(int i=0; i<arrayLength; i++){
array[i] = this.nextDouble();
}
return array;
}
// Returns an array, of length arrayLength, of pseudorandom doubles between 0.0 and top
public double[] doubleArray(int arrayLength, double top){
double[] array = new double[arrayLength];
for(int i=0; i<arrayLength; i++){
array[i] = top*this.nextDouble();
}
return array;
}
// Returns an array, of length arrayLength, of pseudorandom doubles between bottom and top
public double[] doubleArray(int arrayLength, double bottom, double top){
double[] array = new double[arrayLength];
for(int i=0; i<arrayLength; i++){
array[i] = (top - bottom)*this.nextDouble() + bottom;
}
return array;
}
// Park and Miller random number generator with Bays-Durham shuffle
// after ran1 Numerical Recipes in C - W.H. Press et al. (Cambridge)
// 2nd edition 1992 p280.
// return a pseudorandom number between 0.0 and 1.0
public double parkMiller(){
int jj = 0;
long kk = 0L;
double temp = 0.0D;
this.iy = 0L;
if(this.seed <= 0L || iy!=0){
if(-this.seed < 1){
this.seed = 1;
}
else{
this.seed = -this.seed;
}
for(int j=ntab+7; j>=0; j--){
kk = this.seed/iq;
this.seed = ia*( this.seed - kk*iq)- ir*kk;
if(this.seed < 0L) this.seed += im;
if (j < ntab) iv[j] = this.seed;
}
iy = iv[0];
}
kk = this.seed/iq;
this.seed = ia*(this.seed - kk*iq)-ir*kk;
if(this.seed < 0)this.seed += im;
jj = (int)(iy/ndiv);
iy = iv[jj];
iv[jj] = this.seed;
if((temp = am*iy) > rnmx){
return rnmx;
}
else{
return temp;
}
}
// Returns a pseudorandom bit
public int nextBit(){
if(this.methodOptionBinary==1){
return nextBitM1();
}
else{
return nextBitM2();
}
}
// Returns an array, of length arrayLength, of pseudorandom bits
public int[] bitArray(int arrayLength){
int[] bitarray = new int[arrayLength];
for(int i=0; i<arrayLength; i++){
bitarray[i]=nextBit();
}
return bitarray;
}
// Returns a pseudorandom bit - Method 1
// This method is the more cumbersome one in terms of coding (see method 2 below)
// but more readily lends itself to a shift register hardware implementation
public int nextBitM1(){
long newBit;
newBit = ((this.seed & this.powTwo18) >> 17L) ^ ((this.seed & this.powTwo5) >> 4L) ^ ((this.seed & this.powTwo2) >> 1L) ^ (this.seed & this.powTwo1);
this.seed=(this.seed << 1L) | newBit;
return (int) newBit;
}
// Returns a pseudorandom bit - Method 2
// This method is the more suited to software implementation (compare with method 1 above)
// but lends itself less readily to a shift register hardware implementation
public int nextBitM2(){
int randomBit = 0;
if((this.seed & this.powTwo18)<=0L){
this.seed = ((this.seed ^ this.mask) << 1L) | this.powTwo1;
randomBit = 1;
}
else{
this.seed <<= 1L;
randomBit = 0;
}
return randomBit;
}
// Returns a Gaussian (normal) random deviate
// mean = the mean, sd = standard deviation
public double nextGaussian(double mean, double sd){
double ran = 0.0D;
if(this.methodOptionDecimal==1){
ran=this.rr.nextGaussian();
}
else{
ran=this.boxMullerParkMiller();
}
ran = ran*sd+mean;
return ran;
}
// Returns a Gaussian (normal) random deviate
// mean = 0.0, sd = 1.0
public double nextGaussian(){
double ran = 0.0D;
if(this.methodOptionDecimal==1){
ran=this.rr.nextGaussian();
}
else{
ran=this.boxMullerParkMiller();
}
return ran;
}
public double nextNormal(double mean, double sd){
return nextGaussian(mean, sd);
}
public double nextNormal(){
return nextGaussian();
}
// Box-Muller normal deviate generator
// after gasdev (Numerical Recipes in C - W.H. Press et al. (Cambridge)
// 2nd edition 1992 p289
// Uses Park and Miller method for generating pseudorandom numbers
double boxMullerParkMiller(){
double fac = 0.0D, rsq = 0.0D, v1 = 0.0D, v2 = 0.0D;
if (iset==0){
do {
v1=2.0*parkMiller()-1.0D;
v2=2.0*parkMiller()-1.0D;
rsq=v1*v1+v2*v2;
}while (rsq >= 1.0D || rsq == 0.0D);
fac=Math.sqrt(-2.0D*Math.log(rsq)/rsq);
gset=v1*fac;
iset=1;
return v2*fac;
}else{
iset=0;
return gset;
}
}
// Returns a Lorentzian pseudorandom deviate
// mu = the mean, gamma = half-height widthy
public double nextLorentzian (double mu, double gamma){
double ran = Math.tan((this.nextDouble()-0.5)*Math.PI);
ran = ran*gamma/2.0D+mu;
return ran;
}
// Returns an array of Lorentzian pseudorandom deviates
// mu = the mean, gamma = half-height width, n = length of array
public double[] lorentzianArray (double mu, double gamma, int n){
double[] ran = new double[n];
for(int i=0; i<n; i++){
ran[i]=Math.tan((this.nextDouble()-0.5)*Math.PI);
ran[i] = ran[i]*gamma/2.0D+mu;
}
return ran;
}
// Returns a Poissonian pseudorandom deviate
// follows the approach of Numerical Recipes, 2nd Edition, p 294
public double nextPoissonian(double mean){
double ran = 0.0D;
double oldm = -1.0D;
double expt = 0.0D;
double em = 0.0D;
double term = 0.0D;
double sq = 0.0D;
double lnMean = 0.0D;
double yDev = 0.0D;
if(mean < 12.0D){
if(mean != oldm){
oldm = mean;
expt = Math.exp(-mean);
}
em = -1.0D;
term = 1.0D;
do{
++em;
term *= this.nextDouble();
}while(term>expt);
ran = em;
}
else{
if(mean != oldm){
oldm = mean;
sq = Math.sqrt(2.0D*mean);
lnMean = Math.log(mean);
expt = mean*lnMean - logGamma(mean+1.0D);
}
do{
do{
yDev = Math.tan(Math.PI*this.nextDouble());
em = sq*yDev+mean;
}while(em<0.0D);
em = Math.floor(em);
term = 0.9D*(1.0D+yDev*yDev)*Math.exp(em*lnMean - logGamma(em+1.0D)-expt);
}while(this.nextDouble()>term);
ran = em;
}
return ran;
}
// Returns an array of Poisson random deviates
// follows the approach of Numerical Recipes, 2nd Edition, p 294
public double[] poissonianArray(double mean, int n){
double[] ran = new double[n];
double oldm = -1.0D;
double expt = 0.0D;
double em = 0.0D;
double term = 0.0D;
double sq = 0.0D;
double lnMean = 0.0D;
double yDev = 0.0D;
if(mean < 12.0D){
for(int i=0; i<n; i++){
if(mean != oldm){
oldm = mean;
expt = Math.exp(-mean);
}
em = -1.0D;
term = 1.0D;
do{
++em;
term *= this.nextDouble();
}while(term>expt);
ran[i] = em;
}
}
else{
for(int i=0; i<n; i++){
if(mean != oldm){
oldm = mean;
sq = Math.sqrt(2.0D*mean);
lnMean = Math.log(mean);
expt = mean*lnMean - logGamma(mean+1.0D);
}
do{
do{
yDev = Math.tan(Math.PI*this.nextDouble());
em = sq*yDev+mean;
}while(em<0.0D);
em = Math.floor(em);
term = 0.9D*(1.0D+yDev*yDev)*Math.exp(em*lnMean - logGamma(em+1.0D)-expt);
}while(this.nextDouble()>term);
ran[i] = em;
}
}
return ran;
}
// Returns a Binomial pseudorandom deviate from a binomial
// distribution of nTrial trials each of probablity, prob,
// after bndlev Numerical Recipes in C - W.H. Press et al. (Cambridge)
// 2nd edition 1992 p295.
public double nextBinomial(double prob, int nTrials){
if(prob<0.0D || prob>1.0D)throw new IllegalArgumentException("The probablity provided, " + prob + ", must lie between 0 and 1)");
double binomialDeviate = 0.0D; // the binomial deviate to be returned
double deviateMean = 0.0D; // mean of deviate to be produced
double testDeviate = 0.0D; // test deviate
double workingProb = 0.0; // working value of the probability
double logProb = 0.0; // working value of the probability
double probOld = -1.0D; // previous value of the working probability
double probC = -1.0D; // complementary value of the working probability
double logProbC = -1.0D; // log of the complementary value of the working probability
int nOld= -1; // previous value of trials counter
double enTrials = 0.0D; // (double) trials counter
double oldGamma = 0.0D; // a previous log Gamma function value
double tanW = 0.0D; // a working tangent
double hold0 = 0.0D; // a working holding variable
int jj; // counter
workingProb=(prob <= 0.5D ? prob : 1.0-prob); // distribution invariant on swapping prob for 1 - prob
deviateMean = nTrials*workingProb;
if(nTrials < 25) {
// if number of trials greater than 25 use direct method
binomialDeviate=0.0D;
for (jj=1;jj<=nTrials;jj++)if (this.nextDouble() < workingProb) ++binomialDeviate;
}
else if(deviateMean < 1.0D) {
// if fewer than 1 out of 25 events - Poisson approximation is accurate
double expOfMean=Math.exp(-deviateMean);
testDeviate=1.0D;
for(jj=0;jj<=nTrials;jj++) {
testDeviate *= this.nextDouble();
if (testDeviate < expOfMean) break;
}
binomialDeviate=(jj <= nTrials ? jj : nTrials);
}
else{
// use rejection method
if(nTrials != nOld) {
// if nTrials has changed compute useful quantities
enTrials = (double)nTrials;
oldGamma = logGamma(enTrials + 1.0D);
nOld = nTrials;
}
if(workingProb != probOld) {
// if workingProb has changed compute useful quantities
probC = 1.0 - workingProb;
logProb = Math.log(workingProb);
logProbC = Math.log(probC);
probOld = workingProb;
}
double sq = Math.sqrt(2.0*deviateMean*probC);
do{
do{
double angle = Math.PI*this.nextDouble();
tanW = Math.tan(angle);
hold0 = sq*tanW + deviateMean;
}while(hold0 < 0.0D || hold0 >= (enTrials + 1.0D)); // rejection test
hold0 = Math.floor(hold0); // integer value distribution
testDeviate = 1.2D*sq*(1.0D + tanW*tanW)*Math.exp(oldGamma - logGamma(hold0 + 1.0D) - logGamma(enTrials - hold0 + 1.0D) + hold0*logProb + (enTrials - hold0)*logProbC);
}while(this.nextDouble() > testDeviate); // rejection test
binomialDeviate=hold0;
}
if(workingProb != prob) binomialDeviate = nTrials - binomialDeviate; // symmetry transformation
return binomialDeviate;
}
// Returns an array of n Binomial pseudorandom deviates from a binomial
// distribution of nTrial trials each of probablity, prob,
// after bndlev Numerical Recipes in C - W.H. Press et al. (Cambridge)
// 2nd edition 1992 p295.
public double[] binomialArray(double prob, int nTrials, int n){
if(nTrials<n)throw new IllegalArgumentException("Number of deviates requested, " + n + ", must be less than the number of trials, " + nTrials);
if(prob<0.0D || prob>1.0D)throw new IllegalArgumentException("The probablity provided, " + prob + ", must lie between 0 and 1)");
double[] ran = new double[n]; // array of deviates to be returned
double binomialDeviate = 0.0D; // the binomial deviate to be returned
double deviateMean = 0.0D; // mean of deviate to be produced
double testDeviate = 0.0D; // test deviate
double workingProb = 0.0; // working value of the probability
double logProb = 0.0; // working value of the probability
double probOld = -1.0D; // previous value of the working probability
double probC = -1.0D; // complementary value of the working probability
double logProbC = -1.0D; // log of the complementary value of the working probability
int nOld= -1; // previous value of trials counter
double enTrials = 0.0D; // (double) trials counter
double oldGamma = 0.0D; // a previous log Gamma function value
double tanW = 0.0D; // a working tangent
double hold0 = 0.0D; // a working holding variable
int jj; // counter
double probOriginalValue = prob;
for(int i=0; i<n; i++){
prob = probOriginalValue;
workingProb=(prob <= 0.5D ? prob : 1.0-prob); // distribution invariant on swapping prob for 1 - prob
deviateMean = nTrials*workingProb;
if(nTrials < 25) {
// if number of trials greater than 25 use direct method
binomialDeviate=0.0D;
for(jj=1;jj<=nTrials;jj++)if (this.nextDouble() < workingProb) ++binomialDeviate;
}
else if(deviateMean < 1.0D) {
// if fewer than 1 out of 25 events - Poisson approximation is accurate
double expOfMean=Math.exp(-deviateMean);
testDeviate=1.0D;
for (jj=0;jj<=nTrials;jj++) {
testDeviate *= this.nextDouble();
if (testDeviate < expOfMean) break;
}
binomialDeviate=(jj <= nTrials ? jj : nTrials);
}
else{
// use rejection method
if(nTrials != nOld) {
// if nTrials has changed compute useful quantities
enTrials = (double)nTrials;
oldGamma = logGamma(enTrials + 1.0D);
nOld = nTrials;
}
if(workingProb != probOld) {
// if workingProb has changed compute useful quantities
probC = 1.0 - workingProb;
logProb = Math.log(workingProb);
logProbC = Math.log(probC);
probOld = workingProb;
}
double sq = Math.sqrt(2.0*deviateMean*probC);
do{
do{
double angle = Math.PI*this.nextDouble();
tanW = Math.tan(angle);
hold0 = sq*tanW + deviateMean;
}while(hold0 < 0.0D || hold0 >= (enTrials + 1.0D)); // rejection test
hold0 = Math.floor(hold0); // integer value distribution
testDeviate = 1.2D*sq*(1.0D + tanW*tanW)*Math.exp(oldGamma - logGamma(hold0 + 1.0D) - logGamma(enTrials - hold0 + 1.0D) + hold0*logProb + (enTrials - hold0)*logProbC);
}while(this.nextDouble() > testDeviate); // rejection test
binomialDeviate=hold0;
}
if(workingProb != prob) binomialDeviate = nTrials - binomialDeviate; // symmetry transformation
ran[i] = binomialDeviate;
}
return ran;
}
// Returns a Pareto pseudorandom deviate
public double nextPareto(double alpha, double beta){
return Math.pow(1.0D-this.nextDouble(), -1.0D/alpha)*beta;
}
// Returns an array, of Pareto pseudorandom deviates, of length n
public double[] paretoArray (double alpha, double beta, int n){
double[] ran = new double[n];
for(int i=0; i<n; i++){
ran[i] = Math.pow(1.0D-this.nextDouble(), -1.0D/alpha)*beta;
}
return ran;
}
// Returns an exponential pseudorandom deviate
public double nextExponential(double mu, double sigma){
return mu - Math.log(1.0D-this.nextDouble())*sigma;
}
// Returns an array, of exponential pseudorandom deviates, of length n
public double[] exponentialArray (double mu, double sigma, int n){
double[] ran = new double[n];
for(int i=0; i<n; i++){
ran[i] = mu - Math.log(1.0D-this.nextDouble())*sigma;
}
return ran;
}
// Returns a Rayleigh pseudorandom deviate
public double nextRayleigh(double sigma){
return Math.sqrt(-2.0D*Math.log(1.0D-this.nextDouble()))*sigma;
}
// Returns an array, of Rayleigh pseudorandom deviates, of length n
public double[] rayleighArray (double sigma, int n){
double[] ran = new double[n];
for(int i=0; i<n; i++){
ran[i] = Math.sqrt(-2.0D*Math.log(1.0D-this.nextDouble()))*sigma;
}
return ran;
}
// Returns a minimal Gumbel (Type I EVD) random deviate
// mu = location parameter, sigma = scale parameter
public double nextMinimalGumbel(double mu, double sigma){
return Math.log(Math.log(1.0D/(1.0D-this.nextDouble())))*sigma+mu;
}
// Returns an array of minimal Gumbel (Type I EVD) random deviates
// mu = location parameter, sigma = scale parameter, n = length of array
public double[] minimalGumbelArray(double mu, double sigma, int n){
double[] ran = new double[n];
for(int i=0; i<n; i++){
ran[i] = Math.log(Math.log(1.0D/(1.0D-this.nextDouble())))*sigma+mu;
}
return ran;
}
// Returns a maximal Gumbel (Type I EVD) random deviate
// mu = location parameter, sigma = scale parameter
public double nextMaximalGumbel(double mu, double sigma){
return mu-Math.log(Math.log(1.0D/(1.0D-this.nextDouble())))*sigma;
}
// Returns an array of maximal Gumbel (Type I EVD) random deviates
// mu = location parameter, sigma = scale parameter, n = length of array
public double[] maximalGumbelArray(double mu, double sigma, int n){
double[] ran = new double[n];
for(int i=0; i<n; i++){
ran[i] = mu-Math.log(Math.log(1.0D/(1.0D-this.nextDouble())))*sigma;
}
return ran;
}
// Returns a Frechet (Type II EVD) random deviate
// mu = location parameter, sigma = scale parameter, gamma = shape parameter
public double nextFrechet(double mu, double sigma, double gamma){
return Math.pow((1.0D/(Math.log(1.0D/this.nextDouble()))),1.0D/gamma)*sigma + mu;
}
// Returns an array of Frechet (Type II EVD) random deviates
// mu = location parameter, sigma = scale parameter, gamma = shape parameter, n = length of array
public double[] frechetArray(double mu, double sigma, double gamma, int n){
double[] ran = new double[n];
for(int i=0; i<n; i++){
ran[i] = Math.pow((1.0D/(Math.log(1.0D/this.nextDouble()))),1.0D/gamma)*sigma + mu;
}
return ran;
}
// Returns a Weibull (Type III EVD) random deviate
// mu = location parameter, sigma = scale parameter, gamma = shape parameter
public double nextWeibull(double mu, double sigma, double gamma){
return Math.pow(-Math.log(1.0D-this.nextDouble()),1.0D/gamma)*sigma + mu;
}
// Returns an array of Weibull (Type III EVD) random deviates
// mu = location parameter, sigma = scale parameter, gamma = shape parameter, n = length of array
public double[] weibullArray(double mu, double sigma, double gamma, int n){
double[] ran = new double[n];
for(int i=0; i<n; i++){
ran[i] = Math.pow(-Math.log(1.0D-this.nextDouble()),1.0D/gamma)*sigma + mu;
}
return ran;
}
// Returns an array of Logistic distribution random deviates
// mu = location parameter, scale = scale parameter
public double nextLogistic(double mu, double scale){
return 2.0D*scale*atanh(2.0D*this.nextDouble() - 1.0D) + mu;
}
// Returns an array of Logistic distribution random deviate
// mu = location parameter, scale = scale parameter, n is the length of the returned array
public double[] logisticArray(double mu, double scale, int n){
double[] ran = new double[n];
for(int i=0; i<n; i++) ran[i] = 2.0D*scale*atanh(2.0D*this.nextDouble() - 1.0D) + mu;
return ran;
}
// PSEUDORANDOM INTEGERS
// Returns a pseudorandom int integer between 0.0 and top
public int nextInteger(int bottom, int top){
int randint = 0;
switch(this.methodOptionInteger){
case 1: // Java Random class nextInt()
randint = this.rr.nextInt(++top - bottom) + bottom;
break;
case 2: randint = (int)Math.round(this.nextDouble()*(top - bottom)) + bottom;
break;
case 3: randint = (int)Math.floor(this.nextDouble()*(++top - bottom)) + bottom;
break;
default: throw new IllegalArgumentException("methodOptionInteger, " + this.methodOptionInteger + " is not recognised");
}
return randint;
}
// Returns a pseudorandom int integer between bottom (inclusive) and top (inclusive)
public int nextInteger(int top){
return this.nextInteger(0, top);
}
// Returns an array, of length arraylength, of pseudorandom int integers between 0.0 (inclusive) and top (inclusive)
public int[] integerArray(int arrayLength, int top){
int[] array = new int[arrayLength];
for(int i=0; i<arrayLength; i++){
array[i] = this.nextInteger(top);
}
return array;
}
// Returns an array, of length arraylength, of pseudorandom int integers between bottom and top
public int[] integerArray(int arrayLength, int bottom, int top){
int[] array = new int[arrayLength];
for(int i=0; i<arrayLength; i++){
array[i] = this.nextInteger(top - bottom) + bottom;
}
return array;
}
// Returns an array, of length arraylength, of pseudorandom int integers between bottom and top with no repeats
public int[] noRepeatIntegerArray(int arrayLength, int bottom, int top){
int[] array = new int[arrayLength];
boolean test = true;
int hold = -1;
int nFound = 0;
boolean repeatTest = true;
while(test){
hold = this.nextInteger(top - bottom) + bottom;
if(nFound==0){
array[0] = hold;
nFound = 1;
}
else{
repeatTest = true;
for(int i=0; i<nFound; i++)if(array[i]==hold)repeatTest = false;
if(repeatTest){
array[nFound] = hold;
nFound++;
}
}
if(nFound==arrayLength)test = false;
}
return array;
}
// Returns an array, of length top+1, of unique pseudorandom integers between bottom and top
// i.e. no integer is repeated and all integers between bottom and top inclusive are present
public int[] uniqueIntegerArray(int bottom, int top){
int range = top - bottom;
int[] array = uniqueIntegerArray(range);
for(int i=0; i<range+1; i++)array[i] += bottom;
return array;
}
// Returns an array, of length top+1, of unique pseudorandom integers between 0 and top
// i.e. no integer is repeated and all integers between 0 and top inclusive are present
public int[] uniqueIntegerArray(int top){
int numberOfIntegers = top + 1; // number of unique pseudorandom integers returned
int[] array = new int[numberOfIntegers]; // array to contain returned unique pseudorandom integers
boolean allFound = false; // will equal true when all required integers found
int nFound = 0; // number of required pseudorandom integers found
boolean[] found = new boolean[numberOfIntegers]; // = true when integer corresponding to its index is found
for(int i=0; i<numberOfIntegers; i++)found[i] = false;
while(!allFound){
int ii = this.nextInteger(top);
if(!found[ii]){
array[nFound] = ii;
found[ii] = true;
nFound++;
if(nFound==numberOfIntegers)allFound = true;
}
}
return array;
}
// Return the serial version unique identifier
public static long getSerialVersionUID(){
return PsRandom.serialVersionUID;
}
// Inverse hyperbolic tangent of a double number
private static double atanh(double a){
double sgn = 1.0D;
if(a<0.0D){
sgn = -1.0D;
a = -a;
}
if(a>1.0D) throw new IllegalArgumentException("atanh real number argument (" + sgn*a + ") must be >= -1 and <= 1");
return 0.5D*sgn*(Math.log(1.0D + a)-Math.log(1.0D - a));
}
// log to base e of the Gamma function
// Lanczos approximation (6 terms)
// Lanczos Gamma Function approximation - N (number of coefficients -1)
private static int lgfN = 6;
private static double lgfGamma = 5.0;
// Lanczos Gamma Function approximation - Coefficients
private static double[] lgfCoeff = {1.000000000190015, 76.18009172947146, -86.50532032941677, 24.01409824083091, -1.231739572450155, 0.1208650973866179E-2, -0.5395239384953E-5};
// log to base e of the Gamma function
// Lanczos approximation (6 terms)
// Retained for backward compatibility
public static double logGamma(double x){
double xcopy = x;
double fg = 0.0D;
double first = x + lgfGamma + 0.5;
double second = lgfCoeff[0];
if(x>=0.0){
first -= (x + 0.5)*Math.log(first);
for(int i=1; i<=lgfN; i++)second += lgfCoeff[i]/++xcopy;
fg = Math.log(Math.sqrt(2.0*Math.PI)*second/x) - first;
}
else{
fg = Math.PI/(gamma(1.0D-x)*Math.sin(Math.PI*x));
if(fg!=1.0/0.0 && fg!=-1.0/0.0){
if(fg<0){
throw new IllegalArgumentException("\nThe gamma function is negative");
}
else{
fg = Math.log(fg);
}
}
}
return fg;
}
// Gamma function
// Lanczos approximation (6 terms)
// retained for backward compatibity
private static double gamma(double x){
double xcopy = x;
double first = x + lgfGamma + 0.5;
double second = lgfCoeff[0];
double fg = 0.0D;
if(x>=0.0){
first = Math.pow(first, x + 0.5)*Math.exp(-first);
for(int i=1; i<=lgfN; i++)second += lgfCoeff[i]/++xcopy;
fg = first*Math.sqrt(2.0*Math.PI)*second/x;
}
else{
fg = -Math.PI/(x*gamma(-x)*Math.sin(Math.PI*x));
}
return fg;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/tools/Volume.java | .java | 1,784 | 83 | package smlms.tools;
import imageware.Builder;
import imageware.ImageWare;
import smlms.file.Fluorophore;
public class Volume {
public double x1;
public double x2;
public double y1;
public double y2;
public double z1;
public double z2;
public Volume(double x1, double x2, double y1, double y2, double z1, double z2) {
this.x1 = x1;
this.x2 = x2;
this.y1 = y1;
this.y2 = y2;
this.z1 = z1;
this.z2 = z2;
}
public Volume(Point3D origin, Point3D dim) {
this.x1 = origin.x;
this.x2 = origin.x + dim.x;
this.y1 = origin.y;
this.y2 = origin.y + dim.y;
this.z1 = origin.z;
this.z2 = origin.z + dim.z;
}
public boolean contains(Fluorophore point) {
if (point.x < x1)
return false;
if (point.x > x2)
return false;
if (point.y < y1)
return false;
if (point.y > y2)
return false;
if (point.z < z1)
return false;
if (point.z > z2)
return false;
return true;
}
public boolean contains(Point3D point) {
if (point.x < x1)
return false;
if (point.x > x2)
return false;
if (point.y < y1)
return false;
if (point.y > y2)
return false;
if (point.z < z1)
return false;
if (point.z > z2)
return false;
return true;
}
public ImageWare allocate3D(double pixelsize) {
int nx = (int)Math.ceil((x2-x1)/pixelsize);
int ny = (int)Math.ceil((y2-y1)/pixelsize);
int nz = (int)Math.ceil((z2-z1)/pixelsize);
return Builder.create(nx, ny, nz, ImageWare.FLOAT);
}
public ImageWare allocate2D(double pixelsize) {
int nx = (int)Math.ceil((x2-x1)/pixelsize);
int ny = (int)Math.ceil((y2-y1)/pixelsize);
return Builder.create(nx, ny, 1, ImageWare.FLOAT);
}
public String toString() {
return "Volume (" + x1 + ", " + y1 + ", " + z1 + ") " + " (" + x2 + ", " + y2 + ", " + z2 + ") ";
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/tools/Zip.java | .java | 1,935 | 73 | package smlms.tools;
import ij.IJ;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.util.zip.ZipEntry;
import java.util.zip.ZipOutputStream;
public class Zip {
static public void zipFolder(String dir, String zipFileName) {
File dirObj = new File(dir);
try {
ZipOutputStream out = new ZipOutputStream(new FileOutputStream(zipFileName));
IJ.log("Creating : " + zipFileName);
addDir(dirObj, out);
out.close();
}
catch(Exception ex) {
IJ.log(ex.toString());
}
}
static private void addDir(File dirObj, ZipOutputStream out) {
File[] files = dirObj.listFiles();
byte[] tmpBuf = new byte[1024];
for (int i = 0; i < files.length; i++) {
try {
FileInputStream in = new FileInputStream(files[i].getAbsolutePath());
out.putNextEntry(new ZipEntry(files[i].getName()));
int len;
while ((len = in.read(tmpBuf)) > 0) {
out.write(tmpBuf, 0, len);
}
out.closeEntry();
in.close();
}
catch(Exception ex) {
IJ.log(ex.toString());
}
}
}
/*
public static void zipFolder(String folder, String zipfile) {
try {
File inFolder = new File(folder);
File outFolder = new File(zipfile);
ZipOutputStream out = new ZipOutputStream(new BufferedOutputStream(new FileOutputStream(outFolder)));
BufferedInputStream in = null;
byte[] data = new byte[1000];
String files[] = inFolder.list();
for (int i=0; i<files.length; i++) {
in = new BufferedInputStream(new FileInputStream(inFolder.getPath() + "/" + files[i]), 1000);
out.putNextEntry(new ZipEntry(files[i]));
int count;
while((count = in.read(data,0,1000)) != -1) {
out.write(data, 0, count);
}
out.closeEntry();
}
out.flush();
out.close();
}
catch(Exception e) {
IJ.error("" + e);
}
}
*/
} | Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/tools/Vector3D.java | .java | 212 | 13 | package smlms.tools;
public class Vector3D {
public Point3D origin;
public Point3D extremity;
public Vector3D(Point3D origin, Point3D extremity) {
this.origin = origin;
this.extremity = extremity;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/tools/Chrono.java | .java | 2,279 | 85 | //=========================================================================================
//
// Project: Localization Microscopy
//
// Author : Daniel Sage, Biomedical Imaging Group (BIG), http://bigwww.epfl.ch/sage/
//
// Organization: Ecole Polytechnique F�d�rale de Lausanne (EPFL), Lausanne, Switzerland
//
// Conditions of use: You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
package smlms.tools;
import ij.IJ;
public class Chrono {
static private double chrono;
static private double[] chronos = new double[10];
static public void reset() {
chrono = System.nanoTime();
}
static public void reset(int number) {
if (number < 0)
chrono = System.nanoTime();
else if (number >= 10)
chrono = System.nanoTime();
else
chronos[number] = System.nanoTime();
}
static public String string() {
return string("", -1);
}
static public String string(String message) {
return string(message, -1);
}
static public String string(int number) {
return string("", number);
}
static public String string(String message, int number) {
double t = 0;
if (number < 0)
t = System.nanoTime() - chrono;
else if (number >= 10)
t = System.nanoTime() - chrono;
else
t = System.nanoTime() - chronos[number];
String sn = (number < 0 ? " " : " [" + number + "] ");
if (t > 1000000000.0)
return message + " Chrono" + sn + IJ.d2s(t/1000000000.0) + " s";
else if (t > 1000000.0)
return message + " Chrono" + sn + IJ.d2s(t/1000000.0) + " ms";
else if (t > 1000.0)
return message + " Chrono" + sn + IJ.d2s(t/1000.0) + " us";
else
return message + " Chrono" + sn + IJ.d2s(t/1000000000.0) + " ns";
}
static public void print() {
IJ.log(string("", -1));
}
static public void print(String message) {
IJ.log(string(message, -1));
}
static public void print(int number) {
IJ.log(string("" , number));
}
static public void print(String message, int number) {
IJ.log(string(message, number));
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/tools/Point3D.java | .java | 2,119 | 86 | //=========================================================================================
//
// Project: Localization Microscopy
//
// Author : Daniel Sage, Biomedical Imaging Group (BIG), http://bigwww.epfl.ch/sage/
//
// Organization: Ecole Polytechnique F�d�rale de Lausanne (EPFL), Lausanne, Switzerland
//
// Conditions of use: You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
package smlms.tools;
import ij.IJ;
public class Point3D {
public double x;
public double y;
public double z;
public Point3D(double x, double y, double z) {
this.x = x;
this.y = y;
this.z = z;
}
public Point3D(double coord[]) {
this.x = coord[0];
this.y = coord[1];
this.z = coord[2];
}
public double distance(Point3D p) {
return Math.sqrt((p.x-x)*(p.x-x) + (p.y-y)*(p.y-y) + (p.z-z)*(p.z-z));
}
public double distanceLateral(Point3D p) {
return Math.sqrt((p.x-x)*(p.x-x) + (p.y-y)*(p.y-y));
}
public Point3D scale(double scale) {
return new Point3D(x*scale, y*scale, z*scale);
}
public Point3D negate() {
return new Point3D(-x, -y, -z);
}
public void translate(double dx, double dy, double dz) {
x += dx;
y += dy;
z += dz;
}
public void subtract(Point3D delta) {
x = x - delta.x;
y = y - delta.y;
z = z - delta.z;
}
public Point3D translate(Point3D delta) {
return new Point3D(x + delta.x, y + delta.y, z + delta.z);
}
public static Point3D read(String line) {
String[] tokens = line.split("[,]");
if (tokens.length == 3) {
double x = Double.parseDouble(tokens[0]);
double y = Double.parseDouble(tokens[1]);
double z = Double.parseDouble(tokens[2]);
return new Point3D(x, y, z);
}
else {
return new Point3D(0, 0, 0);
}
}
public String toString() {
return " (" + IJ.d2s(x) + ", " + IJ.d2s(y) + ", " + IJ.d2s(z) + ")";
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/tools/ArrayOperations.java | .java | 7,818 | 284 | //=========================================================================================
//
// Project: Localization Microscopy
//
// Author : Daniel Sage, Biomedical Imaging Group (BIG), http://bigwww.epfl.ch/sage/
//
// Organization: Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
//
// Conditions of use: You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
package smlms.tools;
public class ArrayOperations {
public static void main(String args[]) {
double max[] = ArrayOperations.init(3, -Double.MAX_VALUE);
double min[] = ArrayOperations.init(3, Double.MAX_VALUE);
double mean[] = ArrayOperations.init(3, 0);
double var[] = ArrayOperations.init(3, 0);
double count[] = ArrayOperations.init(3, 0);
PsRandom rand = new PsRandom();
for(int i=0; i<10000; i++) {
double a[] = new double[] {rand.nextDouble(3, 5), rand.nextGaussian(3, 5), rand.nextPoissonian(4)};
ArrayOperations.inc(count);
ArrayOperations.mean(count, mean, a);
ArrayOperations.min(min, a);
ArrayOperations.max(max, a);
ArrayOperations.variance(count, var, mean, a);
}
for(int i=0; i<min.length; i++)
System.out.println("Min: " + min[i]);
for(int i=0; i<min.length; i++)
System.out.println("Mean: " + mean[i]);
for(int i=0; i<min.length; i++)
System.out.println("stdev: " + Math.sqrt(var[i]));
for(int i=0; i<min.length; i++)
System.out.println("max: " + max[i]);
}
public static String[] merge(String arr1[], String arr2[], int begin, int end) {
String[] arr = new String[arr1.length + (end-begin) + 1];
for(int i=0; i<arr1.length; i++)
arr[i] = arr1[i];
for(int i=begin; i<=end; i++)
arr[i+arr1.length-begin] = arr2[i];
return arr;
}
public static double[] ramp(int n) {
double array[] = new double[n];
for(int i=0; i<n; i++)
array[i] = i;
return array;
}
public static int getIndexMaximum(double[] array) {
int n = array.length;
double max = -Double.MAX_VALUE;
int imax = 0;
for(int i=0; i<n; i++) {
if (array[i] > max) {
max = array[i];
imax = i;
}
}
return imax;
}
public static double[][] transpose(double[][] array) {
int ni = array.length;
int nj = array[0].length;
double out[][] = new double[nj][ni];
for(int i=0; i<ni; i++)
for(int j=0; j<nj; j++)
out[j][i] = array[i][j];
return out;
}
public static double getMaximum(double[] array) {
int n = array.length;
double max = -Double.MAX_VALUE;
for(int i=0; i<n; i++) {
if (array[i] > max) {
max = array[i];
}
}
return max;
}
public static int getFWMH(double[] array, double max, int posmax) {
int n = array.length;
int i1 = 0;
for(i1=posmax; i1<n; i1++) {
if (max*0.5 > array[i1])
break;
}
int i2 = 0;
for(i2=posmax; i2>=0; i2--) {
if (max*0.5 > array[i2])
break;
}
return i1-i2;
}
public static float getMean(float[][] array) {
int n = array.length;
int m = array[0].length;
float mean = 0f;
for (int i = 0; i < n; i++)
for (int j = 0; j < m; j++)
mean += array[i][j];
return mean / (n*m);
}
public static float getNorm(float[][] array) {
int n = array.length;
int m = array[0].length;
float mean = 0f;
for (int i = 0; i < n; i++)
for (int j = 0; j < m; j++)
mean += array[i][j]*array[i][j];
return (float)Math.sqrt(mean / (n*m));
}
public static void fill(float[][] array, float value) {
int n = array.length;
int m = array[0].length;
for (int i = 0; i < n; i++)
for (int j = 0; j < m; j++)
array[i][j] = value;
}
public static void multiply(float[][] array, float multiply) {
int n = array.length;
int m = array[0].length;
for (int i = 0; i < n; i++)
for (int j = 0; j < m; j++)
array[i][j] *= multiply;
}
public static void copy(float[][] source, float[][] destination) {
int n = source.length;
for (int i = 0; i < n; i++)
System.arraycopy(source[i], 0, destination[i], 0, source[i].length);
}
public static void max(float[][] array, float[][] additiveTerm) {
int n = array.length;
int m = array[0].length;
for (int i = 0; i < n; i++)
for (int j = 0; j < m; j++) {
array[i][j] = Math.max(array[i][j], additiveTerm[i][j]);
}
}
public static void add(float[][] array, float[][] additiveTerm) {
int n = array.length;
int m = array[0].length;
for (int i = 0; i < n; i++)
for (int j = 0; j < m; j++) {
array[i][j] += additiveTerm[i][j];
}
}
public static void linear(float[][] array, float gain, float offset) {
int n = array.length;
int m = array[0].length;
for (int i = 0; i < n; i++)
for (int j = 0; j < m; j++) {
array[i][j] = gain *array[i][j] + offset;
}
}
public static void normalize(float[][] array, float norm) {
int n = array.length;
int m = array[0].length;
float max = 0.0f;
for (int i = 0; i < n; i++)
for (int j = 0; j < m; j++) {
if (array[i][j] > max)
max = array[i][j];
}
multiply(array, norm/max);
}
public static void normalize(float[][] array, float norm, float offset) {
int n = array.length;
int m = array[0].length;
float max = 0.0f;
for (int i = 0; i < n; i++)
for (int j = 0; j < m; j++) {
if (array[i][j] > max)
max = array[i][j];
}
float a = norm/max;
for (int i = 0; i < n; i++)
for (int j = 0; j < m; j++) {
array[i][j] = a * array[i][j] + offset;
}
}
public static void increment(float[][] array, float[][] additiveTerm) {
int n = array.length;
int m = array[0].length;
for (int i = 0; i < n; i++)
for (int j = 0; j < m; j++) {
array[i][j] += additiveTerm[i][j];
}
}
public static void increment(float[][] array, float[] additiveTerm) {
int n = array.length;
int m = array[0].length;
for (int i = 0; i < n; i++)
for (int j = 0; j < m; j++) {
array[i][j] += additiveTerm[i+j*n];
}
}
public static void increment(float[][] array, float value) {
int n = array.length;
int m = array[0].length;
for (int i = 0; i < n; i++)
for (int j = 0; j < m; j++)
array[i][j] += value;
}
public static double[] init(int n, double value) {
double[] array = new double[n];
for(int i=0; i<array.length; i++)
array[i] = value;
return array;
}
public static void setValue(double[] array, double value) {
for(int i=0; i<array.length; i++)
array[i] = value;
}
public static void min(double[] minimun, double operand[]) {
for(int i=0; i<operand.length; i++)
minimun[i] = Math.min(minimun[i], operand[i]);
}
public static void max(double[] maximun, double operand[]) {
for(int i=0; i<operand.length; i++)
maximun[i] = Math.max(maximun[i], operand[i]);
}
public static void sum(double[] out, double operand[]) {
for(int i=0; i<operand.length; i++)
out[i] += operand[i];
}
public static void inc(double[] out) {
for(int i=0; i<out.length; i++)
out[i]++;
}
public static void mean(double count[], double[] currentMean, double update[]) {
for(int i=0; i<update.length; i++)
currentMean[i] = (currentMean[i]*count[i] + update[i]) / (count[i] + 1.0);
}
public static void variance(double count[], double currentVariance[], double currentMean[], double update[]) {
for(int i=0; i<update.length; i++) {
if (count[i] > 1.0) {
double a = currentVariance[i] * (count[i]-2.0) / (count[i]-1.0);
double b = (update[i] - currentMean[i]) * (update[i] - currentMean[i]) / (count[i]+1.0);
currentVariance[i] = a + b;
}
else {
currentVariance[i] = (update[i] - currentMean[i]) * (update[i] - currentMean[i]) ;
}
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/tools/Tools.java | .java | 4,146 | 158 | //=========================================================================================
//
// Project: Localization Microscopy
//
// Author : Daniel Sage, Biomedical Imaging Group (BIG), http://bigwww.epfl.ch/sage/
//
// Organization: Ecole Polytechnique F�d�rale de Lausanne (EPFL), Lausanne, Switzerland
//
// Conditions of use: You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
package smlms.tools;
import ij.IJ;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.FileReader;
import java.io.IOException;
import java.nio.channels.FileChannel;
import java.util.ArrayList;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
import javax.swing.JTextField;
public class Tools {
public static String getSMLMSPath() {
String s = System.getProperty("user.home") + File.separator + "Desktop" + File.separator + "smlms-data" + File.separator;
new File(s).mkdir();
return s;
}
public static ArrayList<Double> extractDoubles(String text) {
Pattern p = Pattern.compile("[-]?[0-9]*\\.?[0-9]+");
Matcher m = p.matcher(text.trim());
ArrayList<Double> doubles = new ArrayList<Double>();
while (m.find()) {
doubles.add(Double.parseDouble(m.group()));
}
return doubles;
}
public static double extractDouble(String text) {
Pattern p = Pattern.compile("[-]?[0-9]*\\.?[0-9]+");
Matcher m = p.matcher(text.trim());
while (m.find()) {
return Double.parseDouble(m.group());
}
return 0.0;
}
public static double extractDouble(JTextField txt) {
return extractDouble(txt.getText().trim());
}
public static String[] concat(String[] list, String element) {
String[] s = new String[list.length+1];
for(int i=0; i<list.length; i++)
s[i] = list[i];
s[list.length] = element;
return s;
}
public static String readFile(String filename) {
try {
StringBuffer fileData = new StringBuffer(1000);
BufferedReader reader = new BufferedReader(new FileReader(filename));
char[] buf = new char[1024];
int numRead = 0;
while ((numRead = reader.read(buf)) != -1) {
String readData = String.valueOf(buf, 0, numRead);
fileData.append(readData);
buf = new char[1024];
}
reader.close();
return fileData.toString();
}
catch(Exception ex) {
}
return "";
}
public static void copyFile(String sourceFile, String destFile) {
try {
copyFile(new File(sourceFile), new File(destFile));
}
catch(IOException e) {}
}
public static void copyFile(File sourceFile, File destFile) throws IOException {
if(!destFile.exists()) {
destFile.createNewFile();
}
FileChannel source = null;
FileChannel destination = null;
try {
source = new FileInputStream(sourceFile).getChannel();
destination = new FileOutputStream(destFile).getChannel();
destination.transferFrom(source, 0, source.size());
}
finally {
if(source != null) {
source.close();
}
if(destination != null) {
destination.close();
}
}
}
public static int round(double a) {
return (int)Math.round(a);
}
public static String format(int n) {
String w = "" + n;
if (n < 10)
w = "0000" + w;
else if (n < 100)
w = "000" + w;
else if (n < 1000)
w = "00" + w;
else if (n < 10000)
w = "0" + w;
return w;
}
public static String formatNanoCubetoString(double volnm) {
if (volnm > 10000000000.0) {
return IJ.d2s(volnm/1000000000.0, 1) + " um3";
}
if (volnm > 1000000000.0) {
return IJ.d2s(volnm/1000000000.0, 2) + " um3";
}
if (volnm > 100000000.0) {
return IJ.d2s(volnm/1000000000.0, 3) + " um3";
}
if (volnm > 1000000.0) {
return IJ.d2s(volnm/1000000000.0, 4) + " um3";
}
return IJ.d2s(volnm, 0) + " nm3";
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/plugins/Test_JComboBox.java | .java | 561 | 26 | package smlms.plugins;
import java.awt.Font;
import javax.swing.JComboBox;
import javax.swing.JFrame;
public class Test_JComboBox {
public static void main(String args[]) {
new Test_JComboBox();
}
public Test_JComboBox() {
JFrame frame = new JFrame("Test");
JComboBox cmb = new JComboBox(new String[] {"x y z", "t u ? ? z"});
cmb.setEditable(true);
Font font = cmb.getFont();
frame.getContentPane().add(cmb);
cmb.setFont(new java.awt.Font(font.getFamily(), Font.BOLD, font.getSize()+10 ));
frame.pack();
frame.setVisible(true);
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/plugins/Format_PSF.java | .java | 6,911 | 251 | package smlms.plugins;
import ij.IJ;
import ij.ImagePlus;
import ij.WindowManager;
import ij.gui.GenericDialog;
import ij.gui.OvalRoi;
import ij.gui.Plot;
import ij.process.ImageConverter;
import ij.process.ImageProcessor;
import imageware.Builder;
import imageware.ImageWare;
public class Format_PSF {
public Format_PSF() {
ImagePlus imp = WindowManager.getCurrentImage();
if (imp == null) {
IJ.error("No open image");
return;
}
GenericDialog dlg = new GenericDialog("Format PSF");
dlg.addCheckbox("Zeroing", true);
dlg.addNumericField("Percentage of histogram", 0.1, 1);
dlg.addCheckbox("Gaussian in Z", true);
dlg.addNumericField("SigmaZ", 1, 1);
dlg.addCheckbox("Attenuation Lateral", true);
dlg.addNumericField("Sigma of sigmoid window", 0.02, 3);
dlg.addCheckbox("Normalization sum of middle slice = N", true);
dlg.addNumericField("N", 1, 1);
dlg.addCheckbox("Surface normalization (centered disk)", true);
dlg.addNumericField("Radius in pixel", 100, 3);
dlg.showDialog();
if (dlg.wasCanceled())
return;
boolean zero = dlg.getNextBoolean();
double percentage = dlg.getNextNumber();
boolean gaussianZ = dlg.getNextBoolean();
double sigmaZ = dlg.getNextNumber();
boolean attenuation = dlg.getNextBoolean();
double att = dlg.getNextNumber();
boolean normalization = dlg.getNextBoolean();
double norm = dlg.getNextNumber();
boolean disk = dlg.getNextBoolean();
double radius = dlg.getNextNumber();
new ImageConverter(imp).convertToGray32();
ImagePlus imp1 = zero ? zeroing(imp, percentage) : imp.duplicate();
ImagePlus imp2 = gaussianZ ? gaussianZ(imp1, sigmaZ) : imp1.duplicate();
ImagePlus imp3 = attenuation ? attenuation(imp2, att) : imp2.duplicate();
ImagePlus imp4 = normalization ? normalization(imp3, norm, disk, (int)radius) : imp3.duplicate();
imp4.show();
}
private ImagePlus zeroing(ImagePlus imp, double percentage) {
ImageWare psf = Builder.wrap(imp);
double max = psf.getMaximum();
double min = psf.getMinimum();
int nbins = 1000000;
int histo[] = new int[nbins];
int nx = psf.getWidth();
int ny = psf.getHeight();
int nz = psf.getSizeZ();
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++)
for(int z=0; z<nz; z++) {
double v = (psf.getPixel(x, y, z) - min) / (max-min) * nbins;
int h = (int)(Math.max(0, Math.min(nbins-1, v)));
histo[h]++;
}
double stop = percentage * nx *ny * ny / 100.0;
double cumul = 0;
int h = 0;
while (cumul < stop && h < nbins-1) {
cumul += histo[h];
h++;
}
double T = min + h * (max-min) / nbins;
IJ.log("zero at T " + T + " h:" + h + " for " +stop + " cumul:" + cumul);
ImagePlus out = imp.duplicate();
for(int z=0; z<nz; z++) {
ImageProcessor in = imp.getStack().getProcessor(z+1);
ImageProcessor ou = out.getStack().getProcessor(z+1);
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++)
ou.putPixelValue(x, y, in.getPixelValue(x, y) < T ? 0.0 : in.getPixelValue(x, y)-T);
}
out.setTitle("After Zeroing");
out.setSlice(nz/2);
out.show();
return out;
}
private ImagePlus gaussianZ(ImagePlus imp, double sigma) {
ImageWare psf = Builder.wrap(imp);
int nz = imp.getStackSize();
psf.smoothGaussian(0, 0, sigma);
ImagePlus out = new ImagePlus("", psf.buildImageStack());
out.setTitle("After Gaussian");
out.setSlice(nz/2);
out.show();
return out;
}
private ImagePlus attenuation(ImagePlus imp, double factorXY) {
int nx = imp.getWidth();
int ny = imp.getHeight();
int nz = imp.getStackSize();
double att[][] = new double[nx][ny];
double xc = nx * 0.5;
double yc = ny * 0.5;
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++) {
double d = 1 - Math.sqrt((x-xc)*(x-xc) + (y-yc)*(y-yc)) / (nx);
att[x][y] = 1.0 / (1.0 + Math.exp(-(d-0.5)/factorXY));
}
Builder.create(att).show("Windowing map " + factorXY);
ImagePlus out = imp.duplicate();
for(int z=0; z<nz; z++) {
ImageProcessor in = imp.getStack().getProcessor(z+1);
ImageProcessor ou = out.getStack().getProcessor(z+1);
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++)
ou.putPixelValue(x, y, in.getPixelValue(x, y) * att[x][y]);
}
out.setTitle("After Attenuation");
out.setSlice(nz/2);
out.show();
return out;
}
private ImagePlus normalization(ImagePlus imp, double norm, boolean disk, int radius) {
ImageWare psf = Builder.create(imp);
int nx = psf.getWidth();
int ny = psf.getHeight();
int nz = psf.getSizeZ();
int xc = nx/2;
int yc = ny/2;
int zo = nz/2;
double sum = 0.0;
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++) {
if (disk == true) {
double r = (xc-x)*(xc-x) + (yc-y)*(yc-y);
if (Math.sqrt(r) < radius)
sum += psf.getPixel(x, y, zo);
}
else {
sum += psf.getPixel(x, y, zo);
}
}
double total = norm / sum;
IJ.log(" Normalization sum=" + String.format("%13.3f", sum) + " disk " + disk + " > normalize factor " + String.format("%3.15f", total));
psf.multiply(total);
double zz[] = new double[nz];
double val[] = new double[nz];
for(int z=0; z<nz; z++) {
zz[z] = z;
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++)
val[z] += psf.getPixel(x, y, z);
}
new Plot("Z integral", "z", "sum", zz, val).show();
ImagePlus out = new ImagePlus("", psf.buildImageStack());
out.setTitle("After Normalization norm=" + total);
out.setSlice(nz/2);
out.show();
if (disk)
out.setRoi(new OvalRoi(xc-radius, yc-radius, radius*2, radius*2));
return out;
}
/*
private ImagePlus normalization(ImagePlus imp, double norm) {
ImageWare psf = Builder.wrap(imp);
int nx = psf.getWidth();
int ny = psf.getHeight();
int nz = psf.getSizeZ();
double max = -Double.MAX_VALUE;
int xmax = 0;
int ymax = 0;
int zmax = 0;
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++)
for(int z=0; z<nz; z++)
if (psf.getPixel(x, y, z) > max) {
max = psf.getPixel(x, y, z);
xmax = x;
ymax = y;
zmax = z;
}
IJ.log(" Max " + max + " at (" + xmax + " " + ymax + " " + zmax + ") == middle (" + (nx/2) + " " + (ny/2) + " " + (nz/2) +")");
double hmax = max * norm;
int swhm = 0;
double sum = 0.0;
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++)
if (psf.getPixel(x, y, zmax) > hmax) {
sum += psf.getPixel(x, y, zmax);
swhm++;
}
IJ.log(" Surface HMax:" + swhm + " fwhm "+ Math.sqrt((swhm/Math.PI)*2) + " norm: " + sum);
double total = 1.0 / sum;
psf.multiply(total);
ImagePlus out = new ImagePlus("", psf.buildImageStack());
double zz[] = new double[nz];
double val[] = new double[nz];
for(int z=0; z<nz; z++) {
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++)
val[z] += psf.getPixel(x, y, z);
}
new Plot("Z integral", "z", "sum", zz, val).show();
out.setTitle("After Normalization");
out.setSlice(nz/2);
out.show();
return out;
}
*/
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/plugins/Test_JFreeChart.java | .java | 423 | 25 | package smlms.plugins;
import smlms.tools.Chart;
public class Test_JFreeChart {
public static void main(String args[]) {
new Test_JFreeChart();
}
public Test_JFreeChart() {
int N = 1000;
double x[] = new double[N];
double y[] = new double[N];
for(int i=0; i<N; i++) {
x[i] = Math.random();
y[i] = Math.random();
}
Chart chart = new Chart("", "x", y);
chart.show("test", 500, 500);
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/plugins/Generate_Beads.java | .java | 2,106 | 70 | package smlms.plugins;
import java.io.File;
import java.util.ArrayList;
import smlms.file.Fluorophore;
import smlms.file.Fluorophores;
import smlms.simulation.CameraModule;
import smlms.simulation.PSFModule;
import smlms.simulation.SequenceFactory;
import smlms.simulation.Viewport;
import smlms.tools.Point3D;
public class Generate_Beads {
double pixelsize = 100;
public Generate_Beads() {
String psf = "/Users/sage/Desktop/beads/PSF/2D-Exp.tif";
String path = "/Users/sage/Desktop/beads/PSF/2D-Exp/";
int fmax = 151;
Fluorophores[] fluos = new Fluorophores[fmax];
for(int i=0; i <fmax; i++) {
fluos[i] = new Fluorophores();
Fluorophore fluo = new Fluorophore(i, 1000, 1000, 10*i-750, i+1, 1000000000.0);
fluos[i].add(fluo);
}
generate(path, psf, fluos, fmax);
}
private void generate(String path, String psf, Fluorophores[] fluos, int fmax) {
int fmin = 0;
int upC = 100;
int upW = 1;
double fwhm = 250;
new File(path).mkdir();
CameraModule camera = createCameraModule();
Viewport vwPSF = createViewport(pixelsize/(upC * upW));
PSFModule module = new PSFModule(psf, vwPSF);
if (psf.startsWith("BP500")) {
module.biplaneAffine = true;
module.affineRotation = 2;
module.affineScale = 1;
module.affineDX = 120;
module.affineDY = 70;
}
ArrayList<PSFModule> psfs = new ArrayList<PSFModule>();
psfs.add(module);
String dataset = "Beads in " + psf;
SequenceFactory sequencer = new SequenceFactory(dataset, fmin, fmax, 1, camera, psfs, null, null, createViewport(pixelsize), upW, upC);
sequencer.generate(path, 2, fluos, fwhm, 0, true, true, null);
}
protected Viewport createViewport(double pixelsize) {
Point3D origin = new Point3D(0, 0, 0);
double fovNano = 64 * pixelsize;
double thickness = 1500;
return new Viewport(origin, fovNano, fovNano, thickness, pixelsize);
}
protected CameraModule createCameraModule() {
double saturation = 65535;
String format = "TIFF 32-bits";
String quantiz = CameraModule.quantizationNames[5];
return new CameraModule(quantiz, saturation, format);
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/plugins/Test_RandomVariable.java | .java | 773 | 31 | package smlms.plugins;
import ij.gui.Plot;
import smlms.tools.NormalizedVariable;
public class Test_RandomVariable {
public static void main(String args[]) {
double xmin = -100;
double xmax = 900;
int n = (int)(xmax - xmin);
double x[] = new double[n];
for(int i=0; i<n; i++)
x[i] = xmin + i;
NormalizedVariable var1 = new NormalizedVariable(10);
var1.addSigmoid(100, 0.5, 0.1);
var1.addCosine(20, 2, 200);
var1.addPoissonRandom(10);
double v1[] = new double[n];
for(int i=0; i<n; i++)
v1[i] = var1.get(x[i]/1000.0);
Plot plot = new Plot("Variable", "x", "value", x, v1);
plot.show();
double array[][][] = new double[2][3][4];
System.out.println( " " + array.length + " " + array[0].length + " " + array[0][0].length);
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/plugins/AffineTransform_PSF.java | .java | 916 | 18 | package smlms.plugins;
import ij.IJ;
public class AffineTransform_PSF {
public AffineTransform_PSF() {
IJ.selectWindow("BP-000.tif");
IJ.run("TransformJ Scale", "x-factor=1.0 y-factor=1.0 z-factor=1.0 interpolation=[cubic B-spline]");
IJ.run("TransformJ Rotate", "z-angle=3 y-angle=0.0 x-angle=0.0 interpolation=[cubic B-spline] background=0.0 anti-alias");
IJ.run("TransformJ Translate", "x-translation=50 y-translation=0.0 z-translation=0.0 interpolation=[cubic convolution] background=0.0");
IJ.selectWindow("BP-500.tif");
IJ.run("TransformJ Scale", "x-factor=1.05 y-factor=1.05 z-factor=1.0 interpolation=[cubic B-spline]");
IJ.run("TransformJ Rotate", "z-angle=-3 y-angle=0.0 x-angle=0.0 interpolation=[cubic B-spline] background=0.0 anti-alias");
IJ.run("TransformJ Translate", "x-translation=-50 y-translation=0.0 z-translation=0.0 interpolation=[cubic convolution] background=0.0");
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/plugins/Normalized_PSF.java | .java | 1,800 | 72 | package smlms.plugins;
import ij.IJ;
import ij.ImagePlus;
import ij.WindowManager;
import ij.io.FileSaver;
import imageware.Builder;
import imageware.ImageWare;
public class Normalized_PSF {
public static void main(String args[]) {
new Normalized_PSF();
}
public Normalized_PSF() {
//String filename = "/Users/sage/Desktop/GL-20nm.tif";
//String filename = "/Users/sage/Desktop/DH-20nm.tif";
//String filename = "/Users/sage/Desktop/psf.tif";
//ImagePlus imp = new Opener().openImage(filename);
ImagePlus imp = WindowManager.getCurrentImage();
if (imp == null)
return;
ImageWare psf = Builder.wrap(imp);
double min = psf.getMinimum();
psf.subtract(min);
int nx = psf.getWidth();
int ny = psf.getHeight();
int nz = psf.getSizeZ();
double att[][] = new double[nx][ny];
double xc = nx * 0.5;
double yc = ny * 0.5;
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++) {
double d = 1 - Math.sqrt((x-xc)*(x-xc) + (y-yc)*(y-yc)) / (nx);
att[x][y] = 1.0 / (1.0 + Math.exp(-(d-0.5)/0.02));
}
Builder.create(att).show("att");
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++)
for(int z=0; z<nz; z++)
psf.putPixel(x, y, z, psf.getPixel(x, y, z) * att[x][y]);
int zo = nz/2;
double sum = 0.0;
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++)
sum += psf.getPixel(x, y, zo);
double total = 1.0 / sum;
psf.multiply(total);
psf.show("" + psf.getTotal());
ImagePlus out = new ImagePlus("", psf.buildImageStack());
FileSaver saver = new FileSaver(out);
saver.saveAsTiffStack("/Users/sage/Desktop/" + imp.getTitle() + "-norm.tif");
for(int z=0; z<nz; z++) {
double s = 0.0;
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++)
s += psf.getPixel(x, y, z);
IJ.log(" slice z=" + z + " " + s);
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/plugins/Test_Random_1.java | .java | 1,294 | 56 | package smlms.plugins;
import smlms.tools.PsRandom;
public class Test_Random_1 {
public static void main(String args[]) {
double mx = 125;
double my = 125;
double mz = 11;
double chrono = System.nanoTime();
for(int i=0; i<1000; i++) {
PsRandom rand = new PsRandom();
double p = rand.nextDouble()*mx*my*mz;
double x = p / (my*mz);
double y = p / (mx*mz);
double z = p / (mx*my);
System.out.println("x= " + x + " y=" + y + " z=" + z);
}
System.out.println(" " + (System.nanoTime() - chrono));
double a = 0.9551002;
int e = (int)Math.log10(a);
double m = a / Math.pow(10, e);
int mi = ((int)Math.round(m*100));
double ai = mi * Math.pow(10, e-2);
System.out.println("a= " + a + " e=" + e + " m=" + m + " " + mi + " " + ai);
int N = 1000;
double x[] = new double[N];
for(int i=0; i<N; i++)
x[i] = Math.random();
double mean = 0.0;
for(int i=0; i<N; i++)
mean += x[i];
mean = mean / N;
double sd = 0.0;
for(int i=0; i<N; i++)
sd += (x[i]-mean)*(x[i]-mean);
sd = Math.sqrt(sd / N);
double sd2 = 0.0;
double xx = 0.0;
double xs = 0.0;
for(int i=0; i<N; i++) {
xs += x[i];
xx += x[i]*x[i];
}
sd2= Math.sqrt(xx*N - xs*xs)/N;
System.out.println(" mean " + mean + " " + sd + " =?= " + sd2);
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/plugins/Merge_Sequence_Frames.java | .java | 1,977 | 57 | package smlms.plugins;
import ij.IJ;
import ij.ImagePlus;
import ij.io.FileSaver;
import ij.io.Opener;
import imageware.Builder;
import imageware.ImageWare;
import java.io.File;
import smlms.tools.Zip;
public class Merge_Sequence_Frames {
public static String dataset = "MT2.N2";
public static void main(String[] arg) {
Merge_Sequence_Frames merge = new Merge_Sequence_Frames();
String path1 = "/Users/sage/Desktop/activation-may/" + dataset +"/BP-000/";
String path2 = "/Users/sage/Desktop/activation-may/" + dataset +"/BP-500/";
String pathout = "/Users/sage/Desktop/activation-may/" + dataset +"/BP-Exp/sequence-MT2.N1-BP-Exp/";
merge.run(path1, path2, pathout);
}
public void run(String path1, String path2, String pathout) {
String list1[] = new File(path1 + "/sequence").list();
String list2[] = new File(path2 + "/sequence").list();
new File(pathout).mkdir();
new File(pathout + "/sequence").mkdir();
Opener opener = new Opener();
IJ.log("Path1 " + path1);
IJ.log("Path2 " + path2);
IJ.log("Number of files " + list1.length + " in " + path1);
IJ.log("Number of files " + list2.length + " in " + path2);
for(int i=0; i<Math.min(list1.length, list2.length); i++) {
IJ.log(" " + list1[i]);
ImagePlus imp1 = opener.openImage(path1 + "/sequence/" + list1[i]);
ImagePlus imp2 = opener.openImage(path2 + "/sequence/" + list2[i]);
ImageWare image1 = Builder.wrap(imp1);
ImageWare image2 = Builder.wrap(imp2);
int nx1 = image1.getSizeX();
int nx2 = image1.getSizeX();
int ny1 = image2.getSizeX();
ImageWare image = Builder.create(nx1+nx2, ny1, 1, image1.getType());
image.putXY(0, 0, 0, image1);
image.putXY(nx1, 0, 0, image2);
ImagePlus imp = new ImagePlus("", image.buildImageStack());
(new FileSaver(imp)).saveAsTiff(pathout + "/sequence/" + File.separator + list1[i]);
}
Zip.zipFolder(pathout + "/sequence/", pathout + "sequence-" + dataset + "-BP-Exp-as-list.zip");
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/plugins/Normalize_PSF_Batch.java | .java | 2,611 | 106 | package smlms.plugins;
import ij.IJ;
import ij.ImagePlus;
import ij.io.FileSaver;
import ij.io.Opener;
import imageware.Builder;
import imageware.ImageWare;
import java.io.File;
public class Normalize_PSF_Batch {
public static String path = "/Users/sage/Desktop/activation/psf/";
public static void main(String args[]) {
new Normalize_PSF_Batch();
}
public Normalize_PSF_Batch() {
String list[] = new File(path).list();
for(String psf : list) {
if (psf.endsWith(".tif")) {
ImagePlus imp = (new Opener()).openImage(path + File.separator + psf);
ImagePlus out = normalize(imp, psf);
FileSaver saver = new FileSaver(out);
saver.saveAsTiffStack(path + File.separator + psf + "-max.tif");
}
}
}
private ImagePlus normalize(ImagePlus imp, String psfname) {
ImageWare psf = Builder.wrap(imp);
double min = psf.getMinimum();
psf.subtract(min);
int nx = psf.getWidth();
int ny = psf.getHeight();
int nz = psf.getSizeZ();
double att[][] = new double[nx][ny];
double xc = nx * 0.5;
double yc = ny * 0.5;
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++) {
double d = 1 - Math.sqrt((x-xc)*(x-xc) + (y-yc)*(y-yc)) / (nx);
att[x][y] = 1.0 / (1.0 + Math.exp(-(d-0.5)/0.02));
}
Builder.create(att).show("Windowing map " + psfname);
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++)
for(int z=0; z<nz; z++)
psf.putPixel(x, y, z, psf.getPixel(x, y, z) * att[x][y]);
double max = -Double.MAX_VALUE;
int xmax = 0;
int ymax = 0;
int zmax = 0;
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++)
for(int z=0; z<nz; z++)
if (psf.getPixel(x, y, z) > max) {
max = psf.getPixel(x, y, z);
xmax = x;
ymax = y;
zmax = z;
}
IJ.log(" Max " + psfname + ": "+ max + " at (" + xmax + " " + ymax + " " + zmax + ") == middle (" + (nx/2) + " " + (ny/2) + " " + (nz/2) +")");
double f = 0.5;
double hmax = (psfname.startsWith("BP") ? max * f * 0.5 : max * f);
int swhm = 0;
double sum = 0.0;
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++)
if (psf.getPixel(x, y, zmax) > hmax) {
sum += psf.getPixel(x, y, zmax);
swhm++;
}
IJ.log(" Surface HMax:" + swhm + " fwhm "+ Math.sqrt((swhm/Math.PI)*2) + " norm: " + sum);
double total = 1.0 / sum;
psf.multiply(total);
psf.show("" + psf.getTotal());
ImagePlus out = new ImagePlus("", psf.buildImageStack());
/*
for(int z=0; z<nz; z++) {
double s = 0.0;
for(int x=0; x<nx; x++)
for(int y=0; y<ny; y++)
s += psf.getPixel(x, y, z);
IJ.log(" slice z=" + z + " " + s);
}
*/
return out;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/sample/Tube.java | .java | 5,042 | 188 | package smlms.sample;
import ij.IJ;
import java.util.ArrayList;
import smlms.tools.NormalizedVariable;
import smlms.tools.Point3D;
public class Tube extends Item {
//private CurveSpline axis;
public CurveSegment axis;
public ArrayList<Point3D> nodes;
public NormalizedVariable radius;
public NormalizedVariable thickness;
public ArrayList<Point3D> list;
public double innerMin;
public double outerMax;
public double density;
public double inner[];
public double outer[];
public Tube(String name, ArrayList<Point3D> nodes, NormalizedVariable radius, NormalizedVariable thickness, double density) {
super(name);
this.radius = radius;
this.thickness = thickness;
this.density = density;
this.nodes = nodes;
axis = new CurveSegment(nodes, 0.1, 0);
//axis = new CurveSpline(nodes, 4);
}
@Override
public String getInfo() {
String s = "\nTUBE " + getName() + "\n";
s += "radius:" + radius.write() + "\n";
s += "thickness:" + thickness.write() + "\n";
s += "density:" + density + "\n";
for(Point3D node : nodes)
s += "node:" + node.x + "," + node.y + "," + node.z + "\n";
return s;
}
public ArrayList<Point3D> getSamplesInterval(double step, double sigma) {
return axis.getSamplesInterval(step, sigma);
}
public Point3D[] getNodes() {
return axis.getNodes();
}
public void initStep(double step, double sigma) {
list = axis.getSamplesInterval(step, sigma);
double n = list.size();
inner = new double[list.size()];
outer = new double[list.size()];
innerMin = 1000000.0;
outerMax = -1000000.0;
for(int i=0; i<list.size(); i++) {
inner[i] = radius.get(i/n) - thickness.get(i/n);
outer[i] = radius.get(i/n);
innerMin = Math.min(inner[i], innerMin);
outerMax = Math.max(outer[i], outerMax);
}
}
public void init(int nsamples) {
list = axis.getSamplesNumber(nsamples);
double n = list.size();
inner = new double[list.size()];
outer = new double[list.size()];
innerMin = 1000000.0;
outerMax = -1000000.0;
for(int i=0; i<list.size(); i++) {
inner[i] = radius.get(i/n) - thickness.get(i/n);
outer[i] = radius.get(i/n);
innerMin = Math.min(inner[i], innerMin);
outerMax = Math.max(outer[i], outerMax);
}
IJ.log("Init nb samples: " + list.size() + " innerMin " + innerMin+ " outerMax " + outerMax);
}
public double[] closestDistance(Point3D p) {
double distance = Double.MAX_VALUE;
double n = list.size();
int index = 0;
for(int i=0; i<n; i++) {
Point3D a = list.get(i);
double d = (p.x-a.x)*(p.x-a.x) + (p.y-a.y)*(p.y-a.y) + (p.z-a.z)*(p.z-a.z);
if (d < distance) {
distance = d;
index = i;
}
}
return new double[] {Math.sqrt(distance), index};
}
@Override
public boolean contains(Point3D p) {
double[] closest = closestDistance(p);
double min = closest[0];
int imin = (int)closest[1];
if (imin == 0)
return false;
if (imin == list.size()-1)
return false;
if (min < inner[imin])
return false;
if (min > outer[imin])
return false;
return true;
}
public int inside(Point3D p, int k1, int k2) {
double min = Double.MAX_VALUE;
int i1 = Math.max(1, k1);
int i2 = Math.min(list.size()-2, k2);
int index = 0;
for(int i=i1; i<=i2; i++) {
Point3D a = list.get(i);
double d = (p.x-a.x)*(p.x-a.x) + (p.y-a.y)*(p.y-a.y) + (p.z-a.z)*(p.z-a.z);
if (d < min) {
min = d;
index = i;
}
}
min = Math.sqrt(min);
if (min < inner[index])
return -1;
if (min > outer[index])
return -1;
return index;
}
public int inside(Point3D p, double resizeFactor) {
double[] closest = closestDistance(p);
int index = (int)closest[1];
if (index <= 0)
return -1;
if (index >= list.size()-1)
return -1;
if (closest[0] < inner[index]/resizeFactor)
return -1;
if (closest[0] > outer[index]*resizeFactor)
return -1;
return index;
}
public static Tube load(ArrayList<String> lines) {
String name = "noname";
ArrayList<Point3D> nodes = new ArrayList<Point3D>();
NormalizedVariable radius = null;
NormalizedVariable thickness = null;
double density = 1;
for(String line : lines) {
String[] tokens = line.split("[:]");
for (int i=0; i<tokens.length; i++) {
if (tokens[i].startsWith("name"))
name = tokens[i+1];
if (tokens[i].startsWith("radius"))
radius = NormalizedVariable.read(tokens[i+1]);
if (tokens[i].startsWith("thickness"))
thickness = NormalizedVariable.read(tokens[i+1]);
if (tokens[i].startsWith("density"))
density = Double.parseDouble(tokens[i+1]);
if (tokens[i].startsWith("node"))
nodes.add(Point3D.read(tokens[i+1]));
}
}
return new Tube(name, nodes, radius, thickness, density);
}
public String save() {
String s = "<TUBE>";
s += "name:" + getName() + "\n";
s += "radius:" + radius.write() + "\n";
s += "thickness:" + thickness.write() + "\n";
s += "density:" + density + "\n";
for(Point3D node : nodes)
s += "node:" + node.x + "," + node.y + "," + node.z + "\n";
return s + "</TUBE>\n";
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/sample/Tolerance.java | .java | 79 | 6 | package smlms.sample;
public enum Tolerance {
TENTH, HUNDREDTH, THOUSANDTH
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/sample/Item.java | .java | 370 | 23 | package smlms.sample;
import smlms.tools.Point3D;
public abstract class Item {
private String name = "";
public Item(String name) {
this.name = name;
}
public String getName() {
return name;
}
public abstract String getInfo();
public abstract String save();
public abstract boolean contains(Point3D p);
public abstract void init(int tolerance);
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/sample/Sample.java | .java | 2,987 | 121 | package smlms.sample;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileOutputStream;
import java.io.FileReader;
import java.io.PrintStream;
import java.util.ArrayList;
import smlms.tools.Point3D;
import smlms.tools.Progress;
public class Sample extends ArrayList<Item> {
public String name;
public int nx;
public int ny;
public int nz;
public ArrayList<Point3D> voxels = new ArrayList<Point3D>();
public ArrayList<Point3D> fluos = new ArrayList<Point3D>();
public Sample(String name, int nx, int ny, int nz) {
this.name = name;
this.nx = nx;
this.ny = ny;
this.nz = nz;
}
public String getInfo() {
return name + " [" + nx + ", " + ny + ", " + nz + "]";
}
public static Sample load(String filename) {
Progress progress = new Progress();
progress.print("Load Sample " + filename);
File file = new File(filename);
if (!file.exists())
return null;
try {
BufferedReader input = new BufferedReader(new FileReader(filename));
String line = input.readLine();
String[] tokens = line.split("[,:]");
if (tokens[0].equals("SAMPLE")) {
String name = "untitled";
int nx = 1;
int ny = 1;
int nz = 1;
for (int i=0; i<tokens.length; i++) {
if (tokens[i].equals("name"))
name = tokens[i+1];
if (tokens[i].equals("size")) {
nx = Integer.parseInt(tokens[i+1]);
ny = Integer.parseInt(tokens[i+2]);
nz = Integer.parseInt(tokens[i+3]);
}
}
Sample sample = new Sample(name, nx, ny, nz);
ArrayList<String> lines = new ArrayList<String>();
while ((line = input.readLine()) != null) {
if (lines.size() % 100 == 0)
progress.print(" line " + line);
if (line.startsWith("<TUBE>"))
lines.clear();
if (line.startsWith("</TUBE>"))
sample.add(Tube.load(lines));
lines.add(line);
}
progress.print("Load " + sample.size() + " items.");
input.close();
return sample;
}
else {
progress.print("Format error in reading " + filename);
}
input.close();
}
catch (Exception ex) {
progress.print(ex.toString());
}
return null;
}
public Tube[] getTubes() {
int ntubes = 0;
for (Item item : this)
if (item instanceof Tube)
ntubes++;
Tube tubes[] = new Tube[ntubes];
ntubes = 0;
for (Item item : this)
tubes[ntubes++] = (Tube) item;
return tubes;
}
public void save(String filename) {
Progress progress = new Progress();
File file = new File(filename);
File parent = new File(file.getParent());
if (!parent.exists()) {
parent.mkdir();
}
try {
PrintStream out = new PrintStream(new FileOutputStream(filename));
progress.print("Save Sample " + filename);
out.print("SAMPLE,name:" + name + ",size:" + nx + "," + ny + "," + nz + "\n");
for (Item item : this) {
String s = item.save();
out.print(s);
//progress.print(s);
}
out.close();
}
catch (Exception ex) {
progress.print(filename + " " + ex.toString());
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/sample/CurveSpline.java | .java | 12,659 | 435 | //=========================================================================================
//
// Project: Localization Microscopy
//
// Author : Daniel Sage, Biomedical Imaging Group (BIG), http://bigwww.epfl.ch/sage/
//
// Organization: Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
//
// Conditions of use: You'll be free to use this software for research purposes, but you
// should not redistribute it without our consent. In addition, we expect you to include a
// citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
package smlms.sample;
import ij.gui.Line;
import ij.gui.OvalRoi;
import ij.gui.Overlay;
import java.awt.Color;
import java.util.ArrayList;
import smlms.tools.Point3D;
import smlms.tools.Vector3D;
public class CurveSpline {
private static double C0 = 6.0;
private static double A = Math.sqrt(3.0) - 2.0;
private static double accuracy = 0.001;
private Point3D nodes[];
private ArrayList<Point3D> samples = new ArrayList<Point3D>();
public CurveSpline(Point3D nodes[], double sampling) {
this.nodes = nodes;
this.samples = getSamplesInterval(sampling);
}
public CurveSpline(ArrayList<Point3D> vnodes, double sampling) {
int n = vnodes.size();
this.nodes = new Point3D[n];
for (int i = 0; i < n; i++)
this.nodes[i] = vnodes.get(i);
this.samples = getSamplesInterval(sampling);
}
public Point3D[] getNodes() {
return nodes;
}
public Point3D getFirstNode() {
return nodes[0];
}
public ArrayList<Point3D> getInitialSamples() {
return samples;
}
public ArrayList<Point3D> getSamplesNumber(int nsamples) {
double len = getLength();
double step = len / (nsamples - 1);
return getSamplesInterval(step);
}
public ArrayList<Point3D> getSamplesInterval(double step) {
Point3D coef[] = doSymmetricalExponentialFilter(extend(nodes));
ArrayList<Point3D> points = new ArrayList<Point3D>();
Point3D current = nodes[0];
points.add(current);
double len = 0;
for (int k = 1; k < coef.length - 2; k++) {
for (double s = 0.0; s <= 1.0; s += accuracy) {
Point3D p = getInterpolatedCubicValue(coef, k + s);
len += p.distance(current);
if (len >= step) {
points.add(p);
len = 0;
}
current = p;
}
}
points.add(nodes[nodes.length - 1]);
return points;
}
public ArrayList<Vector3D> getNormalsInterval(double step, double unit) {
Point3D coef[] = doSymmetricalExponentialFilter(extend(nodes));
ArrayList<Vector3D> normals = new ArrayList<Vector3D>();
Point3D current = nodes[0];
double len = 0;
for (int k = 1; k < coef.length - 2; k++) {
for (double s = 0.0; s <= 1.0; s += accuracy) {
Point3D p = getInterpolatedCubicValue(coef, k + s);
len += p.distance(current);
if (len >= step) {
Point3D p1 = getInterpolatedQuadraticValue(coef, k + s - 0.5);
Point3D p2 = getInterpolatedQuadraticValue(coef, k + s + 0.5);
double dx = p1.y - p2.y;
double dy = p1.x - p2.x;
double dz = p1.z - p2.z;
double d = unit / Math.sqrt(dx * dx + dy * dy + dz * dz);
if (d > 0)
normals.add(new Vector3D(p, new Point3D(dx * d, dy * d, dz * d)));
else
normals.add(new Vector3D(p, p));
len = 0;
}
current = p;
}
}
return normals;
}
public Point3D[] prefilter() {
return doSymmetricalExponentialFilter(extend(nodes));
}
public Point3D getClosestPoint(Point3D[] coef, Point3D a, double tolerance) {
Point3D best = a;
double min = Double.MAX_VALUE;
for (int k = 1; k < coef.length-1; k++) {
for (double s=0; s<1; s+=tolerance) {
Point3D p = getInterpolatedCubicValue(coef, k+s);
double d = p.distance(a);
if (d < min) {
min = d;
best = p;
}
}
}
return best;
}
public ArrayList<Point3D> getSample(double tolerance) {
Point3D[] coef = doSymmetricalExponentialFilter(extend(nodes));
ArrayList<Point3D> samples = new ArrayList<Point3D>();
for (int k = 1; k < coef.length-1; k++)
for (double s=0; s<1; s+=tolerance)
samples.add(getInterpolatedCubicValue(coef, k+s));
return samples;
}
/*
public Point3D getClosestPoint(Point3D[] coef, Point3D a, Tolerance tolerance) {
Point3D best = a;
double pos = 0;
double min = Double.MAX_VALUE;
for (int k = 1; k < coef.length-1; k++) {
for (double s=0; s<1; s+=0.05) {
Point3D p = getInterpolatedCubicValue(coef, k+s);
double d = p.distance(a);
if (d < min) {
min = d;
best = p;
pos = k+s;
}
}
}
if (tolerance == Tolerance.HUNDREDTH || tolerance == Tolerance.THOUSANDTH) {
for (double s=pos-0.05; s<=pos+0.05; s+=0.005) {
Point3D p = getInterpolatedCubicValue(coef, s);
double d = p.distance(a);
if (d < min) {
min = d;
best = p;
pos = s;
}
}
}
if (tolerance == Tolerance.THOUSANDTH) {
for (double s=pos-0.005; s<=pos+0.005; s+=0.0005) {
Point3D p = getInterpolatedCubicValue(coef, s);
double d = p.distance(a);
if (d < min) {
min = d;
best = p;
pos = s;
}
}
}
if (pos <= 1)
return new Point3D(Double.MAX_VALUE, Double.MAX_VALUE, 0);
if (pos >= coef.length-2)
return new Point3D(Double.MAX_VALUE, Double.MAX_VALUE, 0);
//IJ.log("S:" + ((System.nanoTime()-chrono)/1000) + " " + pos + " " + best + " " + min);
return best;
}
*/
public double getLength() {
Point3D coef[] = doSymmetricalExponentialFilter(extend(nodes));
Point3D current = nodes[0];
double len = 0;
for (int k = 1; k < coef.length - 2; k++) {
for (double s = 0; s <= 1; s += accuracy) {
Point3D p = getInterpolatedCubicValue(coef, k + s);
len += p.distance(current);
current = p;
}
}
return len;
}
private Point3D[] extend(Point3D nodes[]) {
int nnodes = nodes.length;
Point3D extendedNodes[] = new Point3D[nnodes + 2];
extendedNodes[0] = nodes[0];
for (int i = 0; i < nnodes; i++)
extendedNodes[i + 1] = nodes[i];
extendedNodes[nnodes + 1] = nodes[nnodes - 1];
return extendedNodes;
}
public void overlayNodes(Overlay overlay) {
int nnodes = nodes.length;
for (int k = 0; k < nnodes; k++) {
int x = (int) Math.round(nodes[k].x);
int y = (int) Math.round(nodes[k].y);
OvalRoi roi = new OvalRoi(x - 5, y - 5, 10, 10);
roi.setStrokeColor(Color.RED);
overlay.add(roi);
}
}
public void overlayInitialSamples(Overlay overlay, Color color, int radiusSample, Color colorPoint) {
overlaySamples(samples, overlay, color, radiusSample, colorPoint);
}
public void overlaySamples(ArrayList<Point3D> points, Overlay overlay, Color color, int radiusSample, Color colorPoint) {
for (int s = 1; s < points.size(); s++) {
int x1 = (int) Math.round(points.get(s - 1).x);
int y1 = (int) Math.round(points.get(s - 1).y);
int x2 = (int) Math.round(points.get(s).x);
int y2 = (int) Math.round(points.get(s).y);
Line line = new Line(x1, y1, x2, y2);
line.setStrokeColor(color);
overlay.add(line);
if (radiusSample > 0) {
OvalRoi roi = new OvalRoi(x2 - radiusSample, y2 - radiusSample, 2 * radiusSample, 2 * radiusSample);
roi.setStrokeColor(colorPoint);
overlay.add(roi);
}
}
if (radiusSample > 0) {
int x2 = (int) Math.round(points.get(0).x);
int y2 = (int) Math.round(points.get(0).y);
OvalRoi roi = new OvalRoi(x2 - radiusSample, y2 - radiusSample, 2 * radiusSample, 2 * radiusSample);
roi.setStrokeColor(colorPoint);
overlay.add(roi);
}
}
/*
* public void overlayDepthColorized(Vector<CurvePoint> points, Viewport
* viewport, Overlay overlay, int stroke) { double z1 =
* viewport.getCornerMinNano().z; double z2 = viewport.getCornerMaxNano().z;
* double thickness = z2 - z1; for (int s = 1; s<points.size(); s++) {
* CurvePoint pnm = points.get(s); //Point p1 =
* points.get(s-1).scale(resolution); //Point p2 = pnm.scale(resolution);
* CurvePoint p1 = viewport.screenPoint(points.get(s)); CurvePoint p2 =
* viewport.screenPoint(points.get(s-1)); //p1.translate(zero);
* //p2.translate(zero); Line line = new Line(p1.x, p1.y, p2.x, p2.y);
* line.setStrokeWidth(stroke); if (pnm.z < z1)
* line.setStrokeColor(Color.gray); else if (pnm.z > z2)
* line.setStrokeColor(Color.white); else { float h =
* (float)(pnm.z/thickness) + 0.5f; line.setStrokeColor(Color.getHSBColor(h,
* 1f, 1f)); } overlay.add(line); //OvalRoi roi = new OvalRoi(x2-2, y2-2, 4,
* 4); //roi.setStrokeColor(Color.GREEN); //overlay.add(roi); } }
*/
private Point3D getInterpolatedQuadraticValue(Point3D coef[], double s) {
double[] cx = new double[coef.length];
double[] cy = new double[coef.length];
double[] cz = new double[coef.length];
for (int i = 0; i < coef.length; i++) {
cx[i] = coef[i].x;
cy[i] = coef[i].y;
cz[i] = coef[i].z;
}
double px = getInterpolatedQuadraticValue(cx, s);
double py = getInterpolatedQuadraticValue(cy, s);
double pz = getInterpolatedQuadraticValue(cz, s);
return new Point3D(px, py, pz);
}
private Point3D getInterpolatedCubicValue(Point3D coef[], double s) {
double[] cx = new double[coef.length];
double[] cy = new double[coef.length];
double[] cz = new double[coef.length];
for (int i = 0; i < coef.length; i++) {
cx[i] = coef[i].x;
cy[i] = coef[i].y;
cz[i] = coef[i].z;
}
double px = getInterpolatedCubicValue(cx, s);
double py = getInterpolatedCubicValue(cy, s);
double pz = getInterpolatedCubicValue(cz, s);
return new Point3D(px, py, pz);
}
public double getInterpolatedQuadraticValue(double[] coef, double x) {
int n = coef.length;
int i = (int) Math.round(x);
double t = x - i;
double v0 = ((t - 0.5) * (t - 0.5)) / 2.0;
double v2 = ((t + 0.5) * (t + 0.5)) / 2.0;
double v1 = 1.0 - v0 - v2;
int i0 = i - 1;
if (i0 < 0)
i0 = n - 2 - i0;
int i1 = i;
if (i1 >= n)
i1 = i1 - n;
int i2 = i + 1;
if (i2 >= n)
i2 = i2 - n;
int i3 = i + 2;
if (i3 >= n)
i3 = i3 - n;
return v0 * coef[i0] + v1 * coef[i1] + v2 * coef[i2];
}
/**
* Return an interpolated value (cubic) from B-spline coefficients at the
* position x. x should be in the range [0..n] n is the number of
* coefficients. Periodic conditions are applied.
*/
private double getInterpolatedCubicValue(double[] coef, double x) {
int n = coef.length;
int i = (int) Math.floor(x);
double t = x - i;
double t1 = 1.0 - t;
double v0 = t1 * t1 * t1;
double v1 = 4.0 + 3.0 * t * t * (t - 2.0);
double v3 = t * t * t;
int i0 = i - 1;
if (i0 < 0)
i0 = n - 2 - i0;
int i1 = i;
if (i1 >= n)
i1 = i1 - n;
int i2 = i + 1;
if (i2 >= n)
i2 = i2 - n;
int i3 = i + 2;
if (i3 >= n)
i3 = i3 - n;
return (v0 * coef[i0] + v1 * coef[i1] + (6.0 - v0 - v1 - v3) * coef[i2] + v3 * coef[i3]) / 6.0;
}
private Point3D[] doSymmetricalExponentialFilter(Point3D[] s) {
double[] sx = new double[s.length];
double[] sy = new double[s.length];
double[] sz = new double[s.length];
for (int i = 0; i < s.length; i++) {
sx[i] = s[i].x;
sy[i] = s[i].y;
sz[i] = s[i].z;
}
doSymmetricalExponentialFilter(sx);
doSymmetricalExponentialFilter(sy);
doSymmetricalExponentialFilter(sz);
Point3D[] coef = new Point3D[s.length];
for (int i = 0; i < s.length; i++) {
coef[i] = new Point3D(sx[i], sy[i], sz[i]);
}
return coef;
}
/**
* Performs the 1D symmetrical exponential filtering.
*/
private void doSymmetricalExponentialFilter(double s[]) {
int n = s.length;
double cn[] = new double[n];
double cp[] = new double[n];
cp[0] = computeInitialValueCausalMirror(s);
for (int k = 1; k < n; k++)
cp[k] = s[k] + A * cp[k - 1];
cn[n - 1] = computeInitialValueAntiCausalMirror(cp);
for (int k = n - 2; k >= 0; k--)
cn[k] = A * (cn[k + 1] - cp[k]);
for (int k = 0; k < n; k++)
s[k] = C0 * cn[k];
}
/**
* Returns the initial value for the causal filter using the mirror boundary
* conditions.
*/
private double computeInitialValueCausalMirror(double signal[]) {
double epsilon = 1e-6; // desired level of precision
int k0 = (int) Math.ceil(Math.log(epsilon) / Math.log(Math.abs(A)));
k0 = Math.min(k0, signal.length);
double polek = A;
double v = signal[0];
for (int k = 1; k < k0; k++) {
v = v + polek * signal[k];
polek = polek * A;
}
return v;
}
/**
* Returns the initial value for the anti-causal filter using the mirror
* boundary conditions.
*/
private double computeInitialValueAntiCausalMirror(double signal[]) {
int n = signal.length;
double v = (A / (A * A - 1.0)) * (signal[n - 1] + A * signal[n - 2]);
return v;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/sample/Microtubules_Generator_2016.java | .java | 10,889 | 312 | package smlms.sample;
import ij.IJ;
import ij.ImagePlus;
import ij.gui.ImageCanvas;
import ij.gui.ImageWindow;
import ij.gui.StackWindow;
import imageware.Builder;
import imageware.ImageWare;
import java.awt.Color;
import java.awt.Graphics;
import java.util.ArrayList;
import smlms.tools.NormalizedVariable;
import smlms.tools.Point3D;
import smlms.tools.PsRandom;
public class Microtubules_Generator_2016 {
private PsRandom rand = new PsRandom(123);
/******
* MT0-1 private int margin = 100; private int nx = 320 + margin + margin;
* // 320 * 40 = 12800nm private int ny = 320 + margin + margin; // 320 * 40
* = 12800nm private int nz = 25; // 25 * 40 = 1000nm private double
* pixelsize = 40; private double lateralRandomExtremity = 10; private
* double jitterExtremityAngleRadian = 0.02; private int nbExtremity = 12;
* private int rangeExtremityOpposition = 2; private double lambdaBackground
* = 1; private double lambdaDirection = 1; private double lambdaRandom =
* 0.1; private double elevationRange = Math.toRadians(5); private double
* azimuthRange = Math.toRadians(10); private double elementLength = 6;
* private int nbTubes = 100; private double radiusTube = 12; private double
* thickTube = 6;
***/
private int margin = 100;
private int nx = 320 + margin + margin;
private int ny = 320 + margin + margin;
private int nz = 75;
private double pixelsize = 20;
private double lateralRandomExtremity = 10;
private double jitterExtremityAngleRadian = 0.02;
private int nbExtremity = 12;
private int rangeExtremityOpposition = 2;
private double lambdaBackground = 1;
private double lambdaDirection = 1;
private double lambdaRandom = 0.4;
private double elevationRange = Math.toRadians(4);
private double azimuthRange = Math.toRadians(10);
private double elementLength = 6;
private int nbTubes = 100;
private double radiusTube = 12;
private double thickTube = 6;
private double density = 1.0;
private ImageWare collision;
private ImageWare background;
private String outfile = "/Users/sage/Desktop/SampleMT2.txt";
public static void main(String args[]) {
new Microtubules_Generator_2016();
}
public Microtubules_Generator_2016() {
collision = Builder.create(nx, ny, nz, ImageWare.BYTE);
background = Builder.create(nx, ny, nz, ImageWare.FLOAT);
for (int i = 0; i < nx; i++)
for (int j = 0; j < ny; j++)
for (int k = 0; k < nz; k++) {
background.putPixel(i, j, k, rand.nextDouble(-20, 20));
}
for (int i = 0; i < 15; i++) {
int sign = i % 2 == 0 ? -1 : 1;
int x = rand.nextInteger(10, nx - 10);
int y = rand.nextInteger(10, nx - 10);
int z = rand.nextInteger(1, nz - 1);
for (int j = 0; j < 1000000; j++) {
x += rand.nextInteger(-2, 2);
y += rand.nextInteger(-1, 1);
z += rand.nextInteger(-1, 1) * (rand.nextDouble(1) > 0.9 ? 1 : 0);
background.putPixel(x, y, z, sign * 20);
}
}
background.smoothGaussian(6, 6, 1);
ImagePlus imp = new ImagePlus("background", background.buildImageStack());
imp.show();
Point3D extremities[] = createExtremity();
ArrayList<ArrayList<Point3D>> lists = new ArrayList<ArrayList<Point3D>>();
for (int i = 0; i < nbTubes; i++) {
ArrayList<Point3D> list = build(i, createTubeExtremity(extremities));
if (list != null) {
boolean flag = true;
for (Point3D p : list)
if (p.z <= 0) {
IJ.log("" + i + " > " + p.z);
flag = false;
break;
}
for (Point3D p : list)
if (p.z <= 0) {
IJ.log("" + i + " > " + p.z);
flag = false;
break;
}
if (list.size() < nx / 10) {
IJ.log("" + i + " > n = " + list.size());
flag = false;
}
if (flag) {
draw(list);
lists.add(list);
}
}
}
IJ.log("Number of tubes: " + lists.size());
new MTCanvas(imp, lists, nz, extremities);
Sample sample = new Sample("name", (int) ((nx - 2 * margin) * pixelsize), (int) ((ny - 2 * margin) * pixelsize), (int) ((nz) * pixelsize));
for (int i = 0; i < lists.size(); i++) {
NormalizedVariable radius = new NormalizedVariable(radiusTube);
NormalizedVariable thickness = new NormalizedVariable(thickTube);
ArrayList<Point3D> nodes = new ArrayList<Point3D>();
ArrayList<Point3D> list = lists.get(i);
for (int k = 0; k < list.size(); k++) {
Point3D p = list.get(k);
nodes.add(new Point3D((p.x - margin) * pixelsize, (p.y - margin) * pixelsize, p.z * pixelsize));
}
Tube tube = new Tube("tube " + i, nodes, radius, thickness, 1);
sample.add(tube);
}
collision.show();
sample.save(outfile);
}
private ArrayList<Point3D> build(int tube, Point3D extremity[]) {
int n = nx;
double dx = (extremity[1].x - extremity[0].x) / n;
double dy = (extremity[1].y - extremity[0].y) / n;
double dz = (extremity[1].z - extremity[0].z) / n;
double d = Math.sqrt(dx * dx + dy * dy + dz * dz);
dx = dx / d;
dy = dy / d;
dz = dz / d;
double x = extremity[0].x;
double y = extremity[0].y;
double z = extremity[0].z;
double azimuth = Math.atan2(dy, dx);
double elevation = Math.acos(dz / d);
ArrayList<Point3D> list = new ArrayList<Point3D>();
for (int p = 0; p < n; p++) {
double max = -Double.MAX_VALUE;
double amax = 0;
double imax = 0;
double er = (rand.nextDouble(1) > 0.1 ? elevationRange : elevationRange * 6);
for (double i = elevation - er; i <= elevation + er; i += er * 0.5) {
double sini = Math.sin(i);
double oz = Math.cos(i);
for (double a = azimuth - azimuthRange; a <= azimuth + azimuthRange; a += azimuthRange * 0.5) {
double ox = Math.cos(a) * sini;
double oy = Math.sin(a) * sini;
double xn = x + elementLength * ox;
double yn = y + elementLength * oy;
double zn = z + elementLength * oz;
double v = 0.0;
v += lambdaBackground * background.getInterpolatedPixel(xn, yn, zn);
v += lambdaDirection * (dx * ox + dy * oy + dz * oz);
v += lambdaRandom * rand.nextGaussian(0, 2);
if (v > max) {
max = v;
amax = a;
imax = i;
}
}
}
azimuth = amax;
x = x + elementLength * Math.cos(amax) * Math.sin(imax);
y = y + elementLength * Math.sin(amax) * Math.sin(imax);
z = z + elementLength * Math.cos(imax);
if (collision.getInterpolatedPixel(x, y, z) > 0.5) {
return null;
}
list.add(new Point3D(x, y, z));
}
double x1 = margin * 0.5;
double y1 = margin * 0.5;
double x2 = nx - margin * 0.5;
double y2 = ny - margin * 0.5;
ArrayList<Point3D> inside = new ArrayList<Point3D>();
for (int i = 0; i < list.size(); i++) {
Point3D p = list.get(i);
if (p.x > x1 && p.x < x2)
if (p.y > y1 && p.y < y2) {
inside.add(p);
}
}
return inside;
}
private void draw(ArrayList<Point3D> list) {
for (int p = 1; p < list.size(); p++) {
int xp = (int) list.get(p).x;
int yp = (int) list.get(p).y;
int zp = (int) list.get(p).z;
int t = 3;
for (int k = 0; k < elementLength; k++) {
double dx = list.get(p).x - list.get(p - 1).x;
double dy = list.get(p).y - list.get(p - 1).y;
double dz = list.get(p).z - list.get(p - 1).z;
int xo = (int) Math.round(list.get(p - 1).x + k * dx / elementLength);
int yo = (int) Math.round(list.get(p - 1).y + k * dy / elementLength);
int zo = (int) Math.round(list.get(p - 1).z + k * dz / elementLength);
int nc = 5;
for(int x1=-nc; x1<=nc; x1++)
for(int y1=-nc; y1<=nc; y1++)
for(int z1=-nc; z1<=nc; z1++) {
collision.putPixel(xo+x1, yo+y1, zo+z1, 1);
}
}
for (int i = -t; i <= t; i++)
for (int j = -t; j <= t; j++)
for (int k = -t; k <= t; k++)
background.putPixel(xp + i, yp + j, zp + k, background.getPixel(xp + i, yp + j, zp + k) - 12);
}
}
private Point3D[] createTubeExtremity(Point3D[] extremities) {
int next = extremities.length;
int ext1 = (int) Math.abs(Math.round(rand.nextDouble(0, next - 1)));
ext1 = ext1 % next;
int ext2 = ext1 + next / 2 + (int) Math.abs(Math.round(rand.nextDouble(-rangeExtremityOpposition, rangeExtremityOpposition)));
ext2 = ext2 % next;
Point3D e1 = new Point3D(extremities[ext1].x, extremities[ext1].y, extremities[ext1].z);
Point3D e2 = new Point3D(extremities[ext2].x, extremities[ext2].y, extremities[ext2].z);
e1.x += rand.nextDouble(-lateralRandomExtremity, lateralRandomExtremity);
e1.y += rand.nextDouble(-lateralRandomExtremity, lateralRandomExtremity);
e1.z += rand.nextDouble(-lateralRandomExtremity, lateralRandomExtremity);
e2.x += rand.nextDouble(-lateralRandomExtremity, lateralRandomExtremity);
e2.y += rand.nextDouble(-lateralRandomExtremity, lateralRandomExtremity);
e2.z += rand.nextDouble(-lateralRandomExtremity, lateralRandomExtremity);
return new Point3D[] { e1, e2 };
}
private Point3D[] createExtremity() {
int xc = nx / 2;
int yc = ny / 2;
Point3D extremities[] = new Point3D[nbExtremity];
for (int a = 0; a < nbExtremity; a++) {
double r = a * 2 * Math.PI / nbExtremity + rand.nextDouble(-jitterExtremityAngleRadian, jitterExtremityAngleRadian);
double xp = xc + nx * Math.cos(r) * 0.49;
double yp = yc + ny * Math.sin(r) * 0.49;
double zp = nz * 0.5 + nz * rand.nextDouble(-0.4, 0.4);
extremities[a] = new Point3D(xp, yp, zp);
IJ.log("Extremity " + extremities[a].x + " " + extremities[a].y + " " + extremities[a].z);
}
return extremities;
}
public class MTCanvas extends ImageCanvas {
private ArrayList<ArrayList<Point3D>> tubes;
private int nz;
private Point3D extremities[];
public MTCanvas(ImagePlus imp, ArrayList<ArrayList<Point3D>> tubes, int nz, Point3D extremities[]) {
super(imp);
this.tubes = tubes;
this.extremities = extremities;
this.nz = nz;
if (imp.getStackSize() > 1)
imp.setWindow(new StackWindow(imp, this));
else
imp.setWindow(new ImageWindow(imp, this));
}
@Override
public void paint(Graphics g) {
super.paint(g);
for (Point3D extremity : extremities) {
g.setColor(Color.WHITE);
g.fillOval(screenXD(extremity.x) - 3, screenYD(extremity.y) - 3, 7, 7);
}
for (ArrayList<Point3D> tube : tubes) {
Color c = Color.getHSBColor((float) (tube.get(0).z / nz), 1, 1);
g.setColor(c);
g.fillOval(screenXD(tube.get(0).x) - 3, screenYD(tube.get(0).y) - 3, 7, 7);
for (int p = 1; p < tube.size(); p++) {
Point3D prev = tube.get(p - 1);
Point3D curr = tube.get(p);
g.drawLine(screenXD(prev.x), screenYD(prev.y), screenXD(curr.x), screenYD(curr.y));
}
}
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/sample/SampleFactory.java | .java | 16,681 | 532 | package smlms.sample;
import static java.lang.Math.PI;
import static java.lang.Math.abs;
import static java.lang.Math.sqrt;
import ij.IJ;
import imageware.Builder;
import imageware.ImageWare;
import java.util.ArrayList;
import java.util.Random;
import smlms.tools.NormalizedVariable;
import smlms.tools.Point3D;
import smlms.tools.PsRandom;
import additionaluserinterface.WalkBar;
public class SampleFactory {
private PsRandom rand = new PsRandom(1234);
private Sample sample;
private WalkBar walk;
private Belong[][][] belongs;
private Tube[] tubes;
private int mx;
private int my;
private int mz;
private double pixelsize;
private ArrayList<int[]> list = new ArrayList<int[]>();
public SampleFactory(Point3D dim, WalkBar walk, Sample sample, double pixelsize) {
this.walk = walk;
this.sample = sample;
this.pixelsize = pixelsize;
mx = (int) Math.ceil(dim.x / pixelsize);
my = (int) Math.ceil(dim.y / pixelsize);
mz = (int) Math.ceil(dim.z / pixelsize);
IJ.log("\nMask size " + mx + " " + my + " " + mz);
// belongs = new Belong[mx][my][mz];
}
public void createPositionsStep(int nbfluos, double timeout, double step, double sigma) {
int count = 0;
double chrono = System.nanoTime();
tubes = sample.getTubes();
int nt = tubes.length;
for (int i = 0; i<nt; i++) {
//
//NormalizedVariable radiusMT = new NormalizedVariable(12);
//NormalizedVariable thicknessMT = new NormalizedVariable(5);
NormalizedVariable radiusER = new NormalizedVariable(150);
NormalizedVariable thicknessER = new NormalizedVariable(6);
radiusER.addLinear(5-i, -35+2*i);
radiusER.addCosine(15, 2.5+i, 0.1);
thicknessER.addCosine(1, 4.5+i, 0.5);
tubes[i].radius = radiusER;
tubes[i].thickness = thicknessER;
}
double cumul[] = new double[nt];
double total = 0;
for (int i = 0; i<nt; i++) {
Point3D nodes[] = tubes[i].axis.getNodes();
for(int k=0; k<nodes.length; k++)
if (k%100==0)
System.out.println("SampleFactory >>>>>>>>>>>>>>>> node avent init tube = " + i + " " + nodes[k]);
tubes[i].initStep(step, sigma);
for (int j = 0; j<20; j++)
System.out.println("SampleFactory list tube=" + i + " " + j + " " + tubes[i].list.get(j));
double rmin = tubes[i].innerMin;
double rmax = tubes[i].outerMax;
double vol = tubes[i].list.size() * Math.PI * (rmax*rmax - rmin*rmin) * tubes[i].density;
IJ.log("Tube " + i + " size = " + tubes[i].list.size() + " rmin=" + rmin + " rmax=" + rmax + " vol=" + vol );
cumul[i] = (i == 0 ? vol : vol + cumul[i-1]);
IJ.log("Init step: nb_list=" + tubes[i].list.size() + " vol " + IJ.d2s(vol * 1e-9, 4) + " um3 // innerRadius: " + tubes[i].innerMin + " outer " + tubes[i].outerMax);
total += vol;
/*
for(int k=0; k<tubes[i].list.size(); k++)
sample.fluos.add(tubes[i].list.get(k));
for(Point3D node : tubes[i].nodes)
for(int r=0; r<20; r++)
sample.fluos.add(new Point3D(node.x, node.y+r, node.z));
*/
}
IJ.log("CREATION nt = " + nt);
do {
double rvol = rand.nextDouble(total - 1);
int tub = 0;
for(tub=0; tub<nt; tub++)
if (rvol < cumul[tub]) {
break;
}
Tube tube = tubes[tub];
int s = rand.nextInteger(tube.list.size() - 1);
double radius = rand.nextDouble(tube.inner[s], tube.outer[s]);
Point3D pa = tube.list.get(Math.max(0, s - 1));
Point3D po = tube.list.get(s);
Point3D pb = tube.list.get(Math.min( tube.list.size() - 1, s + 1));
Point3D p = getPointNormalCircle(rand, pa, po, pb, radius);
sample.fluos.add(p);
count++;
if (count % 1000 == 0)
walk.progress("Fine: " + count, (100.0 * count) / nbfluos);
if (count % 4000 == 0)
IJ.log("" + count + " tub=" + tub + " rvol=" + rvol + " total_vol=" + total);
}
while( count < nbfluos && (System.nanoTime() - chrono) * 1e-9 < timeout);
}
public int createBelongs(double factorDistanceMask, double timeout, int nbsamples) {
int count = 0;
walk.reset();
tubes = sample.getTubes();
for (Tube tube : tubes) {
tube.init(nbsamples);
IJ.log("INIT nbsamples " + nbsamples);
}
double n = mx * my * mz;
ImageWare belongsImage = Builder.create(mx, my, mz, ImageWare.FLOAT);
ArrayList<double[]> shifts = new ArrayList<double[]>();
for (double i = 0; i < 1; i += .36)
for (double j = 0; j < 1; j += .36)
for (double k = 0; k < 1; k += .36)
shifts.add(new double[] { i, j, k });
double chrono = System.nanoTime();
for (int tub = 0; tub < tubes.length; tub++) {
for (int i = 0; i < mx; i++)
for (int j = 0; j < my; j++)
for (int k = 0; k < mz; k++) {
for (double[] shift : shifts) {
Point3D p = new Point3D((i + shift[0]) * pixelsize, (j + shift[1]) * pixelsize, (k + shift[2]) * pixelsize);
int index = tubes[tub].inside(p, factorDistanceMask);
if (index > 0) {
// if (belongs[i][j][k] == null)
// belongs[i][j][k] = new Belong();
count++;
belongsImage.putPixel(i, j, k, list.size());
list.add(new int[] { i, j, k, tub, index });
break;
}
}
if (count % 100 == 0)
walk.progress("Count: " + tub + " " + count, 100.0 * tubes.length * (i * mz * my + j * mz + k) / n);
if ((System.nanoTime() - chrono) * 1e-9 > timeout)
break;
}
}
belongsImage.show("belongsImage " + count);
return count;
}
public class ListPoint extends ArrayList<Point3D> {
}
public class Belong extends ArrayList<int[]> {
}
public void createPositions(int nbfluos, double timeout) {
int count = 0;
IJ.log("\nCreate Position : Mask size " + mx + " " + my + " " + mz);
walk.reset();
IJ.log("Fine size " + mx + " " + my + " " + mz);
sample.fluos.clear();
count = 0;
int bad = 0;
int good = 0;
double chrono = System.nanoTime();
double time = 0.0;
int nitems = list.size() - 1;
do {
int item[] = list.get(rand.nextInteger(nitems));
double x = rand.nextDouble(pixelsize);
double y = rand.nextDouble(pixelsize);
double z = rand.nextDouble(pixelsize);
Point3D point = new Point3D(x + item[0] * pixelsize, y + item[1] * pixelsize, z + item[2] * pixelsize);
int index1 = item[4] - 4;
int index2 = item[4] + 4;
int index = tubes[item[3]].inside(point, index1, index2);
good++;
if (index > 0) {
sample.fluos.add(new Point3D(point.x, point.y, point.z));
count++;
}
else {
bad++;
}
if (count % 1000 == 0)
walk.progress("Fine: " + count, (100.0 * count) / nbfluos);
time = (System.nanoTime() - chrono) * 1e-9;
}
while (count < nbfluos && time < timeout);
IJ.log(" Count " + count + " fluos");
IJ.log(" Bad voxels " + bad + " fluos");
IJ.log(" Good voxels " + good + " fluos");
}
public void removeSteric(double steric) {
int n = sample.fluos.size();
walk.reset();
// Allocation in voxels
ListPoint a[][][] = new ListPoint[mx][my][mz];
int count = 0;
for(Point3D fluo : sample.fluos) {
int i = (int)(fluo.x / pixelsize);
if (i >= 0 && i < mx) {
int j = (int)(fluo.y / pixelsize);
if (j >= 0 && j < my) {
int k = (int)(fluo.z / pixelsize);
if (k >= 0 && k < mz) {
if (a[i][j][k] == null)
a[i][j][k] = new ListPoint();
a[i][j][k].add(fluo);
count++;
}
}
}
if (count % 100 == 0)
walk.progress("Fluo allocation " + count, 100.0 * count / n);
}
ArrayList<Point3D> fluos = new ArrayList<Point3D>();
for(Point3D fluo : sample.fluos)
fluos.add(fluo);
sample.fluos.clear();
int nbins = (int)Math.ceil(pixelsize/steric)+1 ;
byte[][][] test = new byte[nbins][nbins][nbins];
walk.reset();
for(int i=0; i<mx; i++) {
walk.progress("column " + i, (100.0*i)/mx);
for(int j=0; j<my; j++)
for(int k=0; k<mz; k++) {
if (a[i][j][k] != null) {
for(int x=0; x<nbins; x++)
for(int y=0; y<nbins; y++)
for(int z=0; z<nbins; z++) {
test[x][y][z] = 0;
}
ListPoint list = a[i][j][k];
int na = list.size();
int removed[] = new int[na];
for(int u=0; u<na; u++) {
Point3D p = list.get(u);
int xp = (int)((p.x - i*pixelsize)/steric);
int yp = (int)((p.y - j*pixelsize)/steric);
int zp = (int)((p.z - k*pixelsize)/steric);
if (test[xp][yp][zp] == 0)
test[xp][yp][zp] = 1;
else
removed[u]++;
}
for(int u=0; u<na; u++) {
if (removed[u] == 0)
sample.fluos.add(list.get(u));
}
}
}
}
IJ.log(" Count: " + sample.fluos.size());
}
public void crop() {
int n = sample.fluos.size();
walk.reset();
ArrayList<Point3D> fluos = new ArrayList<Point3D>();
for(Point3D fluo : sample.fluos)
fluos.add(fluo);
sample.fluos.clear();
int count = 0;
for(Point3D fluo : fluos) {
boolean in = false;
if (fluo.x > 0)
if (fluo.y > 0)
if (fluo.z > -sample.nz)
if (fluo.x < sample.nx)
if (fluo.y < sample.ny)
if (fluo.z < sample.nz)
in = true;
if (in)
sample.fluos.add(fluo);
else
IJ.log( "Erased: " + fluo.toString());
count++;
if (count % 100 == 0)
walk.progress("Fluo allocation " + count, 100.0 * count / n);
}
IJ.log(" Count: " + sample.fluos.size());
}
public void createPositions_grid(int nbfluos, double timeout) {
int count = 0;
IJ.log("\nMask size " + mx + " " + my + " " + mz);
walk.reset();
PsRandom rand = new PsRandom(1234);
walk.reset();
IJ.log("Fine size " + mx + " " + my + " " + mz);
sample.fluos.clear();
count = 0;
int bad = 0;
int good = 0;
double chrono = System.nanoTime();
double time = 0.0;
do {
double x = rand.nextDouble(0, mx);
double y = rand.nextDouble(0, my);
double z = rand.nextDouble(0, mz);
int i = (int) x;
int j = (int) y;
int k = (int) z;
if (belongs[i][j][k] != null) {
good++;
for (int[] belong : belongs[i][j][k]) {
Point3D point = new Point3D(x * pixelsize, y * pixelsize, z * pixelsize);
int index1 = belong[1] - 40;
int index2 = belong[1] + 40;
int index = tubes[belong[0]].inside(point, index1, index2);
if (index > 0) {
sample.fluos.add(new Point3D(point.x, point.y, point.z));
count++;
}
}
}
else {
bad++;
}
if (count % 1000 == 0)
walk.progress("Fine: " + count, (100.0 * count) / nbfluos);
time = (System.nanoTime() - chrono) * 1e-9;
}
while (count < nbfluos && time < timeout);
IJ.log(" Count " + count + " fluos");
IJ.log(" Bad voxels " + bad + " fluos");
IJ.log(" Good voxels " + good + " fluos");
}
/*
* public ImageWare createMask(Point3D dim, double pixelsize, int nbsamples,
* double factorDistanceMask) { int count = 0; int mx = (int)
* Math.ceil(dim.x / pixelsize); int my = (int) Math.ceil(dim.y /
* pixelsize); int mz = (int) Math.ceil(dim.z / pixelsize);
* IJ.log("\nMask size " + mx + " " + my + " " + mz); ImageWare image =
* Builder.create(mx, my, mz, ImageWare.BYTE); ImageWare distance =
* Builder.create(mx, my, mz, ImageWare.FLOAT); walk.reset(); double n = mx
* * my * mz; for(Item item : sample) { if (item instanceof Tube) { Tube
* tube = (Tube)item; tube.init(nbsamples); } }
*
* Tube[] tubes = sample.getTubes();
*
* ArrayList<double[]> voxels1 = new ArrayList<double[]>(); for (int i = 0;
* i < mx; i++) for (int j = 0; j < my; j++) for (int k = 0; k < mz; k++) {
* Point3D p = new Point3D(i * pixelsize, j * pixelsize, k * pixelsize);
* double min = Double.MAX_VALUE; for(int tub=0; tub<tubes.length; tub++) {
* double[] inside = tubes[tub].inside(p, factorDistanceMask); if (inside[0]
* < min) { min = inside[0]; } if (inside[2] > 0) { image.putPixel(i, j, k,
* 255); voxels1.add(new double[] {i, j, k, tub, inside[1]}); count++; } if
* (count % 100 == 0) walk.progress("Count: " + count, 100.0 * (i * mz * my
* + j * mz + k) / n); } distance.putPixel(i, j, k, min); }
* distance.show("Distance"); IJ.log("Number of voxels Step 1: " +
* voxels1.size());
*
* int n1 = voxels1.size(); walk.progress("2nd step" + voxels1.size(), 0);
* int vcount2 = 0; ArrayList<double[]> voxels2 = new ArrayList<double[]>();
* double f2 = factorDistanceMask*0.1; for(int v=0; v<n1; v++) { double[]
* voxel = voxels1.get(v); int tub = (int)voxel[3]; Tube tube = tubes[tub];
* if (v % 10 == 0) walk.progress("2nd Step " + vcount2, (100.0*v) / n1);
* int k1 = (int)voxel[4] - 2; int k2 = (int)voxel[4] + 2; for (double i =
* voxel[0]-1; i <= voxel[0]+1; i+=0.1) for (double j = voxel[1]-1; j <=
* voxel[1]+1; j+=0.1) for (double k = voxel[2]-1; k <= voxel[2]+1; k+=0.1)
* { Point3D p = new Point3D(i * pixelsize, j * pixelsize, k * pixelsize);
* double[] inside = tube.inside(p, f2, k1, k2); if (inside[2] > 0) {
* voxels2.add(new double[] {i, j, k, tub, inside[1]}); vcount2++; } } } int
* n2 = voxels2.size(); IJ.log("Number of voxels end of step2 : " + n2);
*
* sample.voxels.clear(); for(double[] voxel : voxels2) {
* sample.voxels.add(new Point3D(voxel[0]*pixelsize, voxel[1]*pixelsize,
* voxel[2]*pixelsize)); sample.fluos.add(new Point3D(voxel[0]*pixelsize,
* voxel[1]*pixelsize, voxel[2]*pixelsize)); } return image; }
*/
public void createPositions1(int nbfluos, double timeout) {
double chrono = System.nanoTime();
Tube[] tubes = sample.getTubes();
sample.voxels.clear();
sample.fluos.clear();
for (Tube tube : tubes) {
ArrayList<Point3D> ps = tube.getSamplesInterval(1, 0);
for (Point3D p : ps) {
sample.voxels.add(p);
sample.fluos.add(p);
}
}
IJ.log("Count: " + sample.voxels.size() + " fluos in time:" + ((System.nanoTime() - chrono) * 1e-9) + " s");
}
public void createPositions1(int nbfluos, double timeout, double pixelsize, int nbsamples) {
ArrayList<Point3D> mask = sample.voxels;
Random rand = new Random(1234);
int count = 0;
int nmask = mask.size() - 1;
sample.fluos.clear();
for (Item item : sample) {
if (item instanceof Tube) {
Tube tube = (Tube) item;
tube.init(nbsamples);
}
}
int ntubes = 0;
for (Item item : sample)
if (item instanceof Tube)
ntubes++;
Tube tubes[] = new Tube[ntubes];
ntubes = 0;
for (Item item : sample)
tubes[ntubes++] = (Tube) item;
double chrono = System.nanoTime();
double time = 0.0;
do {
int index = (int) (rand.nextDouble() * nmask);
int inx = (int) mask.get(index).x;
int iny = (int) mask.get(index).y;
int inz = (int) mask.get(index).z;
double x = inx;// + (rand.nextDouble()-0.5)*pixelsize;
double y = iny;// + (rand.nextDouble()-0.5)*pixelsize;
double z = inz;// + (rand.nextDouble()-0.5)*pixelsize;
Point3D p = new Point3D(x, y, z);
for (Tube tube : tubes) {
if (tube.contains(p)) {
sample.fluos.add(p);
count++;
}
}
if (count % 100 == 0)
walk.progress("Count: " + count, 100.0);
time = (System.nanoTime() - chrono) * 1e-9;
}
while (count < nbfluos && time < timeout);
IJ.log("Count: " + count + " fluos in time:" + ((System.nanoTime() - chrono) * 1e-9) + " s");
}
/**
* http://www.groupsrv.com/computers/about216280.html N Normal, (U,V) Plane
* of the circle // X(t) = C + r*(cos(t)*U + sin(t)*V) for 0 <= t < 2*pi. If
* N = (nx,ny,nz), U = (ux,uy,uz), and V = (vx,vy,vz), then
*/
private Point3D getPointNormalCircle(PsRandom psrand, Point3D pa, Point3D po, Point3D pb, double radiusPix) {
double dab = pa.distance(pb);
// Normal
double nx = (pa.x - pb.x) / dab;
double ny = (pa.y - pb.y) / dab;
double nz = (pa.z - pb.z) / dab;
nx += (rand.nextDouble() -0.5) * 0.1;
ny += (rand.nextDouble() -0.5) * 0.1;
nz += (rand.nextDouble() -0.5) * 0.1;
// Plane circle
double ux, uy, uz;
double vx, vy, vz;
if (abs(nx) >= abs(ny)) {
double invLength = 1.0 / sqrt(nx * nx + ny * ny);
ux = -nz * invLength;
uy = 0;
uz = +nx * invLength;
vx = ny * uz;
vy = nz * ux - nx * uz;
vz = -ny * ux;
}
else {
double invLength = 1.0 / sqrt(ny * ny + nz * nz);
ux = 0;
uy = +nz * invLength;
uz = -ny * invLength;
vx = ny * uz - nz * uy;
vy = -nx * uz;
vz = nx * uy;
}
double r = radiusPix;
double a = psrand.nextDouble() * 2.0 * PI;
double normu = Math.sqrt(ux * ux + uy * uy + uz * uz);
double normv = Math.sqrt(vx * vx + vy * vy + vz * vz);
ux /= normu;
uz /= normu;
ux /= normu;
vx /= normv;
vz /= normv;
vx /= normv;
double cosa = Math.cos(a);
double sina = Math.sin(a);
double x = po.x + r * (cosa * ux + sina * vx);
double y = po.y + r * (cosa * uy + sina * vy);
double z = po.z + r * (cosa * uz + sina * vz);
Point3D out = new Point3D(x, y, z);
return out;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/sample/SampleFactoryDialog.java | .java | 13,320 | 381 | package smlms.sample;
import ij.IJ;
import ij.gui.GUI;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.io.File;
import java.util.ArrayList;
import javax.swing.BorderFactory;
import javax.swing.ButtonGroup;
import javax.swing.JButton;
import javax.swing.JComboBox;
import javax.swing.JDialog;
import javax.swing.JFileChooser;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.JRadioButton;
import javax.swing.JTabbedPane;
import javax.swing.JTextField;
import smlms.file.PositionFile;
import smlms.tools.NormalizedVariable;
import smlms.tools.Point3D;
import additionaluserinterface.GridPanel;
import additionaluserinterface.Settings;
import additionaluserinterface.SpinnerDouble;
import additionaluserinterface.SpinnerInteger;
import additionaluserinterface.WalkBar;
public class SampleFactoryDialog extends JDialog implements ActionListener, Runnable {
private WalkBar walk = new WalkBar("(c) 2016 EPFL, BIG", false, false, true);
private Settings settings = new Settings("localization-microscopy", IJ.getDirectory("plugins") + "smlm-2016.txt");
private JButton bnBrowse = new JButton("Browse");
private JButton bnLoad = new JButton("Load");
private JButton bnSave = new JButton("Save");
private JButton bnInfo = new JButton("Information");
private JButton bnCreateBelong = new JButton("Mask");
private JButton bnCreatePos = new JButton("Create");
private JButton bnCreateStep = new JButton("Create Step");
private JButton bnRemoveSteric = new JButton("Steric");
private JButton bnCrop = new JButton("Crop");
private JButton bnSavePositions = new JButton("ZOff & Save");
private JComboBox cmbPredefined = new JComboBox();
private SpinnerInteger spnNbFluo = new SpinnerInteger(1000, 1, 9999999, 1);
private SpinnerInteger spnPixelsize = new SpinnerInteger(100, 1, 10000, 1);
private SpinnerInteger spnNbSamples = new SpinnerInteger(10000, 1, 9999999, 1000);
private SpinnerDouble spnZOffset = new SpinnerDouble(-1000, -999999, 999999, 1);
private SpinnerDouble spnTimeOut = new SpinnerDouble(5, 0.1, 99999, 1);
private SpinnerDouble spnFactorDistanceMask = new SpinnerDouble(1, 0, 1000, 0.1);
private SpinnerDouble spnSteric = new SpinnerDouble(1, 0, 1000, 0.1);
private SpinnerDouble spnSmooth = new SpinnerDouble(10, 0, 10000, 1);
private SpinnerDouble spnStep = new SpinnerDouble(1, 0.0001, 1000, 0.1);
private JTextField txtFileSample = new JTextField("-");
private JTextField txtOutSample = new JTextField("-");
private JLabel txtName = new JLabel("null sample");
private JLabel txtMask = new JLabel("unvalid mask");
private JLabel txtFluo = new JLabel("unvalid fluo");
private JLabel txtSteric = new JLabel("not yet run");
private JLabel txtStep = new JLabel("not yet run");
private JLabel txtCrop = new JLabel("not yet run");
private JRadioButton rbMask = new JRadioButton("Mask", false);
private JRadioButton rbFluos = new JRadioButton("Fluos", true);
private Thread thread = null;
private Sample sample = null;
private ArrayList<Sample> samples = new ArrayList<Sample>();
private JButton job = null;
private SampleFactory builder;
public static void main(String args[]) {
new SampleFactoryDialog();
}
public SampleFactoryDialog() {
super(new JFrame(), "Sample Factory");
samples.add(getSample1());
samples.add(getSample2());
samples.add(getSample3());
ButtonGroup group = new ButtonGroup();
group.add(rbMask);
group.add(rbFluos);
cmbPredefined.addItem("None");
for (Sample sample : samples)
cmbPredefined.addItem(sample.name);
settings.record("sample-spnFactorDistanceMask", spnFactorDistanceMask, "1");
settings.record("sample-txtFileSample", txtFileSample, "-");
settings.record("sample-spnPixelsize", spnPixelsize, "100");
settings.record("sample-spnZOffset", spnZOffset, "-100");
settings.record("sample-spnNbSamples", spnNbSamples, "1000");
settings.record("sample-spnTimeOut", spnTimeOut, "5");
settings.record("sample-spnNbFluo", spnNbFluo, "1000");
settings.record("sample-spnStep", spnStep, "1");
settings.record("sample-spnSmooth", spnSmooth, "800");
settings.loadRecordedItems();
GridPanel samp = new GridPanel("Sample Definition");
samp.place(0, 0, txtFileSample);
samp.place(0, 1, bnBrowse);
samp.place(1, 0, txtName);
samp.place(1, 1, bnLoad);
samp.place(2, 0, cmbPredefined);
samp.place(2, 1, bnSave);
GridPanel pn1 = new GridPanel(false);
pn1.place(3, 0, new JLabel("NbSample"));
pn1.place(3, 1, spnNbSamples);
pn1.place(3, 2, txtMask);
pn1.place(3, 3, bnCreateBelong);
pn1.place(4, 0, new JLabel("Factor Distance"));
pn1.place(4, 1, spnFactorDistanceMask);
pn1.place(4, 2, txtFluo);
pn1.place(4, 3, bnCreatePos);
JTabbedPane tab = new JTabbedPane();
tab.add("Masked", pn1);
GridPanel fact = new GridPanel("Sample Factory");
fact.place(1, 0, new JLabel("Pixelsize"));
fact.place(1, 1, spnPixelsize);
fact.place(1, 2, new JLabel("TimeOut"));
fact.place(1, 3, spnTimeOut);
fact.place(2, 0, new JLabel("Nb Pos."));
fact.place(2, 1, spnNbFluo);
fact.place(2, 2, new JLabel("ZOffset"));
fact.place(2, 3, spnZOffset);
//fact.place(3, 0, 4, 1, tab);
fact.place(4, 0, new JLabel("Step (nm)"));
fact.place(4, 1, spnStep);
fact.place(4, 2, txtStep);
fact.place(4, 3, bnCreateStep);
fact.place(6, 0, new JLabel("Smooth"));
fact.place(6, 1, spnSmooth);
fact.place(6, 2, txtCrop);
fact.place(6, 3, bnCrop);
fact.place(7, 0, new JLabel("Steric (nm)"));
fact.place(7, 1, spnSteric);
fact.place(7, 2, txtSteric);
fact.place(7, 3, bnRemoveSteric);
fact.place(8, 0, 3, 1, txtOutSample);
fact.place(8, 3, bnSavePositions);
txtMask.setBorder(BorderFactory.createEtchedBorder());
txtName.setBorder(BorderFactory.createEtchedBorder());
txtFluo.setBorder(BorderFactory.createEtchedBorder());
txtSteric.setBorder(BorderFactory.createEtchedBorder());
txtStep.setBorder(BorderFactory.createEtchedBorder());
txtCrop.setBorder(BorderFactory.createEtchedBorder());
GridPanel main = new GridPanel(false);
main.place(0, 0, samp);
main.place(1, 0, fact);
main.place(6, 0, walk);
add(main);
pack();
setModal(false);
GUI.center(this);
setVisible(true);
bnCrop.addActionListener(this);
bnRemoveSteric.addActionListener(this);
bnBrowse.addActionListener(this);
bnLoad.addActionListener(this);
walk.getButtonClose().addActionListener(this);
bnInfo.addActionListener(this);
bnCreateStep.addActionListener(this);
bnCreateBelong.addActionListener(this);
bnCreatePos.addActionListener(this);
bnSave.addActionListener(this);
bnSavePositions.addActionListener(this);
cmbPredefined.addActionListener(this);
updateInterface();
}
@Override
public void actionPerformed(ActionEvent e) {
if (e.getSource() == walk.getButtonClose()) {
settings.storeRecordedItems();
dispose();
}
else if (e.getSource() == bnBrowse) {
sample = null;
JFileChooser fc = new JFileChooser();
int ret = fc.showOpenDialog(null);
if (ret == JFileChooser.APPROVE_OPTION) {
String filename = fc.getSelectedFile().getAbsolutePath();
txtFileSample.setText(filename);
}
}
else if (e.getSource() == bnLoad) {
sample = null;
String filename = txtFileSample.getText();
setSample(Sample.load(filename));
}
else if (e.getSource() == bnSave && sample != null) {
JFileChooser fc = new JFileChooser(IJ.getDirectory("plugins") + File.separator + sample + "-" + sample.name + ".txt");
int ret = fc.showSaveDialog(null);
if (ret == JFileChooser.APPROVE_OPTION)
sample.save(fc.getSelectedFile().getAbsolutePath());
}
else if (e.getSource() == bnInfo && sample != null) {
IJ.log("\n Information on :" + sample.name);
for (Item item : sample)
IJ.log(item.getInfo());
}
else if (e.getSource() == bnCreateBelong || e.getSource() == bnCreatePos
|| e.getSource() == bnCreateStep || e.getSource() == bnCrop
|| e.getSource() == bnRemoveSteric
|| e.getSource() == bnSavePositions) {
job = (JButton)e.getSource();
if (thread == null) {
thread = new Thread(this);
thread.setPriority(Thread.MIN_PRIORITY);
thread.start();
}
}
else if (e.getSource() == cmbPredefined) {
String selected = (String) cmbPredefined.getSelectedItem();
sample = null;
for (Sample s : samples) {
if (selected.equals(s.name)) {
setSample(s);
}
}
}
updateInterface();
}
private void updateInterface() {
bnCreateBelong.setEnabled(sample != null);
bnCreatePos.setEnabled(sample != null);
bnCreateStep.setEnabled(sample != null);
bnSave.setEnabled(sample != null);
bnInfo.setEnabled(sample != null);
File file = new File(txtFileSample.getText());
txtOutSample.setText( file.getParent() + File.separator + "positions.csv");
}
public void setSample(Sample sample) {
if (sample == null)
return;
this.sample = sample;
txtName.setText(sample.getInfo());
updateInterface();
}
public void run() {
walk.reset();
double chrono = System.nanoTime();
if (job == this.bnCreateBelong) {
Point3D dim = new Point3D(sample.nx, sample.ny, sample.nz);
builder = new SampleFactory(dim, walk, sample, spnPixelsize.get());
int count = builder.createBelongs(spnFactorDistanceMask.get(), spnTimeOut.get(), spnNbSamples.get());
txtMask.setText("" + count + " voxels ");
/*
ImageWare mask = builder.createMask(dim, spnPixelsizeMask.get(), spnNbSamples.get(), factorDistanceMask);
mask.show("Mask " + spnPixelsizeMask.get() );
if (sample != null) {
double n = sample.voxels.size();
double p = spnPixelsizeMask.get();
double size = (sample.nx * sample.ny * sample.nz) / p;
double ratio = n / size;
txtMask.setText("Mask " + n + " voxels / ratio:" + ratio );
}
*/
IJ.log("Create Mask " + (System.nanoTime() - chrono)*1e-9);
}
else if (job == bnRemoveSteric) {
int n = sample.fluos.size();
builder.removeSteric(spnSteric.get());
txtSteric.setText(" " + n + " > " + sample.fluos.size());
}
else if (job == bnCrop) {
int n = sample.fluos.size();
builder.crop();
txtCrop.setText(" " + n + " > " + sample.fluos.size());
}
else if (job == bnCreatePos) {
sample.fluos.clear();
builder.createPositions(spnNbFluo.get(), spnTimeOut.get());
txtFluo.setText("" + sample.fluos.size());
}
else if (job == bnCreateStep) {
Point3D dim = new Point3D(sample.nx, sample.ny, sample.nz);
builder = new SampleFactory(dim, walk, sample, spnPixelsize.get());
builder.createPositionsStep(spnNbFluo.get(), spnTimeOut.get(), spnStep.get(), spnSmooth.get());
txtStep.setText("" + sample.fluos.size());
}
else if (job == bnSavePositions) {
double zoff = spnZOffset.get();
for(Point3D fluo : sample.fluos)
fluo.z += zoff;
new PositionFile(walk, txtOutSample.getText()).save(sample.fluos);
}
walk.finish();
thread = null;
}
public Sample getSample1() {
int sx = 5000;
int sy = 4000;
int sz = 1000;
ArrayList<Point3D> nodes1 = new ArrayList<Point3D>();
nodes1.add(new Point3D(-500, 1000, 500));
nodes1.add(new Point3D(1000, 1000, 500));
nodes1.add(new Point3D(1000, 500, 500));
ArrayList<Point3D> nodes2 = new ArrayList<Point3D>();
nodes2.add(new Point3D(1000, 2000, 500));
nodes2.add(new Point3D(1500, 2000, 500));
nodes2.add(new Point3D(1500, 2500, 500));
ArrayList<Point3D> nodes3 = new ArrayList<Point3D>();
nodes3.add(new Point3D(2500, 500, 0));
nodes3.add(new Point3D(2500, 500, 1000));
ArrayList<Point3D> nodes4 = new ArrayList<Point3D>();
nodes4.add(new Point3D(2000, 1000, 100));
nodes4.add(new Point3D(2500, 1000, 100));
Sample sample = new Sample("U", sx, sy, sz);
sample.add(new Tube("tube", nodes1, new NormalizedVariable(100), new NormalizedVariable(10), 1));
sample.add(new Tube("tube", nodes2, new NormalizedVariable(100), new NormalizedVariable(10), 1));
sample.add(new Tube("tube", nodes3, new NormalizedVariable(100), new NormalizedVariable(10), 1));
sample.add(new Tube("tube", nodes4, new NormalizedVariable(100), new NormalizedVariable(10), 1));
return sample;
}
public Sample getSample2() {
int sx = 1000;
int sy = 1000;
int sz = 240;
ArrayList<Point3D> nodes1 = new ArrayList<Point3D>();
nodes1.add(new Point3D(100, 500, 120));
nodes1.add(new Point3D(500, 500, 120));
nodes1.add(new Point3D(900, 500, 120));
Sample sample = new Sample("wavy", sx, sy, sz);
NormalizedVariable thickness = new NormalizedVariable(5);
thickness.addCosine(5, 2, 0);
sample.add(new Tube("tube", nodes1, new NormalizedVariable(80), thickness, 1));
return sample;
}
public Sample getSample3() {
int sx = 1000;
int sy = 1000;
int sz = 200;
ArrayList<Point3D> nodes1 = new ArrayList<Point3D>();
nodes1.add(new Point3D(250, 250, 50));
nodes1.add(new Point3D(750, 250, 150));
nodes1.add(new Point3D(750, 750, 150));
nodes1.add(new Point3D(250, 750, 50));
Sample sample = new Sample("small square", sx, sy, sz);
NormalizedVariable radius = new NormalizedVariable(40);
radius.addCosine(5, 5, 0);
sample.add(new Tube("tube", nodes1, radius, new NormalizedVariable(2), 1));
return sample;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/sample/Reticulum2T_Generator_2016.java | .java | 9,200 | 320 | package smlms.sample;
import ij.IJ;
import ij.ImagePlus;
import ij.gui.ImageCanvas;
import ij.gui.ImageWindow;
import ij.gui.StackWindow;
import ij.io.FileSaver;
import imageware.Builder;
import imageware.ImageWare;
import java.awt.Color;
import java.awt.Graphics;
import java.util.ArrayList;
import smlms.tools.NormalizedVariable;
import smlms.tools.Point3D;
import smlms.tools.PsRandom;
public class Reticulum2T_Generator_2016 {
private PsRandom rand = new PsRandom(13234);
private int nx = 640;
private int ny = 640;
private int nz = 150;
private double pixelsize = 10;
private double radiusTube = 150;
private double thickTube = 6;
private ImageWare substrat;
ArrayList<Point3D> anchors = new ArrayList<Point3D>();
public static void main(String args[]) {
Reticulum2T_Generator_2016 mt = new Reticulum2T_Generator_2016();
mt.run(1807, -10);
mt.run(2709, +10);
}
public Reticulum2T_Generator_2016() {
}
public void run(long random, double rotation) {
rand = new PsRandom(random);
String outfile = "/Users/sage/Desktop/SampleMT-" + random + ".txt";
substrat = createSubstrat();
ImagePlus imp = new ImagePlus("Substrat-"+ random + "-" + rotation, substrat.buildImageStack());
imp.show();
ArrayList<ArrayList<Point3D>> lists = build(rotation);
IJ.log("Number of tubes: " + lists.size());
new MTCanvas(imp, lists, anchors);
Sample sample = new Sample("name", (int)(nx * pixelsize), (int) (ny * pixelsize), (int) (nz * pixelsize));
for (int i = 0; i < lists.size(); i++) {
NormalizedVariable radius = new NormalizedVariable(radiusTube);
NormalizedVariable thickness = new NormalizedVariable(thickTube);
ArrayList<Point3D> nodes = new ArrayList<Point3D>();
ArrayList<Point3D> list = lists.get(i);
for (int k = 0; k < list.size(); k++) {
Point3D p = list.get(k);
nodes.add(new Point3D(p.x * pixelsize, p.y * pixelsize, p.z * pixelsize + 750));
}
Tube tube = new Tube("tube " + i, nodes, radius, thickness, 1);
sample.add(tube);
}
sample.save(outfile);
}
private ArrayList<ArrayList<Point3D>> build(double rotation) {
ArrayList<Point3D> my1 = new ArrayList<Point3D>();
Point3D a1 = new Point3D(r(400, 420), r(-30, -40), r(-24, -27));
Point3D b1 = new Point3D(r(200, 240), r(320, 340), r(-13, -18));
Point3D c1 = new Point3D(r(180, 200), r(450, 460), r(13, 18));
Point3D d1 = new Point3D(r(50, 80), r(700, 720), r(22, 25));
my1.add(a1);
my1.add(b1);
my1.add(c1);
my1.add(d1);
ArrayList<Point3D> my3 = new ArrayList<Point3D>();
Point3D a3 = new Point3D(d1.x+r(160, 170), r(690, 700), r(-15, -17));
Point3D b3 = new Point3D(c1.x-r(80, 100), r(350, 360), r(-7, -8));
Point3D c3 = new Point3D(b1.x+r(100,110), r(150, 170), r(9, 11));
Point3D d3 = new Point3D(r(680, 700), r(90, 120), r(15, 18));
my3.add(a3);
my3.add(b3);
my3.add(c3);
my3.add(d3);
ArrayList<Point3D> myss1 = smooth(perturbe(attract(my1), r(0.15, 0.25)));
ArrayList<Point3D> py1 = perturbe(attract(my3), r(0.15, 0.25));
ArrayList<Point3D> py2 = perturbe(py1, r(0.45, 0.55));
ArrayList<Point3D> py3 = perturbe(py2, r(0.70, 0.72));
ArrayList<Point3D> myss3 = smooth(perturbe(py3, r(0.75, 0.75)));
rotate(myss1, rotation);
rotate(myss3, rotation);
ArrayList<ArrayList<Point3D>> lists = new ArrayList<ArrayList<Point3D>>();
lists.add(myss1);
lists.add(myss3);
return lists;
}
private void rotate(ArrayList<Point3D> points, double angle) {
double cosa = Math.cos(Math.toRadians(angle));
double sina = Math.sin(Math.toRadians(angle));
for(Point3D point : points) {
double x = point.x - nx/2;
double y = point.y - ny/2;
point.x = cosa * x + sina * y + nx/2;
point.y = -sina * x + cosa * y + ny/2;
}
}
private ArrayList<Point3D> perturbe(ArrayList<Point3D> in, double pos) {
int k = (int)(pos*in.size());
double dx = in.get(k-1).x - in.get(k+1).x;
double dy = in.get(k-1).y - in.get(k+1).y;
double d = Math.sqrt(dy*dy+dx*dx);
double r = rand.nextDouble(-1, 1) < 0 ? r(-5, -6) : r(3 , 5);
in.get(k).x += -r*dy/d;
in.get(k).y += +r*dx/d;
return in;
}
private ArrayList<Point3D> smooth(ArrayList<Point3D> in) {
CurveSpline spline1 = new CurveSpline(in, 1);
return spline1.getSamplesInterval(1);
}
private ArrayList<Point3D> attract(ArrayList<Point3D> in) {
CurveSpline spline1 = new CurveSpline(in, 1);
ArrayList<Point3D> s = spline1.getSamplesInterval(50);
ArrayList<Point3D> out = new ArrayList<Point3D>();
out.add(s.get(0));
for(int k=1; k<s.size()-1; k++) {
int x = (int)s.get(k).x;
int y = (int)s.get(k).y;
double dx = s.get(k-1).x - s.get(k+1).x;
double dy = s.get(k-1).y - s.get(k+1).y;
double min = Double.MAX_VALUE;
int dirx = dx <= dy ? 0 : 1;
int diry = dx >= dy ? 1 : 0;
int imin = 0;
for(int i=-50; i<50; i++) {
double cost = Math.abs(i) + substrat.getPixel(x + dirx*i, y + diry*i, 0);
if (cost < min) {
imin = i;
min = cost;
}
}
out.add(new Point3D(s.get(k).x + dirx*imin, s.get(k).y + diry*imin, s.get(k).z));
}
out.add(s.get(s.size()-1));
return out;
}
/*
private ArrayList<Point3D> build(Point3D a, Point3D b) {
int n = nx;
double dx = (b.x - a.x) / n;
double dy = (b.y - a.y) / n;
double d = Math.sqrt(dx * dx + dy * dy);
dx = dx / d;
dy = dy / d;
double x = a.x;
double y = a.y;
double azimuth = Math.atan2(dy, dx);
ArrayList<Point3D> list = new ArrayList<Point3D>();
boolean flag = true;
double diprev = Double.MAX_VALUE;
for (int p = 0; p < n & flag; p++) {
double max = -Double.MAX_VALUE;
double amax = 0;
for (double an = azimuth - azimuthRange; an <= azimuth + azimuthRange; an += azimuthRange * 0.5) {
double ox = Math.cos(an);
double oy = Math.sin(an);
double xn = x + elementLength * ox;
double yn = y + elementLength * oy;
double v = 0.0;
double goal = 1000 -b.distanceLateral(new Point3D(xn, yn, 0));
v += lambdaBackground * substrat.getInterpolatedPixel(xn, yn, 0);
//v += lambdaDirection * (dx * ox + dy * oy );
v += lambdaRandom * rand.nextGaussian(0, 2);
v += lambdaDirection*goal;
if (v > max) {
max = v;
amax = an;
}
}
azimuth = amax;
x = x + elementLength * Math.cos(amax);
y = y + elementLength * Math.sin(amax);
Point3D pc = new Point3D(x, y, 0);
double di = b.distanceLateral(pc);
System.out.println("Distanace to b " + di);
if (di < 10) {
list.add(b);
break;
}
if (diprev < di) {
list.add(b);
break;
}
list.add(pc);
diprev = di;
}
ArrayList<Point3D> flat = new ArrayList<Point3D>();
for (int i = 0; i < list.size(); i++) {
Point3D p = list.get(i);
if (p.x > 0 && p.x < nx)
if (p.y > 0 && p.y < ny) {
flat.add(p);
}
}
double dz = (b.z - a.z) / flat.size();
for (int i = 0; i < flat.size(); i++)
list.get(i).z = a.z + i*dz;
ArrayList<Point3D> inside = new ArrayList<Point3D>();
for (int i = 0; i < flat.size(); i++) {
Point3D p = flat.get(i);
if (p.x > -nz/2 && p.z < nz/2)
inside.add(p);
}
System.out.println("nbpoints " + list.size() + " > " + " > " + flat.size() + " > " + inside.size());
return inside;
}
*/
private double r(double bottom, double top) {
if (bottom < top)
return rand.nextDouble(bottom, top);
else
return rand.nextDouble(top, bottom);
}
public ImageWare createSubstrat() {
ImageWare substrat = Builder.create(nx, ny, 1, ImageWare.FLOAT);
for (int i = 0; i < nx; i++)
for (int j = 0; j < ny; j++)
{
substrat.putPixel(i, j, 0, rand.nextDouble(-20, 20));
}
for (int i = 0; i < 15; i++) {
int sign = i % 2 == 0 ? -1 : 1;
int x = rand.nextInteger(10, nx - 10);
int y = rand.nextInteger(10, nx - 10);
for (int j = 0; j < 1000000; j++) {
x += rand.nextInteger(-2, 2);
y += rand.nextInteger(-1, 1);
substrat.putPixel(x, y, 0, sign * 20);
}
}
substrat.smoothGaussian(6);
return substrat;
}
public class MTCanvas extends ImageCanvas {
private ArrayList<ArrayList<Point3D>> tubes;
private ArrayList<Point3D> points;
public MTCanvas(ImagePlus imp, ArrayList<ArrayList<Point3D>> tubes, ArrayList<Point3D> points) {
super(imp);
this.tubes = tubes;
this.points = points;
if (imp.getStackSize() > 1)
imp.setWindow(new StackWindow(imp, this));
else
imp.setWindow(new ImageWindow(imp, this));
}
@Override
public void paint(Graphics g) {
super.paint(g);
for (Point3D point : points) {
g.setColor(Color.ORANGE);
g.fillOval(screenXD(point.x) - 3, screenYD(point.y) - 3, 7, 7);
}
for (ArrayList<Point3D> tube : tubes) {
Color c = Color.getHSBColor((float) (tube.get(0).z / nz), 1, 1);
g.setColor(c);
for (int p = 1; p < tube.size(); p++) {
Point3D prev = tube.get(p - 1);
Point3D curr = tube.get(p);
g.drawLine(screenXD(prev.x), screenYD(prev.y), screenXD(curr.x), screenYD(curr.y));
}
}
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/sample/Node.java | .java | 327 | 18 | package smlms.sample;
public class Node {
public double x;
public double y;
public double z;
public double radius;
public double thickness;
public Node(double x, double y, double z, double radius, double thickness) {
this.x = x;
this.y = y;
this.z = z;
this.radius = radius;
this.thickness = thickness;
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/sample/Microtubules3T_Generator_2016.java | .java | 9,567 | 332 | package smlms.sample;
import ij.IJ;
import ij.ImagePlus;
import ij.gui.ImageCanvas;
import ij.gui.ImageWindow;
import ij.gui.StackWindow;
import ij.io.FileSaver;
import imageware.Builder;
import imageware.ImageWare;
import java.awt.Color;
import java.awt.Graphics;
import java.util.ArrayList;
import smlms.tools.NormalizedVariable;
import smlms.tools.Point3D;
import smlms.tools.PsRandom;
public class Microtubules3T_Generator_2016 {
private PsRandom rand = new PsRandom(13234);
private int nx = 640;
private int ny = 640;
private int nz = 150;
private double pixelsize = 10;
private double radiusTube = 12;
private double thickTube = 6;
private ImageWare substrat;
ArrayList<Point3D> anchors = new ArrayList<Point3D>();
public static void main(String args[]) {
Microtubules3T_Generator_2016 mt = new Microtubules3T_Generator_2016();
mt.run(1107, -3);
mt.run(2707, 36);
mt.run(1807, 18);
mt.run(9628, -12);
mt.run(9907, -8);
}
public Microtubules3T_Generator_2016() {
}
public void run(long random, double rotation) {
rand = new PsRandom(random);
String outfile = "/Users/sage/Desktop/SampleMT-" + random + ".txt";
substrat = createSubstrat();
ImagePlus imp = new ImagePlus("Substrat-"+ random + "-" + rotation, substrat.buildImageStack());
imp.show();
ArrayList<ArrayList<Point3D>> lists = build(rotation);
IJ.log("Number of tubes: " + lists.size());
new MTCanvas(imp, lists, anchors);
Sample sample = new Sample("name", (int)(nx * pixelsize), (int) (ny * pixelsize), (int) (nz * pixelsize));
for (int i = 0; i < lists.size(); i++) {
NormalizedVariable radius = new NormalizedVariable(radiusTube);
NormalizedVariable thickness = new NormalizedVariable(thickTube);
ArrayList<Point3D> nodes = new ArrayList<Point3D>();
ArrayList<Point3D> list = lists.get(i);
for (int k = 0; k < list.size(); k++) {
Point3D p = list.get(k);
nodes.add(new Point3D(p.x * pixelsize, p.y * pixelsize, p.z * pixelsize + 750));
}
Tube tube = new Tube("tube " + i, nodes, radius, thickness, 1);
sample.add(tube);
}
sample.save(outfile);
}
private ArrayList<ArrayList<Point3D>> build(double rotation) {
ArrayList<Point3D> my1 = new ArrayList<Point3D>();
Point3D a1 = new Point3D(r(360, 400), r(-30, -40), r(-71, -69));
Point3D b1 = new Point3D(r(200, 240), r(320, 340), r(-10, 10));
Point3D c1 = new Point3D(r(180, 200), r(450, 460), r(31, 39));
Point3D d1 = new Point3D(r(140, 180), r(700, 720), r(71, 69));
my1.add(a1);
my1.add(b1);
my1.add(c1);
my1.add(d1);
ArrayList<Point3D> my2 = new ArrayList<Point3D>();
Point3D a2 = new Point3D(r(-50, -70), r(80, 120), r(34, 41));
Point3D b2 = new Point3D(r(180, 200), r(180, 240), r(14, 24));
Point3D c2 = new Point3D(r(450, 460), r(200, 260), r(-1, -8)) ;
Point3D d2 = new Point3D(r(740, 760), r(300, 310), r(-18, -22));
my2.add(a2);
my2.add(b2);
my2.add(c2);
my2.add(d2);
ArrayList<Point3D> my3 = new ArrayList<Point3D>();
Point3D a3 = new Point3D(d1.x+r(60, 70), r(690, 700), r(-15, -25));
Point3D b3 = new Point3D(c1.x, c1.y, r(0, 8));
Point3D c3 = new Point3D(c2.x, c2.y-r(3, 4), r(12, 15));
Point3D d3 = new Point3D(r(680, 700), d2.y+r(60, 70), r(18, 25));
my3.add(a3);
my3.add(b3);
my3.add(c3);
my3.add(d3);
ArrayList<Point3D> myss1 = smooth(perturbe(attract(my1), r(0.15, 0.25)));
ArrayList<Point3D> myss3 = smooth(perturbe(attract(my3), r(0.45, 0.55)));
ArrayList<Point3D> myss2 = smooth(perturbe(attract(my2), r(0.15, 0.25)));
rotate(myss1, rotation);
rotate(myss2, rotation);
rotate(myss3, rotation);
ArrayList<ArrayList<Point3D>> lists = new ArrayList<ArrayList<Point3D>>();
lists.add(myss1);
lists.add(myss2);
lists.add(myss3);
return lists;
}
private void rotate(ArrayList<Point3D> points, double angle) {
double cosa = Math.cos(Math.toRadians(angle));
double sina = Math.sin(Math.toRadians(angle));
for(Point3D point : points) {
double x = point.x - nx/2;
double y = point.y - ny/2;
point.x = cosa * x + sina * y + nx/2;
point.y = -sina * x + cosa * y + ny/2;
}
}
private ArrayList<Point3D> perturbe(ArrayList<Point3D> in, double pos) {
int k = (int)(pos*in.size());
double dx = in.get(k-1).x - in.get(k+1).x;
double dy = in.get(k-1).y - in.get(k+1).y;
double d = Math.sqrt(dy*dy+dx*dx);
double r = rand.nextDouble(-1, 1) < 0 ? r(-20, -30) : r(20 , 30);
in.get(k).x += -r*dy/d;
in.get(k).y += +r*dx/d;
return in;
}
private ArrayList<Point3D> smooth(ArrayList<Point3D> in) {
CurveSpline spline1 = new CurveSpline(in, 1);
return spline1.getSamplesInterval(1);
}
private ArrayList<Point3D> attract(ArrayList<Point3D> in) {
CurveSpline spline1 = new CurveSpline(in, 1);
ArrayList<Point3D> s = spline1.getSamplesInterval(50);
ArrayList<Point3D> out = new ArrayList<Point3D>();
out.add(s.get(0));
for(int k=1; k<s.size()-1; k++) {
int x = (int)s.get(k).x;
int y = (int)s.get(k).y;
double dx = s.get(k-1).x - s.get(k+1).x;
double dy = s.get(k-1).y - s.get(k+1).y;
double min = Double.MAX_VALUE;
int dirx = dx <= dy ? 0 : 1;
int diry = dx >= dy ? 1 : 0;
int imin = 0;
for(int i=-50; i<50; i++) {
double cost = Math.abs(i) + substrat.getPixel(x + dirx*i, y + diry*i, 0);
if (cost < min) {
imin = i;
min = cost;
}
}
out.add(new Point3D(s.get(k).x + dirx*imin, s.get(k).y + diry*imin, s.get(k).z));
}
out.add(s.get(s.size()-1));
return out;
}
/*
private ArrayList<Point3D> build(Point3D a, Point3D b) {
int n = nx;
double dx = (b.x - a.x) / n;
double dy = (b.y - a.y) / n;
double d = Math.sqrt(dx * dx + dy * dy);
dx = dx / d;
dy = dy / d;
double x = a.x;
double y = a.y;
double azimuth = Math.atan2(dy, dx);
ArrayList<Point3D> list = new ArrayList<Point3D>();
boolean flag = true;
double diprev = Double.MAX_VALUE;
for (int p = 0; p < n & flag; p++) {
double max = -Double.MAX_VALUE;
double amax = 0;
for (double an = azimuth - azimuthRange; an <= azimuth + azimuthRange; an += azimuthRange * 0.5) {
double ox = Math.cos(an);
double oy = Math.sin(an);
double xn = x + elementLength * ox;
double yn = y + elementLength * oy;
double v = 0.0;
double goal = 1000 -b.distanceLateral(new Point3D(xn, yn, 0));
v += lambdaBackground * substrat.getInterpolatedPixel(xn, yn, 0);
//v += lambdaDirection * (dx * ox + dy * oy );
v += lambdaRandom * rand.nextGaussian(0, 2);
v += lambdaDirection*goal;
if (v > max) {
max = v;
amax = an;
}
}
azimuth = amax;
x = x + elementLength * Math.cos(amax);
y = y + elementLength * Math.sin(amax);
Point3D pc = new Point3D(x, y, 0);
double di = b.distanceLateral(pc);
System.out.println("Distanace to b " + di);
if (di < 10) {
list.add(b);
break;
}
if (diprev < di) {
list.add(b);
break;
}
list.add(pc);
diprev = di;
}
ArrayList<Point3D> flat = new ArrayList<Point3D>();
for (int i = 0; i < list.size(); i++) {
Point3D p = list.get(i);
if (p.x > 0 && p.x < nx)
if (p.y > 0 && p.y < ny) {
flat.add(p);
}
}
double dz = (b.z - a.z) / flat.size();
for (int i = 0; i < flat.size(); i++)
list.get(i).z = a.z + i*dz;
ArrayList<Point3D> inside = new ArrayList<Point3D>();
for (int i = 0; i < flat.size(); i++) {
Point3D p = flat.get(i);
if (p.x > -nz/2 && p.z < nz/2)
inside.add(p);
}
System.out.println("nbpoints " + list.size() + " > " + " > " + flat.size() + " > " + inside.size());
return inside;
}
*/
private double r(double bottom, double top) {
if (bottom < top)
return rand.nextDouble(bottom, top);
else
return rand.nextDouble(top, bottom);
}
public ImageWare createSubstrat() {
ImageWare substrat = Builder.create(nx, ny, 1, ImageWare.FLOAT);
for (int i = 0; i < nx; i++)
for (int j = 0; j < ny; j++)
{
substrat.putPixel(i, j, 0, rand.nextDouble(-20, 20));
}
for (int i = 0; i < 15; i++) {
int sign = i % 2 == 0 ? -1 : 1;
int x = rand.nextInteger(10, nx - 10);
int y = rand.nextInteger(10, nx - 10);
for (int j = 0; j < 1000000; j++) {
x += rand.nextInteger(-2, 2);
y += rand.nextInteger(-1, 1);
substrat.putPixel(x, y, 0, sign * 20);
}
}
substrat.smoothGaussian(6);
return substrat;
}
public class MTCanvas extends ImageCanvas {
private ArrayList<ArrayList<Point3D>> tubes;
private ArrayList<Point3D> points;
public MTCanvas(ImagePlus imp, ArrayList<ArrayList<Point3D>> tubes, ArrayList<Point3D> points) {
super(imp);
this.tubes = tubes;
this.points = points;
if (imp.getStackSize() > 1)
imp.setWindow(new StackWindow(imp, this));
else
imp.setWindow(new ImageWindow(imp, this));
}
@Override
public void paint(Graphics g) {
super.paint(g);
for (Point3D point : points) {
g.setColor(Color.ORANGE);
g.fillOval(screenXD(point.x) - 3, screenYD(point.y) - 3, 7, 7);
}
for (ArrayList<Point3D> tube : tubes) {
Color c = Color.getHSBColor((float) (tube.get(0).z / nz), 1, 1);
g.setColor(c);
for (int p = 1; p < tube.size(); p++) {
Point3D prev = tube.get(p - 1);
Point3D curr = tube.get(p);
g.drawLine(screenXD(prev.x), screenYD(prev.y), screenXD(curr.x), screenYD(curr.y));
}
}
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/sample/Reticulum_Generator_2016.java | .java | 12,617 | 368 | package smlms.sample;
import ij.IJ;
import ij.ImagePlus;
import ij.gui.ImageCanvas;
import ij.gui.ImageWindow;
import ij.gui.StackWindow;
import ij.process.FloatProcessor;
import java.awt.Color;
import java.awt.Graphics;
import java.util.ArrayList;
import smlms.tools.NormalizedVariable;
import smlms.tools.Point3D;
import smlms.tools.PsRandom;
public class Reticulum_Generator_2016 {
private PsRandom rand = new PsRandom(123);
private int margin = 0;
private int nx = 640 + margin + margin; // 320 * 20 = 6400nm
private int ny = 640 + margin + margin; // 320 * 20 = 6400nm
private int nz = 150; // 25 * 20 = 1500nm
private double pixelsize = 10;
private double jitterExtremityAngleRadian = 0; //0.02;
private int nbExtremity = 12;
private double radiusTube = 150;
private double thickTube = 6;
private String outfile = "/Users/sage/Desktop/SampleER0.txt";
public static void main(String args[]) {
new Reticulum_Generator_2016();
}
public Reticulum_Generator_2016() {
System.out.println("Endoplasmic Reticulum");
ImagePlus imp = new ImagePlus("background", new FloatProcessor(640, 640));
imp.show();
Point3D extremities[] = createExtremity();
ArrayList<ArrayList<Point3D>> lists = build();
IJ.log("Number of tubes: " + lists.size());
new MTCanvas(imp, lists, nz, extremities);
Sample sample = new Sample("name", (int)((nx-2*margin)*pixelsize), (int)((ny-2*margin)*pixelsize), (int)((nz)*pixelsize));
for(int i=0; i<lists.size(); i++) {
NormalizedVariable radius = new NormalizedVariable(radiusTube);
NormalizedVariable thickness = new NormalizedVariable(thickTube);
ArrayList<Point3D> nodes = new ArrayList<Point3D>();
ArrayList<Point3D> list = lists.get(i);
for(int k=0; k<list.size(); k++) {
Point3D p = list.get(k);
nodes.add(new Point3D((p.x-margin)*pixelsize, (p.y-margin)*pixelsize, p.z*pixelsize));
}
Tube tube = new Tube("tube " + i, nodes, radius, thickness, 1);
IJ.log(" Radius 0.0: " + radius.get(0.0));
IJ.log(" Radius 0.5: " + radius.get(0.5));
IJ.log(" Radius 1.0: " + radius.get(1.0));
sample.add(tube);
}
sample.save(outfile);
}
private ArrayList<ArrayList<Point3D>> build() {
ArrayList<Point3D> my1 = new ArrayList<Point3D>();
my1.add(new Point3D( -20 , 266.5 , 700 ));
my1.add(new Point3D( 44 , 268.5 , 147 ));
my1.add(new Point3D( 74 , 262.5 , 148 ));
my1.add(new Point3D( 104 , 260.5 , 149 ));
my1.add(new Point3D( 128 , 266.5 , 150 ));
my1.add(new Point3D( 144 , 287.5 , 151 ));
my1.add(new Point3D( 164 , 298.5 , 152 ));
my1.add(new Point3D( 190 , 302.5 , 153 ));
my1.add(new Point3D( 216 , 313.5 , 154 ));
my1.add(new Point3D( 246 , 327.5 , 155 ));
my1.add(new Point3D( 271 , 329.5 , 156 ));
my1.add(new Point3D( 299 , 325.5 , 157 ));
my1.add(new Point3D( 320 , 305.5 , 158 ));
my1.add(new Point3D( 343 , 290.5 , 159 ));
my1.add(new Point3D( 367 , 286.5 , 160 ));
my1.add(new Point3D( 379 , 259.5 , 161 ));
my1.add(new Point3D( 378 , 229.5 , 162 ));
my1.add(new Point3D( 385 , 205.5 , 163 ));
my1.add(new Point3D( 404 , 195.5 , 164 ));
my1.add(new Point3D( 445 , 202.5 , 165 ));
my1.add(new Point3D( 469 , 224.5 , 166 ));
my1.add(new Point3D( 490 , 242.5 , 167 ));
my1.add(new Point3D( 509 , 268.5 , 168 ));
my1.add(new Point3D( 533 , 272.5 , 169 ));
my1.add(new Point3D( 564 , 275.5 , 170 ));
my1.add(new Point3D( 581 , 254.5 , 171 ));
my1.add(new Point3D( 603 , 225.5 , 172 ));
my1.add(new Point3D( 618 , 200.5 , 173 ));
my1.add(new Point3D( 650 , 187.5 , 174 ));
ArrayList<Point3D> my2 = new ArrayList<Point3D>();
my2.add(new Point3D( 35 , 401.5 , 176 ));
my2.add(new Point3D( 73 , 409.5 , 177 ));
my2.add(new Point3D( 97 , 403.5 , 178 ));
my2.add(new Point3D( 120 , 383.5 , 179 ));
my2.add(new Point3D( 138 , 363.5 , 180 ));
my2.add(new Point3D( 151 , 327.5 , 181 ));
my2.add(new Point3D( 170 , 292.5 , 182 ));
my2.add(new Point3D( 181 , 258.5 , 183 ));
my2.add(new Point3D( 195 , 215.5 , 184 ));
my2.add(new Point3D( 209 , 178.5 , 185 ));
my2.add(new Point3D( 221 , 150.5 , 186 ));
my2.add(new Point3D( 247 , 134.5 , 187 ));
my2.add(new Point3D( 280 , 129.5 , 188 ));
my2.add(new Point3D( 317 , 136.5 , 189 ));
my2.add(new Point3D( 357 , 140.5 , 190 ));
my2.add(new Point3D( 392 , 157.5 , 191 ));
my2.add(new Point3D( 413 , 185.5 , 192 ));
my2.add(new Point3D( 432 , 221.5 , 193 ));
my2.add(new Point3D( 423 , 261.5 , 194 ));
ArrayList<Point3D> my3 = new ArrayList<Point3D>();
my3.add(new Point3D( 149 , 124.5 , 196 ));
my3.add(new Point3D( 186 , 155.5 , 197 ));
my3.add(new Point3D( 202 , 166.5 , 198 ));
my3.add(new Point3D( 225 , 188.5 , 199 ));
my3.add(new Point3D( 239 , 220.5 , 200 ));
my3.add(new Point3D( 247 , 247.5 , 201 ));
my3.add(new Point3D( 238 , 270.5 , 202 ));
my3.add(new Point3D( 211 , 285.5 , 203 ));
my3.add(new Point3D( 190 , 281.5 , 204 ));
my3.add(new Point3D( 169 , 269.5 , 205 ));
my3.add(new Point3D( 146 , 252.5 , 206 ));
my3.add(new Point3D( 125 , 254.5 , 207 ));
my3.add(new Point3D( 101 , 270.5 , 208 ));
my3.add(new Point3D( 80 , 299.5 , 209 ));
my3.add(new Point3D( 57 , 338.5 , 210 ));
my3.add(new Point3D( 55 , 373.5 , 211 ));
my3.add(new Point3D( 55 , 417.5 , 212 ));
my3.add(new Point3D( 59 , 454.5 , 213 ));
my3.add(new Point3D( 64 , 487.5 , 214 ));
my3.add(new Point3D( 73 , 518.5 , 215 ));
ArrayList<Point3D> my4 = new ArrayList<Point3D>();
my4.add(new Point3D( 142 , -20 , 217 ));
my4.add(new Point3D( 148 , 31.5 , 218 ));
my4.add(new Point3D( 176 , 54.5 , 219 ));
my4.add(new Point3D( 207 , 85.5 , 220 ));
my4.add(new Point3D( 239 , 104.5 , 221 ));
my4.add(new Point3D( 261 , 122.5 , 222 ));
my4.add(new Point3D( 279 , 153.5 , 223 ));
my4.add(new Point3D( 296 , 184.5 , 224 ));
my4.add(new Point3D( 307 , 217.5 , 225 ));
my4.add(new Point3D( 325 , 233.5 , 226 ));
my4.add(new Point3D( 343 , 258.5 , 227 ));
my4.add(new Point3D( 334 , 292.5 , 228 ));
my4.add(new Point3D( 347 , 335.5 , 229 ));
my4.add(new Point3D( 364 , 365.5 , 230 ));
my4.add(new Point3D( 384 , 389.5 , 231 ));
my4.add(new Point3D( 416 , 404.5 , 232 ));
my4.add(new Point3D( 459 , 407.5 , 233 ));
my4.add(new Point3D( 495 , 409.5 , 234 ));
my4.add(new Point3D( 534 , 408.5 , 235 ));
my4.add(new Point3D( 564 , 406.5 , 236 ));
my4.add(new Point3D( 601 , 413.5 , 237 ));
my4.add(new Point3D( 650 , 432.5 , 238 ));
ArrayList<Point3D> my5 = new ArrayList<Point3D>();
my5.add(new Point3D( 562 , 650 , 241 ));
my5.add(new Point3D( 547 , 605.5 , 242 ));
my5.add(new Point3D( 545 , 569.5 , 243 ));
my5.add(new Point3D( 538 , 524.5 , 244 ));
my5.add(new Point3D( 534 , 485.5 , 245 ));
my5.add(new Point3D( 528 , 449.5 , 246 ));
my5.add(new Point3D( 520 , 413.5 , 247 ));
my5.add(new Point3D( 498 , 381.5 , 248 ));
my5.add(new Point3D( 480 , 346.5 , 249 ));
my5.add(new Point3D( 445 , 330.5 , 250 ));
my5.add(new Point3D( 425 , 313.5 , 251 ));
my5.add(new Point3D( 419 , 287.5 , 252 ));
my5.add(new Point3D( 410 , 261.5 , 253 ));
my5.add(new Point3D( 395 , 250.5 , 254 ));
my5.add(new Point3D( 367 , 240.5 , 255 ));
my5.add(new Point3D( 355 , 242.5 , 256 ));
ArrayList<Point3D> my6= new ArrayList<Point3D>();
my6.add(new Point3D( -20 , 327.5 , 258 ));
my6.add(new Point3D( 28 , 322.5 , 259 ));
my6.add(new Point3D( 56 , 322.5 , 260 ));
my6.add(new Point3D( 97 , 325.5 , 261 ));
my6.add(new Point3D( 131 , 330.5 , 262 ));
my6.add(new Point3D( 156 , 338.5 , 263 ));
my6.add(new Point3D( 182 , 352.5 , 264 ));
my6.add(new Point3D( 204 , 376.5 , 265 ));
my6.add(new Point3D( 218 , 402.5 , 266 ));
my6.add(new Point3D( 250 , 414.5 , 267 ));
my6.add(new Point3D( 276 , 424.5 , 268 ));
my6.add(new Point3D( 297 , 425.5 , 269 ));
my6.add(new Point3D( 330 , 419.5 , 270 ));
my6.add(new Point3D( 351 , 412.5 , 271 ));
my6.add(new Point3D( 373 , 390.5 , 272 ));
my6.add(new Point3D( 392 , 371.5 , 273 ));
my6.add(new Point3D( 412 , 338.5 , 274 ));
my6.add(new Point3D( 431 , 313.5 , 275 ));
my6.add(new Point3D( 453 , 291.5 , 276 ));
my6.add(new Point3D( 478 , 258.5 , 277 ));
my6.add(new Point3D( 491 , 233.5 , 278 ));
my6.add(new Point3D( 507 , 209.5 , 279 ));
my6.add(new Point3D( 519 , 195.5 , 280 ));
my6.add(new Point3D( 540 , 181.5 , 281 ));
ArrayList<ArrayList<Point3D>> lists = new ArrayList<ArrayList<Point3D>>();
lists.add(my1);
lists.add(my2);
lists.add(my3);
lists.add(my4);
lists.add(my5);
lists.add(my6);
for ( ArrayList<Point3D> list : lists)
for (Point3D p : list) {
double t = p.x; p.x = p.y; p.y = t;
}
IJ.log(" \n");
int er = 0;
NormalizedVariable[] z = new NormalizedVariable[6];
double n[] = new double[6];
// 1
n[er] = lists.get(er).size();
z[er] = new NormalizedVariable(68);
z[er].addLinear(00, -32);
z[er].addCosine(3, 2.5, 0.3);
z[er].addUniformRandom(0, 3);
for (int k=0; k<n[er]; k++) { // 1
lists.get(er).get(k).z = z[er].get(k/(n[er]-1));
IJ.log(" ER" + (er+1) + " " + lists.get(er).get(k).z);
}
IJ.log(" \n");
er++;
// 2
n[er] = lists.get(er).size();
z[er] = new NormalizedVariable(0);
z[er].addLinear(170, -50);
z[er].addCosine(5, 0.3, 1.57);
z[er].addUniformRandom(0, 3);
for (int k=0; k<n[er]; k++) { // 1
lists.get(er).get(k).z = z[er].get(k/(n[er]-1));
IJ.log(" ER" + (er+1) + " " + lists.get(er).get(k).z);
}
IJ.log(" \n");
er++;
// 3
n[er] = lists.get(er).size();
z[er] = new NormalizedVariable(18);
z[er].addCosine(19, 1.1, 1.57);
z[er].addUniformRandom(0, 3);
for (int k=0; k<n[er]; k++) { // 1
lists.get(er).get(k).z = z[er].get(k/(n[er]-1));
IJ.log(" ER" + (er+1) + " " + lists.get(er).get(k).z);
}
IJ.log(" \n");
er++;
// 4
n[er] = lists.get(er).size();
z[er] = new NormalizedVariable(0);
z[er].addLinear(95, 50);
z[er].addCosine(2, 2.1, 1.57);
z[er].addUniformRandom(0, 3);
for (int k=0; k<n[er]; k++) { // 1
lists.get(er).get(k).z = z[er].get(k/(n[er]-1));
IJ.log(" ER" + (er+1) + " " + lists.get(er).get(k).z);
}
IJ.log(" \n");
er++;
// 4
n[er] = lists.get(er).size();
z[er] = new NormalizedVariable(0);
z[er].addLinear(90, 160);
z[er].addCosine(2, 3.1, 1.57);
z[er].addUniformRandom(0, 3);
for (int k=0; k<n[er]; k++) { // 1
lists.get(er).get(k).z = z[er].get(k/(n[er]-1));
IJ.log(" ER" + (er+1) + " " + lists.get(er).get(k).z);
}
IJ.log(" \n");
er++;
// 6
n[er] = lists.get(er).size();
z[er] = new NormalizedVariable(0);
z[er].addLinear(100, -1);
z[er].addCosine(10, 2.1, 1.57);
z[er].addUniformRandom(0, 3);
for (int k=0; k<n[er]; k++) { // 1
lists.get(er).get(k).z = z[er].get(k/(n[er]-1));
IJ.log(" ER" + (er+1) + " " + lists.get(er).get(k).z);
}
return lists;
}
private Point3D[] createExtremity() {
int xc = nx / 2;
int yc = ny / 2;
Point3D extremities[] = new Point3D[nbExtremity];
for (int a = 0; a < nbExtremity; a++) {
double r = a * 2 * Math.PI / nbExtremity + rand.nextDouble(-jitterExtremityAngleRadian, jitterExtremityAngleRadian);
double xp = xc + nx * Math.cos(r)* 0.49;
double yp = yc + ny * Math.sin(r) * 0.49;
double zp = nz * 0.5 + nz*rand.nextDouble(-0.4, 0.4);
extremities[a] = new Point3D(xp, yp, zp);
IJ.log("Extremity " + extremities[a].x + " " + extremities[a].y + " " + extremities[a].z);
}
return extremities;
}
public class MTCanvas extends ImageCanvas {
private ArrayList<ArrayList<Point3D>> tubes;
private int nz;
private Point3D extremities[];
public MTCanvas(ImagePlus imp, ArrayList<ArrayList<Point3D>> tubes, int nz, Point3D extremities[]) {
super(imp);
this.tubes = tubes;
this.extremities = extremities;
this.nz = nz;
if (imp.getStackSize() > 1)
imp.setWindow(new StackWindow(imp, this));
else
imp.setWindow(new ImageWindow(imp, this));
}
@Override
public void paint(Graphics g) {
super.paint(g);
for(Point3D extremity : extremities) {
g.setColor(Color.WHITE);
g.fillOval(screenXD(extremity.x)-3, screenYD(extremity.y)-3, 7, 7);
}
for(ArrayList<Point3D> tube : tubes) {
Color c = Color.getHSBColor((float)(tube.get(0).z/nz), 1, 1);
g.setColor(c);
g.fillOval(screenXD(tube.get(0).x)-3, screenYD(tube.get(0).y)-3, 7, 7);
for(int p=1; p<tube.size(); p++) {
Point3D prev = tube.get(p-1);
Point3D curr = tube.get(p);
g.drawLine(screenXD(prev.x), screenYD(prev.y), screenXD(curr.x), screenYD(curr.y));
}
}
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/sample/CurveSegment.java | .java | 5,504 | 188 | package smlms.sample;
//=========================================================================================
//
//Project: Localization Microscopy
//
//Author : Daniel Sage, Biomedical Imaging Group (BIG), http://bigwww.epfl.ch/sage/
//
//Organization: Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
//
//Conditions of use: You'll be free to use this software for research purposes, but you
//should not redistribute it without our consent. In addition, we expect you to include a
//citation or acknowledgment whenever you present or publish results that are based on it.
//
//=========================================================================================
import ij.IJ;
import ij.gui.Line;
import ij.gui.OvalRoi;
import ij.gui.Overlay;
import imageware.Builder;
import imageware.ImageWare;
import java.awt.Color;
import java.util.ArrayList;
import smlms.tools.Point3D;
public class CurveSegment {
private static double accuracy = 0.001;
private Point3D nodes[];
private ArrayList<Point3D> samples = new ArrayList<Point3D>();
public CurveSegment(Point3D nodes[], double sampling, double sigma) {
this.nodes = nodes;
this.samples = getSamplesInterval(sampling, sigma);
}
public CurveSegment(ArrayList<Point3D> vnodes, double sampling, double sigma) {
int n = vnodes.size();
this.nodes = new Point3D[n];
for (int i = 0; i < n; i++)
this.nodes[i] = vnodes.get(i);
this.samples = getSamplesInterval(sampling, sigma);
}
public Point3D[] getNodes() {
return nodes;
}
public Point3D getFirstNode() {
return nodes[0];
}
public ArrayList<Point3D> getInitialSamples() {
return samples;
}
public ArrayList<Point3D> getSamplesNumber(int nsamples) {
double len = getLength();
double step = len / (nsamples - 1);
return getSamplesInterval(step, 0);
}
public ArrayList<Point3D> getSamplesInterval(double step, double sigma) {
ArrayList<Point3D> points = new ArrayList<Point3D>();
for (int k = 1; k < nodes.length; k++) {
double d = nodes[k-1].distance(nodes[k]);
if (d > step*0.1) {
int n = (int)(d/step);
double stepCorr = d / n;
double nn = d / stepCorr;
double dx = (nodes[k].x - nodes[k-1].x) / nn;
double dy = (nodes[k].y - nodes[k-1].y) / nn;
double dz = (nodes[k].z - nodes[k-1].z) / nn;
for (int i = 0; i<=n; i++) {
Point3D p = new Point3D(dx * i + nodes[k-1].x, dy * i + nodes[k-1].y, dz * i + nodes[k-1].z);
points.add(p);
}
}
}
IJ.log(" CurveSegment : nodes= " + nodes.length + " len:" +getLength() + " step=" + step + " nbsamples=" + points.size());
int n = points.size();
double x[] = new double[n];
double y[] = new double[n];
double z[] = new double[n];
for(int i=0; i<n; i++) {
Point3D p = points.get(i);
x[i] = p.x;
y[i] = p.y;
z[i] = p.z;
}
ImageWare ix = Builder.create(x);
ImageWare iy = Builder.create(y);
ImageWare iz = Builder.create(z);
System.out.print(" ---------------------------------------------- SMOOTH " + sigma);
if (sigma > 0.1) {
ix.smoothGaussian(sigma);
iy.smoothGaussian(sigma);
iz.smoothGaussian(sigma);
}
ArrayList<Point3D> spoints = new ArrayList<Point3D>();
for(int i=0; i<n; i++) {
spoints.add(new Point3D(ix.getPixel(i, 0, 0), iy.getPixel(i, 0, 0), iz.getPixel(i, 0, 0)));
}
return spoints;
}
public Point3D getClosestPoint(ArrayList<Point3D> list, Point3D a) {
Point3D best = a;
double min = Double.MAX_VALUE;
for (Point3D p : list) {
double d = p.distance(a);
if (d < min) {
min = d;
best = p;
}
}
return best;
}
public double getLength() {
double len = 0.0;
for (int k = 1; k < nodes.length; k++) {
len += nodes[k-1].distance(nodes[k]);
}
return len;
}
private Point3D[] extend(Point3D nodes[]) {
int nnodes = nodes.length;
Point3D extendedNodes[] = new Point3D[nnodes + 2];
extendedNodes[0] = nodes[0];
for (int i = 0; i < nnodes; i++)
extendedNodes[i + 1] = nodes[i];
extendedNodes[nnodes + 1] = nodes[nnodes - 1];
return extendedNodes;
}
public void overlayNodes(Overlay overlay) {
int nnodes = nodes.length;
for (int k = 0; k < nnodes; k++) {
int x = (int) Math.round(nodes[k].x);
int y = (int) Math.round(nodes[k].y);
OvalRoi roi = new OvalRoi(x - 5, y - 5, 10, 10);
roi.setStrokeColor(Color.RED);
overlay.add(roi);
}
}
public void overlayInitialSamples(Overlay overlay, Color color, int radiusSample, Color colorPoint) {
overlaySamples(samples, overlay, color, radiusSample, colorPoint);
}
public void overlaySamples(ArrayList<Point3D> points, Overlay overlay, Color color, int radiusSample, Color colorPoint) {
for (int s = 1; s < points.size(); s++) {
int x1 = (int) Math.round(points.get(s - 1).x);
int y1 = (int) Math.round(points.get(s - 1).y);
int x2 = (int) Math.round(points.get(s).x);
int y2 = (int) Math.round(points.get(s).y);
Line line = new Line(x1, y1, x2, y2);
line.setStrokeColor(color);
overlay.add(line);
if (radiusSample > 0) {
OvalRoi roi = new OvalRoi(x2 - radiusSample, y2 - radiusSample, 2 * radiusSample, 2 * radiusSample);
roi.setStrokeColor(colorPoint);
overlay.add(roi);
}
}
if (radiusSample > 0) {
int x2 = (int) Math.round(points.get(0).x);
int y2 = (int) Math.round(points.get(0).y);
OvalRoi roi = new OvalRoi(x2 - radiusSample, y2 - radiusSample, 2 * radiusSample, 2 * radiusSample);
roi.setStrokeColor(colorPoint);
overlay.add(roi);
}
}
}
| Java |
2D | SMLM-Challenge/Challenge2016 | Simulator/Simulator-Java/src/smlms/sample/Microtubules_Generator_Training.java | .java | 10,240 | 286 | package smlms.sample;
import ij.IJ;
import ij.ImagePlus;
import ij.gui.ImageCanvas;
import ij.gui.ImageWindow;
import ij.gui.StackWindow;
import ij.process.FloatProcessor;
import imageware.ImageWare;
import java.awt.Color;
import java.awt.Graphics;
import java.util.ArrayList;
import smlms.tools.NormalizedVariable;
import smlms.tools.Point3D;
import smlms.tools.PsRandom;
public class Microtubules_Generator_Training {
private PsRandom rand = new PsRandom(123);
private int margin = 0;
private int nx = 320 + margin + margin;
private int ny = 320 + margin + margin;
private int nz = 75;
private double pixelsize = 10;
private double lateralRandomExtremity = 10;
private double jitterExtremityAngleRadian = 0.02;
private int nbExtremity = 12;
private int rangeExtremityOpposition = 2;
private double elementLength = 6;
private double radiusTube = 12;
private double thickTube = 6;
private ImageWare collision;
private ImageWare background;
private String outfile = "/Users/sage/Desktop/SampleMT-Training.txt";
public static void main(String args[]) {
new Microtubules_Generator_Training();
}
public Microtubules_Generator_Training() {
System.out.println("Microtubule Training Reticulum");
ImagePlus imp = new ImagePlus("background", new FloatProcessor(640, 640));
imp.show();
Point3D extremities[] = createExtremity();
ArrayList<ArrayList<Point3D>> lists = build();
IJ.log("Number of tubes: " + lists.size());
new MTCanvas(imp, lists, nz, extremities);
Sample sample = new Sample("name", 6400, 6400, 1500);
for(int i=0; i<lists.size(); i++) {
NormalizedVariable radius = new NormalizedVariable(radiusTube);
NormalizedVariable thickness = new NormalizedVariable(thickTube);
ArrayList<Point3D> nodes = new ArrayList<Point3D>();
ArrayList<Point3D> list = lists.get(i);
for(int k=0; k<list.size(); k++) {
Point3D p = list.get(k);
nodes.add(new Point3D((p.x-margin)*pixelsize, (p.y-margin)*pixelsize, p.z*pixelsize));
}
Tube tube = new Tube("tube " + i, nodes, radius, thickness, 1);
IJ.log(" list: " + i + " " + list.size() + " first " + list.get(0));
IJ.log(" Radius 0.0: " + radius.get(0.0));
IJ.log(" Radius 0.5: " + radius.get(0.5));
IJ.log(" Radius 1.0: " + radius.get(1.0));
sample.add(tube);
}
sample.save(outfile); }
private ArrayList<ArrayList<Point3D>> build() {
ArrayList<Point3D> my1 = new ArrayList<Point3D>();
my1.add(new Point3D( -20 , 266.5 , 10.0 + 75 ));
my1.add(new Point3D( 74 , 268.3 , 11.7 + 75 ));
my1.add(new Point3D( 124 , 262.5 , 12.8 + 75 ));
my1.add(new Point3D( 184 , 240.4 , 13.9 + 75 ));
my1.add(new Point3D( 228 , 256.5 , 14.0 + 75 ));
my1.add(new Point3D( 284 , 267.5 , 15.1 + 75));
my1.add(new Point3D( 324 , 298.5 , 16.2 + 75 ));
my1.add(new Point3D( 380 , 302.6 , 16.3 + 75 ));
my1.add(new Point3D( 446 , 313.5 , 16.4 + 75 ));
my1.add(new Point3D( 506 , 327.7 , 16.5 + 75 ));
my1.add(new Point3D( 561 , 340.5 , 16.0 + 75 ));
my1.add(new Point3D( 619 , 325.5 , 15.7 + 75 ));
my1.add(new Point3D( 650 , 305.5 , 15.8 + 75 ));
CurveSpline c1 = new CurveSpline(my1, 1);
ArrayList<Point3D> mys1 = c1.getSamplesInterval(5);
ArrayList<Point3D> my2 = new ArrayList<Point3D>(); // green
my2.add(new Point3D( -20 , 401.5 , -70.6 + 75 ));
my2.add(new Point3D( 73 , 409.5 , -65.7 + 75 ));
my2.add(new Point3D( 97 , 403.5 , -60.8 + 75 ));
my2.add(new Point3D( 120 , 383.5 , -52.9 + 75 ));
my2.add(new Point3D( 138 , 363.5 , -42.0 + 75 ));
my2.add(new Point3D( 201 , 327.5 , -38.1 + 75 ));
my2.add(new Point3D( 270 , 292.5 , -28.2 + 75 ));
my2.add(new Point3D( 381 , 308.5 , -23.3 + 75 ));
my2.add(new Point3D( 405 , 315.5 , -21.4 + 75 ));
my2.add(new Point3D( 495 , 325.5 , -21.5 + 75 ));
my2.add(new Point3D( 609 , 335.5 , -22.5 + 75 ));
my2.add(new Point3D( 641 , 345.5 , -23.4 + 75 ));
CurveSpline c2 = new CurveSpline(my2, 1);
ArrayList<Point3D> mys2 = c2.getSamplesInterval(5);
ArrayList<Point3D> my3 = new ArrayList<Point3D>(); // top, horiz
my3.add(new Point3D( 89 , -20 , 20.0 + 75 ));
my3.add(new Point3D( 80 , 105.5 , 21.2 + 75 ));
my3.add(new Point3D( 102 , 143.5 , 24.0 + 75 ));
my3.add(new Point3D( 142 , 216.5 , 24.0 + 75 ));
my3.add(new Point3D( 205 , 236.5 , 26.2 + 75 ));
my3.add(new Point3D( 239 , 269.5 , 28.0 + 75 ));
my3.add(new Point3D( 200 , 330.5 , 29.0 + 75 ));
my3.add(new Point3D( 207 , 385.5 , 32.1 + 75 ));//
my3.add(new Point3D( 244 , 405.5 , 52.6 + 75 ));
my3.add(new Point3D( 260 , 513.5 , 61.0 + 75 ));
my3.add(new Point3D( 305 , 609.5 , 63.5 + 75 ));
my3.add(new Point3D( 385 , 669.5 , 70.4 + 75 ));
CurveSpline c3 = new CurveSpline(my3, 1);
ArrayList<Point3D> mys3 = c3.getSamplesInterval(5);
ArrayList<ArrayList<Point3D>> lists = new ArrayList<ArrayList<Point3D>>();
lists.add(mys1);
lists.add(mys2);
lists.add(mys3);
for ( ArrayList<Point3D> list : lists)
for (Point3D p : list) {
double t = p.x; p.x = p.y; p.y = t;
}
/*
IJ.log(" \n");
int er = 0;
NormalizedVariable[] z = new NormalizedVariable[3];
double n[] = new double[3];
// 1
n[er] = lists.get(er).size();
z[er] = new NormalizedVariable(68);
z[er].addLinear(00, -32);
z[er].addCosine(3, 2.5, 0.3);
z[er].addUniformRandom(0, 3);
for (int k=0; k<n[er]; k++) { // 1
lists.get(er).get(k).z = z[er].get(k/(n[er]-1));
IJ.log(" ER" + (er+1) + " " + lists.get(er).get(k).z);
}
IJ.log(" \n");
er++;
// 2
n[er] = lists.get(er).size();
z[er] = new NormalizedVariable(0);
z[er].addLinear(170, -50);
z[er].addCosine(5, 0.3, 1.57);
z[er].addUniformRandom(0, 3);
for (int k=0; k<n[er]; k++) { // 1
lists.get(er).get(k).z = z[er].get(k/(n[er]-1));
IJ.log(" ER" + (er+1) + " " + lists.get(er).get(k).z);
}
IJ.log(" \n");
er++;
// 3
n[er] = lists.get(er).size();
z[er] = new NormalizedVariable(18);
z[er].addCosine(19, 1.1, 1.57);
z[er].addUniformRandom(0, 3);
for (int k=0; k<n[er]; k++) { // 1
lists.get(er).get(k).z = z[er].get(k/(n[er]-1));
IJ.log(" ER" + (er+1) + " " + lists.get(er).get(k).z);
}
*/
return lists;
}
private void draw(ArrayList<Point3D> list) {
for (int p = 1; p < list.size(); p++) {
int xp = (int) list.get(p).x;
int yp = (int) list.get(p).y;
int zp = (int) list.get(p).z;
int t = 3;
for (int k = 0; k < elementLength; k++) {
double dx = list.get(p).x - list.get(p - 1).x;
double dy = list.get(p).y - list.get(p - 1).y;
double dz = list.get(p).z - list.get(p - 1).z;
int xo = (int) Math.round(list.get(p - 1).x + k * dx / elementLength);
int yo = (int) Math.round(list.get(p - 1).y + k * dy / elementLength);
int zo = (int) Math.round(list.get(p - 1).z + k * dz / elementLength);
int nc = 5;
for(int x1=-nc; x1<=nc; x1++)
for(int y1=-nc; y1<=nc; y1++)
for(int z1=-nc; z1<=nc; z1++) {
collision.putPixel(xo+x1, yo+y1, zo+z1, 1);
}
}
for (int i = -t; i <= t; i++)
for (int j = -t; j <= t; j++)
for (int k = -t; k <= t; k++)
background.putPixel(xp + i, yp + j, zp + k, background.getPixel(xp + i, yp + j, zp + k) - 12);
}
}
private Point3D[] createTubeExtremity(Point3D[] extremities) {
int next = extremities.length;
int ext1 = (int) Math.abs(Math.round(rand.nextDouble(0, next - 1)));
ext1 = ext1 % next;
int ext2 = ext1 + next / 2 + (int) Math.abs(Math.round(rand.nextDouble(-rangeExtremityOpposition, rangeExtremityOpposition)));
ext2 = ext2 % next;
Point3D e1 = new Point3D(extremities[ext1].x, extremities[ext1].y, extremities[ext1].z);
Point3D e2 = new Point3D(extremities[ext2].x, extremities[ext2].y, extremities[ext2].z);
e1.x += rand.nextDouble(-lateralRandomExtremity, lateralRandomExtremity);
e1.y += rand.nextDouble(-lateralRandomExtremity, lateralRandomExtremity);
e1.z += rand.nextDouble(-lateralRandomExtremity, lateralRandomExtremity);
e2.x += rand.nextDouble(-lateralRandomExtremity, lateralRandomExtremity);
e2.y += rand.nextDouble(-lateralRandomExtremity, lateralRandomExtremity);
e2.z += rand.nextDouble(-lateralRandomExtremity, lateralRandomExtremity);
return new Point3D[] { e1, e2 };
}
private Point3D[] createExtremity() {
int xc = nx / 2;
int yc = ny / 2;
Point3D extremities[] = new Point3D[nbExtremity];
for (int a = 0; a < nbExtremity; a++) {
double r = a * 2 * Math.PI / nbExtremity + rand.nextDouble(-jitterExtremityAngleRadian, jitterExtremityAngleRadian);
double xp = xc + nx * Math.cos(r) * 0.49;
double yp = yc + ny * Math.sin(r) * 0.49;
double zp = nz * 0.5 + nz * rand.nextDouble(-0.4, 0.4);
extremities[a] = new Point3D(xp, yp, zp);
IJ.log("Extremity " + extremities[a].x + " " + extremities[a].y + " " + extremities[a].z);
}
return extremities;
}
public class MTCanvas extends ImageCanvas {
private ArrayList<ArrayList<Point3D>> tubes;
private int nz;
private Point3D extremities[];
public MTCanvas(ImagePlus imp, ArrayList<ArrayList<Point3D>> tubes, int nz, Point3D extremities[]) {
super(imp);
this.tubes = tubes;
this.extremities = extremities;
this.nz = nz;
if (imp.getStackSize() > 1)
imp.setWindow(new StackWindow(imp, this));
else
imp.setWindow(new ImageWindow(imp, this));
}
@Override
public void paint(Graphics g) {
super.paint(g);
for (Point3D extremity : extremities) {
g.setColor(Color.WHITE);
g.fillOval(screenXD(extremity.x) - 3, screenYD(extremity.y) - 3, 7, 7);
}
for (ArrayList<Point3D> tube : tubes) {
Color c = Color.getHSBColor((float) (tube.get(0).z / nz), 1, 1);
g.setColor(c);
g.fillOval(screenXD(tube.get(0).x) - 3, screenYD(tube.get(0).y) - 3, 7, 7);
for (int p = 1; p < tube.size(); p++) {
Point3D prev = tube.get(p - 1);
Point3D curr = tube.get(p);
g.drawLine(screenXD(prev.x), screenYD(prev.y), screenXD(curr.x), screenYD(curr.y));
}
}
}
}
}
| Java |
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