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 |
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2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/diagonalmatrices.cpp | .cpp | 7,157 | 167 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
using namespace std;
template<typename MatrixType> void diagonalmatrices(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime };
typedef Matrix<Scalar, Rows, 1> VectorType;
typedef Matrix<Scalar, 1, Cols> RowVectorType;
typedef Matrix<Scalar, Rows, Rows> SquareMatrixType;
typedef Matrix<Scalar, Dynamic, Dynamic> DynMatrixType;
typedef DiagonalMatrix<Scalar, Rows> LeftDiagonalMatrix;
typedef DiagonalMatrix<Scalar, Cols> RightDiagonalMatrix;
typedef Matrix<Scalar, Rows==Dynamic?Dynamic:2*Rows, Cols==Dynamic?Dynamic:2*Cols> BigMatrix;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols);
VectorType v1 = VectorType::Random(rows),
v2 = VectorType::Random(rows);
RowVectorType rv1 = RowVectorType::Random(cols),
rv2 = RowVectorType::Random(cols);
LeftDiagonalMatrix ldm1(v1), ldm2(v2);
RightDiagonalMatrix rdm1(rv1), rdm2(rv2);
Scalar s1 = internal::random<Scalar>();
SquareMatrixType sq_m1 (v1.asDiagonal());
VERIFY_IS_APPROX(sq_m1, v1.asDiagonal().toDenseMatrix());
sq_m1 = v1.asDiagonal();
VERIFY_IS_APPROX(sq_m1, v1.asDiagonal().toDenseMatrix());
SquareMatrixType sq_m2 = v1.asDiagonal();
VERIFY_IS_APPROX(sq_m1, sq_m2);
ldm1 = v1.asDiagonal();
LeftDiagonalMatrix ldm3(v1);
VERIFY_IS_APPROX(ldm1.diagonal(), ldm3.diagonal());
LeftDiagonalMatrix ldm4 = v1.asDiagonal();
VERIFY_IS_APPROX(ldm1.diagonal(), ldm4.diagonal());
sq_m1.block(0,0,rows,rows) = ldm1;
VERIFY_IS_APPROX(sq_m1, ldm1.toDenseMatrix());
sq_m1.transpose() = ldm1;
VERIFY_IS_APPROX(sq_m1, ldm1.toDenseMatrix());
Index i = internal::random<Index>(0, rows-1);
Index j = internal::random<Index>(0, cols-1);
VERIFY_IS_APPROX( ((ldm1 * m1)(i,j)) , ldm1.diagonal()(i) * m1(i,j) );
VERIFY_IS_APPROX( ((ldm1 * (m1+m2))(i,j)) , ldm1.diagonal()(i) * (m1+m2)(i,j) );
VERIFY_IS_APPROX( ((m1 * rdm1)(i,j)) , rdm1.diagonal()(j) * m1(i,j) );
VERIFY_IS_APPROX( ((v1.asDiagonal() * m1)(i,j)) , v1(i) * m1(i,j) );
VERIFY_IS_APPROX( ((m1 * rv1.asDiagonal())(i,j)) , rv1(j) * m1(i,j) );
VERIFY_IS_APPROX( (((v1+v2).asDiagonal() * m1)(i,j)) , (v1+v2)(i) * m1(i,j) );
VERIFY_IS_APPROX( (((v1+v2).asDiagonal() * (m1+m2))(i,j)) , (v1+v2)(i) * (m1+m2)(i,j) );
VERIFY_IS_APPROX( ((m1 * (rv1+rv2).asDiagonal())(i,j)) , (rv1+rv2)(j) * m1(i,j) );
VERIFY_IS_APPROX( (((m1+m2) * (rv1+rv2).asDiagonal())(i,j)) , (rv1+rv2)(j) * (m1+m2)(i,j) );
if(rows>1)
{
DynMatrixType tmp = m1.topRows(rows/2), res;
VERIFY_IS_APPROX( (res = m1.topRows(rows/2) * rv1.asDiagonal()), tmp * rv1.asDiagonal() );
VERIFY_IS_APPROX( (res = v1.head(rows/2).asDiagonal()*m1.topRows(rows/2)), v1.head(rows/2).asDiagonal()*tmp );
}
BigMatrix big;
big.setZero(2*rows, 2*cols);
big.block(i,j,rows,cols) = m1;
big.block(i,j,rows,cols) = v1.asDiagonal() * big.block(i,j,rows,cols);
VERIFY_IS_APPROX((big.block(i,j,rows,cols)) , v1.asDiagonal() * m1 );
big.block(i,j,rows,cols) = m1;
big.block(i,j,rows,cols) = big.block(i,j,rows,cols) * rv1.asDiagonal();
VERIFY_IS_APPROX((big.block(i,j,rows,cols)) , m1 * rv1.asDiagonal() );
// scalar multiple
VERIFY_IS_APPROX(LeftDiagonalMatrix(ldm1*s1).diagonal(), ldm1.diagonal() * s1);
VERIFY_IS_APPROX(LeftDiagonalMatrix(s1*ldm1).diagonal(), s1 * ldm1.diagonal());
VERIFY_IS_APPROX(m1 * (rdm1 * s1), (m1 * rdm1) * s1);
VERIFY_IS_APPROX(m1 * (s1 * rdm1), (m1 * rdm1) * s1);
// Diagonal to dense
sq_m1.setRandom();
sq_m2 = sq_m1;
VERIFY_IS_APPROX( (sq_m1 += (s1*v1).asDiagonal()), sq_m2 += (s1*v1).asDiagonal().toDenseMatrix() );
VERIFY_IS_APPROX( (sq_m1 -= (s1*v1).asDiagonal()), sq_m2 -= (s1*v1).asDiagonal().toDenseMatrix() );
VERIFY_IS_APPROX( (sq_m1 = (s1*v1).asDiagonal()), (s1*v1).asDiagonal().toDenseMatrix() );
sq_m1.setRandom();
sq_m2 = v1.asDiagonal();
sq_m2 = sq_m1 * sq_m2;
VERIFY_IS_APPROX( (sq_m1*v1.asDiagonal()).col(i), sq_m2.col(i) );
VERIFY_IS_APPROX( (sq_m1*v1.asDiagonal()).row(i), sq_m2.row(i) );
}
template<typename MatrixType> void as_scalar_product(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
typedef Matrix<Scalar, Dynamic, Dynamic> DynMatrixType;
typedef Matrix<Scalar, Dynamic, 1> DynVectorType;
typedef Matrix<Scalar, 1, Dynamic> DynRowVectorType;
Index rows = m.rows();
Index depth = internal::random<Index>(1,EIGEN_TEST_MAX_SIZE);
VectorType v1 = VectorType::Random(rows);
DynVectorType dv1 = DynVectorType::Random(depth);
DynRowVectorType drv1 = DynRowVectorType::Random(depth);
DynMatrixType dm1 = dv1;
DynMatrixType drm1 = drv1;
Scalar s = v1(0);
VERIFY_IS_APPROX( v1.asDiagonal() * drv1, s*drv1 );
VERIFY_IS_APPROX( dv1 * v1.asDiagonal(), dv1*s );
VERIFY_IS_APPROX( v1.asDiagonal() * drm1, s*drm1 );
VERIFY_IS_APPROX( dm1 * v1.asDiagonal(), dm1*s );
}
template<int>
void bug987()
{
Matrix3Xd points = Matrix3Xd::Random(3, 3);
Vector2d diag = Vector2d::Random();
Matrix2Xd tmp1 = points.topRows<2>(), res1, res2;
VERIFY_IS_APPROX( res1 = diag.asDiagonal() * points.topRows<2>(), res2 = diag.asDiagonal() * tmp1 );
Matrix2d tmp2 = points.topLeftCorner<2,2>();
VERIFY_IS_APPROX(( res1 = points.topLeftCorner<2,2>()*diag.asDiagonal()) , res2 = tmp2*diag.asDiagonal() );
}
void test_diagonalmatrices()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( diagonalmatrices(Matrix<float, 1, 1>()) );
CALL_SUBTEST_1( as_scalar_product(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( diagonalmatrices(Matrix3f()) );
CALL_SUBTEST_3( diagonalmatrices(Matrix<double,3,3,RowMajor>()) );
CALL_SUBTEST_4( diagonalmatrices(Matrix4d()) );
CALL_SUBTEST_5( diagonalmatrices(Matrix<float,4,4,RowMajor>()) );
CALL_SUBTEST_6( diagonalmatrices(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_6( as_scalar_product(MatrixXcf(1,1)) );
CALL_SUBTEST_7( diagonalmatrices(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_8( diagonalmatrices(Matrix<double,Dynamic,Dynamic,RowMajor>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_9( diagonalmatrices(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_9( diagonalmatrices(MatrixXf(1,1)) );
CALL_SUBTEST_9( as_scalar_product(MatrixXf(1,1)) );
}
CALL_SUBTEST_10( bug987<0>() );
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/basicstuff.cpp | .cpp | 10,604 | 279 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_NO_STATIC_ASSERT
#include "main.h"
template<typename MatrixType> void basicStuff(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
Index rows = m.rows();
Index cols = m.cols();
// this test relies a lot on Random.h, and there's not much more that we can do
// to test it, hence I consider that we will have tested Random.h
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols),
mzero = MatrixType::Zero(rows, cols),
square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>::Random(rows, rows);
VectorType v1 = VectorType::Random(rows),
vzero = VectorType::Zero(rows);
SquareMatrixType sm1 = SquareMatrixType::Random(rows,rows), sm2(rows,rows);
Scalar x = 0;
while(x == Scalar(0)) x = internal::random<Scalar>();
Index r = internal::random<Index>(0, rows-1),
c = internal::random<Index>(0, cols-1);
m1.coeffRef(r,c) = x;
VERIFY_IS_APPROX(x, m1.coeff(r,c));
m1(r,c) = x;
VERIFY_IS_APPROX(x, m1(r,c));
v1.coeffRef(r) = x;
VERIFY_IS_APPROX(x, v1.coeff(r));
v1(r) = x;
VERIFY_IS_APPROX(x, v1(r));
v1[r] = x;
VERIFY_IS_APPROX(x, v1[r]);
VERIFY_IS_APPROX( v1, v1);
VERIFY_IS_NOT_APPROX( v1, 2*v1);
VERIFY_IS_MUCH_SMALLER_THAN( vzero, v1);
VERIFY_IS_MUCH_SMALLER_THAN( vzero, v1.squaredNorm());
VERIFY_IS_NOT_MUCH_SMALLER_THAN(v1, v1);
VERIFY_IS_APPROX( vzero, v1-v1);
VERIFY_IS_APPROX( m1, m1);
VERIFY_IS_NOT_APPROX( m1, 2*m1);
VERIFY_IS_MUCH_SMALLER_THAN( mzero, m1);
VERIFY_IS_NOT_MUCH_SMALLER_THAN(m1, m1);
VERIFY_IS_APPROX( mzero, m1-m1);
// always test operator() on each read-only expression class,
// in order to check const-qualifiers.
// indeed, if an expression class (here Zero) is meant to be read-only,
// hence has no _write() method, the corresponding MatrixBase method (here zero())
// should return a const-qualified object so that it is the const-qualified
// operator() that gets called, which in turn calls _read().
VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows,cols)(r,c), static_cast<Scalar>(1));
// now test copying a row-vector into a (column-)vector and conversely.
square.col(r) = square.row(r).eval();
Matrix<Scalar, 1, MatrixType::RowsAtCompileTime> rv(rows);
Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> cv(rows);
rv = square.row(r);
cv = square.col(r);
VERIFY_IS_APPROX(rv, cv.transpose());
if(cols!=1 && rows!=1 && MatrixType::SizeAtCompileTime!=Dynamic)
{
VERIFY_RAISES_ASSERT(m1 = (m2.block(0,0, rows-1, cols-1)));
}
if(cols!=1 && rows!=1)
{
VERIFY_RAISES_ASSERT(m1[0]);
VERIFY_RAISES_ASSERT((m1+m1)[0]);
}
VERIFY_IS_APPROX(m3 = m1,m1);
MatrixType m4;
VERIFY_IS_APPROX(m4 = m1,m1);
m3.real() = m1.real();
VERIFY_IS_APPROX(static_cast<const MatrixType&>(m3).real(), static_cast<const MatrixType&>(m1).real());
VERIFY_IS_APPROX(static_cast<const MatrixType&>(m3).real(), m1.real());
// check == / != operators
VERIFY(m1==m1);
VERIFY(m1!=m2);
VERIFY(!(m1==m2));
VERIFY(!(m1!=m1));
m1 = m2;
VERIFY(m1==m2);
VERIFY(!(m1!=m2));
// check automatic transposition
sm2.setZero();
for(typename MatrixType::Index i=0;i<rows;++i)
sm2.col(i) = sm1.row(i);
VERIFY_IS_APPROX(sm2,sm1.transpose());
sm2.setZero();
for(typename MatrixType::Index i=0;i<rows;++i)
sm2.col(i).noalias() = sm1.row(i);
VERIFY_IS_APPROX(sm2,sm1.transpose());
sm2.setZero();
for(typename MatrixType::Index i=0;i<rows;++i)
sm2.col(i).noalias() += sm1.row(i);
VERIFY_IS_APPROX(sm2,sm1.transpose());
sm2.setZero();
for(typename MatrixType::Index i=0;i<rows;++i)
sm2.col(i).noalias() -= sm1.row(i);
VERIFY_IS_APPROX(sm2,-sm1.transpose());
// check ternary usage
{
bool b = internal::random<int>(0,10)>5;
m3 = b ? m1 : m2;
if(b) VERIFY_IS_APPROX(m3,m1);
else VERIFY_IS_APPROX(m3,m2);
m3 = b ? -m1 : m2;
if(b) VERIFY_IS_APPROX(m3,-m1);
else VERIFY_IS_APPROX(m3,m2);
m3 = b ? m1 : -m2;
if(b) VERIFY_IS_APPROX(m3,m1);
else VERIFY_IS_APPROX(m3,-m2);
}
}
template<typename MatrixType> void basicStuffComplex(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime> RealMatrixType;
Index rows = m.rows();
Index cols = m.cols();
Scalar s1 = internal::random<Scalar>(),
s2 = internal::random<Scalar>();
VERIFY(numext::real(s1)==numext::real_ref(s1));
VERIFY(numext::imag(s1)==numext::imag_ref(s1));
numext::real_ref(s1) = numext::real(s2);
numext::imag_ref(s1) = numext::imag(s2);
VERIFY(internal::isApprox(s1, s2, NumTraits<RealScalar>::epsilon()));
// extended precision in Intel FPUs means that s1 == s2 in the line above is not guaranteed.
RealMatrixType rm1 = RealMatrixType::Random(rows,cols),
rm2 = RealMatrixType::Random(rows,cols);
MatrixType cm(rows,cols);
cm.real() = rm1;
cm.imag() = rm2;
VERIFY_IS_APPROX(static_cast<const MatrixType&>(cm).real(), rm1);
VERIFY_IS_APPROX(static_cast<const MatrixType&>(cm).imag(), rm2);
rm1.setZero();
rm2.setZero();
rm1 = cm.real();
rm2 = cm.imag();
VERIFY_IS_APPROX(static_cast<const MatrixType&>(cm).real(), rm1);
VERIFY_IS_APPROX(static_cast<const MatrixType&>(cm).imag(), rm2);
cm.real().setZero();
VERIFY(static_cast<const MatrixType&>(cm).real().isZero());
VERIFY(!static_cast<const MatrixType&>(cm).imag().isZero());
}
#ifdef EIGEN_TEST_PART_2
void casting()
{
Matrix4f m = Matrix4f::Random(), m2;
Matrix4d n = m.cast<double>();
VERIFY(m.isApprox(n.cast<float>()));
m2 = m.cast<float>(); // check the specialization when NewType == Type
VERIFY(m.isApprox(m2));
}
#endif
template <typename Scalar>
void fixedSizeMatrixConstruction()
{
Scalar raw[4];
for(int k=0; k<4; ++k)
raw[k] = internal::random<Scalar>();
{
Matrix<Scalar,4,1> m(raw);
Array<Scalar,4,1> a(raw);
for(int k=0; k<4; ++k) VERIFY(m(k) == raw[k]);
for(int k=0; k<4; ++k) VERIFY(a(k) == raw[k]);
VERIFY_IS_EQUAL(m,(Matrix<Scalar,4,1>(raw[0],raw[1],raw[2],raw[3])));
VERIFY((a==(Array<Scalar,4,1>(raw[0],raw[1],raw[2],raw[3]))).all());
}
{
Matrix<Scalar,3,1> m(raw);
Array<Scalar,3,1> a(raw);
for(int k=0; k<3; ++k) VERIFY(m(k) == raw[k]);
for(int k=0; k<3; ++k) VERIFY(a(k) == raw[k]);
VERIFY_IS_EQUAL(m,(Matrix<Scalar,3,1>(raw[0],raw[1],raw[2])));
VERIFY((a==Array<Scalar,3,1>(raw[0],raw[1],raw[2])).all());
}
{
Matrix<Scalar,2,1> m(raw), m2( (DenseIndex(raw[0])), (DenseIndex(raw[1])) );
Array<Scalar,2,1> a(raw), a2( (DenseIndex(raw[0])), (DenseIndex(raw[1])) );
for(int k=0; k<2; ++k) VERIFY(m(k) == raw[k]);
for(int k=0; k<2; ++k) VERIFY(a(k) == raw[k]);
VERIFY_IS_EQUAL(m,(Matrix<Scalar,2,1>(raw[0],raw[1])));
VERIFY((a==Array<Scalar,2,1>(raw[0],raw[1])).all());
for(int k=0; k<2; ++k) VERIFY(m2(k) == DenseIndex(raw[k]));
for(int k=0; k<2; ++k) VERIFY(a2(k) == DenseIndex(raw[k]));
}
{
Matrix<Scalar,1,2> m(raw),
m2( (DenseIndex(raw[0])), (DenseIndex(raw[1])) ),
m3( (int(raw[0])), (int(raw[1])) ),
m4( (float(raw[0])), (float(raw[1])) );
Array<Scalar,1,2> a(raw), a2( (DenseIndex(raw[0])), (DenseIndex(raw[1])) );
for(int k=0; k<2; ++k) VERIFY(m(k) == raw[k]);
for(int k=0; k<2; ++k) VERIFY(a(k) == raw[k]);
VERIFY_IS_EQUAL(m,(Matrix<Scalar,1,2>(raw[0],raw[1])));
VERIFY((a==Array<Scalar,1,2>(raw[0],raw[1])).all());
for(int k=0; k<2; ++k) VERIFY(m2(k) == DenseIndex(raw[k]));
for(int k=0; k<2; ++k) VERIFY(a2(k) == DenseIndex(raw[k]));
for(int k=0; k<2; ++k) VERIFY(m3(k) == int(raw[k]));
for(int k=0; k<2; ++k) VERIFY((m4(k)) == Scalar(float(raw[k])));
}
{
Matrix<Scalar,1,1> m(raw), m1(raw[0]), m2( (DenseIndex(raw[0])) ), m3( (int(raw[0])) );
Array<Scalar,1,1> a(raw), a1(raw[0]), a2( (DenseIndex(raw[0])) );
VERIFY(m(0) == raw[0]);
VERIFY(a(0) == raw[0]);
VERIFY(m1(0) == raw[0]);
VERIFY(a1(0) == raw[0]);
VERIFY(m2(0) == DenseIndex(raw[0]));
VERIFY(a2(0) == DenseIndex(raw[0]));
VERIFY(m3(0) == int(raw[0]));
VERIFY_IS_EQUAL(m,(Matrix<Scalar,1,1>(raw[0])));
VERIFY((a==Array<Scalar,1,1>(raw[0])).all());
}
}
void test_basicstuff()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( basicStuff(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( basicStuff(Matrix4d()) );
CALL_SUBTEST_3( basicStuff(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_4( basicStuff(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_5( basicStuff(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_6( basicStuff(Matrix<float, 100, 100>()) );
CALL_SUBTEST_7( basicStuff(Matrix<long double,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_3( basicStuffComplex(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_5( basicStuffComplex(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
CALL_SUBTEST_1(fixedSizeMatrixConstruction<unsigned char>());
CALL_SUBTEST_1(fixedSizeMatrixConstruction<float>());
CALL_SUBTEST_1(fixedSizeMatrixConstruction<double>());
CALL_SUBTEST_1(fixedSizeMatrixConstruction<int>());
CALL_SUBTEST_1(fixedSizeMatrixConstruction<long int>());
CALL_SUBTEST_1(fixedSizeMatrixConstruction<std::ptrdiff_t>());
CALL_SUBTEST_2(casting());
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/product_trmv.cpp | .cpp | 4,242 | 91 | // This file is triangularView of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename MatrixType> void trmv(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
RealScalar largerEps = 10*test_precision<RealScalar>();
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m3(rows, cols);
VectorType v1 = VectorType::Random(rows);
Scalar s1 = internal::random<Scalar>();
m1 = MatrixType::Random(rows, cols);
// check with a column-major matrix
m3 = m1.template triangularView<Eigen::Lower>();
VERIFY((m3 * v1).isApprox(m1.template triangularView<Eigen::Lower>() * v1, largerEps));
m3 = m1.template triangularView<Eigen::Upper>();
VERIFY((m3 * v1).isApprox(m1.template triangularView<Eigen::Upper>() * v1, largerEps));
m3 = m1.template triangularView<Eigen::UnitLower>();
VERIFY((m3 * v1).isApprox(m1.template triangularView<Eigen::UnitLower>() * v1, largerEps));
m3 = m1.template triangularView<Eigen::UnitUpper>();
VERIFY((m3 * v1).isApprox(m1.template triangularView<Eigen::UnitUpper>() * v1, largerEps));
// check conjugated and scalar multiple expressions (col-major)
m3 = m1.template triangularView<Eigen::Lower>();
VERIFY(((s1*m3).conjugate() * v1).isApprox((s1*m1).conjugate().template triangularView<Eigen::Lower>() * v1, largerEps));
m3 = m1.template triangularView<Eigen::Upper>();
VERIFY((m3.conjugate() * v1.conjugate()).isApprox(m1.conjugate().template triangularView<Eigen::Upper>() * v1.conjugate(), largerEps));
// check with a row-major matrix
m3 = m1.template triangularView<Eigen::Upper>();
VERIFY((m3.transpose() * v1).isApprox(m1.transpose().template triangularView<Eigen::Lower>() * v1, largerEps));
m3 = m1.template triangularView<Eigen::Lower>();
VERIFY((m3.transpose() * v1).isApprox(m1.transpose().template triangularView<Eigen::Upper>() * v1, largerEps));
m3 = m1.template triangularView<Eigen::UnitUpper>();
VERIFY((m3.transpose() * v1).isApprox(m1.transpose().template triangularView<Eigen::UnitLower>() * v1, largerEps));
m3 = m1.template triangularView<Eigen::UnitLower>();
VERIFY((m3.transpose() * v1).isApprox(m1.transpose().template triangularView<Eigen::UnitUpper>() * v1, largerEps));
// check conjugated and scalar multiple expressions (row-major)
m3 = m1.template triangularView<Eigen::Upper>();
VERIFY((m3.adjoint() * v1).isApprox(m1.adjoint().template triangularView<Eigen::Lower>() * v1, largerEps));
m3 = m1.template triangularView<Eigen::Lower>();
VERIFY((m3.adjoint() * (s1*v1.conjugate())).isApprox(m1.adjoint().template triangularView<Eigen::Upper>() * (s1*v1.conjugate()), largerEps));
m3 = m1.template triangularView<Eigen::UnitUpper>();
// check transposed cases:
m3 = m1.template triangularView<Eigen::Lower>();
VERIFY((v1.transpose() * m3).isApprox(v1.transpose() * m1.template triangularView<Eigen::Lower>(), largerEps));
VERIFY((v1.adjoint() * m3).isApprox(v1.adjoint() * m1.template triangularView<Eigen::Lower>(), largerEps));
VERIFY((v1.adjoint() * m3.adjoint()).isApprox(v1.adjoint() * m1.template triangularView<Eigen::Lower>().adjoint(), largerEps));
// TODO check with sub-matrices
}
void test_product_trmv()
{
int s = 0;
for(int i = 0; i < g_repeat ; i++) {
CALL_SUBTEST_1( trmv(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( trmv(Matrix<float, 2, 2>()) );
CALL_SUBTEST_3( trmv(Matrix3d()) );
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
CALL_SUBTEST_4( trmv(MatrixXcf(s,s)) );
CALL_SUBTEST_5( trmv(MatrixXcd(s,s)) );
TEST_SET_BUT_UNUSED_VARIABLE(s)
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
CALL_SUBTEST_6( trmv(Matrix<float,Dynamic,Dynamic,RowMajor>(s, s)) );
TEST_SET_BUT_UNUSED_VARIABLE(s)
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/linearstructure.cpp | .cpp | 6,089 | 149 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
static bool g_called;
#define EIGEN_SCALAR_BINARY_OP_PLUGIN { g_called |= (!internal::is_same<LhsScalar,RhsScalar>::value); }
#include "main.h"
template<typename MatrixType> void linearStructure(const MatrixType& m)
{
using std::abs;
/* this test covers the following files:
CwiseUnaryOp.h, CwiseBinaryOp.h, SelfCwiseBinaryOp.h
*/
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
Index rows = m.rows();
Index cols = m.cols();
// this test relies a lot on Random.h, and there's not much more that we can do
// to test it, hence I consider that we will have tested Random.h
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols);
Scalar s1 = internal::random<Scalar>();
while (abs(s1)<RealScalar(1e-3)) s1 = internal::random<Scalar>();
Index r = internal::random<Index>(0, rows-1),
c = internal::random<Index>(0, cols-1);
VERIFY_IS_APPROX(-(-m1), m1);
VERIFY_IS_APPROX(m1+m1, 2*m1);
VERIFY_IS_APPROX(m1+m2-m1, m2);
VERIFY_IS_APPROX(-m2+m1+m2, m1);
VERIFY_IS_APPROX(m1*s1, s1*m1);
VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2);
VERIFY_IS_APPROX((-m1+m2)*s1, -s1*m1+s1*m2);
m3 = m2; m3 += m1;
VERIFY_IS_APPROX(m3, m1+m2);
m3 = m2; m3 -= m1;
VERIFY_IS_APPROX(m3, m2-m1);
m3 = m2; m3 *= s1;
VERIFY_IS_APPROX(m3, s1*m2);
if(!NumTraits<Scalar>::IsInteger)
{
m3 = m2; m3 /= s1;
VERIFY_IS_APPROX(m3, m2/s1);
}
// again, test operator() to check const-qualification
VERIFY_IS_APPROX((-m1)(r,c), -(m1(r,c)));
VERIFY_IS_APPROX((m1-m2)(r,c), (m1(r,c))-(m2(r,c)));
VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c)));
VERIFY_IS_APPROX((s1*m1)(r,c), s1*(m1(r,c)));
VERIFY_IS_APPROX((m1*s1)(r,c), (m1(r,c))*s1);
if(!NumTraits<Scalar>::IsInteger)
VERIFY_IS_APPROX((m1/s1)(r,c), (m1(r,c))/s1);
// use .block to disable vectorization and compare to the vectorized version
VERIFY_IS_APPROX(m1+m1.block(0,0,rows,cols), m1+m1);
VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), m1.cwiseProduct(m1));
VERIFY_IS_APPROX(m1 - m1.block(0,0,rows,cols), m1 - m1);
VERIFY_IS_APPROX(m1.block(0,0,rows,cols) * s1, m1 * s1);
}
// Make sure that complex * real and real * complex are properly optimized
template<typename MatrixType> void real_complex(DenseIndex rows = MatrixType::RowsAtCompileTime, DenseIndex cols = MatrixType::ColsAtCompileTime)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
RealScalar s = internal::random<RealScalar>();
MatrixType m1 = MatrixType::Random(rows, cols);
g_called = false;
VERIFY_IS_APPROX(s*m1, Scalar(s)*m1);
VERIFY(g_called && "real * matrix<complex> not properly optimized");
g_called = false;
VERIFY_IS_APPROX(m1*s, m1*Scalar(s));
VERIFY(g_called && "matrix<complex> * real not properly optimized");
g_called = false;
VERIFY_IS_APPROX(m1/s, m1/Scalar(s));
VERIFY(g_called && "matrix<complex> / real not properly optimized");
g_called = false;
VERIFY_IS_APPROX(s+m1.array(), Scalar(s)+m1.array());
VERIFY(g_called && "real + matrix<complex> not properly optimized");
g_called = false;
VERIFY_IS_APPROX(m1.array()+s, m1.array()+Scalar(s));
VERIFY(g_called && "matrix<complex> + real not properly optimized");
g_called = false;
VERIFY_IS_APPROX(s-m1.array(), Scalar(s)-m1.array());
VERIFY(g_called && "real - matrix<complex> not properly optimized");
g_called = false;
VERIFY_IS_APPROX(m1.array()-s, m1.array()-Scalar(s));
VERIFY(g_called && "matrix<complex> - real not properly optimized");
}
void test_linearstructure()
{
g_called = true;
VERIFY(g_called); // avoid `unneeded-internal-declaration` warning.
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( linearStructure(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( linearStructure(Matrix2f()) );
CALL_SUBTEST_3( linearStructure(Vector3d()) );
CALL_SUBTEST_4( linearStructure(Matrix4d()) );
CALL_SUBTEST_5( linearStructure(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
CALL_SUBTEST_6( linearStructure(MatrixXf (internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_7( linearStructure(MatrixXi (internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_8( linearStructure(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
CALL_SUBTEST_9( linearStructure(ArrayXXf (internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_10( linearStructure(ArrayXXcf (internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_11( real_complex<Matrix4cd>() );
CALL_SUBTEST_11( real_complex<MatrixXcf>(10,10) );
CALL_SUBTEST_11( real_complex<ArrayXXcf>(10,10) );
}
#ifdef EIGEN_TEST_PART_4
{
// make sure that /=scalar and /scalar do not overflow
// rational: 1.0/4.94e-320 overflow, but m/4.94e-320 should not
Matrix4d m2, m3;
m3 = m2 = Matrix4d::Random()*1e-20;
m2 = m2 / 4.9e-320;
VERIFY_IS_APPROX(m2.cwiseQuotient(m2), Matrix4d::Ones());
m3 /= 4.9e-320;
VERIFY_IS_APPROX(m3.cwiseQuotient(m3), Matrix4d::Ones());
}
#endif
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/sparse_basic.cpp | .cpp | 26,098 | 698 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com>
// Copyright (C) 2013 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
static long g_realloc_count = 0;
#define EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN g_realloc_count++;
#include "sparse.h"
template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref)
{
typedef typename SparseMatrixType::StorageIndex StorageIndex;
typedef Matrix<StorageIndex,2,1> Vector2;
const Index rows = ref.rows();
const Index cols = ref.cols();
//const Index inner = ref.innerSize();
//const Index outer = ref.outerSize();
typedef typename SparseMatrixType::Scalar Scalar;
typedef typename SparseMatrixType::RealScalar RealScalar;
enum { Flags = SparseMatrixType::Flags };
double density = (std::max)(8./(rows*cols), 0.01);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
Scalar eps = 1e-6;
Scalar s1 = internal::random<Scalar>();
{
SparseMatrixType m(rows, cols);
DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
DenseVector vec1 = DenseVector::Random(rows);
std::vector<Vector2> zeroCoords;
std::vector<Vector2> nonzeroCoords;
initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
// test coeff and coeffRef
for (std::size_t i=0; i<zeroCoords.size(); ++i)
{
VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps );
if(internal::is_same<SparseMatrixType,SparseMatrix<Scalar,Flags> >::value)
VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[i].x(),zeroCoords[i].y()) = 5 );
}
VERIFY_IS_APPROX(m, refMat);
if(!nonzeroCoords.empty()) {
m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
}
VERIFY_IS_APPROX(m, refMat);
// test assertion
VERIFY_RAISES_ASSERT( m.coeffRef(-1,1) = 0 );
VERIFY_RAISES_ASSERT( m.coeffRef(0,m.cols()) = 0 );
}
// test insert (inner random)
{
DenseMatrix m1(rows,cols);
m1.setZero();
SparseMatrixType m2(rows,cols);
bool call_reserve = internal::random<int>()%2;
Index nnz = internal::random<int>(1,int(rows)/2);
if(call_reserve)
{
if(internal::random<int>()%2)
m2.reserve(VectorXi::Constant(m2.outerSize(), int(nnz)));
else
m2.reserve(m2.outerSize() * nnz);
}
g_realloc_count = 0;
for (Index j=0; j<cols; ++j)
{
for (Index k=0; k<nnz; ++k)
{
Index i = internal::random<Index>(0,rows-1);
if (m1.coeff(i,j)==Scalar(0))
m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
}
}
if(call_reserve && !SparseMatrixType::IsRowMajor)
{
VERIFY(g_realloc_count==0);
}
m2.finalize();
VERIFY_IS_APPROX(m2,m1);
}
// test insert (fully random)
{
DenseMatrix m1(rows,cols);
m1.setZero();
SparseMatrixType m2(rows,cols);
if(internal::random<int>()%2)
m2.reserve(VectorXi::Constant(m2.outerSize(), 2));
for (int k=0; k<rows*cols; ++k)
{
Index i = internal::random<Index>(0,rows-1);
Index j = internal::random<Index>(0,cols-1);
if ((m1.coeff(i,j)==Scalar(0)) && (internal::random<int>()%2))
m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
else
{
Scalar v = internal::random<Scalar>();
m2.coeffRef(i,j) += v;
m1(i,j) += v;
}
}
VERIFY_IS_APPROX(m2,m1);
}
// test insert (un-compressed)
for(int mode=0;mode<4;++mode)
{
DenseMatrix m1(rows,cols);
m1.setZero();
SparseMatrixType m2(rows,cols);
VectorXi r(VectorXi::Constant(m2.outerSize(), ((mode%2)==0) ? int(m2.innerSize()) : std::max<int>(1,int(m2.innerSize())/8)));
m2.reserve(r);
for (Index k=0; k<rows*cols; ++k)
{
Index i = internal::random<Index>(0,rows-1);
Index j = internal::random<Index>(0,cols-1);
if (m1.coeff(i,j)==Scalar(0))
m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
if(mode==3)
m2.reserve(r);
}
if(internal::random<int>()%2)
m2.makeCompressed();
VERIFY_IS_APPROX(m2,m1);
}
// test basic computations
{
DenseMatrix refM1 = DenseMatrix::Zero(rows, cols);
DenseMatrix refM2 = DenseMatrix::Zero(rows, cols);
DenseMatrix refM3 = DenseMatrix::Zero(rows, cols);
DenseMatrix refM4 = DenseMatrix::Zero(rows, cols);
SparseMatrixType m1(rows, cols);
SparseMatrixType m2(rows, cols);
SparseMatrixType m3(rows, cols);
SparseMatrixType m4(rows, cols);
initSparse<Scalar>(density, refM1, m1);
initSparse<Scalar>(density, refM2, m2);
initSparse<Scalar>(density, refM3, m3);
initSparse<Scalar>(density, refM4, m4);
if(internal::random<bool>())
m1.makeCompressed();
Index m1_nnz = m1.nonZeros();
VERIFY_IS_APPROX(m1*s1, refM1*s1);
VERIFY_IS_APPROX(m1+m2, refM1+refM2);
VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3);
VERIFY_IS_APPROX(m3.cwiseProduct(m1+m2), refM3.cwiseProduct(refM1+refM2));
VERIFY_IS_APPROX(m1*s1-m2, refM1*s1-refM2);
VERIFY_IS_APPROX(m4=m1/s1, refM1/s1);
VERIFY_IS_EQUAL(m4.nonZeros(), m1_nnz);
if(SparseMatrixType::IsRowMajor)
VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.row(0)), refM1.row(0).dot(refM2.row(0)));
else
VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.col(0)), refM1.col(0).dot(refM2.col(0)));
DenseVector rv = DenseVector::Random(m1.cols());
DenseVector cv = DenseVector::Random(m1.rows());
Index r = internal::random<Index>(0,m1.rows()-2);
Index c = internal::random<Index>(0,m1.cols()-1);
VERIFY_IS_APPROX(( m1.template block<1,Dynamic>(r,0,1,m1.cols()).dot(rv)) , refM1.row(r).dot(rv));
VERIFY_IS_APPROX(m1.row(r).dot(rv), refM1.row(r).dot(rv));
VERIFY_IS_APPROX(m1.col(c).dot(cv), refM1.col(c).dot(cv));
VERIFY_IS_APPROX(m1.conjugate(), refM1.conjugate());
VERIFY_IS_APPROX(m1.real(), refM1.real());
refM4.setRandom();
// sparse cwise* dense
VERIFY_IS_APPROX(m3.cwiseProduct(refM4), refM3.cwiseProduct(refM4));
// dense cwise* sparse
VERIFY_IS_APPROX(refM4.cwiseProduct(m3), refM4.cwiseProduct(refM3));
// VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4);
VERIFY_IS_APPROX(refM4 + m3, refM4 + refM3);
VERIFY_IS_APPROX(m3 + refM4, refM3 + refM4);
VERIFY_IS_APPROX(refM4 - m3, refM4 - refM3);
VERIFY_IS_APPROX(m3 - refM4, refM3 - refM4);
VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3*RealScalar(0.5)).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3.cwiseProduct(m3)).eval(), RealScalar(0.5)*refM4 + refM3.cwiseProduct(refM3));
VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3*RealScalar(0.5)).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (m3+m3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3));
VERIFY_IS_APPROX(((refM3+m3)+RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM3 + (refM3+refM3));
VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (refM3+m3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3));
VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (m3+refM3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3));
VERIFY_IS_APPROX(m1.sum(), refM1.sum());
m4 = m1; refM4 = m4;
VERIFY_IS_APPROX(m1*=s1, refM1*=s1);
VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
VERIFY_IS_APPROX(m1/=s1, refM1/=s1);
VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
VERIFY_IS_APPROX(m1+=m2, refM1+=refM2);
VERIFY_IS_APPROX(m1-=m2, refM1-=refM2);
if (rows>=2 && cols>=2)
{
VERIFY_RAISES_ASSERT( m1 += m1.innerVector(0) );
VERIFY_RAISES_ASSERT( m1 -= m1.innerVector(0) );
VERIFY_RAISES_ASSERT( refM1 -= m1.innerVector(0) );
VERIFY_RAISES_ASSERT( refM1 += m1.innerVector(0) );
}
m1 = m4; refM1 = refM4;
// test aliasing
VERIFY_IS_APPROX((m1 = -m1), (refM1 = -refM1));
VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
m1 = m4; refM1 = refM4;
VERIFY_IS_APPROX((m1 = m1.transpose()), (refM1 = refM1.transpose().eval()));
VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
m1 = m4; refM1 = refM4;
VERIFY_IS_APPROX((m1 = -m1.transpose()), (refM1 = -refM1.transpose().eval()));
VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
m1 = m4; refM1 = refM4;
VERIFY_IS_APPROX((m1 += -m1), (refM1 += -refM1));
VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
m1 = m4; refM1 = refM4;
if(m1.isCompressed())
{
VERIFY_IS_APPROX(m1.coeffs().sum(), m1.sum());
m1.coeffs() += s1;
for(Index j = 0; j<m1.outerSize(); ++j)
for(typename SparseMatrixType::InnerIterator it(m1,j); it; ++it)
refM1(it.row(), it.col()) += s1;
VERIFY_IS_APPROX(m1, refM1);
}
// and/or
{
typedef SparseMatrix<bool, SparseMatrixType::Options, typename SparseMatrixType::StorageIndex> SpBool;
SpBool mb1 = m1.real().template cast<bool>();
SpBool mb2 = m2.real().template cast<bool>();
VERIFY_IS_EQUAL(mb1.template cast<int>().sum(), refM1.real().template cast<bool>().count());
VERIFY_IS_EQUAL((mb1 && mb2).template cast<int>().sum(), (refM1.real().template cast<bool>() && refM2.real().template cast<bool>()).count());
VERIFY_IS_EQUAL((mb1 || mb2).template cast<int>().sum(), (refM1.real().template cast<bool>() || refM2.real().template cast<bool>()).count());
SpBool mb3 = mb1 && mb2;
if(mb1.coeffs().all() && mb2.coeffs().all())
{
VERIFY_IS_EQUAL(mb3.nonZeros(), (refM1.real().template cast<bool>() && refM2.real().template cast<bool>()).count());
}
}
}
// test reverse iterators
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
SparseMatrixType m2(rows, cols);
initSparse<Scalar>(density, refMat2, m2);
std::vector<Scalar> ref_value(m2.innerSize());
std::vector<Index> ref_index(m2.innerSize());
if(internal::random<bool>())
m2.makeCompressed();
for(Index j = 0; j<m2.outerSize(); ++j)
{
Index count_forward = 0;
for(typename SparseMatrixType::InnerIterator it(m2,j); it; ++it)
{
ref_value[ref_value.size()-1-count_forward] = it.value();
ref_index[ref_index.size()-1-count_forward] = it.index();
count_forward++;
}
Index count_reverse = 0;
for(typename SparseMatrixType::ReverseInnerIterator it(m2,j); it; --it)
{
VERIFY_IS_APPROX( std::abs(ref_value[ref_value.size()-count_forward+count_reverse])+1, std::abs(it.value())+1);
VERIFY_IS_EQUAL( ref_index[ref_index.size()-count_forward+count_reverse] , it.index());
count_reverse++;
}
VERIFY_IS_EQUAL(count_forward, count_reverse);
}
}
// test transpose
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
SparseMatrixType m2(rows, cols);
initSparse<Scalar>(density, refMat2, m2);
VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval());
VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose());
VERIFY_IS_APPROX(SparseMatrixType(m2.adjoint()), refMat2.adjoint());
// check isApprox handles opposite storage order
typename Transpose<SparseMatrixType>::PlainObject m3(m2);
VERIFY(m2.isApprox(m3));
}
// test prune
{
SparseMatrixType m2(rows, cols);
DenseMatrix refM2(rows, cols);
refM2.setZero();
int countFalseNonZero = 0;
int countTrueNonZero = 0;
m2.reserve(VectorXi::Constant(m2.outerSize(), int(m2.innerSize())));
for (Index j=0; j<m2.cols(); ++j)
{
for (Index i=0; i<m2.rows(); ++i)
{
float x = internal::random<float>(0,1);
if (x<0.1f)
{
// do nothing
}
else if (x<0.5f)
{
countFalseNonZero++;
m2.insert(i,j) = Scalar(0);
}
else
{
countTrueNonZero++;
m2.insert(i,j) = Scalar(1);
refM2(i,j) = Scalar(1);
}
}
}
if(internal::random<bool>())
m2.makeCompressed();
VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros());
if(countTrueNonZero>0)
VERIFY_IS_APPROX(m2, refM2);
m2.prune(Scalar(1));
VERIFY(countTrueNonZero==m2.nonZeros());
VERIFY_IS_APPROX(m2, refM2);
}
// test setFromTriplets
{
typedef Triplet<Scalar,StorageIndex> TripletType;
std::vector<TripletType> triplets;
Index ntriplets = rows*cols;
triplets.reserve(ntriplets);
DenseMatrix refMat_sum = DenseMatrix::Zero(rows,cols);
DenseMatrix refMat_prod = DenseMatrix::Zero(rows,cols);
DenseMatrix refMat_last = DenseMatrix::Zero(rows,cols);
for(Index i=0;i<ntriplets;++i)
{
StorageIndex r = internal::random<StorageIndex>(0,StorageIndex(rows-1));
StorageIndex c = internal::random<StorageIndex>(0,StorageIndex(cols-1));
Scalar v = internal::random<Scalar>();
triplets.push_back(TripletType(r,c,v));
refMat_sum(r,c) += v;
if(std::abs(refMat_prod(r,c))==0)
refMat_prod(r,c) = v;
else
refMat_prod(r,c) *= v;
refMat_last(r,c) = v;
}
SparseMatrixType m(rows,cols);
m.setFromTriplets(triplets.begin(), triplets.end());
VERIFY_IS_APPROX(m, refMat_sum);
m.setFromTriplets(triplets.begin(), triplets.end(), std::multiplies<Scalar>());
VERIFY_IS_APPROX(m, refMat_prod);
#if (defined(__cplusplus) && __cplusplus >= 201103L)
m.setFromTriplets(triplets.begin(), triplets.end(), [] (Scalar,Scalar b) { return b; });
VERIFY_IS_APPROX(m, refMat_last);
#endif
}
// test Map
{
DenseMatrix refMat2(rows, cols), refMat3(rows, cols);
SparseMatrixType m2(rows, cols), m3(rows, cols);
initSparse<Scalar>(density, refMat2, m2);
initSparse<Scalar>(density, refMat3, m3);
{
Map<SparseMatrixType> mapMat2(m2.rows(), m2.cols(), m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr());
Map<SparseMatrixType> mapMat3(m3.rows(), m3.cols(), m3.nonZeros(), m3.outerIndexPtr(), m3.innerIndexPtr(), m3.valuePtr(), m3.innerNonZeroPtr());
VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
}
{
MappedSparseMatrix<Scalar,SparseMatrixType::Options,StorageIndex> mapMat2(m2.rows(), m2.cols(), m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr());
MappedSparseMatrix<Scalar,SparseMatrixType::Options,StorageIndex> mapMat3(m3.rows(), m3.cols(), m3.nonZeros(), m3.outerIndexPtr(), m3.innerIndexPtr(), m3.valuePtr(), m3.innerNonZeroPtr());
VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
}
Index i = internal::random<Index>(0,rows-1);
Index j = internal::random<Index>(0,cols-1);
m2.coeffRef(i,j) = 123;
if(internal::random<bool>())
m2.makeCompressed();
Map<SparseMatrixType> mapMat2(rows, cols, m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr());
VERIFY_IS_EQUAL(m2.coeff(i,j),Scalar(123));
VERIFY_IS_EQUAL(mapMat2.coeff(i,j),Scalar(123));
mapMat2.coeffRef(i,j) = -123;
VERIFY_IS_EQUAL(m2.coeff(i,j),Scalar(-123));
}
// test triangularView
{
DenseMatrix refMat2(rows, cols), refMat3(rows, cols);
SparseMatrixType m2(rows, cols), m3(rows, cols);
initSparse<Scalar>(density, refMat2, m2);
refMat3 = refMat2.template triangularView<Lower>();
m3 = m2.template triangularView<Lower>();
VERIFY_IS_APPROX(m3, refMat3);
refMat3 = refMat2.template triangularView<Upper>();
m3 = m2.template triangularView<Upper>();
VERIFY_IS_APPROX(m3, refMat3);
{
refMat3 = refMat2.template triangularView<UnitUpper>();
m3 = m2.template triangularView<UnitUpper>();
VERIFY_IS_APPROX(m3, refMat3);
refMat3 = refMat2.template triangularView<UnitLower>();
m3 = m2.template triangularView<UnitLower>();
VERIFY_IS_APPROX(m3, refMat3);
}
refMat3 = refMat2.template triangularView<StrictlyUpper>();
m3 = m2.template triangularView<StrictlyUpper>();
VERIFY_IS_APPROX(m3, refMat3);
refMat3 = refMat2.template triangularView<StrictlyLower>();
m3 = m2.template triangularView<StrictlyLower>();
VERIFY_IS_APPROX(m3, refMat3);
// check sparse-triangular to dense
refMat3 = m2.template triangularView<StrictlyUpper>();
VERIFY_IS_APPROX(refMat3, DenseMatrix(refMat2.template triangularView<StrictlyUpper>()));
}
// test selfadjointView
if(!SparseMatrixType::IsRowMajor)
{
DenseMatrix refMat2(rows, rows), refMat3(rows, rows);
SparseMatrixType m2(rows, rows), m3(rows, rows);
initSparse<Scalar>(density, refMat2, m2);
refMat3 = refMat2.template selfadjointView<Lower>();
m3 = m2.template selfadjointView<Lower>();
VERIFY_IS_APPROX(m3, refMat3);
refMat3 += refMat2.template selfadjointView<Lower>();
m3 += m2.template selfadjointView<Lower>();
VERIFY_IS_APPROX(m3, refMat3);
refMat3 -= refMat2.template selfadjointView<Lower>();
m3 -= m2.template selfadjointView<Lower>();
VERIFY_IS_APPROX(m3, refMat3);
// selfadjointView only works for square matrices:
SparseMatrixType m4(rows, rows+1);
VERIFY_RAISES_ASSERT(m4.template selfadjointView<Lower>());
VERIFY_RAISES_ASSERT(m4.template selfadjointView<Upper>());
}
// test sparseView
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
SparseMatrixType m2(rows, rows);
initSparse<Scalar>(density, refMat2, m2);
VERIFY_IS_APPROX(m2.eval(), refMat2.sparseView().eval());
// sparse view on expressions:
VERIFY_IS_APPROX((s1*m2).eval(), (s1*refMat2).sparseView().eval());
VERIFY_IS_APPROX((m2+m2).eval(), (refMat2+refMat2).sparseView().eval());
VERIFY_IS_APPROX((m2*m2).eval(), (refMat2.lazyProduct(refMat2)).sparseView().eval());
VERIFY_IS_APPROX((m2*m2).eval(), (refMat2*refMat2).sparseView().eval());
}
// test diagonal
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
SparseMatrixType m2(rows, cols);
initSparse<Scalar>(density, refMat2, m2);
VERIFY_IS_APPROX(m2.diagonal(), refMat2.diagonal().eval());
DenseVector d = m2.diagonal();
VERIFY_IS_APPROX(d, refMat2.diagonal().eval());
d = m2.diagonal().array();
VERIFY_IS_APPROX(d, refMat2.diagonal().eval());
VERIFY_IS_APPROX(const_cast<const SparseMatrixType&>(m2).diagonal(), refMat2.diagonal().eval());
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag);
m2.diagonal() += refMat2.diagonal();
refMat2.diagonal() += refMat2.diagonal();
VERIFY_IS_APPROX(m2, refMat2);
}
// test diagonal to sparse
{
DenseVector d = DenseVector::Random(rows);
DenseMatrix refMat2 = d.asDiagonal();
SparseMatrixType m2(rows, rows);
m2 = d.asDiagonal();
VERIFY_IS_APPROX(m2, refMat2);
SparseMatrixType m3(d.asDiagonal());
VERIFY_IS_APPROX(m3, refMat2);
refMat2 += d.asDiagonal();
m2 += d.asDiagonal();
VERIFY_IS_APPROX(m2, refMat2);
}
// test conservative resize
{
std::vector< std::pair<StorageIndex,StorageIndex> > inc;
if(rows > 3 && cols > 2)
inc.push_back(std::pair<StorageIndex,StorageIndex>(-3,-2));
inc.push_back(std::pair<StorageIndex,StorageIndex>(0,0));
inc.push_back(std::pair<StorageIndex,StorageIndex>(3,2));
inc.push_back(std::pair<StorageIndex,StorageIndex>(3,0));
inc.push_back(std::pair<StorageIndex,StorageIndex>(0,3));
for(size_t i = 0; i< inc.size(); i++) {
StorageIndex incRows = inc[i].first;
StorageIndex incCols = inc[i].second;
SparseMatrixType m1(rows, cols);
DenseMatrix refMat1 = DenseMatrix::Zero(rows, cols);
initSparse<Scalar>(density, refMat1, m1);
m1.conservativeResize(rows+incRows, cols+incCols);
refMat1.conservativeResize(rows+incRows, cols+incCols);
if (incRows > 0) refMat1.bottomRows(incRows).setZero();
if (incCols > 0) refMat1.rightCols(incCols).setZero();
VERIFY_IS_APPROX(m1, refMat1);
// Insert new values
if (incRows > 0)
m1.insert(m1.rows()-1, 0) = refMat1(refMat1.rows()-1, 0) = 1;
if (incCols > 0)
m1.insert(0, m1.cols()-1) = refMat1(0, refMat1.cols()-1) = 1;
VERIFY_IS_APPROX(m1, refMat1);
}
}
// test Identity matrix
{
DenseMatrix refMat1 = DenseMatrix::Identity(rows, rows);
SparseMatrixType m1(rows, rows);
m1.setIdentity();
VERIFY_IS_APPROX(m1, refMat1);
for(int k=0; k<rows*rows/4; ++k)
{
Index i = internal::random<Index>(0,rows-1);
Index j = internal::random<Index>(0,rows-1);
Scalar v = internal::random<Scalar>();
m1.coeffRef(i,j) = v;
refMat1.coeffRef(i,j) = v;
VERIFY_IS_APPROX(m1, refMat1);
if(internal::random<Index>(0,10)<2)
m1.makeCompressed();
}
m1.setIdentity();
refMat1.setIdentity();
VERIFY_IS_APPROX(m1, refMat1);
}
// test array/vector of InnerIterator
{
typedef typename SparseMatrixType::InnerIterator IteratorType;
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
SparseMatrixType m2(rows, cols);
initSparse<Scalar>(density, refMat2, m2);
IteratorType static_array[2];
static_array[0] = IteratorType(m2,0);
static_array[1] = IteratorType(m2,m2.outerSize()-1);
VERIFY( static_array[0] || m2.innerVector(static_array[0].outer()).nonZeros() == 0 );
VERIFY( static_array[1] || m2.innerVector(static_array[1].outer()).nonZeros() == 0 );
if(static_array[0] && static_array[1])
{
++(static_array[1]);
static_array[1] = IteratorType(m2,0);
VERIFY( static_array[1] );
VERIFY( static_array[1].index() == static_array[0].index() );
VERIFY( static_array[1].outer() == static_array[0].outer() );
VERIFY( static_array[1].value() == static_array[0].value() );
}
std::vector<IteratorType> iters(2);
iters[0] = IteratorType(m2,0);
iters[1] = IteratorType(m2,m2.outerSize()-1);
}
// test reserve with empty rows/columns
{
SparseMatrixType m1(0,cols);
m1.reserve(ArrayXi::Constant(m1.outerSize(),1));
SparseMatrixType m2(rows,0);
m2.reserve(ArrayXi::Constant(m2.outerSize(),1));
}
}
template<typename SparseMatrixType>
void big_sparse_triplet(Index rows, Index cols, double density) {
typedef typename SparseMatrixType::StorageIndex StorageIndex;
typedef typename SparseMatrixType::Scalar Scalar;
typedef Triplet<Scalar,Index> TripletType;
std::vector<TripletType> triplets;
double nelements = density * rows*cols;
VERIFY(nelements>=0 && nelements < NumTraits<StorageIndex>::highest());
Index ntriplets = Index(nelements);
triplets.reserve(ntriplets);
Scalar sum = Scalar(0);
for(Index i=0;i<ntriplets;++i)
{
Index r = internal::random<Index>(0,rows-1);
Index c = internal::random<Index>(0,cols-1);
// use positive values to prevent numerical cancellation errors in sum
Scalar v = numext::abs(internal::random<Scalar>());
triplets.push_back(TripletType(r,c,v));
sum += v;
}
SparseMatrixType m(rows,cols);
m.setFromTriplets(triplets.begin(), triplets.end());
VERIFY(m.nonZeros() <= ntriplets);
VERIFY_IS_APPROX(sum, m.sum());
}
void test_sparse_basic()
{
for(int i = 0; i < g_repeat; i++) {
int r = Eigen::internal::random<int>(1,200), c = Eigen::internal::random<int>(1,200);
if(Eigen::internal::random<int>(0,4) == 0) {
r = c; // check square matrices in 25% of tries
}
EIGEN_UNUSED_VARIABLE(r+c);
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(1, 1)) ));
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(8, 8)) ));
CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, ColMajor>(r, c)) ));
CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, RowMajor>(r, c)) ));
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(r, c)) ));
CALL_SUBTEST_5(( sparse_basic(SparseMatrix<double,ColMajor,long int>(r, c)) ));
CALL_SUBTEST_5(( sparse_basic(SparseMatrix<double,RowMajor,long int>(r, c)) ));
r = Eigen::internal::random<int>(1,100);
c = Eigen::internal::random<int>(1,100);
if(Eigen::internal::random<int>(0,4) == 0) {
r = c; // check square matrices in 25% of tries
}
CALL_SUBTEST_6(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(r), short(c))) ));
CALL_SUBTEST_6(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(r), short(c))) ));
}
// Regression test for bug 900: (manually insert higher values here, if you have enough RAM):
CALL_SUBTEST_3((big_sparse_triplet<SparseMatrix<float, RowMajor, int> >(10000, 10000, 0.125)));
CALL_SUBTEST_4((big_sparse_triplet<SparseMatrix<double, ColMajor, long int> >(10000, 10000, 0.125)));
// Regression test for bug 1105
#ifdef EIGEN_TEST_PART_7
{
int n = Eigen::internal::random<int>(200,600);
SparseMatrix<std::complex<double>,0, long> mat(n, n);
std::complex<double> val;
for(int i=0; i<n; ++i)
{
mat.coeffRef(i, i%(n/10)) = val;
VERIFY(mat.data().allocatedSize()<20*n);
}
}
#endif
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/array_replicate.cpp | .cpp | 2,334 | 82 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename MatrixType> void replicate(const MatrixType& m)
{
/* this test covers the following files:
Replicate.cpp
*/
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
typedef Matrix<Scalar, Dynamic, Dynamic> MatrixX;
typedef Matrix<Scalar, Dynamic, 1> VectorX;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols);
VectorType v1 = VectorType::Random(rows);
MatrixX x1, x2;
VectorX vx1;
int f1 = internal::random<int>(1,10),
f2 = internal::random<int>(1,10);
x1.resize(rows*f1,cols*f2);
for(int j=0; j<f2; j++)
for(int i=0; i<f1; i++)
x1.block(i*rows,j*cols,rows,cols) = m1;
VERIFY_IS_APPROX(x1, m1.replicate(f1,f2));
x2.resize(2*rows,3*cols);
x2 << m2, m2, m2,
m2, m2, m2;
VERIFY_IS_APPROX(x2, (m2.template replicate<2,3>()));
x2.resize(rows,3*cols);
x2 << m2, m2, m2;
VERIFY_IS_APPROX(x2, (m2.template replicate<1,3>()));
vx1.resize(3*rows,cols);
vx1 << m2, m2, m2;
VERIFY_IS_APPROX(vx1+vx1, vx1+(m2.template replicate<3,1>()));
vx1=m2+(m2.colwise().replicate(1));
if(m2.cols()==1)
VERIFY_IS_APPROX(m2.coeff(0), (m2.template replicate<3,1>().coeff(m2.rows())));
x2.resize(rows,f1);
for (int j=0; j<f1; ++j)
x2.col(j) = v1;
VERIFY_IS_APPROX(x2, v1.rowwise().replicate(f1));
vx1.resize(rows*f2);
for (int j=0; j<f2; ++j)
vx1.segment(j*rows,rows) = v1;
VERIFY_IS_APPROX(vx1, v1.colwise().replicate(f2));
}
void test_array_replicate()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( replicate(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( replicate(Vector2f()) );
CALL_SUBTEST_3( replicate(Vector3d()) );
CALL_SUBTEST_4( replicate(Vector4f()) );
CALL_SUBTEST_5( replicate(VectorXf(16)) );
CALL_SUBTEST_6( replicate(VectorXcd(10)) );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/permutationmatrices.cpp | .cpp | 6,298 | 168 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define TEST_ENABLE_TEMPORARY_TRACKING
#include "main.h"
using namespace std;
template<typename MatrixType> void permutationmatrices(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime,
Options = MatrixType::Options };
typedef PermutationMatrix<Rows> LeftPermutationType;
typedef Transpositions<Rows> LeftTranspositionsType;
typedef Matrix<int, Rows, 1> LeftPermutationVectorType;
typedef Map<LeftPermutationType> MapLeftPerm;
typedef PermutationMatrix<Cols> RightPermutationType;
typedef Transpositions<Cols> RightTranspositionsType;
typedef Matrix<int, Cols, 1> RightPermutationVectorType;
typedef Map<RightPermutationType> MapRightPerm;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m_original = MatrixType::Random(rows,cols);
LeftPermutationVectorType lv;
randomPermutationVector(lv, rows);
LeftPermutationType lp(lv);
RightPermutationVectorType rv;
randomPermutationVector(rv, cols);
RightPermutationType rp(rv);
LeftTranspositionsType lt(lv);
RightTranspositionsType rt(rv);
MatrixType m_permuted = MatrixType::Random(rows,cols);
VERIFY_EVALUATION_COUNT(m_permuted = lp * m_original * rp, 1); // 1 temp for sub expression "lp * m_original"
for (int i=0; i<rows; i++)
for (int j=0; j<cols; j++)
VERIFY_IS_APPROX(m_permuted(lv(i),j), m_original(i,rv(j)));
Matrix<Scalar,Rows,Rows> lm(lp);
Matrix<Scalar,Cols,Cols> rm(rp);
VERIFY_IS_APPROX(m_permuted, lm*m_original*rm);
m_permuted = m_original;
VERIFY_EVALUATION_COUNT(m_permuted = lp * m_permuted * rp, 1);
VERIFY_IS_APPROX(m_permuted, lm*m_original*rm);
VERIFY_IS_APPROX(lp.inverse()*m_permuted*rp.inverse(), m_original);
VERIFY_IS_APPROX(lv.asPermutation().inverse()*m_permuted*rv.asPermutation().inverse(), m_original);
VERIFY_IS_APPROX(MapLeftPerm(lv.data(),lv.size()).inverse()*m_permuted*MapRightPerm(rv.data(),rv.size()).inverse(), m_original);
VERIFY((lp*lp.inverse()).toDenseMatrix().isIdentity());
VERIFY((lv.asPermutation()*lv.asPermutation().inverse()).toDenseMatrix().isIdentity());
VERIFY((MapLeftPerm(lv.data(),lv.size())*MapLeftPerm(lv.data(),lv.size()).inverse()).toDenseMatrix().isIdentity());
LeftPermutationVectorType lv2;
randomPermutationVector(lv2, rows);
LeftPermutationType lp2(lv2);
Matrix<Scalar,Rows,Rows> lm2(lp2);
VERIFY_IS_APPROX((lp*lp2).toDenseMatrix().template cast<Scalar>(), lm*lm2);
VERIFY_IS_APPROX((lv.asPermutation()*lv2.asPermutation()).toDenseMatrix().template cast<Scalar>(), lm*lm2);
VERIFY_IS_APPROX((MapLeftPerm(lv.data(),lv.size())*MapLeftPerm(lv2.data(),lv2.size())).toDenseMatrix().template cast<Scalar>(), lm*lm2);
LeftPermutationType identityp;
identityp.setIdentity(rows);
VERIFY_IS_APPROX(m_original, identityp*m_original);
// check inplace permutations
m_permuted = m_original;
VERIFY_EVALUATION_COUNT(m_permuted.noalias()= lp.inverse() * m_permuted, 1); // 1 temp to allocate the mask
VERIFY_IS_APPROX(m_permuted, lp.inverse()*m_original);
m_permuted = m_original;
VERIFY_EVALUATION_COUNT(m_permuted.noalias() = m_permuted * rp.inverse(), 1); // 1 temp to allocate the mask
VERIFY_IS_APPROX(m_permuted, m_original*rp.inverse());
m_permuted = m_original;
VERIFY_EVALUATION_COUNT(m_permuted.noalias() = lp * m_permuted, 1); // 1 temp to allocate the mask
VERIFY_IS_APPROX(m_permuted, lp*m_original);
m_permuted = m_original;
VERIFY_EVALUATION_COUNT(m_permuted.noalias() = m_permuted * rp, 1); // 1 temp to allocate the mask
VERIFY_IS_APPROX(m_permuted, m_original*rp);
if(rows>1 && cols>1)
{
lp2 = lp;
Index i = internal::random<Index>(0, rows-1);
Index j;
do j = internal::random<Index>(0, rows-1); while(j==i);
lp2.applyTranspositionOnTheLeft(i, j);
lm = lp;
lm.row(i).swap(lm.row(j));
VERIFY_IS_APPROX(lm, lp2.toDenseMatrix().template cast<Scalar>());
RightPermutationType rp2 = rp;
i = internal::random<Index>(0, cols-1);
do j = internal::random<Index>(0, cols-1); while(j==i);
rp2.applyTranspositionOnTheRight(i, j);
rm = rp;
rm.col(i).swap(rm.col(j));
VERIFY_IS_APPROX(rm, rp2.toDenseMatrix().template cast<Scalar>());
}
{
// simple compilation check
Matrix<Scalar, Cols, Cols> A = rp;
Matrix<Scalar, Cols, Cols> B = rp.transpose();
VERIFY_IS_APPROX(A, B.transpose());
}
m_permuted = m_original;
lp = lt;
rp = rt;
VERIFY_EVALUATION_COUNT(m_permuted = lt * m_permuted * rt, 1);
VERIFY_IS_APPROX(m_permuted, lp*m_original*rp.transpose());
VERIFY_IS_APPROX(lt.inverse()*m_permuted*rt.inverse(), m_original);
}
template<typename T>
void bug890()
{
typedef Matrix<T, Dynamic, Dynamic> MatrixType;
typedef Matrix<T, Dynamic, 1> VectorType;
typedef Stride<Dynamic,Dynamic> S;
typedef Map<MatrixType, Aligned, S> MapType;
typedef PermutationMatrix<Dynamic> Perm;
VectorType v1(2), v2(2), op(4), rhs(2);
v1 << 666,667;
op << 1,0,0,1;
rhs << 42,42;
Perm P(2);
P.indices() << 1, 0;
MapType(v1.data(),2,1,S(1,1)) = P * MapType(rhs.data(),2,1,S(1,1));
VERIFY_IS_APPROX(v1, (P * rhs).eval());
MapType(v1.data(),2,1,S(1,1)) = P.inverse() * MapType(rhs.data(),2,1,S(1,1));
VERIFY_IS_APPROX(v1, (P.inverse() * rhs).eval());
}
void test_permutationmatrices()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( permutationmatrices(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( permutationmatrices(Matrix3f()) );
CALL_SUBTEST_3( permutationmatrices(Matrix<double,3,3,RowMajor>()) );
CALL_SUBTEST_4( permutationmatrices(Matrix4d()) );
CALL_SUBTEST_5( permutationmatrices(Matrix<double,40,60>()) );
CALL_SUBTEST_6( permutationmatrices(Matrix<double,Dynamic,Dynamic,RowMajor>(20, 30)) );
CALL_SUBTEST_7( permutationmatrices(MatrixXcf(15, 10)) );
}
CALL_SUBTEST_5( bug890<double>() );
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/geo_parametrizedline.cpp | .cpp | 3,916 | 104 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/Geometry>
#include <Eigen/LU>
#include <Eigen/QR>
template<typename LineType> void parametrizedline(const LineType& _line)
{
/* this test covers the following files:
ParametrizedLine.h
*/
using std::abs;
const Index dim = _line.dim();
typedef typename LineType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, LineType::AmbientDimAtCompileTime, 1> VectorType;
typedef Hyperplane<Scalar,LineType::AmbientDimAtCompileTime> HyperplaneType;
VectorType p0 = VectorType::Random(dim);
VectorType p1 = VectorType::Random(dim);
VectorType d0 = VectorType::Random(dim).normalized();
LineType l0(p0, d0);
Scalar s0 = internal::random<Scalar>();
Scalar s1 = abs(internal::random<Scalar>());
VERIFY_IS_MUCH_SMALLER_THAN( l0.distance(p0), RealScalar(1) );
VERIFY_IS_MUCH_SMALLER_THAN( l0.distance(p0+s0*d0), RealScalar(1) );
VERIFY_IS_APPROX( (l0.projection(p1)-p1).norm(), l0.distance(p1) );
VERIFY_IS_MUCH_SMALLER_THAN( l0.distance(l0.projection(p1)), RealScalar(1) );
VERIFY_IS_APPROX( Scalar(l0.distance((p0+s0*d0) + d0.unitOrthogonal() * s1)), s1 );
// casting
const int Dim = LineType::AmbientDimAtCompileTime;
typedef typename GetDifferentType<Scalar>::type OtherScalar;
ParametrizedLine<OtherScalar,Dim> hp1f = l0.template cast<OtherScalar>();
VERIFY_IS_APPROX(hp1f.template cast<Scalar>(),l0);
ParametrizedLine<Scalar,Dim> hp1d = l0.template cast<Scalar>();
VERIFY_IS_APPROX(hp1d.template cast<Scalar>(),l0);
// intersections
VectorType p2 = VectorType::Random(dim);
VectorType n2 = VectorType::Random(dim).normalized();
HyperplaneType hp(p2,n2);
Scalar t = l0.intersectionParameter(hp);
VectorType pi = l0.pointAt(t);
VERIFY_IS_MUCH_SMALLER_THAN(hp.signedDistance(pi), RealScalar(1));
VERIFY_IS_MUCH_SMALLER_THAN(l0.distance(pi), RealScalar(1));
VERIFY_IS_APPROX(l0.intersectionPoint(hp), pi);
}
template<typename Scalar> void parametrizedline_alignment()
{
typedef ParametrizedLine<Scalar,4,AutoAlign> Line4a;
typedef ParametrizedLine<Scalar,4,DontAlign> Line4u;
EIGEN_ALIGN_MAX Scalar array1[16];
EIGEN_ALIGN_MAX Scalar array2[16];
EIGEN_ALIGN_MAX Scalar array3[16+1];
Scalar* array3u = array3+1;
Line4a *p1 = ::new(reinterpret_cast<void*>(array1)) Line4a;
Line4u *p2 = ::new(reinterpret_cast<void*>(array2)) Line4u;
Line4u *p3 = ::new(reinterpret_cast<void*>(array3u)) Line4u;
p1->origin().setRandom();
p1->direction().setRandom();
*p2 = *p1;
*p3 = *p1;
VERIFY_IS_APPROX(p1->origin(), p2->origin());
VERIFY_IS_APPROX(p1->origin(), p3->origin());
VERIFY_IS_APPROX(p1->direction(), p2->direction());
VERIFY_IS_APPROX(p1->direction(), p3->direction());
#if defined(EIGEN_VECTORIZE) && EIGEN_MAX_STATIC_ALIGN_BYTES>0
if(internal::packet_traits<Scalar>::Vectorizable && internal::packet_traits<Scalar>::size<=4)
VERIFY_RAISES_ASSERT((::new(reinterpret_cast<void*>(array3u)) Line4a));
#endif
}
void test_geo_parametrizedline()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( parametrizedline(ParametrizedLine<float,2>()) );
CALL_SUBTEST_2( parametrizedline(ParametrizedLine<float,3>()) );
CALL_SUBTEST_2( parametrizedline_alignment<float>() );
CALL_SUBTEST_3( parametrizedline(ParametrizedLine<double,4>()) );
CALL_SUBTEST_3( parametrizedline_alignment<double>() );
CALL_SUBTEST_4( parametrizedline(ParametrizedLine<std::complex<double>,5>()) );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/diagonal.cpp | .cpp | 4,107 | 106 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename MatrixType> void diagonal(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols);
Scalar s1 = internal::random<Scalar>();
//check diagonal()
VERIFY_IS_APPROX(m1.diagonal(), m1.transpose().diagonal());
m2.diagonal() = 2 * m1.diagonal();
m2.diagonal()[0] *= 3;
if (rows>2)
{
enum {
N1 = MatrixType::RowsAtCompileTime>2 ? 2 : 0,
N2 = MatrixType::RowsAtCompileTime>1 ? -1 : 0
};
// check sub/super diagonal
if(MatrixType::SizeAtCompileTime!=Dynamic)
{
VERIFY(m1.template diagonal<N1>().RowsAtCompileTime == m1.diagonal(N1).size());
VERIFY(m1.template diagonal<N2>().RowsAtCompileTime == m1.diagonal(N2).size());
}
m2.template diagonal<N1>() = 2 * m1.template diagonal<N1>();
VERIFY_IS_APPROX(m2.template diagonal<N1>(), static_cast<Scalar>(2) * m1.diagonal(N1));
m2.template diagonal<N1>()[0] *= 3;
VERIFY_IS_APPROX(m2.template diagonal<N1>()[0], static_cast<Scalar>(6) * m1.template diagonal<N1>()[0]);
m2.template diagonal<N2>() = 2 * m1.template diagonal<N2>();
m2.template diagonal<N2>()[0] *= 3;
VERIFY_IS_APPROX(m2.template diagonal<N2>()[0], static_cast<Scalar>(6) * m1.template diagonal<N2>()[0]);
m2.diagonal(N1) = 2 * m1.diagonal(N1);
VERIFY_IS_APPROX(m2.template diagonal<N1>(), static_cast<Scalar>(2) * m1.diagonal(N1));
m2.diagonal(N1)[0] *= 3;
VERIFY_IS_APPROX(m2.diagonal(N1)[0], static_cast<Scalar>(6) * m1.diagonal(N1)[0]);
m2.diagonal(N2) = 2 * m1.diagonal(N2);
VERIFY_IS_APPROX(m2.template diagonal<N2>(), static_cast<Scalar>(2) * m1.diagonal(N2));
m2.diagonal(N2)[0] *= 3;
VERIFY_IS_APPROX(m2.diagonal(N2)[0], static_cast<Scalar>(6) * m1.diagonal(N2)[0]);
m2.diagonal(N2).x() = s1;
VERIFY_IS_APPROX(m2.diagonal(N2).x(), s1);
m2.diagonal(N2).coeffRef(0) = Scalar(2)*s1;
VERIFY_IS_APPROX(m2.diagonal(N2).coeff(0), Scalar(2)*s1);
}
VERIFY( m1.diagonal( cols).size()==0 );
VERIFY( m1.diagonal(-rows).size()==0 );
}
template<typename MatrixType> void diagonal_assert(const MatrixType& m) {
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols);
if (rows>=2 && cols>=2)
{
VERIFY_RAISES_ASSERT( m1 += m1.diagonal() );
VERIFY_RAISES_ASSERT( m1 -= m1.diagonal() );
VERIFY_RAISES_ASSERT( m1.array() *= m1.diagonal().array() );
VERIFY_RAISES_ASSERT( m1.array() /= m1.diagonal().array() );
}
VERIFY_RAISES_ASSERT( m1.diagonal(cols+1) );
VERIFY_RAISES_ASSERT( m1.diagonal(-(rows+1)) );
}
void test_diagonal()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( diagonal(Matrix<float, 1, 1>()) );
CALL_SUBTEST_1( diagonal(Matrix<float, 4, 9>()) );
CALL_SUBTEST_1( diagonal(Matrix<float, 7, 3>()) );
CALL_SUBTEST_2( diagonal(Matrix4d()) );
CALL_SUBTEST_2( diagonal(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_2( diagonal(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_2( diagonal(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_1( diagonal(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_1( diagonal(Matrix<float,Dynamic,4>(3, 4)) );
CALL_SUBTEST_1( diagonal_assert(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/umeyama.cpp | .cpp | 5,853 | 184 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Hauke Heibel <hauke.heibel@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/Core>
#include <Eigen/Geometry>
#include <Eigen/LU> // required for MatrixBase::determinant
#include <Eigen/SVD> // required for SVD
using namespace Eigen;
// Constructs a random matrix from the unitary group U(size).
template <typename T>
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> randMatrixUnitary(int size)
{
typedef T Scalar;
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> MatrixType;
MatrixType Q;
int max_tries = 40;
double is_unitary = false;
while (!is_unitary && max_tries > 0)
{
// initialize random matrix
Q = MatrixType::Random(size, size);
// orthogonalize columns using the Gram-Schmidt algorithm
for (int col = 0; col < size; ++col)
{
typename MatrixType::ColXpr colVec = Q.col(col);
for (int prevCol = 0; prevCol < col; ++prevCol)
{
typename MatrixType::ColXpr prevColVec = Q.col(prevCol);
colVec -= colVec.dot(prevColVec)*prevColVec;
}
Q.col(col) = colVec.normalized();
}
// this additional orthogonalization is not necessary in theory but should enhance
// the numerical orthogonality of the matrix
for (int row = 0; row < size; ++row)
{
typename MatrixType::RowXpr rowVec = Q.row(row);
for (int prevRow = 0; prevRow < row; ++prevRow)
{
typename MatrixType::RowXpr prevRowVec = Q.row(prevRow);
rowVec -= rowVec.dot(prevRowVec)*prevRowVec;
}
Q.row(row) = rowVec.normalized();
}
// final check
is_unitary = Q.isUnitary();
--max_tries;
}
if (max_tries == 0)
eigen_assert(false && "randMatrixUnitary: Could not construct unitary matrix!");
return Q;
}
// Constructs a random matrix from the special unitary group SU(size).
template <typename T>
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> randMatrixSpecialUnitary(int size)
{
typedef T Scalar;
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> MatrixType;
// initialize unitary matrix
MatrixType Q = randMatrixUnitary<Scalar>(size);
// tweak the first column to make the determinant be 1
Q.col(0) *= numext::conj(Q.determinant());
return Q;
}
template <typename MatrixType>
void run_test(int dim, int num_elements)
{
using std::abs;
typedef typename internal::traits<MatrixType>::Scalar Scalar;
typedef Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> MatrixX;
typedef Matrix<Scalar, Eigen::Dynamic, 1> VectorX;
// MUST be positive because in any other case det(cR_t) may become negative for
// odd dimensions!
const Scalar c = abs(internal::random<Scalar>());
MatrixX R = randMatrixSpecialUnitary<Scalar>(dim);
VectorX t = Scalar(50)*VectorX::Random(dim,1);
MatrixX cR_t = MatrixX::Identity(dim+1,dim+1);
cR_t.block(0,0,dim,dim) = c*R;
cR_t.block(0,dim,dim,1) = t;
MatrixX src = MatrixX::Random(dim+1, num_elements);
src.row(dim) = Matrix<Scalar, 1, Dynamic>::Constant(num_elements, Scalar(1));
MatrixX dst = cR_t*src;
MatrixX cR_t_umeyama = umeyama(src.block(0,0,dim,num_elements), dst.block(0,0,dim,num_elements));
const Scalar error = ( cR_t_umeyama*src - dst ).norm() / dst.norm();
VERIFY(error < Scalar(40)*std::numeric_limits<Scalar>::epsilon());
}
template<typename Scalar, int Dimension>
void run_fixed_size_test(int num_elements)
{
using std::abs;
typedef Matrix<Scalar, Dimension+1, Dynamic> MatrixX;
typedef Matrix<Scalar, Dimension+1, Dimension+1> HomMatrix;
typedef Matrix<Scalar, Dimension, Dimension> FixedMatrix;
typedef Matrix<Scalar, Dimension, 1> FixedVector;
const int dim = Dimension;
// MUST be positive because in any other case det(cR_t) may become negative for
// odd dimensions!
// Also if c is to small compared to t.norm(), problem is ill-posed (cf. Bug 744)
const Scalar c = internal::random<Scalar>(0.5, 2.0);
FixedMatrix R = randMatrixSpecialUnitary<Scalar>(dim);
FixedVector t = Scalar(32)*FixedVector::Random(dim,1);
HomMatrix cR_t = HomMatrix::Identity(dim+1,dim+1);
cR_t.block(0,0,dim,dim) = c*R;
cR_t.block(0,dim,dim,1) = t;
MatrixX src = MatrixX::Random(dim+1, num_elements);
src.row(dim) = Matrix<Scalar, 1, Dynamic>::Constant(num_elements, Scalar(1));
MatrixX dst = cR_t*src;
Block<MatrixX, Dimension, Dynamic> src_block(src,0,0,dim,num_elements);
Block<MatrixX, Dimension, Dynamic> dst_block(dst,0,0,dim,num_elements);
HomMatrix cR_t_umeyama = umeyama(src_block, dst_block);
const Scalar error = ( cR_t_umeyama*src - dst ).squaredNorm();
VERIFY(error < Scalar(16)*std::numeric_limits<Scalar>::epsilon());
}
void test_umeyama()
{
for (int i=0; i<g_repeat; ++i)
{
const int num_elements = internal::random<int>(40,500);
// works also for dimensions bigger than 3...
for (int dim=2; dim<8; ++dim)
{
CALL_SUBTEST_1(run_test<MatrixXd>(dim, num_elements));
CALL_SUBTEST_2(run_test<MatrixXf>(dim, num_elements));
}
CALL_SUBTEST_3((run_fixed_size_test<float, 2>(num_elements)));
CALL_SUBTEST_4((run_fixed_size_test<float, 3>(num_elements)));
CALL_SUBTEST_5((run_fixed_size_test<float, 4>(num_elements)));
CALL_SUBTEST_6((run_fixed_size_test<double, 2>(num_elements)));
CALL_SUBTEST_7((run_fixed_size_test<double, 3>(num_elements)));
CALL_SUBTEST_8((run_fixed_size_test<double, 4>(num_elements)));
}
// Those two calls don't compile and result in meaningful error messages!
// umeyama(MatrixXcf(),MatrixXcf());
// umeyama(MatrixXcd(),MatrixXcd());
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/is_same_dense.cpp | .cpp | 1,095 | 34 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
using internal::is_same_dense;
void test_is_same_dense()
{
typedef Matrix<double,Dynamic,Dynamic,ColMajor> ColMatrixXd;
ColMatrixXd m1(10,10);
Ref<ColMatrixXd> ref_m1(m1);
Ref<const ColMatrixXd> const_ref_m1(m1);
VERIFY(is_same_dense(m1,m1));
VERIFY(is_same_dense(m1,ref_m1));
VERIFY(is_same_dense(const_ref_m1,m1));
VERIFY(is_same_dense(const_ref_m1,ref_m1));
VERIFY(is_same_dense(m1.block(0,0,m1.rows(),m1.cols()),m1));
VERIFY(!is_same_dense(m1.row(0),m1.col(0)));
Ref<const ColMatrixXd> const_ref_m1_row(m1.row(1));
VERIFY(!is_same_dense(m1.row(1),const_ref_m1_row));
Ref<const ColMatrixXd> const_ref_m1_col(m1.col(1));
VERIFY(is_same_dense(m1.col(1),const_ref_m1_col));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/stable_norm.cpp | .cpp | 8,474 | 202 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename T> EIGEN_DONT_INLINE T copy(const T& x)
{
return x;
}
template<typename MatrixType> void stable_norm(const MatrixType& m)
{
/* this test covers the following files:
StableNorm.h
*/
using std::sqrt;
using std::abs;
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
bool complex_real_product_ok = true;
// Check the basic machine-dependent constants.
{
int ibeta, it, iemin, iemax;
ibeta = std::numeric_limits<RealScalar>::radix; // base for floating-point numbers
it = std::numeric_limits<RealScalar>::digits; // number of base-beta digits in mantissa
iemin = std::numeric_limits<RealScalar>::min_exponent; // minimum exponent
iemax = std::numeric_limits<RealScalar>::max_exponent; // maximum exponent
VERIFY( (!(iemin > 1 - 2*it || 1+it>iemax || (it==2 && ibeta<5) || (it<=4 && ibeta <= 3 ) || it<2))
&& "the stable norm algorithm cannot be guaranteed on this computer");
Scalar inf = std::numeric_limits<RealScalar>::infinity();
if(NumTraits<Scalar>::IsComplex && (numext::isnan)(inf*RealScalar(1)) )
{
complex_real_product_ok = false;
static bool first = true;
if(first)
std::cerr << "WARNING: compiler mess up complex*real product, " << inf << " * " << 1.0 << " = " << inf*RealScalar(1) << std::endl;
first = false;
}
}
Index rows = m.rows();
Index cols = m.cols();
// get a non-zero random factor
Scalar factor = internal::random<Scalar>();
while(numext::abs2(factor)<RealScalar(1e-4))
factor = internal::random<Scalar>();
Scalar big = factor * ((std::numeric_limits<RealScalar>::max)() * RealScalar(1e-4));
factor = internal::random<Scalar>();
while(numext::abs2(factor)<RealScalar(1e-4))
factor = internal::random<Scalar>();
Scalar small = factor * ((std::numeric_limits<RealScalar>::min)() * RealScalar(1e4));
Scalar one(1);
MatrixType vzero = MatrixType::Zero(rows, cols),
vrand = MatrixType::Random(rows, cols),
vbig(rows, cols),
vsmall(rows,cols);
vbig.fill(big);
vsmall.fill(small);
VERIFY_IS_MUCH_SMALLER_THAN(vzero.norm(), static_cast<RealScalar>(1));
VERIFY_IS_APPROX(vrand.stableNorm(), vrand.norm());
VERIFY_IS_APPROX(vrand.blueNorm(), vrand.norm());
VERIFY_IS_APPROX(vrand.hypotNorm(), vrand.norm());
// test with expressions as input
VERIFY_IS_APPROX((one*vrand).stableNorm(), vrand.norm());
VERIFY_IS_APPROX((one*vrand).blueNorm(), vrand.norm());
VERIFY_IS_APPROX((one*vrand).hypotNorm(), vrand.norm());
VERIFY_IS_APPROX((one*vrand+one*vrand-one*vrand).stableNorm(), vrand.norm());
VERIFY_IS_APPROX((one*vrand+one*vrand-one*vrand).blueNorm(), vrand.norm());
VERIFY_IS_APPROX((one*vrand+one*vrand-one*vrand).hypotNorm(), vrand.norm());
RealScalar size = static_cast<RealScalar>(m.size());
// test numext::isfinite
VERIFY(!(numext::isfinite)( std::numeric_limits<RealScalar>::infinity()));
VERIFY(!(numext::isfinite)(sqrt(-abs(big))));
// test overflow
VERIFY((numext::isfinite)(sqrt(size)*abs(big)));
VERIFY_IS_NOT_APPROX(sqrt(copy(vbig.squaredNorm())), abs(sqrt(size)*big)); // here the default norm must fail
VERIFY_IS_APPROX(vbig.stableNorm(), sqrt(size)*abs(big));
VERIFY_IS_APPROX(vbig.blueNorm(), sqrt(size)*abs(big));
VERIFY_IS_APPROX(vbig.hypotNorm(), sqrt(size)*abs(big));
// test underflow
VERIFY((numext::isfinite)(sqrt(size)*abs(small)));
VERIFY_IS_NOT_APPROX(sqrt(copy(vsmall.squaredNorm())), abs(sqrt(size)*small)); // here the default norm must fail
VERIFY_IS_APPROX(vsmall.stableNorm(), sqrt(size)*abs(small));
VERIFY_IS_APPROX(vsmall.blueNorm(), sqrt(size)*abs(small));
VERIFY_IS_APPROX(vsmall.hypotNorm(), sqrt(size)*abs(small));
// Test compilation of cwise() version
VERIFY_IS_APPROX(vrand.colwise().stableNorm(), vrand.colwise().norm());
VERIFY_IS_APPROX(vrand.colwise().blueNorm(), vrand.colwise().norm());
VERIFY_IS_APPROX(vrand.colwise().hypotNorm(), vrand.colwise().norm());
VERIFY_IS_APPROX(vrand.rowwise().stableNorm(), vrand.rowwise().norm());
VERIFY_IS_APPROX(vrand.rowwise().blueNorm(), vrand.rowwise().norm());
VERIFY_IS_APPROX(vrand.rowwise().hypotNorm(), vrand.rowwise().norm());
// test NaN, +inf, -inf
MatrixType v;
Index i = internal::random<Index>(0,rows-1);
Index j = internal::random<Index>(0,cols-1);
// NaN
{
v = vrand;
v(i,j) = std::numeric_limits<RealScalar>::quiet_NaN();
VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY((numext::isnan)(v.squaredNorm()));
VERIFY(!(numext::isfinite)(v.norm())); VERIFY((numext::isnan)(v.norm()));
VERIFY(!(numext::isfinite)(v.stableNorm())); VERIFY((numext::isnan)(v.stableNorm()));
VERIFY(!(numext::isfinite)(v.blueNorm())); VERIFY((numext::isnan)(v.blueNorm()));
VERIFY(!(numext::isfinite)(v.hypotNorm())); VERIFY((numext::isnan)(v.hypotNorm()));
}
// +inf
{
v = vrand;
v(i,j) = std::numeric_limits<RealScalar>::infinity();
VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY(isPlusInf(v.squaredNorm()));
VERIFY(!(numext::isfinite)(v.norm())); VERIFY(isPlusInf(v.norm()));
VERIFY(!(numext::isfinite)(v.stableNorm()));
if(complex_real_product_ok){
VERIFY(isPlusInf(v.stableNorm()));
}
VERIFY(!(numext::isfinite)(v.blueNorm())); VERIFY(isPlusInf(v.blueNorm()));
VERIFY(!(numext::isfinite)(v.hypotNorm())); VERIFY(isPlusInf(v.hypotNorm()));
}
// -inf
{
v = vrand;
v(i,j) = -std::numeric_limits<RealScalar>::infinity();
VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY(isPlusInf(v.squaredNorm()));
VERIFY(!(numext::isfinite)(v.norm())); VERIFY(isPlusInf(v.norm()));
VERIFY(!(numext::isfinite)(v.stableNorm()));
if(complex_real_product_ok) {
VERIFY(isPlusInf(v.stableNorm()));
}
VERIFY(!(numext::isfinite)(v.blueNorm())); VERIFY(isPlusInf(v.blueNorm()));
VERIFY(!(numext::isfinite)(v.hypotNorm())); VERIFY(isPlusInf(v.hypotNorm()));
}
// mix
{
Index i2 = internal::random<Index>(0,rows-1);
Index j2 = internal::random<Index>(0,cols-1);
v = vrand;
v(i,j) = -std::numeric_limits<RealScalar>::infinity();
v(i2,j2) = std::numeric_limits<RealScalar>::quiet_NaN();
VERIFY(!(numext::isfinite)(v.squaredNorm())); VERIFY((numext::isnan)(v.squaredNorm()));
VERIFY(!(numext::isfinite)(v.norm())); VERIFY((numext::isnan)(v.norm()));
VERIFY(!(numext::isfinite)(v.stableNorm())); VERIFY((numext::isnan)(v.stableNorm()));
VERIFY(!(numext::isfinite)(v.blueNorm())); VERIFY((numext::isnan)(v.blueNorm()));
VERIFY(!(numext::isfinite)(v.hypotNorm())); VERIFY((numext::isnan)(v.hypotNorm()));
}
// stableNormalize[d]
{
VERIFY_IS_APPROX(vrand.stableNormalized(), vrand.normalized());
MatrixType vcopy(vrand);
vcopy.stableNormalize();
VERIFY_IS_APPROX(vcopy, vrand.normalized());
VERIFY_IS_APPROX((vrand.stableNormalized()).norm(), RealScalar(1));
VERIFY_IS_APPROX(vcopy.norm(), RealScalar(1));
VERIFY_IS_APPROX((vbig.stableNormalized()).norm(), RealScalar(1));
VERIFY_IS_APPROX((vsmall.stableNormalized()).norm(), RealScalar(1));
RealScalar big_scaling = ((std::numeric_limits<RealScalar>::max)() * RealScalar(1e-4));
VERIFY_IS_APPROX(vbig/big_scaling, (vbig.stableNorm() * vbig.stableNormalized()).eval()/big_scaling);
VERIFY_IS_APPROX(vsmall, vsmall.stableNorm() * vsmall.stableNormalized());
}
}
void test_stable_norm()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( stable_norm(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( stable_norm(Vector4d()) );
CALL_SUBTEST_3( stable_norm(VectorXd(internal::random<int>(10,2000))) );
CALL_SUBTEST_4( stable_norm(VectorXf(internal::random<int>(10,2000))) );
CALL_SUBTEST_5( stable_norm(VectorXcd(internal::random<int>(10,2000))) );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/mixingtypes.cpp | .cpp | 17,752 | 329 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#if defined(EIGEN_TEST_PART_7)
#ifndef EIGEN_NO_STATIC_ASSERT
#define EIGEN_NO_STATIC_ASSERT // turn static asserts into runtime asserts in order to check them
#endif
// ignore double-promotion diagnostic for clang and gcc, if we check for static assertion anyway:
// TODO do the same for MSVC?
#if defined(__clang__)
# if (__clang_major__ * 100 + __clang_minor__) >= 308
# pragma clang diagnostic ignored "-Wdouble-promotion"
# endif
#elif defined(__GNUC__)
// TODO is there a minimal GCC version for this? At least g++-4.7 seems to be fine with this.
# pragma GCC diagnostic ignored "-Wdouble-promotion"
#endif
#endif
#if defined(EIGEN_TEST_PART_1) || defined(EIGEN_TEST_PART_2) || defined(EIGEN_TEST_PART_3)
#ifndef EIGEN_DONT_VECTORIZE
#define EIGEN_DONT_VECTORIZE
#endif
#endif
static bool g_called;
#define EIGEN_SCALAR_BINARY_OP_PLUGIN { g_called |= (!internal::is_same<LhsScalar,RhsScalar>::value); }
#include "main.h"
using namespace std;
#define VERIFY_MIX_SCALAR(XPR,REF) \
g_called = false; \
VERIFY_IS_APPROX(XPR,REF); \
VERIFY( g_called && #XPR" not properly optimized");
template<int SizeAtCompileType>
void raise_assertion(Index size = SizeAtCompileType)
{
// VERIFY_RAISES_ASSERT(mf+md); // does not even compile
Matrix<float, SizeAtCompileType, 1> vf; vf.setRandom(size);
Matrix<double, SizeAtCompileType, 1> vd; vd.setRandom(size);
VERIFY_RAISES_ASSERT(vf=vd);
VERIFY_RAISES_ASSERT(vf+=vd);
VERIFY_RAISES_ASSERT(vf-=vd);
VERIFY_RAISES_ASSERT(vd=vf);
VERIFY_RAISES_ASSERT(vd+=vf);
VERIFY_RAISES_ASSERT(vd-=vf);
// vd.asDiagonal() * mf; // does not even compile
// vcd.asDiagonal() * mf; // does not even compile
#if 0 // we get other compilation errors here than just static asserts
VERIFY_RAISES_ASSERT(vd.dot(vf));
#endif
}
template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType)
{
typedef std::complex<float> CF;
typedef std::complex<double> CD;
typedef Matrix<float, SizeAtCompileType, SizeAtCompileType> Mat_f;
typedef Matrix<double, SizeAtCompileType, SizeAtCompileType> Mat_d;
typedef Matrix<std::complex<float>, SizeAtCompileType, SizeAtCompileType> Mat_cf;
typedef Matrix<std::complex<double>, SizeAtCompileType, SizeAtCompileType> Mat_cd;
typedef Matrix<float, SizeAtCompileType, 1> Vec_f;
typedef Matrix<double, SizeAtCompileType, 1> Vec_d;
typedef Matrix<std::complex<float>, SizeAtCompileType, 1> Vec_cf;
typedef Matrix<std::complex<double>, SizeAtCompileType, 1> Vec_cd;
Mat_f mf = Mat_f::Random(size,size);
Mat_d md = mf.template cast<double>();
//Mat_d rd = md;
Mat_cf mcf = Mat_cf::Random(size,size);
Mat_cd mcd = mcf.template cast<complex<double> >();
Mat_cd rcd = mcd;
Vec_f vf = Vec_f::Random(size,1);
Vec_d vd = vf.template cast<double>();
Vec_cf vcf = Vec_cf::Random(size,1);
Vec_cd vcd = vcf.template cast<complex<double> >();
float sf = internal::random<float>();
double sd = internal::random<double>();
complex<float> scf = internal::random<complex<float> >();
complex<double> scd = internal::random<complex<double> >();
mf+mf;
float epsf = std::sqrt(std::numeric_limits<float> ::min EIGEN_EMPTY ());
double epsd = std::sqrt(std::numeric_limits<double>::min EIGEN_EMPTY ());
while(std::abs(sf )<epsf) sf = internal::random<float>();
while(std::abs(sd )<epsd) sd = internal::random<double>();
while(std::abs(scf)<epsf) scf = internal::random<CF>();
while(std::abs(scd)<epsd) scd = internal::random<CD>();
// check scalar products
VERIFY_MIX_SCALAR(vcf * sf , vcf * complex<float>(sf));
VERIFY_MIX_SCALAR(sd * vcd , complex<double>(sd) * vcd);
VERIFY_MIX_SCALAR(vf * scf , vf.template cast<complex<float> >() * scf);
VERIFY_MIX_SCALAR(scd * vd , scd * vd.template cast<complex<double> >());
VERIFY_MIX_SCALAR(vcf * 2 , vcf * complex<float>(2));
VERIFY_MIX_SCALAR(vcf * 2.1 , vcf * complex<float>(2.1));
VERIFY_MIX_SCALAR(2 * vcf, vcf * complex<float>(2));
VERIFY_MIX_SCALAR(2.1 * vcf , vcf * complex<float>(2.1));
// check scalar quotients
VERIFY_MIX_SCALAR(vcf / sf , vcf / complex<float>(sf));
VERIFY_MIX_SCALAR(vf / scf , vf.template cast<complex<float> >() / scf);
VERIFY_MIX_SCALAR(vf.array() / scf, vf.template cast<complex<float> >().array() / scf);
VERIFY_MIX_SCALAR(scd / vd.array() , scd / vd.template cast<complex<double> >().array());
// check scalar increment
VERIFY_MIX_SCALAR(vcf.array() + sf , vcf.array() + complex<float>(sf));
VERIFY_MIX_SCALAR(sd + vcd.array(), complex<double>(sd) + vcd.array());
VERIFY_MIX_SCALAR(vf.array() + scf, vf.template cast<complex<float> >().array() + scf);
VERIFY_MIX_SCALAR(scd + vd.array() , scd + vd.template cast<complex<double> >().array());
// check scalar subtractions
VERIFY_MIX_SCALAR(vcf.array() - sf , vcf.array() - complex<float>(sf));
VERIFY_MIX_SCALAR(sd - vcd.array(), complex<double>(sd) - vcd.array());
VERIFY_MIX_SCALAR(vf.array() - scf, vf.template cast<complex<float> >().array() - scf);
VERIFY_MIX_SCALAR(scd - vd.array() , scd - vd.template cast<complex<double> >().array());
// check scalar powers
VERIFY_MIX_SCALAR( pow(vcf.array(), sf), Eigen::pow(vcf.array(), complex<float>(sf)) );
VERIFY_MIX_SCALAR( vcf.array().pow(sf) , Eigen::pow(vcf.array(), complex<float>(sf)) );
VERIFY_MIX_SCALAR( pow(sd, vcd.array()), Eigen::pow(complex<double>(sd), vcd.array()) );
VERIFY_MIX_SCALAR( Eigen::pow(vf.array(), scf), Eigen::pow(vf.template cast<complex<float> >().array(), scf) );
VERIFY_MIX_SCALAR( vf.array().pow(scf) , Eigen::pow(vf.template cast<complex<float> >().array(), scf) );
VERIFY_MIX_SCALAR( Eigen::pow(scd, vd.array()), Eigen::pow(scd, vd.template cast<complex<double> >().array()) );
// check dot product
vf.dot(vf);
VERIFY_IS_APPROX(vcf.dot(vf), vcf.dot(vf.template cast<complex<float> >()));
// check diagonal product
VERIFY_IS_APPROX(vf.asDiagonal() * mcf, vf.template cast<complex<float> >().asDiagonal() * mcf);
VERIFY_IS_APPROX(vcd.asDiagonal() * md, vcd.asDiagonal() * md.template cast<complex<double> >());
VERIFY_IS_APPROX(mcf * vf.asDiagonal(), mcf * vf.template cast<complex<float> >().asDiagonal());
VERIFY_IS_APPROX(md * vcd.asDiagonal(), md.template cast<complex<double> >() * vcd.asDiagonal());
// check inner product
VERIFY_IS_APPROX((vf.transpose() * vcf).value(), (vf.template cast<complex<float> >().transpose() * vcf).value());
// check outer product
VERIFY_IS_APPROX((vf * vcf.transpose()).eval(), (vf.template cast<complex<float> >() * vcf.transpose()).eval());
// coeff wise product
VERIFY_IS_APPROX((vf * vcf.transpose()).eval(), (vf.template cast<complex<float> >() * vcf.transpose()).eval());
Mat_cd mcd2 = mcd;
VERIFY_IS_APPROX(mcd.array() *= md.array(), mcd2.array() *= md.array().template cast<std::complex<double> >());
// check matrix-matrix products
VERIFY_IS_APPROX(sd*md*mcd, (sd*md).template cast<CD>().eval()*mcd);
VERIFY_IS_APPROX(sd*mcd*md, sd*mcd*md.template cast<CD>());
VERIFY_IS_APPROX(scd*md*mcd, scd*md.template cast<CD>().eval()*mcd);
VERIFY_IS_APPROX(scd*mcd*md, scd*mcd*md.template cast<CD>());
VERIFY_IS_APPROX(sf*mf*mcf, sf*mf.template cast<CF>()*mcf);
VERIFY_IS_APPROX(sf*mcf*mf, sf*mcf*mf.template cast<CF>());
VERIFY_IS_APPROX(scf*mf*mcf, scf*mf.template cast<CF>()*mcf);
VERIFY_IS_APPROX(scf*mcf*mf, scf*mcf*mf.template cast<CF>());
VERIFY_IS_APPROX(sd*md.adjoint()*mcd, (sd*md).template cast<CD>().eval().adjoint()*mcd);
VERIFY_IS_APPROX(sd*mcd.adjoint()*md, sd*mcd.adjoint()*md.template cast<CD>());
VERIFY_IS_APPROX(sd*md.adjoint()*mcd.adjoint(), (sd*md).template cast<CD>().eval().adjoint()*mcd.adjoint());
VERIFY_IS_APPROX(sd*mcd.adjoint()*md.adjoint(), sd*mcd.adjoint()*md.template cast<CD>().adjoint());
VERIFY_IS_APPROX(sd*md*mcd.adjoint(), (sd*md).template cast<CD>().eval()*mcd.adjoint());
VERIFY_IS_APPROX(sd*mcd*md.adjoint(), sd*mcd*md.template cast<CD>().adjoint());
VERIFY_IS_APPROX(sf*mf.adjoint()*mcf, (sf*mf).template cast<CF>().eval().adjoint()*mcf);
VERIFY_IS_APPROX(sf*mcf.adjoint()*mf, sf*mcf.adjoint()*mf.template cast<CF>());
VERIFY_IS_APPROX(sf*mf.adjoint()*mcf.adjoint(), (sf*mf).template cast<CF>().eval().adjoint()*mcf.adjoint());
VERIFY_IS_APPROX(sf*mcf.adjoint()*mf.adjoint(), sf*mcf.adjoint()*mf.template cast<CF>().adjoint());
VERIFY_IS_APPROX(sf*mf*mcf.adjoint(), (sf*mf).template cast<CF>().eval()*mcf.adjoint());
VERIFY_IS_APPROX(sf*mcf*mf.adjoint(), sf*mcf*mf.template cast<CF>().adjoint());
VERIFY_IS_APPROX(sf*mf*vcf, (sf*mf).template cast<CF>().eval()*vcf);
VERIFY_IS_APPROX(scf*mf*vcf,(scf*mf.template cast<CF>()).eval()*vcf);
VERIFY_IS_APPROX(sf*mcf*vf, sf*mcf*vf.template cast<CF>());
VERIFY_IS_APPROX(scf*mcf*vf,scf*mcf*vf.template cast<CF>());
VERIFY_IS_APPROX(sf*vcf.adjoint()*mf, sf*vcf.adjoint()*mf.template cast<CF>().eval());
VERIFY_IS_APPROX(scf*vcf.adjoint()*mf, scf*vcf.adjoint()*mf.template cast<CF>().eval());
VERIFY_IS_APPROX(sf*vf.adjoint()*mcf, sf*vf.adjoint().template cast<CF>().eval()*mcf);
VERIFY_IS_APPROX(scf*vf.adjoint()*mcf, scf*vf.adjoint().template cast<CF>().eval()*mcf);
VERIFY_IS_APPROX(sd*md*vcd, (sd*md).template cast<CD>().eval()*vcd);
VERIFY_IS_APPROX(scd*md*vcd,(scd*md.template cast<CD>()).eval()*vcd);
VERIFY_IS_APPROX(sd*mcd*vd, sd*mcd*vd.template cast<CD>().eval());
VERIFY_IS_APPROX(scd*mcd*vd,scd*mcd*vd.template cast<CD>().eval());
VERIFY_IS_APPROX(sd*vcd.adjoint()*md, sd*vcd.adjoint()*md.template cast<CD>().eval());
VERIFY_IS_APPROX(scd*vcd.adjoint()*md, scd*vcd.adjoint()*md.template cast<CD>().eval());
VERIFY_IS_APPROX(sd*vd.adjoint()*mcd, sd*vd.adjoint().template cast<CD>().eval()*mcd);
VERIFY_IS_APPROX(scd*vd.adjoint()*mcd, scd*vd.adjoint().template cast<CD>().eval()*mcd);
VERIFY_IS_APPROX( sd*vcd.adjoint()*md.template triangularView<Upper>(), sd*vcd.adjoint()*md.template cast<CD>().eval().template triangularView<Upper>());
VERIFY_IS_APPROX(scd*vcd.adjoint()*md.template triangularView<Lower>(), scd*vcd.adjoint()*md.template cast<CD>().eval().template triangularView<Lower>());
VERIFY_IS_APPROX( sd*vcd.adjoint()*md.transpose().template triangularView<Upper>(), sd*vcd.adjoint()*md.transpose().template cast<CD>().eval().template triangularView<Upper>());
VERIFY_IS_APPROX(scd*vcd.adjoint()*md.transpose().template triangularView<Lower>(), scd*vcd.adjoint()*md.transpose().template cast<CD>().eval().template triangularView<Lower>());
VERIFY_IS_APPROX( sd*vd.adjoint()*mcd.template triangularView<Lower>(), sd*vd.adjoint().template cast<CD>().eval()*mcd.template triangularView<Lower>());
VERIFY_IS_APPROX(scd*vd.adjoint()*mcd.template triangularView<Upper>(), scd*vd.adjoint().template cast<CD>().eval()*mcd.template triangularView<Upper>());
VERIFY_IS_APPROX( sd*vd.adjoint()*mcd.transpose().template triangularView<Lower>(), sd*vd.adjoint().template cast<CD>().eval()*mcd.transpose().template triangularView<Lower>());
VERIFY_IS_APPROX(scd*vd.adjoint()*mcd.transpose().template triangularView<Upper>(), scd*vd.adjoint().template cast<CD>().eval()*mcd.transpose().template triangularView<Upper>());
// Not supported yet: trmm
// VERIFY_IS_APPROX(sd*mcd*md.template triangularView<Lower>(), sd*mcd*md.template cast<CD>().eval().template triangularView<Lower>());
// VERIFY_IS_APPROX(scd*mcd*md.template triangularView<Upper>(), scd*mcd*md.template cast<CD>().eval().template triangularView<Upper>());
// VERIFY_IS_APPROX(sd*md*mcd.template triangularView<Lower>(), sd*md.template cast<CD>().eval()*mcd.template triangularView<Lower>());
// VERIFY_IS_APPROX(scd*md*mcd.template triangularView<Upper>(), scd*md.template cast<CD>().eval()*mcd.template triangularView<Upper>());
// Not supported yet: symv
// VERIFY_IS_APPROX(sd*vcd.adjoint()*md.template selfadjointView<Upper>(), sd*vcd.adjoint()*md.template cast<CD>().eval().template selfadjointView<Upper>());
// VERIFY_IS_APPROX(scd*vcd.adjoint()*md.template selfadjointView<Lower>(), scd*vcd.adjoint()*md.template cast<CD>().eval().template selfadjointView<Lower>());
// VERIFY_IS_APPROX(sd*vd.adjoint()*mcd.template selfadjointView<Lower>(), sd*vd.adjoint().template cast<CD>().eval()*mcd.template selfadjointView<Lower>());
// VERIFY_IS_APPROX(scd*vd.adjoint()*mcd.template selfadjointView<Upper>(), scd*vd.adjoint().template cast<CD>().eval()*mcd.template selfadjointView<Upper>());
// Not supported yet: symm
// VERIFY_IS_APPROX(sd*vcd.adjoint()*md.template selfadjointView<Upper>(), sd*vcd.adjoint()*md.template cast<CD>().eval().template selfadjointView<Upper>());
// VERIFY_IS_APPROX(scd*vcd.adjoint()*md.template selfadjointView<Upper>(), scd*vcd.adjoint()*md.template cast<CD>().eval().template selfadjointView<Upper>());
// VERIFY_IS_APPROX(sd*vd.adjoint()*mcd.template selfadjointView<Upper>(), sd*vd.adjoint().template cast<CD>().eval()*mcd.template selfadjointView<Upper>());
// VERIFY_IS_APPROX(scd*vd.adjoint()*mcd.template selfadjointView<Upper>(), scd*vd.adjoint().template cast<CD>().eval()*mcd.template selfadjointView<Upper>());
rcd.setZero();
VERIFY_IS_APPROX(Mat_cd(rcd.template triangularView<Upper>() = sd * mcd * md),
Mat_cd((sd * mcd * md.template cast<CD>().eval()).template triangularView<Upper>()));
VERIFY_IS_APPROX(Mat_cd(rcd.template triangularView<Upper>() = sd * md * mcd),
Mat_cd((sd * md.template cast<CD>().eval() * mcd).template triangularView<Upper>()));
VERIFY_IS_APPROX(Mat_cd(rcd.template triangularView<Upper>() = scd * mcd * md),
Mat_cd((scd * mcd * md.template cast<CD>().eval()).template triangularView<Upper>()));
VERIFY_IS_APPROX(Mat_cd(rcd.template triangularView<Upper>() = scd * md * mcd),
Mat_cd((scd * md.template cast<CD>().eval() * mcd).template triangularView<Upper>()));
VERIFY_IS_APPROX( md.array() * mcd.array(), md.template cast<CD>().eval().array() * mcd.array() );
VERIFY_IS_APPROX( mcd.array() * md.array(), mcd.array() * md.template cast<CD>().eval().array() );
VERIFY_IS_APPROX( md.array() + mcd.array(), md.template cast<CD>().eval().array() + mcd.array() );
VERIFY_IS_APPROX( mcd.array() + md.array(), mcd.array() + md.template cast<CD>().eval().array() );
VERIFY_IS_APPROX( md.array() - mcd.array(), md.template cast<CD>().eval().array() - mcd.array() );
VERIFY_IS_APPROX( mcd.array() - md.array(), mcd.array() - md.template cast<CD>().eval().array() );
if(mcd.array().abs().minCoeff()>epsd)
{
VERIFY_IS_APPROX( md.array() / mcd.array(), md.template cast<CD>().eval().array() / mcd.array() );
}
if(md.array().abs().minCoeff()>epsd)
{
VERIFY_IS_APPROX( mcd.array() / md.array(), mcd.array() / md.template cast<CD>().eval().array() );
}
if(md.array().abs().minCoeff()>epsd || mcd.array().abs().minCoeff()>epsd)
{
VERIFY_IS_APPROX( md.array().pow(mcd.array()), md.template cast<CD>().eval().array().pow(mcd.array()) );
VERIFY_IS_APPROX( mcd.array().pow(md.array()), mcd.array().pow(md.template cast<CD>().eval().array()) );
VERIFY_IS_APPROX( pow(md.array(),mcd.array()), md.template cast<CD>().eval().array().pow(mcd.array()) );
VERIFY_IS_APPROX( pow(mcd.array(),md.array()), mcd.array().pow(md.template cast<CD>().eval().array()) );
}
rcd = mcd;
VERIFY_IS_APPROX( rcd = md, md.template cast<CD>().eval() );
rcd = mcd;
VERIFY_IS_APPROX( rcd += md, mcd + md.template cast<CD>().eval() );
rcd = mcd;
VERIFY_IS_APPROX( rcd -= md, mcd - md.template cast<CD>().eval() );
rcd = mcd;
VERIFY_IS_APPROX( rcd.array() *= md.array(), mcd.array() * md.template cast<CD>().eval().array() );
rcd = mcd;
if(md.array().abs().minCoeff()>epsd)
{
VERIFY_IS_APPROX( rcd.array() /= md.array(), mcd.array() / md.template cast<CD>().eval().array() );
}
rcd = mcd;
VERIFY_IS_APPROX( rcd.noalias() += md + mcd*md, mcd + (md.template cast<CD>().eval()) + mcd*(md.template cast<CD>().eval()));
VERIFY_IS_APPROX( rcd.noalias() = md*md, ((md*md).eval().template cast<CD>()) );
rcd = mcd;
VERIFY_IS_APPROX( rcd.noalias() += md*md, mcd + ((md*md).eval().template cast<CD>()) );
rcd = mcd;
VERIFY_IS_APPROX( rcd.noalias() -= md*md, mcd - ((md*md).eval().template cast<CD>()) );
VERIFY_IS_APPROX( rcd.noalias() = mcd + md*md, mcd + ((md*md).eval().template cast<CD>()) );
rcd = mcd;
VERIFY_IS_APPROX( rcd.noalias() += mcd + md*md, mcd + mcd + ((md*md).eval().template cast<CD>()) );
rcd = mcd;
VERIFY_IS_APPROX( rcd.noalias() -= mcd + md*md, - ((md*md).eval().template cast<CD>()) );
}
void test_mixingtypes()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(mixingtypes<3>());
CALL_SUBTEST_2(mixingtypes<4>());
CALL_SUBTEST_3(mixingtypes<Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)));
CALL_SUBTEST_4(mixingtypes<3>());
CALL_SUBTEST_5(mixingtypes<4>());
CALL_SUBTEST_6(mixingtypes<Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)));
CALL_SUBTEST_7(raise_assertion<Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)));
}
CALL_SUBTEST_7(raise_assertion<0>());
CALL_SUBTEST_7(raise_assertion<3>());
CALL_SUBTEST_7(raise_assertion<4>());
CALL_SUBTEST_7(raise_assertion<Dynamic>(0));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/simplicial_cholesky.cpp | .cpp | 2,147 | 48 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "sparse_solver.h"
template<typename T, typename I> void test_simplicial_cholesky_T()
{
typedef SparseMatrix<T,0,I> SparseMatrixType;
SimplicialCholesky<SparseMatrixType, Lower> chol_colmajor_lower_amd;
SimplicialCholesky<SparseMatrixType, Upper> chol_colmajor_upper_amd;
SimplicialLLT< SparseMatrixType, Lower> llt_colmajor_lower_amd;
SimplicialLLT< SparseMatrixType, Upper> llt_colmajor_upper_amd;
SimplicialLDLT< SparseMatrixType, Lower> ldlt_colmajor_lower_amd;
SimplicialLDLT< SparseMatrixType, Upper> ldlt_colmajor_upper_amd;
SimplicialLDLT< SparseMatrixType, Lower, NaturalOrdering<I> > ldlt_colmajor_lower_nat;
SimplicialLDLT< SparseMatrixType, Upper, NaturalOrdering<I> > ldlt_colmajor_upper_nat;
check_sparse_spd_solving(chol_colmajor_lower_amd);
check_sparse_spd_solving(chol_colmajor_upper_amd);
check_sparse_spd_solving(llt_colmajor_lower_amd);
check_sparse_spd_solving(llt_colmajor_upper_amd);
check_sparse_spd_solving(ldlt_colmajor_lower_amd);
check_sparse_spd_solving(ldlt_colmajor_upper_amd);
check_sparse_spd_determinant(chol_colmajor_lower_amd);
check_sparse_spd_determinant(chol_colmajor_upper_amd);
check_sparse_spd_determinant(llt_colmajor_lower_amd);
check_sparse_spd_determinant(llt_colmajor_upper_amd);
check_sparse_spd_determinant(ldlt_colmajor_lower_amd);
check_sparse_spd_determinant(ldlt_colmajor_upper_amd);
check_sparse_spd_solving(ldlt_colmajor_lower_nat, 300, 1000);
check_sparse_spd_solving(ldlt_colmajor_upper_nat, 300, 1000);
}
void test_simplicial_cholesky()
{
CALL_SUBTEST_1(( test_simplicial_cholesky_T<double,int>() ));
CALL_SUBTEST_2(( test_simplicial_cholesky_T<std::complex<double>, int>() ));
CALL_SUBTEST_3(( test_simplicial_cholesky_T<double,long int>() ));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/triangular.cpp | .cpp | 9,470 | 247 | // This file is triangularView of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename MatrixType> void triangular_square(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
RealScalar largerEps = 10*test_precision<RealScalar>();
typename MatrixType::Index rows = m.rows();
typename MatrixType::Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols),
m4(rows, cols),
r1(rows, cols),
r2(rows, cols);
VectorType v2 = VectorType::Random(rows);
MatrixType m1up = m1.template triangularView<Upper>();
MatrixType m2up = m2.template triangularView<Upper>();
if (rows*cols>1)
{
VERIFY(m1up.isUpperTriangular());
VERIFY(m2up.transpose().isLowerTriangular());
VERIFY(!m2.isLowerTriangular());
}
// VERIFY_IS_APPROX(m1up.transpose() * m2, m1.upper().transpose().lower() * m2);
// test overloaded operator+=
r1.setZero();
r2.setZero();
r1.template triangularView<Upper>() += m1;
r2 += m1up;
VERIFY_IS_APPROX(r1,r2);
// test overloaded operator=
m1.setZero();
m1.template triangularView<Upper>() = m2.transpose() + m2;
m3 = m2.transpose() + m2;
VERIFY_IS_APPROX(m3.template triangularView<Lower>().transpose().toDenseMatrix(), m1);
// test overloaded operator=
m1.setZero();
m1.template triangularView<Lower>() = m2.transpose() + m2;
VERIFY_IS_APPROX(m3.template triangularView<Lower>().toDenseMatrix(), m1);
VERIFY_IS_APPROX(m3.template triangularView<Lower>().conjugate().toDenseMatrix(),
m3.conjugate().template triangularView<Lower>().toDenseMatrix());
m1 = MatrixType::Random(rows, cols);
for (int i=0; i<rows; ++i)
while (numext::abs2(m1(i,i))<RealScalar(1e-1)) m1(i,i) = internal::random<Scalar>();
Transpose<MatrixType> trm4(m4);
// test back and forward subsitution with a vector as the rhs
m3 = m1.template triangularView<Upper>();
VERIFY(v2.isApprox(m3.adjoint() * (m1.adjoint().template triangularView<Lower>().solve(v2)), largerEps));
m3 = m1.template triangularView<Lower>();
VERIFY(v2.isApprox(m3.transpose() * (m1.transpose().template triangularView<Upper>().solve(v2)), largerEps));
m3 = m1.template triangularView<Upper>();
VERIFY(v2.isApprox(m3 * (m1.template triangularView<Upper>().solve(v2)), largerEps));
m3 = m1.template triangularView<Lower>();
VERIFY(v2.isApprox(m3.conjugate() * (m1.conjugate().template triangularView<Lower>().solve(v2)), largerEps));
// test back and forward substitution with a matrix as the rhs
m3 = m1.template triangularView<Upper>();
VERIFY(m2.isApprox(m3.adjoint() * (m1.adjoint().template triangularView<Lower>().solve(m2)), largerEps));
m3 = m1.template triangularView<Lower>();
VERIFY(m2.isApprox(m3.transpose() * (m1.transpose().template triangularView<Upper>().solve(m2)), largerEps));
m3 = m1.template triangularView<Upper>();
VERIFY(m2.isApprox(m3 * (m1.template triangularView<Upper>().solve(m2)), largerEps));
m3 = m1.template triangularView<Lower>();
VERIFY(m2.isApprox(m3.conjugate() * (m1.conjugate().template triangularView<Lower>().solve(m2)), largerEps));
// check M * inv(L) using in place API
m4 = m3;
m1.transpose().template triangularView<Eigen::Upper>().solveInPlace(trm4);
VERIFY_IS_APPROX(m4 * m1.template triangularView<Eigen::Lower>(), m3);
// check M * inv(U) using in place API
m3 = m1.template triangularView<Upper>();
m4 = m3;
m3.transpose().template triangularView<Eigen::Lower>().solveInPlace(trm4);
VERIFY_IS_APPROX(m4 * m1.template triangularView<Eigen::Upper>(), m3);
// check solve with unit diagonal
m3 = m1.template triangularView<UnitUpper>();
VERIFY(m2.isApprox(m3 * (m1.template triangularView<UnitUpper>().solve(m2)), largerEps));
// VERIFY(( m1.template triangularView<Upper>()
// * m2.template triangularView<Upper>()).isUpperTriangular());
// test swap
m1.setOnes();
m2.setZero();
m2.template triangularView<Upper>().swap(m1);
m3.setZero();
m3.template triangularView<Upper>().setOnes();
VERIFY_IS_APPROX(m2,m3);
m1.setRandom();
m3 = m1.template triangularView<Upper>();
Matrix<Scalar, MatrixType::ColsAtCompileTime, Dynamic> m5(cols, internal::random<int>(1,20)); m5.setRandom();
Matrix<Scalar, Dynamic, MatrixType::RowsAtCompileTime> m6(internal::random<int>(1,20), rows); m6.setRandom();
VERIFY_IS_APPROX(m1.template triangularView<Upper>() * m5, m3*m5);
VERIFY_IS_APPROX(m6*m1.template triangularView<Upper>(), m6*m3);
m1up = m1.template triangularView<Upper>();
VERIFY_IS_APPROX(m1.template selfadjointView<Upper>().template triangularView<Upper>().toDenseMatrix(), m1up);
VERIFY_IS_APPROX(m1up.template selfadjointView<Upper>().template triangularView<Upper>().toDenseMatrix(), m1up);
VERIFY_IS_APPROX(m1.template selfadjointView<Upper>().template triangularView<Lower>().toDenseMatrix(), m1up.adjoint());
VERIFY_IS_APPROX(m1up.template selfadjointView<Upper>().template triangularView<Lower>().toDenseMatrix(), m1up.adjoint());
VERIFY_IS_APPROX(m1.template selfadjointView<Upper>().diagonal(), m1.diagonal());
}
template<typename MatrixType> void triangular_rect(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime };
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols),
m4(rows, cols),
r1(rows, cols),
r2(rows, cols);
MatrixType m1up = m1.template triangularView<Upper>();
MatrixType m2up = m2.template triangularView<Upper>();
if (rows>1 && cols>1)
{
VERIFY(m1up.isUpperTriangular());
VERIFY(m2up.transpose().isLowerTriangular());
VERIFY(!m2.isLowerTriangular());
}
// test overloaded operator+=
r1.setZero();
r2.setZero();
r1.template triangularView<Upper>() += m1;
r2 += m1up;
VERIFY_IS_APPROX(r1,r2);
// test overloaded operator=
m1.setZero();
m1.template triangularView<Upper>() = 3 * m2;
m3 = 3 * m2;
VERIFY_IS_APPROX(m3.template triangularView<Upper>().toDenseMatrix(), m1);
m1.setZero();
m1.template triangularView<Lower>() = 3 * m2;
VERIFY_IS_APPROX(m3.template triangularView<Lower>().toDenseMatrix(), m1);
m1.setZero();
m1.template triangularView<StrictlyUpper>() = 3 * m2;
VERIFY_IS_APPROX(m3.template triangularView<StrictlyUpper>().toDenseMatrix(), m1);
m1.setZero();
m1.template triangularView<StrictlyLower>() = 3 * m2;
VERIFY_IS_APPROX(m3.template triangularView<StrictlyLower>().toDenseMatrix(), m1);
m1.setRandom();
m2 = m1.template triangularView<Upper>();
VERIFY(m2.isUpperTriangular());
VERIFY(!m2.isLowerTriangular());
m2 = m1.template triangularView<StrictlyUpper>();
VERIFY(m2.isUpperTriangular());
VERIFY(m2.diagonal().isMuchSmallerThan(RealScalar(1)));
m2 = m1.template triangularView<UnitUpper>();
VERIFY(m2.isUpperTriangular());
m2.diagonal().array() -= Scalar(1);
VERIFY(m2.diagonal().isMuchSmallerThan(RealScalar(1)));
m2 = m1.template triangularView<Lower>();
VERIFY(m2.isLowerTriangular());
VERIFY(!m2.isUpperTriangular());
m2 = m1.template triangularView<StrictlyLower>();
VERIFY(m2.isLowerTriangular());
VERIFY(m2.diagonal().isMuchSmallerThan(RealScalar(1)));
m2 = m1.template triangularView<UnitLower>();
VERIFY(m2.isLowerTriangular());
m2.diagonal().array() -= Scalar(1);
VERIFY(m2.diagonal().isMuchSmallerThan(RealScalar(1)));
// test swap
m1.setOnes();
m2.setZero();
m2.template triangularView<Upper>().swap(m1);
m3.setZero();
m3.template triangularView<Upper>().setOnes();
VERIFY_IS_APPROX(m2,m3);
}
void bug_159()
{
Matrix3d m = Matrix3d::Random().triangularView<Lower>();
EIGEN_UNUSED_VARIABLE(m)
}
void test_triangular()
{
int maxsize = (std::min)(EIGEN_TEST_MAX_SIZE,20);
for(int i = 0; i < g_repeat ; i++)
{
int r = internal::random<int>(2,maxsize); TEST_SET_BUT_UNUSED_VARIABLE(r)
int c = internal::random<int>(2,maxsize); TEST_SET_BUT_UNUSED_VARIABLE(c)
CALL_SUBTEST_1( triangular_square(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( triangular_square(Matrix<float, 2, 2>()) );
CALL_SUBTEST_3( triangular_square(Matrix3d()) );
CALL_SUBTEST_4( triangular_square(Matrix<std::complex<float>,8, 8>()) );
CALL_SUBTEST_5( triangular_square(MatrixXcd(r,r)) );
CALL_SUBTEST_6( triangular_square(Matrix<float,Dynamic,Dynamic,RowMajor>(r, r)) );
CALL_SUBTEST_7( triangular_rect(Matrix<float, 4, 5>()) );
CALL_SUBTEST_8( triangular_rect(Matrix<double, 6, 2>()) );
CALL_SUBTEST_9( triangular_rect(MatrixXcf(r, c)) );
CALL_SUBTEST_5( triangular_rect(MatrixXcd(r, c)) );
CALL_SUBTEST_6( triangular_rect(Matrix<float,Dynamic,Dynamic,RowMajor>(r, c)) );
}
CALL_SUBTEST_1( bug_159() );
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/product_notemporary.cpp | .cpp | 8,803 | 160 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define TEST_ENABLE_TEMPORARY_TRACKING
#include "main.h"
template<typename MatrixType> void product_notemporary(const MatrixType& m)
{
/* This test checks the number of temporaries created
* during the evaluation of a complex expression */
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef Matrix<Scalar, 1, Dynamic> RowVectorType;
typedef Matrix<Scalar, Dynamic, 1> ColVectorType;
typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> ColMajorMatrixType;
typedef Matrix<Scalar, Dynamic, Dynamic, RowMajor> RowMajorMatrixType;
Index rows = m.rows();
Index cols = m.cols();
ColMajorMatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols);
RowVectorType rv1 = RowVectorType::Random(rows), rvres(rows);
ColVectorType cv1 = ColVectorType::Random(cols), cvres(cols);
RowMajorMatrixType rm3(rows, cols);
Scalar s1 = internal::random<Scalar>(),
s2 = internal::random<Scalar>(),
s3 = internal::random<Scalar>();
Index c0 = internal::random<Index>(4,cols-8),
c1 = internal::random<Index>(8,cols-c0),
r0 = internal::random<Index>(4,cols-8),
r1 = internal::random<Index>(8,rows-r0);
VERIFY_EVALUATION_COUNT( m3 = (m1 * m2.adjoint()), 1);
VERIFY_EVALUATION_COUNT( m3 = (m1 * m2.adjoint()).transpose(), 1);
VERIFY_EVALUATION_COUNT( m3.noalias() = m1 * m2.adjoint(), 0);
VERIFY_EVALUATION_COUNT( m3 = s1 * (m1 * m2.transpose()), 1);
// VERIFY_EVALUATION_COUNT( m3 = m3 + s1 * (m1 * m2.transpose()), 1);
VERIFY_EVALUATION_COUNT( m3.noalias() = s1 * (m1 * m2.transpose()), 0);
VERIFY_EVALUATION_COUNT( m3 = m3 + (m1 * m2.adjoint()), 1);
VERIFY_EVALUATION_COUNT( m3 = m3 - (m1 * m2.adjoint()), 1);
VERIFY_EVALUATION_COUNT( m3 = m3 + (m1 * m2.adjoint()).transpose(), 1);
VERIFY_EVALUATION_COUNT( m3.noalias() = m3 + m1 * m2.transpose(), 0);
VERIFY_EVALUATION_COUNT( m3.noalias() += m3 + m1 * m2.transpose(), 0);
VERIFY_EVALUATION_COUNT( m3.noalias() -= m3 + m1 * m2.transpose(), 0);
VERIFY_EVALUATION_COUNT( m3.noalias() = m3 - m1 * m2.transpose(), 0);
VERIFY_EVALUATION_COUNT( m3.noalias() += m3 - m1 * m2.transpose(), 0);
VERIFY_EVALUATION_COUNT( m3.noalias() -= m3 - m1 * m2.transpose(), 0);
VERIFY_EVALUATION_COUNT( m3.noalias() = s1 * m1 * s2 * m2.adjoint(), 0);
VERIFY_EVALUATION_COUNT( m3.noalias() = s1 * m1 * s2 * (m1*s3+m2*s2).adjoint(), 1);
VERIFY_EVALUATION_COUNT( m3.noalias() = (s1 * m1).adjoint() * s2 * m2, 0);
VERIFY_EVALUATION_COUNT( m3.noalias() += s1 * (-m1*s3).adjoint() * (s2 * m2 * s3), 0);
VERIFY_EVALUATION_COUNT( m3.noalias() -= s1 * (m1.transpose() * m2), 0);
VERIFY_EVALUATION_COUNT(( m3.block(r0,r0,r1,r1).noalias() += -m1.block(r0,c0,r1,c1) * (s2*m2.block(r0,c0,r1,c1)).adjoint() ), 0);
VERIFY_EVALUATION_COUNT(( m3.block(r0,r0,r1,r1).noalias() -= s1 * m1.block(r0,c0,r1,c1) * m2.block(c0,r0,c1,r1) ), 0);
// NOTE this is because the Block expression is not handled yet by our expression analyser
VERIFY_EVALUATION_COUNT(( m3.block(r0,r0,r1,r1).noalias() = s1 * m1.block(r0,c0,r1,c1) * (s1*m2).block(c0,r0,c1,r1) ), 1);
VERIFY_EVALUATION_COUNT( m3.noalias() -= (s1 * m1).template triangularView<Lower>() * m2, 0);
VERIFY_EVALUATION_COUNT( rm3.noalias() = (s1 * m1.adjoint()).template triangularView<Upper>() * (m2+m2), 1);
VERIFY_EVALUATION_COUNT( rm3.noalias() = (s1 * m1.adjoint()).template triangularView<UnitUpper>() * m2.adjoint(), 0);
VERIFY_EVALUATION_COUNT( m3.template triangularView<Upper>() = (m1 * m2.adjoint()), 0);
VERIFY_EVALUATION_COUNT( m3.template triangularView<Upper>() -= (m1 * m2.adjoint()), 0);
// NOTE this is because the blas_traits require innerstride==1 to avoid a temporary, but that doesn't seem to be actually needed for the triangular products
VERIFY_EVALUATION_COUNT( rm3.col(c0).noalias() = (s1 * m1.adjoint()).template triangularView<UnitUpper>() * (s2*m2.row(c0)).adjoint(), 1);
VERIFY_EVALUATION_COUNT( m1.template triangularView<Lower>().solveInPlace(m3), 0);
VERIFY_EVALUATION_COUNT( m1.adjoint().template triangularView<Lower>().solveInPlace(m3.transpose()), 0);
VERIFY_EVALUATION_COUNT( m3.noalias() -= (s1 * m1).adjoint().template selfadjointView<Lower>() * (-m2*s3).adjoint(), 0);
VERIFY_EVALUATION_COUNT( m3.noalias() = s2 * m2.adjoint() * (s1 * m1.adjoint()).template selfadjointView<Upper>(), 0);
VERIFY_EVALUATION_COUNT( rm3.noalias() = (s1 * m1.adjoint()).template selfadjointView<Lower>() * m2.adjoint(), 0);
// NOTE this is because the blas_traits require innerstride==1 to avoid a temporary, but that doesn't seem to be actually needed for the triangular products
VERIFY_EVALUATION_COUNT( m3.col(c0).noalias() = (s1 * m1).adjoint().template selfadjointView<Lower>() * (-m2.row(c0)*s3).adjoint(), 1);
VERIFY_EVALUATION_COUNT( m3.col(c0).noalias() -= (s1 * m1).adjoint().template selfadjointView<Upper>() * (-m2.row(c0)*s3).adjoint(), 1);
VERIFY_EVALUATION_COUNT( m3.block(r0,c0,r1,c1).noalias() += m1.block(r0,r0,r1,r1).template selfadjointView<Upper>() * (s1*m2.block(r0,c0,r1,c1)), 0);
VERIFY_EVALUATION_COUNT( m3.block(r0,c0,r1,c1).noalias() = m1.block(r0,r0,r1,r1).template selfadjointView<Upper>() * m2.block(r0,c0,r1,c1), 0);
VERIFY_EVALUATION_COUNT( m3.template selfadjointView<Lower>().rankUpdate(m2.adjoint()), 0);
// Here we will get 1 temporary for each resize operation of the lhs operator; resize(r1,c1) would lead to zero temporaries
m3.resize(1,1);
VERIFY_EVALUATION_COUNT( m3.noalias() = m1.block(r0,r0,r1,r1).template selfadjointView<Lower>() * m2.block(r0,c0,r1,c1), 1);
m3.resize(1,1);
VERIFY_EVALUATION_COUNT( m3.noalias() = m1.block(r0,r0,r1,r1).template triangularView<UnitUpper>() * m2.block(r0,c0,r1,c1), 1);
// Zero temporaries for lazy products ...
VERIFY_EVALUATION_COUNT( Scalar tmp = 0; tmp += Scalar(RealScalar(1)) / (m3.transpose().lazyProduct(m3)).diagonal().sum(), 0 );
// ... and even no temporary for even deeply (>=2) nested products
VERIFY_EVALUATION_COUNT( Scalar tmp = 0; tmp += Scalar(RealScalar(1)) / (m3.transpose() * m3).diagonal().sum(), 0 );
VERIFY_EVALUATION_COUNT( Scalar tmp = 0; tmp += Scalar(RealScalar(1)) / (m3.transpose() * m3).diagonal().array().abs().sum(), 0 );
// Zero temporaries for ... CoeffBasedProductMode
VERIFY_EVALUATION_COUNT( m3.col(0).template head<5>() * m3.col(0).transpose() + m3.col(0).template head<5>() * m3.col(0).transpose(), 0 );
// Check matrix * vectors
VERIFY_EVALUATION_COUNT( cvres.noalias() = m1 * cv1, 0 );
VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * cv1, 0 );
VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * m2.col(0), 0 );
VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * rv1.adjoint(), 0 );
VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * m2.row(0).transpose(), 0 );
VERIFY_EVALUATION_COUNT( cvres.noalias() = (m1+m1) * cv1, 0 );
VERIFY_EVALUATION_COUNT( cvres.noalias() = (rm3+rm3) * cv1, 0 );
VERIFY_EVALUATION_COUNT( cvres.noalias() = (m1+m1) * (m1*cv1), 1 );
VERIFY_EVALUATION_COUNT( cvres.noalias() = (rm3+rm3) * (m1*cv1), 1 );
// Check outer products
m3 = cv1 * rv1;
VERIFY_EVALUATION_COUNT( m3.noalias() = cv1 * rv1, 0 );
VERIFY_EVALUATION_COUNT( m3.noalias() = (cv1+cv1) * (rv1+rv1), 1 );
VERIFY_EVALUATION_COUNT( m3.noalias() = (m1*cv1) * (rv1), 1 );
VERIFY_EVALUATION_COUNT( m3.noalias() += (m1*cv1) * (rv1), 1 );
VERIFY_EVALUATION_COUNT( rm3.noalias() = (cv1) * (rv1 * m1), 1 );
VERIFY_EVALUATION_COUNT( rm3.noalias() -= (cv1) * (rv1 * m1), 1 );
VERIFY_EVALUATION_COUNT( rm3.noalias() = (m1*cv1) * (rv1 * m1), 2 );
VERIFY_EVALUATION_COUNT( rm3.noalias() += (m1*cv1) * (rv1 * m1), 2 );
// Check nested products
VERIFY_EVALUATION_COUNT( cvres.noalias() = m1.adjoint() * m1 * cv1, 1 );
VERIFY_EVALUATION_COUNT( rvres.noalias() = rv1 * (m1 * m2.adjoint()), 1 );
}
void test_product_notemporary()
{
int s;
for(int i = 0; i < g_repeat; i++) {
s = internal::random<int>(16,EIGEN_TEST_MAX_SIZE);
CALL_SUBTEST_1( product_notemporary(MatrixXf(s, s)) );
CALL_SUBTEST_2( product_notemporary(MatrixXd(s, s)) );
TEST_SET_BUT_UNUSED_VARIABLE(s)
s = internal::random<int>(16,EIGEN_TEST_MAX_SIZE/2);
CALL_SUBTEST_3( product_notemporary(MatrixXcf(s,s)) );
CALL_SUBTEST_4( product_notemporary(MatrixXcd(s,s)) );
TEST_SET_BUT_UNUSED_VARIABLE(s)
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/special_numbers.cpp | .cpp | 1,754 | 59 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2013 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename Scalar> void special_numbers()
{
typedef Matrix<Scalar, Dynamic,Dynamic> MatType;
int rows = internal::random<int>(1,300);
int cols = internal::random<int>(1,300);
Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
Scalar inf = std::numeric_limits<Scalar>::infinity();
Scalar s1 = internal::random<Scalar>();
MatType m1 = MatType::Random(rows,cols),
mnan = MatType::Random(rows,cols),
minf = MatType::Random(rows,cols),
mboth = MatType::Random(rows,cols);
int n = internal::random<int>(1,10);
for(int k=0; k<n; ++k)
{
mnan(internal::random<int>(0,rows-1), internal::random<int>(0,cols-1)) = nan;
minf(internal::random<int>(0,rows-1), internal::random<int>(0,cols-1)) = inf;
}
mboth = mnan + minf;
VERIFY(!m1.hasNaN());
VERIFY(m1.allFinite());
VERIFY(mnan.hasNaN());
VERIFY((s1*mnan).hasNaN());
VERIFY(!minf.hasNaN());
VERIFY(!(2*minf).hasNaN());
VERIFY(mboth.hasNaN());
VERIFY(mboth.array().hasNaN());
VERIFY(!mnan.allFinite());
VERIFY(!minf.allFinite());
VERIFY(!(minf-mboth).allFinite());
VERIFY(!mboth.allFinite());
VERIFY(!mboth.array().allFinite());
}
void test_special_numbers()
{
for(int i = 0; i < 10*g_repeat; i++) {
CALL_SUBTEST_1( special_numbers<float>() );
CALL_SUBTEST_1( special_numbers<double>() );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/dynalloc.cpp | .cpp | 4,630 | 176 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#if EIGEN_MAX_ALIGN_BYTES>0
#define ALIGNMENT EIGEN_MAX_ALIGN_BYTES
#else
#define ALIGNMENT 1
#endif
typedef Matrix<float,8,1> Vector8f;
void check_handmade_aligned_malloc()
{
for(int i = 1; i < 1000; i++)
{
char *p = (char*)internal::handmade_aligned_malloc(i);
VERIFY(internal::UIntPtr(p)%ALIGNMENT==0);
// if the buffer is wrongly allocated this will give a bad write --> check with valgrind
for(int j = 0; j < i; j++) p[j]=0;
internal::handmade_aligned_free(p);
}
}
void check_aligned_malloc()
{
for(int i = ALIGNMENT; i < 1000; i++)
{
char *p = (char*)internal::aligned_malloc(i);
VERIFY(internal::UIntPtr(p)%ALIGNMENT==0);
// if the buffer is wrongly allocated this will give a bad write --> check with valgrind
for(int j = 0; j < i; j++) p[j]=0;
internal::aligned_free(p);
}
}
void check_aligned_new()
{
for(int i = ALIGNMENT; i < 1000; i++)
{
float *p = internal::aligned_new<float>(i);
VERIFY(internal::UIntPtr(p)%ALIGNMENT==0);
// if the buffer is wrongly allocated this will give a bad write --> check with valgrind
for(int j = 0; j < i; j++) p[j]=0;
internal::aligned_delete(p,i);
}
}
void check_aligned_stack_alloc()
{
for(int i = ALIGNMENT; i < 400; i++)
{
ei_declare_aligned_stack_constructed_variable(float,p,i,0);
VERIFY(internal::UIntPtr(p)%ALIGNMENT==0);
// if the buffer is wrongly allocated this will give a bad write --> check with valgrind
for(int j = 0; j < i; j++) p[j]=0;
}
}
// test compilation with both a struct and a class...
struct MyStruct
{
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
char dummychar;
Vector8f avec;
};
class MyClassA
{
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
char dummychar;
Vector8f avec;
};
template<typename T> void check_dynaligned()
{
// TODO have to be updated once we support multiple alignment values
if(T::SizeAtCompileTime % ALIGNMENT == 0)
{
T* obj = new T;
VERIFY(T::NeedsToAlign==1);
VERIFY(internal::UIntPtr(obj)%ALIGNMENT==0);
delete obj;
}
}
template<typename T> void check_custom_new_delete()
{
{
T* t = new T;
delete t;
}
{
std::size_t N = internal::random<std::size_t>(1,10);
T* t = new T[N];
delete[] t;
}
#if EIGEN_MAX_ALIGN_BYTES>0
{
T* t = static_cast<T *>((T::operator new)(sizeof(T)));
(T::operator delete)(t, sizeof(T));
}
{
T* t = static_cast<T *>((T::operator new)(sizeof(T)));
(T::operator delete)(t);
}
#endif
}
void test_dynalloc()
{
// low level dynamic memory allocation
CALL_SUBTEST(check_handmade_aligned_malloc());
CALL_SUBTEST(check_aligned_malloc());
CALL_SUBTEST(check_aligned_new());
CALL_SUBTEST(check_aligned_stack_alloc());
for (int i=0; i<g_repeat*100; ++i)
{
CALL_SUBTEST( check_custom_new_delete<Vector4f>() );
CALL_SUBTEST( check_custom_new_delete<Vector2f>() );
CALL_SUBTEST( check_custom_new_delete<Matrix4f>() );
CALL_SUBTEST( check_custom_new_delete<MatrixXi>() );
}
// check static allocation, who knows ?
#if EIGEN_MAX_STATIC_ALIGN_BYTES
for (int i=0; i<g_repeat*100; ++i)
{
CALL_SUBTEST(check_dynaligned<Vector4f>() );
CALL_SUBTEST(check_dynaligned<Vector2d>() );
CALL_SUBTEST(check_dynaligned<Matrix4f>() );
CALL_SUBTEST(check_dynaligned<Vector4d>() );
CALL_SUBTEST(check_dynaligned<Vector4i>() );
CALL_SUBTEST(check_dynaligned<Vector8f>() );
}
{
MyStruct foo0; VERIFY(internal::UIntPtr(foo0.avec.data())%ALIGNMENT==0);
MyClassA fooA; VERIFY(internal::UIntPtr(fooA.avec.data())%ALIGNMENT==0);
}
// dynamic allocation, single object
for (int i=0; i<g_repeat*100; ++i)
{
MyStruct *foo0 = new MyStruct(); VERIFY(internal::UIntPtr(foo0->avec.data())%ALIGNMENT==0);
MyClassA *fooA = new MyClassA(); VERIFY(internal::UIntPtr(fooA->avec.data())%ALIGNMENT==0);
delete foo0;
delete fooA;
}
// dynamic allocation, array
const int N = 10;
for (int i=0; i<g_repeat*100; ++i)
{
MyStruct *foo0 = new MyStruct[N]; VERIFY(internal::UIntPtr(foo0->avec.data())%ALIGNMENT==0);
MyClassA *fooA = new MyClassA[N]; VERIFY(internal::UIntPtr(fooA->avec.data())%ALIGNMENT==0);
delete[] foo0;
delete[] fooA;
}
#endif
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/evaluator_common.h | .h | 0 | 0 | null | Unknown |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/sparse_permutations.cpp | .cpp | 9,989 | 237 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
static long int nb_transposed_copies;
#define EIGEN_SPARSE_TRANSPOSED_COPY_PLUGIN {nb_transposed_copies++;}
#define VERIFY_TRANSPOSITION_COUNT(XPR,N) {\
nb_transposed_copies = 0; \
XPR; \
if(nb_transposed_copies!=N) std::cerr << "nb_transposed_copies == " << nb_transposed_copies << "\n"; \
VERIFY( (#XPR) && nb_transposed_copies==N ); \
}
#include "sparse.h"
template<typename T>
bool is_sorted(const T& mat) {
for(Index k = 0; k<mat.outerSize(); ++k)
{
Index prev = -1;
for(typename T::InnerIterator it(mat,k); it; ++it)
{
if(prev>=it.index())
return false;
prev = it.index();
}
}
return true;
}
template<typename T>
typename internal::nested_eval<T,1>::type eval(const T &xpr)
{
VERIFY( int(internal::nested_eval<T,1>::type::Flags&RowMajorBit) == int(internal::evaluator<T>::Flags&RowMajorBit) );
return xpr;
}
template<int OtherStorage, typename SparseMatrixType> void sparse_permutations(const SparseMatrixType& ref)
{
const Index rows = ref.rows();
const Index cols = ref.cols();
typedef typename SparseMatrixType::Scalar Scalar;
typedef typename SparseMatrixType::StorageIndex StorageIndex;
typedef SparseMatrix<Scalar, OtherStorage, StorageIndex> OtherSparseMatrixType;
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<StorageIndex,Dynamic,1> VectorI;
// bool IsRowMajor1 = SparseMatrixType::IsRowMajor;
// bool IsRowMajor2 = OtherSparseMatrixType::IsRowMajor;
double density = (std::max)(8./(rows*cols), 0.01);
SparseMatrixType mat(rows, cols), up(rows,cols), lo(rows,cols);
OtherSparseMatrixType res;
DenseMatrix mat_d = DenseMatrix::Zero(rows, cols), up_sym_d, lo_sym_d, res_d;
initSparse<Scalar>(density, mat_d, mat, 0);
up = mat.template triangularView<Upper>();
lo = mat.template triangularView<Lower>();
up_sym_d = mat_d.template selfadjointView<Upper>();
lo_sym_d = mat_d.template selfadjointView<Lower>();
VERIFY_IS_APPROX(mat, mat_d);
VERIFY_IS_APPROX(up, DenseMatrix(mat_d.template triangularView<Upper>()));
VERIFY_IS_APPROX(lo, DenseMatrix(mat_d.template triangularView<Lower>()));
PermutationMatrix<Dynamic> p, p_null;
VectorI pi;
randomPermutationVector(pi, cols);
p.indices() = pi;
VERIFY( is_sorted( ::eval(mat*p) ));
VERIFY( is_sorted( res = mat*p ));
VERIFY_TRANSPOSITION_COUNT( ::eval(mat*p), 0);
//VERIFY_TRANSPOSITION_COUNT( res = mat*p, IsRowMajor ? 1 : 0 );
res_d = mat_d*p;
VERIFY(res.isApprox(res_d) && "mat*p");
VERIFY( is_sorted( ::eval(p*mat) ));
VERIFY( is_sorted( res = p*mat ));
VERIFY_TRANSPOSITION_COUNT( ::eval(p*mat), 0);
res_d = p*mat_d;
VERIFY(res.isApprox(res_d) && "p*mat");
VERIFY( is_sorted( (mat*p).eval() ));
VERIFY( is_sorted( res = mat*p.inverse() ));
VERIFY_TRANSPOSITION_COUNT( ::eval(mat*p.inverse()), 0);
res_d = mat*p.inverse();
VERIFY(res.isApprox(res_d) && "mat*inv(p)");
VERIFY( is_sorted( (p*mat+p*mat).eval() ));
VERIFY( is_sorted( res = p.inverse()*mat ));
VERIFY_TRANSPOSITION_COUNT( ::eval(p.inverse()*mat), 0);
res_d = p.inverse()*mat_d;
VERIFY(res.isApprox(res_d) && "inv(p)*mat");
VERIFY( is_sorted( (p * mat * p.inverse()).eval() ));
VERIFY( is_sorted( res = mat.twistedBy(p) ));
VERIFY_TRANSPOSITION_COUNT( ::eval(p * mat * p.inverse()), 0);
res_d = (p * mat_d) * p.inverse();
VERIFY(res.isApprox(res_d) && "p*mat*inv(p)");
VERIFY( is_sorted( res = mat.template selfadjointView<Upper>().twistedBy(p_null) ));
res_d = up_sym_d;
VERIFY(res.isApprox(res_d) && "full selfadjoint upper to full");
VERIFY( is_sorted( res = mat.template selfadjointView<Lower>().twistedBy(p_null) ));
res_d = lo_sym_d;
VERIFY(res.isApprox(res_d) && "full selfadjoint lower to full");
VERIFY( is_sorted( res = up.template selfadjointView<Upper>().twistedBy(p_null) ));
res_d = up_sym_d;
VERIFY(res.isApprox(res_d) && "upper selfadjoint to full");
VERIFY( is_sorted( res = lo.template selfadjointView<Lower>().twistedBy(p_null) ));
res_d = lo_sym_d;
VERIFY(res.isApprox(res_d) && "lower selfadjoint full");
VERIFY( is_sorted( res = mat.template selfadjointView<Upper>() ));
res_d = up_sym_d;
VERIFY(res.isApprox(res_d) && "full selfadjoint upper to full");
VERIFY( is_sorted( res = mat.template selfadjointView<Lower>() ));
res_d = lo_sym_d;
VERIFY(res.isApprox(res_d) && "full selfadjoint lower to full");
VERIFY( is_sorted( res = up.template selfadjointView<Upper>() ));
res_d = up_sym_d;
VERIFY(res.isApprox(res_d) && "upper selfadjoint to full");
VERIFY( is_sorted( res = lo.template selfadjointView<Lower>() ));
res_d = lo_sym_d;
VERIFY(res.isApprox(res_d) && "lower selfadjoint full");
res.template selfadjointView<Upper>() = mat.template selfadjointView<Upper>();
res_d = up_sym_d.template triangularView<Upper>();
VERIFY(res.isApprox(res_d) && "full selfadjoint upper to upper");
res.template selfadjointView<Lower>() = mat.template selfadjointView<Upper>();
res_d = up_sym_d.template triangularView<Lower>();
VERIFY(res.isApprox(res_d) && "full selfadjoint upper to lower");
res.template selfadjointView<Upper>() = mat.template selfadjointView<Lower>();
res_d = lo_sym_d.template triangularView<Upper>();
VERIFY(res.isApprox(res_d) && "full selfadjoint lower to upper");
res.template selfadjointView<Lower>() = mat.template selfadjointView<Lower>();
res_d = lo_sym_d.template triangularView<Lower>();
VERIFY(res.isApprox(res_d) && "full selfadjoint lower to lower");
res.template selfadjointView<Upper>() = mat.template selfadjointView<Upper>().twistedBy(p);
res_d = ((p * up_sym_d) * p.inverse()).eval().template triangularView<Upper>();
VERIFY(res.isApprox(res_d) && "full selfadjoint upper twisted to upper");
res.template selfadjointView<Upper>() = mat.template selfadjointView<Lower>().twistedBy(p);
res_d = ((p * lo_sym_d) * p.inverse()).eval().template triangularView<Upper>();
VERIFY(res.isApprox(res_d) && "full selfadjoint lower twisted to upper");
res.template selfadjointView<Lower>() = mat.template selfadjointView<Lower>().twistedBy(p);
res_d = ((p * lo_sym_d) * p.inverse()).eval().template triangularView<Lower>();
VERIFY(res.isApprox(res_d) && "full selfadjoint lower twisted to lower");
res.template selfadjointView<Lower>() = mat.template selfadjointView<Upper>().twistedBy(p);
res_d = ((p * up_sym_d) * p.inverse()).eval().template triangularView<Lower>();
VERIFY(res.isApprox(res_d) && "full selfadjoint upper twisted to lower");
res.template selfadjointView<Upper>() = up.template selfadjointView<Upper>().twistedBy(p);
res_d = ((p * up_sym_d) * p.inverse()).eval().template triangularView<Upper>();
VERIFY(res.isApprox(res_d) && "upper selfadjoint twisted to upper");
res.template selfadjointView<Upper>() = lo.template selfadjointView<Lower>().twistedBy(p);
res_d = ((p * lo_sym_d) * p.inverse()).eval().template triangularView<Upper>();
VERIFY(res.isApprox(res_d) && "lower selfadjoint twisted to upper");
res.template selfadjointView<Lower>() = lo.template selfadjointView<Lower>().twistedBy(p);
res_d = ((p * lo_sym_d) * p.inverse()).eval().template triangularView<Lower>();
VERIFY(res.isApprox(res_d) && "lower selfadjoint twisted to lower");
res.template selfadjointView<Lower>() = up.template selfadjointView<Upper>().twistedBy(p);
res_d = ((p * up_sym_d) * p.inverse()).eval().template triangularView<Lower>();
VERIFY(res.isApprox(res_d) && "upper selfadjoint twisted to lower");
VERIFY( is_sorted( res = mat.template selfadjointView<Upper>().twistedBy(p) ));
res_d = (p * up_sym_d) * p.inverse();
VERIFY(res.isApprox(res_d) && "full selfadjoint upper twisted to full");
VERIFY( is_sorted( res = mat.template selfadjointView<Lower>().twistedBy(p) ));
res_d = (p * lo_sym_d) * p.inverse();
VERIFY(res.isApprox(res_d) && "full selfadjoint lower twisted to full");
VERIFY( is_sorted( res = up.template selfadjointView<Upper>().twistedBy(p) ));
res_d = (p * up_sym_d) * p.inverse();
VERIFY(res.isApprox(res_d) && "upper selfadjoint twisted to full");
VERIFY( is_sorted( res = lo.template selfadjointView<Lower>().twistedBy(p) ));
res_d = (p * lo_sym_d) * p.inverse();
VERIFY(res.isApprox(res_d) && "lower selfadjoint twisted to full");
}
template<typename Scalar> void sparse_permutations_all(int size)
{
CALL_SUBTEST(( sparse_permutations<ColMajor>(SparseMatrix<Scalar, ColMajor>(size,size)) ));
CALL_SUBTEST(( sparse_permutations<ColMajor>(SparseMatrix<Scalar, RowMajor>(size,size)) ));
CALL_SUBTEST(( sparse_permutations<RowMajor>(SparseMatrix<Scalar, ColMajor>(size,size)) ));
CALL_SUBTEST(( sparse_permutations<RowMajor>(SparseMatrix<Scalar, RowMajor>(size,size)) ));
}
void test_sparse_permutations()
{
for(int i = 0; i < g_repeat; i++) {
int s = Eigen::internal::random<int>(1,50);
CALL_SUBTEST_1(( sparse_permutations_all<double>(s) ));
CALL_SUBTEST_2(( sparse_permutations_all<std::complex<double> >(s) ));
}
VERIFY((internal::is_same<internal::permutation_matrix_product<SparseMatrix<double>,OnTheRight,false,SparseShape>::ReturnType,
internal::nested_eval<Product<SparseMatrix<double>,PermutationMatrix<Dynamic,Dynamic>,AliasFreeProduct>,1>::type>::value));
VERIFY((internal::is_same<internal::permutation_matrix_product<SparseMatrix<double>,OnTheLeft,false,SparseShape>::ReturnType,
internal::nested_eval<Product<PermutationMatrix<Dynamic,Dynamic>,SparseMatrix<double>,AliasFreeProduct>,1>::type>::value));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/vectorization_logic.cpp | .cpp | 20,436 | 434 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifdef EIGEN_TEST_PART_1
#define EIGEN_UNALIGNED_VECTORIZE 1
#endif
#ifdef EIGEN_TEST_PART_2
#define EIGEN_UNALIGNED_VECTORIZE 0
#endif
#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR
#undef EIGEN_DEFAULT_TO_ROW_MAJOR
#endif
#define EIGEN_DEBUG_ASSIGN
#include "main.h"
#include <typeinfo>
// Disable "ignoring attributes on template argument"
// for packet_traits<Packet*>
// => The only workaround would be to wrap _m128 and the likes
// within wrappers.
#if EIGEN_GNUC_AT_LEAST(6,0)
#pragma GCC diagnostic ignored "-Wignored-attributes"
#endif
using internal::demangle_flags;
using internal::demangle_traversal;
using internal::demangle_unrolling;
template<typename Dst, typename Src>
bool test_assign(const Dst&, const Src&, int traversal, int unrolling)
{
typedef internal::copy_using_evaluator_traits<internal::evaluator<Dst>,internal::evaluator<Src>, internal::assign_op<typename Dst::Scalar,typename Src::Scalar> > traits;
bool res = traits::Traversal==traversal;
if(unrolling==InnerUnrolling+CompleteUnrolling)
res = res && (int(traits::Unrolling)==InnerUnrolling || int(traits::Unrolling)==CompleteUnrolling);
else
res = res && int(traits::Unrolling)==unrolling;
if(!res)
{
std::cerr << "Src: " << demangle_flags(Src::Flags) << std::endl;
std::cerr << " " << demangle_flags(internal::evaluator<Src>::Flags) << std::endl;
std::cerr << "Dst: " << demangle_flags(Dst::Flags) << std::endl;
std::cerr << " " << demangle_flags(internal::evaluator<Dst>::Flags) << std::endl;
traits::debug();
std::cerr << " Expected Traversal == " << demangle_traversal(traversal)
<< " got " << demangle_traversal(traits::Traversal) << "\n";
std::cerr << " Expected Unrolling == " << demangle_unrolling(unrolling)
<< " got " << demangle_unrolling(traits::Unrolling) << "\n";
}
return res;
}
template<typename Dst, typename Src>
bool test_assign(int traversal, int unrolling)
{
typedef internal::copy_using_evaluator_traits<internal::evaluator<Dst>,internal::evaluator<Src>, internal::assign_op<typename Dst::Scalar,typename Src::Scalar> > traits;
bool res = traits::Traversal==traversal && traits::Unrolling==unrolling;
if(!res)
{
std::cerr << "Src: " << demangle_flags(Src::Flags) << std::endl;
std::cerr << " " << demangle_flags(internal::evaluator<Src>::Flags) << std::endl;
std::cerr << "Dst: " << demangle_flags(Dst::Flags) << std::endl;
std::cerr << " " << demangle_flags(internal::evaluator<Dst>::Flags) << std::endl;
traits::debug();
std::cerr << " Expected Traversal == " << demangle_traversal(traversal)
<< " got " << demangle_traversal(traits::Traversal) << "\n";
std::cerr << " Expected Unrolling == " << demangle_unrolling(unrolling)
<< " got " << demangle_unrolling(traits::Unrolling) << "\n";
}
return res;
}
template<typename Xpr>
bool test_redux(const Xpr&, int traversal, int unrolling)
{
typedef typename Xpr::Scalar Scalar;
typedef internal::redux_traits<internal::scalar_sum_op<Scalar,Scalar>,internal::redux_evaluator<Xpr> > traits;
bool res = traits::Traversal==traversal && traits::Unrolling==unrolling;
if(!res)
{
std::cerr << demangle_flags(Xpr::Flags) << std::endl;
std::cerr << demangle_flags(internal::evaluator<Xpr>::Flags) << std::endl;
traits::debug();
std::cerr << " Expected Traversal == " << demangle_traversal(traversal)
<< " got " << demangle_traversal(traits::Traversal) << "\n";
std::cerr << " Expected Unrolling == " << demangle_unrolling(unrolling)
<< " got " << demangle_unrolling(traits::Unrolling) << "\n";
}
return res;
}
template<typename Scalar, bool Enable = internal::packet_traits<Scalar>::Vectorizable>
struct vectorization_logic
{
typedef internal::packet_traits<Scalar> PacketTraits;
typedef typename internal::packet_traits<Scalar>::type PacketType;
typedef typename internal::unpacket_traits<PacketType>::half HalfPacketType;
enum {
PacketSize = internal::unpacket_traits<PacketType>::size,
HalfPacketSize = internal::unpacket_traits<HalfPacketType>::size
};
static void run()
{
typedef Matrix<Scalar,PacketSize,1> Vector1;
typedef Matrix<Scalar,Dynamic,1> VectorX;
typedef Matrix<Scalar,Dynamic,Dynamic> MatrixXX;
typedef Matrix<Scalar,PacketSize,PacketSize> Matrix11;
typedef Matrix<Scalar,2*PacketSize,2*PacketSize> Matrix22;
typedef Matrix<Scalar,(Matrix11::Flags&RowMajorBit)?16:4*PacketSize,(Matrix11::Flags&RowMajorBit)?4*PacketSize:16> Matrix44;
typedef Matrix<Scalar,(Matrix11::Flags&RowMajorBit)?16:4*PacketSize,(Matrix11::Flags&RowMajorBit)?4*PacketSize:16,DontAlign|EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION> Matrix44u;
typedef Matrix<Scalar,4*PacketSize,4*PacketSize,ColMajor> Matrix44c;
typedef Matrix<Scalar,4*PacketSize,4*PacketSize,RowMajor> Matrix44r;
typedef Matrix<Scalar,
(PacketSize==8 ? 4 : PacketSize==4 ? 2 : PacketSize==2 ? 1 : /*PacketSize==1 ?*/ 1),
(PacketSize==8 ? 2 : PacketSize==4 ? 2 : PacketSize==2 ? 2 : /*PacketSize==1 ?*/ 1)
> Matrix1;
typedef Matrix<Scalar,
(PacketSize==8 ? 4 : PacketSize==4 ? 2 : PacketSize==2 ? 1 : /*PacketSize==1 ?*/ 1),
(PacketSize==8 ? 2 : PacketSize==4 ? 2 : PacketSize==2 ? 2 : /*PacketSize==1 ?*/ 1),
DontAlign|((Matrix1::Flags&RowMajorBit)?RowMajor:ColMajor)> Matrix1u;
// this type is made such that it can only be vectorized when viewed as a linear 1D vector
typedef Matrix<Scalar,
(PacketSize==8 ? 4 : PacketSize==4 ? 6 : PacketSize==2 ? ((Matrix11::Flags&RowMajorBit)?2:3) : /*PacketSize==1 ?*/ 1),
(PacketSize==8 ? 6 : PacketSize==4 ? 2 : PacketSize==2 ? ((Matrix11::Flags&RowMajorBit)?3:2) : /*PacketSize==1 ?*/ 3)
> Matrix3;
#if !EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT
VERIFY(test_assign(Vector1(),Vector1(),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),Vector1()+Vector1(),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),Vector1().cwiseProduct(Vector1()),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),Vector1().template cast<Scalar>(),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),Vector1(),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),Vector1()+Vector1(),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),Vector1().cwiseProduct(Vector1()),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Matrix44(),Matrix44()+Matrix44(),
InnerVectorizedTraversal,InnerUnrolling));
VERIFY(test_assign(Matrix44u(),Matrix44()+Matrix44(),
EIGEN_UNALIGNED_VECTORIZE ? InnerVectorizedTraversal : LinearTraversal,
EIGEN_UNALIGNED_VECTORIZE ? InnerUnrolling : NoUnrolling));
VERIFY(test_assign(Matrix1(),Matrix1()+Matrix1(),
(Matrix1::InnerSizeAtCompileTime % PacketSize)==0 ? InnerVectorizedTraversal : LinearVectorizedTraversal,
CompleteUnrolling));
VERIFY(test_assign(Matrix1u(),Matrix1()+Matrix1(),
EIGEN_UNALIGNED_VECTORIZE ? ((Matrix1::InnerSizeAtCompileTime % PacketSize)==0 ? InnerVectorizedTraversal : LinearVectorizedTraversal)
: LinearTraversal, CompleteUnrolling));
VERIFY(test_assign(Matrix44c().col(1),Matrix44c().col(2)+Matrix44c().col(3),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Matrix44r().row(2),Matrix44r().row(1)+Matrix44r().row(1),
InnerVectorizedTraversal,CompleteUnrolling));
if(PacketSize>1)
{
typedef Matrix<Scalar,3,3,ColMajor> Matrix33c;
typedef Matrix<Scalar,3,1,ColMajor> Vector3;
VERIFY(test_assign(Matrix33c().row(2),Matrix33c().row(1)+Matrix33c().row(1),
LinearTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector3(),Vector3()+Vector3(),
EIGEN_UNALIGNED_VECTORIZE ? (HalfPacketSize==1 ? InnerVectorizedTraversal : LinearVectorizedTraversal) : (HalfPacketSize==1 ? InnerVectorizedTraversal : LinearTraversal), CompleteUnrolling));
VERIFY(test_assign(Matrix33c().col(0),Matrix33c().col(1)+Matrix33c().col(1),
EIGEN_UNALIGNED_VECTORIZE ? (HalfPacketSize==1 ? InnerVectorizedTraversal : LinearVectorizedTraversal) : (HalfPacketSize==1 ? SliceVectorizedTraversal : LinearTraversal),
((!EIGEN_UNALIGNED_VECTORIZE) && HalfPacketSize==1) ? NoUnrolling : CompleteUnrolling));
VERIFY(test_assign(Matrix3(),Matrix3().cwiseProduct(Matrix3()),
LinearVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Matrix<Scalar,17,17>(),Matrix<Scalar,17,17>()+Matrix<Scalar,17,17>(),
HalfPacketSize==1 ? InnerVectorizedTraversal :
EIGEN_UNALIGNED_VECTORIZE ? LinearVectorizedTraversal :
LinearTraversal,
NoUnrolling));
VERIFY(test_assign(Matrix11(), Matrix11()+Matrix11(),InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Matrix11(),Matrix<Scalar,17,17>().template block<PacketSize,PacketSize>(2,3)+Matrix<Scalar,17,17>().template block<PacketSize,PacketSize>(8,4),
(EIGEN_UNALIGNED_VECTORIZE) ? InnerVectorizedTraversal : DefaultTraversal, CompleteUnrolling|InnerUnrolling));
VERIFY(test_assign(Vector1(),Matrix11()*Vector1(),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Matrix11(),Matrix11().lazyProduct(Matrix11()),
InnerVectorizedTraversal,InnerUnrolling+CompleteUnrolling));
}
VERIFY(test_redux(Vector1(),
LinearVectorizedTraversal,CompleteUnrolling));
VERIFY(test_redux(Vector1().array()*Vector1().array(),
LinearVectorizedTraversal,CompleteUnrolling));
VERIFY(test_redux((Vector1().array()*Vector1().array()).col(0),
LinearVectorizedTraversal,CompleteUnrolling));
VERIFY(test_redux(Matrix<Scalar,PacketSize,3>(),
LinearVectorizedTraversal,CompleteUnrolling));
VERIFY(test_redux(Matrix3(),
LinearVectorizedTraversal,CompleteUnrolling));
VERIFY(test_redux(Matrix44(),
LinearVectorizedTraversal,NoUnrolling));
VERIFY(test_redux(Matrix44().template block<(Matrix1::Flags&RowMajorBit)?4:PacketSize,(Matrix1::Flags&RowMajorBit)?PacketSize:4>(1,2),
DefaultTraversal,CompleteUnrolling));
VERIFY(test_redux(Matrix44c().template block<2*PacketSize,1>(1,2),
LinearVectorizedTraversal,CompleteUnrolling));
VERIFY(test_redux(Matrix44r().template block<1,2*PacketSize>(2,1),
LinearVectorizedTraversal,CompleteUnrolling));
VERIFY((test_assign<
Map<Matrix22, AlignedMax, OuterStride<3*PacketSize> >,
Matrix22
>(InnerVectorizedTraversal,CompleteUnrolling)));
VERIFY((test_assign<
Map<Matrix<Scalar,EIGEN_PLAIN_ENUM_MAX(2,PacketSize),EIGEN_PLAIN_ENUM_MAX(2,PacketSize)>, AlignedMax, InnerStride<3*PacketSize> >,
Matrix<Scalar,EIGEN_PLAIN_ENUM_MAX(2,PacketSize),EIGEN_PLAIN_ENUM_MAX(2,PacketSize)>
>(DefaultTraversal,PacketSize>=8?InnerUnrolling:CompleteUnrolling)));
VERIFY((test_assign(Matrix11(), Matrix<Scalar,PacketSize,EIGEN_PLAIN_ENUM_MIN(2,PacketSize)>()*Matrix<Scalar,EIGEN_PLAIN_ENUM_MIN(2,PacketSize),PacketSize>(),
InnerVectorizedTraversal, CompleteUnrolling)));
#endif
VERIFY(test_assign(MatrixXX(10,10),MatrixXX(20,20).block(10,10,2,3),
SliceVectorizedTraversal,NoUnrolling));
VERIFY(test_redux(VectorX(10),
LinearVectorizedTraversal,NoUnrolling));
}
};
template<typename Scalar> struct vectorization_logic<Scalar,false>
{
static void run() {}
};
template<typename Scalar, bool Enable = !internal::is_same<typename internal::unpacket_traits<typename internal::packet_traits<Scalar>::type>::half,
typename internal::packet_traits<Scalar>::type>::value >
struct vectorization_logic_half
{
typedef internal::packet_traits<Scalar> PacketTraits;
typedef typename internal::unpacket_traits<typename internal::packet_traits<Scalar>::type>::half PacketType;
enum {
PacketSize = internal::unpacket_traits<PacketType>::size
};
static void run()
{
typedef Matrix<Scalar,PacketSize,1> Vector1;
typedef Matrix<Scalar,PacketSize,PacketSize> Matrix11;
typedef Matrix<Scalar,5*PacketSize,7,ColMajor> Matrix57;
typedef Matrix<Scalar,3*PacketSize,5,ColMajor> Matrix35;
typedef Matrix<Scalar,5*PacketSize,7,DontAlign|ColMajor> Matrix57u;
// typedef Matrix<Scalar,(Matrix11::Flags&RowMajorBit)?16:4*PacketSize,(Matrix11::Flags&RowMajorBit)?4*PacketSize:16> Matrix44;
// typedef Matrix<Scalar,(Matrix11::Flags&RowMajorBit)?16:4*PacketSize,(Matrix11::Flags&RowMajorBit)?4*PacketSize:16,DontAlign|EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION> Matrix44u;
// typedef Matrix<Scalar,4*PacketSize,4*PacketSize,ColMajor> Matrix44c;
// typedef Matrix<Scalar,4*PacketSize,4*PacketSize,RowMajor> Matrix44r;
typedef Matrix<Scalar,
(PacketSize==8 ? 4 : PacketSize==4 ? 2 : PacketSize==2 ? 1 : /*PacketSize==1 ?*/ 1),
(PacketSize==8 ? 2 : PacketSize==4 ? 2 : PacketSize==2 ? 2 : /*PacketSize==1 ?*/ 1)
> Matrix1;
typedef Matrix<Scalar,
(PacketSize==8 ? 4 : PacketSize==4 ? 2 : PacketSize==2 ? 1 : /*PacketSize==1 ?*/ 1),
(PacketSize==8 ? 2 : PacketSize==4 ? 2 : PacketSize==2 ? 2 : /*PacketSize==1 ?*/ 1),
DontAlign|((Matrix1::Flags&RowMajorBit)?RowMajor:ColMajor)> Matrix1u;
// this type is made such that it can only be vectorized when viewed as a linear 1D vector
typedef Matrix<Scalar,
(PacketSize==8 ? 4 : PacketSize==4 ? 6 : PacketSize==2 ? ((Matrix11::Flags&RowMajorBit)?2:3) : /*PacketSize==1 ?*/ 1),
(PacketSize==8 ? 6 : PacketSize==4 ? 2 : PacketSize==2 ? ((Matrix11::Flags&RowMajorBit)?3:2) : /*PacketSize==1 ?*/ 3)
> Matrix3;
#if !EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT
VERIFY(test_assign(Vector1(),Vector1(),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),Vector1()+Vector1(),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),Vector1().template segment<PacketSize>(0).derived(),
EIGEN_UNALIGNED_VECTORIZE ? InnerVectorizedTraversal : LinearVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),Scalar(2.1)*Vector1()-Vector1(),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),(Scalar(2.1)*Vector1().template segment<PacketSize>(0)-Vector1().template segment<PacketSize>(0)).derived(),
EIGEN_UNALIGNED_VECTORIZE ? InnerVectorizedTraversal : LinearVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),Vector1().cwiseProduct(Vector1()),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),Vector1().template cast<Scalar>(),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),Vector1(),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),Vector1()+Vector1(),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Vector1(),Vector1().cwiseProduct(Vector1()),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Matrix57(),Matrix57()+Matrix57(),
InnerVectorizedTraversal,InnerUnrolling));
VERIFY(test_assign(Matrix57u(),Matrix57()+Matrix57(),
EIGEN_UNALIGNED_VECTORIZE ? InnerVectorizedTraversal : LinearTraversal,
EIGEN_UNALIGNED_VECTORIZE ? InnerUnrolling : NoUnrolling));
VERIFY(test_assign(Matrix1u(),Matrix1()+Matrix1(),
EIGEN_UNALIGNED_VECTORIZE ? ((Matrix1::InnerSizeAtCompileTime % PacketSize)==0 ? InnerVectorizedTraversal : LinearVectorizedTraversal) : LinearTraversal,CompleteUnrolling));
if(PacketSize>1)
{
typedef Matrix<Scalar,3,3,ColMajor> Matrix33c;
VERIFY(test_assign(Matrix33c().row(2),Matrix33c().row(1)+Matrix33c().row(1),
LinearTraversal,CompleteUnrolling));
VERIFY(test_assign(Matrix33c().col(0),Matrix33c().col(1)+Matrix33c().col(1),
EIGEN_UNALIGNED_VECTORIZE ? (PacketSize==1 ? InnerVectorizedTraversal : LinearVectorizedTraversal) : LinearTraversal,CompleteUnrolling));
VERIFY(test_assign(Matrix3(),Matrix3().cwiseQuotient(Matrix3()),
PacketTraits::HasDiv ? LinearVectorizedTraversal : LinearTraversal,CompleteUnrolling));
VERIFY(test_assign(Matrix<Scalar,17,17>(),Matrix<Scalar,17,17>()+Matrix<Scalar,17,17>(),
EIGEN_UNALIGNED_VECTORIZE ? (PacketSize==1 ? InnerVectorizedTraversal : LinearVectorizedTraversal) : LinearTraversal,
NoUnrolling));
VERIFY(test_assign(Matrix11(),Matrix<Scalar,17,17>().template block<PacketSize,PacketSize>(2,3)+Matrix<Scalar,17,17>().template block<PacketSize,PacketSize>(8,4),
EIGEN_UNALIGNED_VECTORIZE ? InnerVectorizedTraversal : DefaultTraversal,PacketSize>4?InnerUnrolling:CompleteUnrolling));
VERIFY(test_assign(Vector1(),Matrix11()*Vector1(),
InnerVectorizedTraversal,CompleteUnrolling));
VERIFY(test_assign(Matrix11(),Matrix11().lazyProduct(Matrix11()),
InnerVectorizedTraversal,InnerUnrolling+CompleteUnrolling));
}
VERIFY(test_redux(Vector1(),
LinearVectorizedTraversal,CompleteUnrolling));
VERIFY(test_redux(Matrix<Scalar,PacketSize,3>(),
LinearVectorizedTraversal,CompleteUnrolling));
VERIFY(test_redux(Matrix3(),
LinearVectorizedTraversal,CompleteUnrolling));
VERIFY(test_redux(Matrix35(),
LinearVectorizedTraversal,CompleteUnrolling));
VERIFY(test_redux(Matrix57().template block<PacketSize,3>(1,0),
DefaultTraversal,CompleteUnrolling));
VERIFY((test_assign<
Map<Matrix<Scalar,EIGEN_PLAIN_ENUM_MAX(2,PacketSize),EIGEN_PLAIN_ENUM_MAX(2,PacketSize)>, AlignedMax, InnerStride<3*PacketSize> >,
Matrix<Scalar,EIGEN_PLAIN_ENUM_MAX(2,PacketSize),EIGEN_PLAIN_ENUM_MAX(2,PacketSize)>
>(DefaultTraversal,CompleteUnrolling)));
VERIFY((test_assign(Matrix57(), Matrix<Scalar,5*PacketSize,3>()*Matrix<Scalar,3,7>(),
InnerVectorizedTraversal, InnerUnrolling|CompleteUnrolling)));
#endif
}
};
template<typename Scalar> struct vectorization_logic_half<Scalar,false>
{
static void run() {}
};
void test_vectorization_logic()
{
#ifdef EIGEN_VECTORIZE
CALL_SUBTEST( vectorization_logic<int>::run() );
CALL_SUBTEST( vectorization_logic<float>::run() );
CALL_SUBTEST( vectorization_logic<double>::run() );
CALL_SUBTEST( vectorization_logic<std::complex<float> >::run() );
CALL_SUBTEST( vectorization_logic<std::complex<double> >::run() );
CALL_SUBTEST( vectorization_logic_half<int>::run() );
CALL_SUBTEST( vectorization_logic_half<float>::run() );
CALL_SUBTEST( vectorization_logic_half<double>::run() );
CALL_SUBTEST( vectorization_logic_half<std::complex<float> >::run() );
CALL_SUBTEST( vectorization_logic_half<std::complex<double> >::run() );
if(internal::packet_traits<float>::Vectorizable)
{
VERIFY(test_assign(Matrix<float,3,3>(),Matrix<float,3,3>()+Matrix<float,3,3>(),
EIGEN_UNALIGNED_VECTORIZE ? LinearVectorizedTraversal : LinearTraversal,CompleteUnrolling));
VERIFY(test_redux(Matrix<float,5,2>(),
EIGEN_UNALIGNED_VECTORIZE ? LinearVectorizedTraversal : DefaultTraversal,CompleteUnrolling));
}
if(internal::packet_traits<double>::Vectorizable)
{
VERIFY(test_assign(Matrix<double,3,3>(),Matrix<double,3,3>()+Matrix<double,3,3>(),
EIGEN_UNALIGNED_VECTORIZE ? LinearVectorizedTraversal : LinearTraversal,CompleteUnrolling));
VERIFY(test_redux(Matrix<double,7,3>(),
EIGEN_UNALIGNED_VECTORIZE ? LinearVectorizedTraversal : DefaultTraversal,CompleteUnrolling));
}
#endif // EIGEN_VECTORIZE
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/eigensolver_generalized_real.cpp | .cpp | 4,038 | 104 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_RUNTIME_NO_MALLOC
#include "main.h"
#include <limits>
#include <Eigen/Eigenvalues>
#include <Eigen/LU>
template<typename MatrixType> void generalized_eigensolver_real(const MatrixType& m)
{
/* this test covers the following files:
GeneralizedEigenSolver.h
*/
Index rows = m.rows();
Index cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
typedef std::complex<Scalar> ComplexScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
MatrixType a = MatrixType::Random(rows,cols);
MatrixType b = MatrixType::Random(rows,cols);
MatrixType a1 = MatrixType::Random(rows,cols);
MatrixType b1 = MatrixType::Random(rows,cols);
MatrixType spdA = a.adjoint() * a + a1.adjoint() * a1;
MatrixType spdB = b.adjoint() * b + b1.adjoint() * b1;
// lets compare to GeneralizedSelfAdjointEigenSolver
{
GeneralizedSelfAdjointEigenSolver<MatrixType> symmEig(spdA, spdB);
GeneralizedEigenSolver<MatrixType> eig(spdA, spdB);
VERIFY_IS_EQUAL(eig.eigenvalues().imag().cwiseAbs().maxCoeff(), 0);
VectorType realEigenvalues = eig.eigenvalues().real();
std::sort(realEigenvalues.data(), realEigenvalues.data()+realEigenvalues.size());
VERIFY_IS_APPROX(realEigenvalues, symmEig.eigenvalues());
// check eigenvectors
typename GeneralizedEigenSolver<MatrixType>::EigenvectorsType D = eig.eigenvalues().asDiagonal();
typename GeneralizedEigenSolver<MatrixType>::EigenvectorsType V = eig.eigenvectors();
VERIFY_IS_APPROX(spdA*V, spdB*V*D);
}
// non symmetric case:
{
GeneralizedEigenSolver<MatrixType> eig(rows);
// TODO enable full-prealocation of required memory, this probably requires an in-place mode for HessenbergDecomposition
//Eigen::internal::set_is_malloc_allowed(false);
eig.compute(a,b);
//Eigen::internal::set_is_malloc_allowed(true);
for(Index k=0; k<cols; ++k)
{
Matrix<ComplexScalar,Dynamic,Dynamic> tmp = (eig.betas()(k)*a).template cast<ComplexScalar>() - eig.alphas()(k)*b;
if(tmp.size()>1 && tmp.norm()>(std::numeric_limits<Scalar>::min)())
tmp /= tmp.norm();
VERIFY_IS_MUCH_SMALLER_THAN( std::abs(tmp.determinant()), Scalar(1) );
}
// check eigenvectors
typename GeneralizedEigenSolver<MatrixType>::EigenvectorsType D = eig.eigenvalues().asDiagonal();
typename GeneralizedEigenSolver<MatrixType>::EigenvectorsType V = eig.eigenvectors();
VERIFY_IS_APPROX(a*V, b*V*D);
}
// regression test for bug 1098
{
GeneralizedSelfAdjointEigenSolver<MatrixType> eig1(a.adjoint() * a,b.adjoint() * b);
eig1.compute(a.adjoint() * a,b.adjoint() * b);
GeneralizedEigenSolver<MatrixType> eig2(a.adjoint() * a,b.adjoint() * b);
eig2.compute(a.adjoint() * a,b.adjoint() * b);
}
// check without eigenvectors
{
GeneralizedEigenSolver<MatrixType> eig1(spdA, spdB, true);
GeneralizedEigenSolver<MatrixType> eig2(spdA, spdB, false);
VERIFY_IS_APPROX(eig1.eigenvalues(), eig2.eigenvalues());
}
}
void test_eigensolver_generalized_real()
{
for(int i = 0; i < g_repeat; i++) {
int s = 0;
CALL_SUBTEST_1( generalized_eigensolver_real(Matrix4f()) );
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4);
CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(s,s)) );
// some trivial but implementation-wise special cases
CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(1,1)) );
CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(2,2)) );
CALL_SUBTEST_3( generalized_eigensolver_real(Matrix<double,1,1>()) );
CALL_SUBTEST_4( generalized_eigensolver_real(Matrix2d()) );
TEST_SET_BUT_UNUSED_VARIABLE(s)
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/product_extra.cpp | .cpp | 14,544 | 375 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename MatrixType> void product_extra(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, 1, Dynamic> RowVectorType;
typedef Matrix<Scalar, Dynamic, 1> ColVectorType;
typedef Matrix<Scalar, Dynamic, Dynamic,
MatrixType::Flags&RowMajorBit> OtherMajorMatrixType;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols),
mzero = MatrixType::Zero(rows, cols),
identity = MatrixType::Identity(rows, rows),
square = MatrixType::Random(rows, rows),
res = MatrixType::Random(rows, rows),
square2 = MatrixType::Random(cols, cols),
res2 = MatrixType::Random(cols, cols);
RowVectorType v1 = RowVectorType::Random(rows), vrres(rows);
ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols);
OtherMajorMatrixType tm1 = m1;
Scalar s1 = internal::random<Scalar>(),
s2 = internal::random<Scalar>(),
s3 = internal::random<Scalar>();
VERIFY_IS_APPROX(m3.noalias() = m1 * m2.adjoint(), m1 * m2.adjoint().eval());
VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * square.adjoint(), m1.adjoint().eval() * square.adjoint().eval());
VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * m2, m1.adjoint().eval() * m2);
VERIFY_IS_APPROX(m3.noalias() = (s1 * m1.adjoint()) * m2, (s1 * m1.adjoint()).eval() * m2);
VERIFY_IS_APPROX(m3.noalias() = ((s1 * m1).adjoint()) * m2, (numext::conj(s1) * m1.adjoint()).eval() * m2);
VERIFY_IS_APPROX(m3.noalias() = (- m1.adjoint() * s1) * (s3 * m2), (- m1.adjoint() * s1).eval() * (s3 * m2).eval());
VERIFY_IS_APPROX(m3.noalias() = (s2 * m1.adjoint() * s1) * m2, (s2 * m1.adjoint() * s1).eval() * m2);
VERIFY_IS_APPROX(m3.noalias() = (-m1*s2) * s1*m2.adjoint(), (-m1*s2).eval() * (s1*m2.adjoint()).eval());
// a very tricky case where a scale factor has to be automatically conjugated:
VERIFY_IS_APPROX( m1.adjoint() * (s1*m2).conjugate(), (m1.adjoint()).eval() * ((s1*m2).conjugate()).eval());
// test all possible conjugate combinations for the four matrix-vector product cases:
VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2),
(-m1.conjugate()*s2).eval() * (s1 * vc2).eval());
VERIFY_IS_APPROX((-m1 * s2) * (s1 * vc2.conjugate()),
(-m1*s2).eval() * (s1 * vc2.conjugate()).eval());
VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2.conjugate()),
(-m1.conjugate()*s2).eval() * (s1 * vc2.conjugate()).eval());
VERIFY_IS_APPROX((s1 * vc2.transpose()) * (-m1.adjoint() * s2),
(s1 * vc2.transpose()).eval() * (-m1.adjoint()*s2).eval());
VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.transpose() * s2),
(s1 * vc2.adjoint()).eval() * (-m1.transpose()*s2).eval());
VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.adjoint() * s2),
(s1 * vc2.adjoint()).eval() * (-m1.adjoint()*s2).eval());
VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.transpose()),
(-m1.adjoint()*s2).eval() * (s1 * v1.transpose()).eval());
VERIFY_IS_APPROX((-m1.transpose() * s2) * (s1 * v1.adjoint()),
(-m1.transpose()*s2).eval() * (s1 * v1.adjoint()).eval());
VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()),
(-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval());
VERIFY_IS_APPROX((s1 * v1) * (-m1.conjugate() * s2),
(s1 * v1).eval() * (-m1.conjugate()*s2).eval());
VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1 * s2),
(s1 * v1.conjugate()).eval() * (-m1*s2).eval());
VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1.conjugate() * s2),
(s1 * v1.conjugate()).eval() * (-m1.conjugate()*s2).eval());
VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()),
(-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval());
// test the vector-matrix product with non aligned starts
Index i = internal::random<Index>(0,m1.rows()-2);
Index j = internal::random<Index>(0,m1.cols()-2);
Index r = internal::random<Index>(1,m1.rows()-i);
Index c = internal::random<Index>(1,m1.cols()-j);
Index i2 = internal::random<Index>(0,m1.rows()-1);
Index j2 = internal::random<Index>(0,m1.cols()-1);
VERIFY_IS_APPROX(m1.col(j2).adjoint() * m1.block(0,j,m1.rows(),c), m1.col(j2).adjoint().eval() * m1.block(0,j,m1.rows(),c).eval());
VERIFY_IS_APPROX(m1.block(i,0,r,m1.cols()) * m1.row(i2).adjoint(), m1.block(i,0,r,m1.cols()).eval() * m1.row(i2).adjoint().eval());
// regression test
MatrixType tmp = m1 * m1.adjoint() * s1;
VERIFY_IS_APPROX(tmp, m1 * m1.adjoint() * s1);
// regression test for bug 1343, assignment to arrays
Array<Scalar,Dynamic,1> a1 = m1 * vc2;
VERIFY_IS_APPROX(a1.matrix(),m1*vc2);
Array<Scalar,Dynamic,1> a2 = s1 * (m1 * vc2);
VERIFY_IS_APPROX(a2.matrix(),s1*m1*vc2);
Array<Scalar,1,Dynamic> a3 = v1 * m1;
VERIFY_IS_APPROX(a3.matrix(),v1*m1);
Array<Scalar,Dynamic,Dynamic> a4 = m1 * m2.adjoint();
VERIFY_IS_APPROX(a4.matrix(),m1*m2.adjoint());
}
// Regression test for bug reported at http://forum.kde.org/viewtopic.php?f=74&t=96947
void mat_mat_scalar_scalar_product()
{
Eigen::Matrix2Xd dNdxy(2, 3);
dNdxy << -0.5, 0.5, 0,
-0.3, 0, 0.3;
double det = 6.0, wt = 0.5;
VERIFY_IS_APPROX(dNdxy.transpose()*dNdxy*det*wt, det*wt*dNdxy.transpose()*dNdxy);
}
template <typename MatrixType>
void zero_sized_objects(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
const int PacketSize = internal::packet_traits<Scalar>::size;
const int PacketSize1 = PacketSize>1 ? PacketSize-1 : 1;
Index rows = m.rows();
Index cols = m.cols();
{
MatrixType res, a(rows,0), b(0,cols);
VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(rows,cols) );
VERIFY_IS_APPROX( (res=a*a.transpose()), MatrixType::Zero(rows,rows) );
VERIFY_IS_APPROX( (res=b.transpose()*b), MatrixType::Zero(cols,cols) );
VERIFY_IS_APPROX( (res=b.transpose()*a.transpose()), MatrixType::Zero(cols,rows) );
}
{
MatrixType res, a(rows,cols), b(cols,0);
res = a*b;
VERIFY(res.rows()==rows && res.cols()==0);
b.resize(0,rows);
res = b*a;
VERIFY(res.rows()==0 && res.cols()==cols);
}
{
Matrix<Scalar,PacketSize,0> a;
Matrix<Scalar,0,1> b;
Matrix<Scalar,PacketSize,1> res;
VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize,1) );
VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize,1) );
}
{
Matrix<Scalar,PacketSize1,0> a;
Matrix<Scalar,0,1> b;
Matrix<Scalar,PacketSize1,1> res;
VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize1,1) );
VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize1,1) );
}
{
Matrix<Scalar,PacketSize,Dynamic> a(PacketSize,0);
Matrix<Scalar,Dynamic,1> b(0,1);
Matrix<Scalar,PacketSize,1> res;
VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize,1) );
VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize,1) );
}
{
Matrix<Scalar,PacketSize1,Dynamic> a(PacketSize1,0);
Matrix<Scalar,Dynamic,1> b(0,1);
Matrix<Scalar,PacketSize1,1> res;
VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize1,1) );
VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize1,1) );
}
}
template<int>
void bug_127()
{
// Bug 127
//
// a product of the form lhs*rhs with
//
// lhs:
// rows = 1, cols = 4
// RowsAtCompileTime = 1, ColsAtCompileTime = -1
// MaxRowsAtCompileTime = 1, MaxColsAtCompileTime = 5
//
// rhs:
// rows = 4, cols = 0
// RowsAtCompileTime = -1, ColsAtCompileTime = -1
// MaxRowsAtCompileTime = 5, MaxColsAtCompileTime = 1
//
// was failing on a runtime assertion, because it had been mis-compiled as a dot product because Product.h was using the
// max-sizes to detect size 1 indicating vectors, and that didn't account for 0-sized object with max-size 1.
Matrix<float,1,Dynamic,RowMajor,1,5> a(1,4);
Matrix<float,Dynamic,Dynamic,ColMajor,5,1> b(4,0);
a*b;
}
template<int> void bug_817()
{
ArrayXXf B = ArrayXXf::Random(10,10), C;
VectorXf x = VectorXf::Random(10);
C = (x.transpose()*B.matrix());
B = (x.transpose()*B.matrix());
VERIFY_IS_APPROX(B,C);
}
template<int>
void unaligned_objects()
{
// Regression test for the bug reported here:
// http://forum.kde.org/viewtopic.php?f=74&t=107541
// Recall the matrix*vector kernel avoid unaligned loads by loading two packets and then reassemble then.
// There was a mistake in the computation of the valid range for fully unaligned objects: in some rare cases,
// memory was read outside the allocated matrix memory. Though the values were not used, this might raise segfault.
for(int m=450;m<460;++m)
{
for(int n=8;n<12;++n)
{
MatrixXf M(m, n);
VectorXf v1(n), r1(500);
RowVectorXf v2(m), r2(16);
M.setRandom();
v1.setRandom();
v2.setRandom();
for(int o=0; o<4; ++o)
{
r1.segment(o,m).noalias() = M * v1;
VERIFY_IS_APPROX(r1.segment(o,m), M * MatrixXf(v1));
r2.segment(o,n).noalias() = v2 * M;
VERIFY_IS_APPROX(r2.segment(o,n), MatrixXf(v2) * M);
}
}
}
}
template<typename T>
EIGEN_DONT_INLINE
Index test_compute_block_size(Index m, Index n, Index k)
{
Index mc(m), nc(n), kc(k);
internal::computeProductBlockingSizes<T,T>(kc, mc, nc);
return kc+mc+nc;
}
template<typename T>
Index compute_block_size()
{
Index ret = 0;
ret += test_compute_block_size<T>(0,1,1);
ret += test_compute_block_size<T>(1,0,1);
ret += test_compute_block_size<T>(1,1,0);
ret += test_compute_block_size<T>(0,0,1);
ret += test_compute_block_size<T>(0,1,0);
ret += test_compute_block_size<T>(1,0,0);
ret += test_compute_block_size<T>(0,0,0);
return ret;
}
template<typename>
void aliasing_with_resize()
{
Index m = internal::random<Index>(10,50);
Index n = internal::random<Index>(10,50);
MatrixXd A, B, C(m,n), D(m,m);
VectorXd a, b, c(n);
C.setRandom();
D.setRandom();
c.setRandom();
double s = internal::random<double>(1,10);
A = C;
B = A * A.transpose();
A = A * A.transpose();
VERIFY_IS_APPROX(A,B);
A = C;
B = (A * A.transpose())/s;
A = (A * A.transpose())/s;
VERIFY_IS_APPROX(A,B);
A = C;
B = (A * A.transpose()) + D;
A = (A * A.transpose()) + D;
VERIFY_IS_APPROX(A,B);
A = C;
B = D + (A * A.transpose());
A = D + (A * A.transpose());
VERIFY_IS_APPROX(A,B);
A = C;
B = s * (A * A.transpose());
A = s * (A * A.transpose());
VERIFY_IS_APPROX(A,B);
A = C;
a = c;
b = (A * a)/s;
a = (A * a)/s;
VERIFY_IS_APPROX(a,b);
}
template<int>
void bug_1308()
{
int n = 10;
MatrixXd r(n,n);
VectorXd v = VectorXd::Random(n);
r = v * RowVectorXd::Ones(n);
VERIFY_IS_APPROX(r, v.rowwise().replicate(n));
r = VectorXd::Ones(n) * v.transpose();
VERIFY_IS_APPROX(r, v.rowwise().replicate(n).transpose());
Matrix4d ones44 = Matrix4d::Ones();
Matrix4d m44 = Matrix4d::Ones() * Matrix4d::Ones();
VERIFY_IS_APPROX(m44,Matrix4d::Constant(4));
VERIFY_IS_APPROX(m44.noalias()=ones44*Matrix4d::Ones(), Matrix4d::Constant(4));
VERIFY_IS_APPROX(m44.noalias()=ones44.transpose()*Matrix4d::Ones(), Matrix4d::Constant(4));
VERIFY_IS_APPROX(m44.noalias()=Matrix4d::Ones()*ones44, Matrix4d::Constant(4));
VERIFY_IS_APPROX(m44.noalias()=Matrix4d::Ones()*ones44.transpose(), Matrix4d::Constant(4));
typedef Matrix<double,4,4,RowMajor> RMatrix4d;
RMatrix4d r44 = Matrix4d::Ones() * Matrix4d::Ones();
VERIFY_IS_APPROX(r44,Matrix4d::Constant(4));
VERIFY_IS_APPROX(r44.noalias()=ones44*Matrix4d::Ones(), Matrix4d::Constant(4));
VERIFY_IS_APPROX(r44.noalias()=ones44.transpose()*Matrix4d::Ones(), Matrix4d::Constant(4));
VERIFY_IS_APPROX(r44.noalias()=Matrix4d::Ones()*ones44, Matrix4d::Constant(4));
VERIFY_IS_APPROX(r44.noalias()=Matrix4d::Ones()*ones44.transpose(), Matrix4d::Constant(4));
VERIFY_IS_APPROX(r44.noalias()=ones44*RMatrix4d::Ones(), Matrix4d::Constant(4));
VERIFY_IS_APPROX(r44.noalias()=ones44.transpose()*RMatrix4d::Ones(), Matrix4d::Constant(4));
VERIFY_IS_APPROX(r44.noalias()=RMatrix4d::Ones()*ones44, Matrix4d::Constant(4));
VERIFY_IS_APPROX(r44.noalias()=RMatrix4d::Ones()*ones44.transpose(), Matrix4d::Constant(4));
// RowVector4d r4;
m44.setOnes();
r44.setZero();
VERIFY_IS_APPROX(r44.noalias() += m44.row(0).transpose() * RowVector4d::Ones(), ones44);
r44.setZero();
VERIFY_IS_APPROX(r44.noalias() += m44.col(0) * RowVector4d::Ones(), ones44);
r44.setZero();
VERIFY_IS_APPROX(r44.noalias() += Vector4d::Ones() * m44.row(0), ones44);
r44.setZero();
VERIFY_IS_APPROX(r44.noalias() += Vector4d::Ones() * m44.col(0).transpose(), ones44);
}
void test_product_extra()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( product_extra(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_2( product_extra(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_2( mat_mat_scalar_scalar_product() );
CALL_SUBTEST_3( product_extra(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
CALL_SUBTEST_4( product_extra(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
CALL_SUBTEST_1( zero_sized_objects(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
CALL_SUBTEST_5( bug_127<0>() );
CALL_SUBTEST_5( bug_817<0>() );
CALL_SUBTEST_5( bug_1308<0>() );
CALL_SUBTEST_6( unaligned_objects<0>() );
CALL_SUBTEST_7( compute_block_size<float>() );
CALL_SUBTEST_7( compute_block_size<double>() );
CALL_SUBTEST_7( compute_block_size<std::complex<double> >() );
CALL_SUBTEST_8( aliasing_with_resize<void>() );
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/bdcsvd.cpp | .cpp | 4,275 | 117 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/
// discard stack allocation as that too bypasses malloc
#define EIGEN_STACK_ALLOCATION_LIMIT 0
#define EIGEN_RUNTIME_NO_MALLOC
#include "main.h"
#include <Eigen/SVD>
#include <iostream>
#include <Eigen/LU>
#define SVD_DEFAULT(M) BDCSVD<M>
#define SVD_FOR_MIN_NORM(M) BDCSVD<M>
#include "svd_common.h"
// Check all variants of JacobiSVD
template<typename MatrixType>
void bdcsvd(const MatrixType& a = MatrixType(), bool pickrandom = true)
{
MatrixType m;
if(pickrandom) {
m.resizeLike(a);
svd_fill_random(m);
}
else
m = a;
CALL_SUBTEST(( svd_test_all_computation_options<BDCSVD<MatrixType> >(m, false) ));
}
template<typename MatrixType>
void bdcsvd_method()
{
enum { Size = MatrixType::RowsAtCompileTime };
typedef typename MatrixType::RealScalar RealScalar;
typedef Matrix<RealScalar, Size, 1> RealVecType;
MatrixType m = MatrixType::Identity();
VERIFY_IS_APPROX(m.bdcSvd().singularValues(), RealVecType::Ones());
VERIFY_RAISES_ASSERT(m.bdcSvd().matrixU());
VERIFY_RAISES_ASSERT(m.bdcSvd().matrixV());
VERIFY_IS_APPROX(m.bdcSvd(ComputeFullU|ComputeFullV).solve(m), m);
}
// compare the Singular values returned with Jacobi and Bdc
template<typename MatrixType>
void compare_bdc_jacobi(const MatrixType& a = MatrixType(), unsigned int computationOptions = 0)
{
MatrixType m = MatrixType::Random(a.rows(), a.cols());
BDCSVD<MatrixType> bdc_svd(m);
JacobiSVD<MatrixType> jacobi_svd(m);
VERIFY_IS_APPROX(bdc_svd.singularValues(), jacobi_svd.singularValues());
if(computationOptions & ComputeFullU) VERIFY_IS_APPROX(bdc_svd.matrixU(), jacobi_svd.matrixU());
if(computationOptions & ComputeThinU) VERIFY_IS_APPROX(bdc_svd.matrixU(), jacobi_svd.matrixU());
if(computationOptions & ComputeFullV) VERIFY_IS_APPROX(bdc_svd.matrixV(), jacobi_svd.matrixV());
if(computationOptions & ComputeThinV) VERIFY_IS_APPROX(bdc_svd.matrixV(), jacobi_svd.matrixV());
}
void test_bdcsvd()
{
CALL_SUBTEST_3(( svd_verify_assert<BDCSVD<Matrix3f> >(Matrix3f()) ));
CALL_SUBTEST_4(( svd_verify_assert<BDCSVD<Matrix4d> >(Matrix4d()) ));
CALL_SUBTEST_7(( svd_verify_assert<BDCSVD<MatrixXf> >(MatrixXf(10,12)) ));
CALL_SUBTEST_8(( svd_verify_assert<BDCSVD<MatrixXcd> >(MatrixXcd(7,5)) ));
CALL_SUBTEST_101(( svd_all_trivial_2x2(bdcsvd<Matrix2cd>) ));
CALL_SUBTEST_102(( svd_all_trivial_2x2(bdcsvd<Matrix2d>) ));
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_3(( bdcsvd<Matrix3f>() ));
CALL_SUBTEST_4(( bdcsvd<Matrix4d>() ));
CALL_SUBTEST_5(( bdcsvd<Matrix<float,3,5> >() ));
int r = internal::random<int>(1, EIGEN_TEST_MAX_SIZE/2),
c = internal::random<int>(1, EIGEN_TEST_MAX_SIZE/2);
TEST_SET_BUT_UNUSED_VARIABLE(r)
TEST_SET_BUT_UNUSED_VARIABLE(c)
CALL_SUBTEST_6(( bdcsvd(Matrix<double,Dynamic,2>(r,2)) ));
CALL_SUBTEST_7(( bdcsvd(MatrixXf(r,c)) ));
CALL_SUBTEST_7(( compare_bdc_jacobi(MatrixXf(r,c)) ));
CALL_SUBTEST_10(( bdcsvd(MatrixXd(r,c)) ));
CALL_SUBTEST_10(( compare_bdc_jacobi(MatrixXd(r,c)) ));
CALL_SUBTEST_8(( bdcsvd(MatrixXcd(r,c)) ));
CALL_SUBTEST_8(( compare_bdc_jacobi(MatrixXcd(r,c)) ));
// Test on inf/nan matrix
CALL_SUBTEST_7( (svd_inf_nan<BDCSVD<MatrixXf>, MatrixXf>()) );
CALL_SUBTEST_10( (svd_inf_nan<BDCSVD<MatrixXd>, MatrixXd>()) );
}
// test matrixbase method
CALL_SUBTEST_1(( bdcsvd_method<Matrix2cd>() ));
CALL_SUBTEST_3(( bdcsvd_method<Matrix3f>() ));
// Test problem size constructors
CALL_SUBTEST_7( BDCSVD<MatrixXf>(10,10) );
// Check that preallocation avoids subsequent mallocs
// Disbaled because not supported by BDCSVD
// CALL_SUBTEST_9( svd_preallocate<void>() );
CALL_SUBTEST_2( svd_underoverflow<void>() );
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/geo_orthomethods.cpp | .cpp | 4,830 | 134 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/Geometry>
#include <Eigen/LU>
#include <Eigen/SVD>
/* this test covers the following files:
Geometry/OrthoMethods.h
*/
template<typename Scalar> void orthomethods_3()
{
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar,3,3> Matrix3;
typedef Matrix<Scalar,3,1> Vector3;
typedef Matrix<Scalar,4,1> Vector4;
Vector3 v0 = Vector3::Random(),
v1 = Vector3::Random(),
v2 = Vector3::Random();
// cross product
VERIFY_IS_MUCH_SMALLER_THAN(v1.cross(v2).dot(v1), Scalar(1));
VERIFY_IS_MUCH_SMALLER_THAN(v1.dot(v1.cross(v2)), Scalar(1));
VERIFY_IS_MUCH_SMALLER_THAN(v1.cross(v2).dot(v2), Scalar(1));
VERIFY_IS_MUCH_SMALLER_THAN(v2.dot(v1.cross(v2)), Scalar(1));
VERIFY_IS_MUCH_SMALLER_THAN(v1.cross(Vector3::Random()).dot(v1), Scalar(1));
Matrix3 mat3;
mat3 << v0.normalized(),
(v0.cross(v1)).normalized(),
(v0.cross(v1).cross(v0)).normalized();
VERIFY(mat3.isUnitary());
mat3.setRandom();
VERIFY_IS_APPROX(v0.cross(mat3*v1), -(mat3*v1).cross(v0));
VERIFY_IS_APPROX(v0.cross(mat3.lazyProduct(v1)), -(mat3.lazyProduct(v1)).cross(v0));
// colwise/rowwise cross product
mat3.setRandom();
Vector3 vec3 = Vector3::Random();
Matrix3 mcross;
int i = internal::random<int>(0,2);
mcross = mat3.colwise().cross(vec3);
VERIFY_IS_APPROX(mcross.col(i), mat3.col(i).cross(vec3));
VERIFY_IS_MUCH_SMALLER_THAN((mat3.adjoint() * mat3.colwise().cross(vec3)).diagonal().cwiseAbs().sum(), Scalar(1));
VERIFY_IS_MUCH_SMALLER_THAN((mat3.adjoint() * mat3.colwise().cross(Vector3::Random())).diagonal().cwiseAbs().sum(), Scalar(1));
VERIFY_IS_MUCH_SMALLER_THAN((vec3.adjoint() * mat3.colwise().cross(vec3)).cwiseAbs().sum(), Scalar(1));
VERIFY_IS_MUCH_SMALLER_THAN((vec3.adjoint() * Matrix3::Random().colwise().cross(vec3)).cwiseAbs().sum(), Scalar(1));
mcross = mat3.rowwise().cross(vec3);
VERIFY_IS_APPROX(mcross.row(i), mat3.row(i).cross(vec3));
// cross3
Vector4 v40 = Vector4::Random(),
v41 = Vector4::Random(),
v42 = Vector4::Random();
v40.w() = v41.w() = v42.w() = 0;
v42.template head<3>() = v40.template head<3>().cross(v41.template head<3>());
VERIFY_IS_APPROX(v40.cross3(v41), v42);
VERIFY_IS_MUCH_SMALLER_THAN(v40.cross3(Vector4::Random()).dot(v40), Scalar(1));
// check mixed product
typedef Matrix<RealScalar, 3, 1> RealVector3;
RealVector3 rv1 = RealVector3::Random();
VERIFY_IS_APPROX(v1.cross(rv1.template cast<Scalar>()), v1.cross(rv1));
VERIFY_IS_APPROX(rv1.template cast<Scalar>().cross(v1), rv1.cross(v1));
}
template<typename Scalar, int Size> void orthomethods(int size=Size)
{
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar,Size,1> VectorType;
typedef Matrix<Scalar,3,Size> Matrix3N;
typedef Matrix<Scalar,Size,3> MatrixN3;
typedef Matrix<Scalar,3,1> Vector3;
VectorType v0 = VectorType::Random(size);
// unitOrthogonal
VERIFY_IS_MUCH_SMALLER_THAN(v0.unitOrthogonal().dot(v0), Scalar(1));
VERIFY_IS_APPROX(v0.unitOrthogonal().norm(), RealScalar(1));
if (size>=3)
{
v0.template head<2>().setZero();
v0.tail(size-2).setRandom();
VERIFY_IS_MUCH_SMALLER_THAN(v0.unitOrthogonal().dot(v0), Scalar(1));
VERIFY_IS_APPROX(v0.unitOrthogonal().norm(), RealScalar(1));
}
// colwise/rowwise cross product
Vector3 vec3 = Vector3::Random();
int i = internal::random<int>(0,size-1);
Matrix3N mat3N(3,size), mcross3N(3,size);
mat3N.setRandom();
mcross3N = mat3N.colwise().cross(vec3);
VERIFY_IS_APPROX(mcross3N.col(i), mat3N.col(i).cross(vec3));
MatrixN3 matN3(size,3), mcrossN3(size,3);
matN3.setRandom();
mcrossN3 = matN3.rowwise().cross(vec3);
VERIFY_IS_APPROX(mcrossN3.row(i), matN3.row(i).cross(vec3));
}
void test_geo_orthomethods()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( orthomethods_3<float>() );
CALL_SUBTEST_2( orthomethods_3<double>() );
CALL_SUBTEST_4( orthomethods_3<std::complex<double> >() );
CALL_SUBTEST_1( (orthomethods<float,2>()) );
CALL_SUBTEST_2( (orthomethods<double,2>()) );
CALL_SUBTEST_1( (orthomethods<float,3>()) );
CALL_SUBTEST_2( (orthomethods<double,3>()) );
CALL_SUBTEST_3( (orthomethods<float,7>()) );
CALL_SUBTEST_4( (orthomethods<std::complex<double>,8>()) );
CALL_SUBTEST_5( (orthomethods<float,Dynamic>(36)) );
CALL_SUBTEST_6( (orthomethods<double,Dynamic>(35)) );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/inplace_decomposition.cpp | .cpp | 3,872 | 111 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/LU>
#include <Eigen/Cholesky>
#include <Eigen/QR>
// This file test inplace decomposition through Ref<>, as supported by Cholesky, LU, and QR decompositions.
template<typename DecType,typename MatrixType> void inplace(bool square = false, bool SPD = false)
{
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> RhsType;
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> ResType;
Index rows = MatrixType::RowsAtCompileTime==Dynamic ? internal::random<Index>(2,EIGEN_TEST_MAX_SIZE/2) : Index(MatrixType::RowsAtCompileTime);
Index cols = MatrixType::ColsAtCompileTime==Dynamic ? (square?rows:internal::random<Index>(2,rows)) : Index(MatrixType::ColsAtCompileTime);
MatrixType A = MatrixType::Random(rows,cols);
RhsType b = RhsType::Random(rows);
ResType x(cols);
if(SPD)
{
assert(square);
A.topRows(cols) = A.topRows(cols).adjoint() * A.topRows(cols);
A.diagonal().array() += 1e-3;
}
MatrixType A0 = A;
MatrixType A1 = A;
DecType dec(A);
// Check that the content of A has been modified
VERIFY_IS_NOT_APPROX( A, A0 );
// Check that the decomposition is correct:
if(rows==cols)
{
VERIFY_IS_APPROX( A0 * (x = dec.solve(b)), b );
}
else
{
VERIFY_IS_APPROX( A0.transpose() * A0 * (x = dec.solve(b)), A0.transpose() * b );
}
// Check that modifying A breaks the current dec:
A.setRandom();
if(rows==cols)
{
VERIFY_IS_NOT_APPROX( A0 * (x = dec.solve(b)), b );
}
else
{
VERIFY_IS_NOT_APPROX( A0.transpose() * A0 * (x = dec.solve(b)), A0.transpose() * b );
}
// Check that calling compute(A1) does not modify A1:
A = A0;
dec.compute(A1);
VERIFY_IS_EQUAL(A0,A1);
VERIFY_IS_NOT_APPROX( A, A0 );
if(rows==cols)
{
VERIFY_IS_APPROX( A0 * (x = dec.solve(b)), b );
}
else
{
VERIFY_IS_APPROX( A0.transpose() * A0 * (x = dec.solve(b)), A0.transpose() * b );
}
}
void test_inplace_decomposition()
{
EIGEN_UNUSED typedef Matrix<double,4,3> Matrix43d;
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(( inplace<LLT<Ref<MatrixXd> >, MatrixXd>(true,true) ));
CALL_SUBTEST_1(( inplace<LLT<Ref<Matrix4d> >, Matrix4d>(true,true) ));
CALL_SUBTEST_2(( inplace<LDLT<Ref<MatrixXd> >, MatrixXd>(true,true) ));
CALL_SUBTEST_2(( inplace<LDLT<Ref<Matrix4d> >, Matrix4d>(true,true) ));
CALL_SUBTEST_3(( inplace<PartialPivLU<Ref<MatrixXd> >, MatrixXd>(true,false) ));
CALL_SUBTEST_3(( inplace<PartialPivLU<Ref<Matrix4d> >, Matrix4d>(true,false) ));
CALL_SUBTEST_4(( inplace<FullPivLU<Ref<MatrixXd> >, MatrixXd>(true,false) ));
CALL_SUBTEST_4(( inplace<FullPivLU<Ref<Matrix4d> >, Matrix4d>(true,false) ));
CALL_SUBTEST_5(( inplace<HouseholderQR<Ref<MatrixXd> >, MatrixXd>(false,false) ));
CALL_SUBTEST_5(( inplace<HouseholderQR<Ref<Matrix43d> >, Matrix43d>(false,false) ));
CALL_SUBTEST_6(( inplace<ColPivHouseholderQR<Ref<MatrixXd> >, MatrixXd>(false,false) ));
CALL_SUBTEST_6(( inplace<ColPivHouseholderQR<Ref<Matrix43d> >, Matrix43d>(false,false) ));
CALL_SUBTEST_7(( inplace<FullPivHouseholderQR<Ref<MatrixXd> >, MatrixXd>(false,false) ));
CALL_SUBTEST_7(( inplace<FullPivHouseholderQR<Ref<Matrix43d> >, Matrix43d>(false,false) ));
CALL_SUBTEST_8(( inplace<CompleteOrthogonalDecomposition<Ref<MatrixXd> >, MatrixXd>(false,false) ));
CALL_SUBTEST_8(( inplace<CompleteOrthogonalDecomposition<Ref<Matrix43d> >, Matrix43d>(false,false) ));
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/qtvector.cpp | .cpp | 4,613 | 157 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_WORK_AROUND_QT_BUG_CALLING_WRONG_OPERATOR_NEW_FIXED_IN_QT_4_5
#include "main.h"
#include <QtCore/QVector>
#include <Eigen/Geometry>
#include <Eigen/QtAlignedMalloc>
template<typename MatrixType>
void check_qtvector_matrix(const MatrixType& m)
{
Index rows = m.rows();
Index cols = m.cols();
MatrixType x = MatrixType::Random(rows,cols), y = MatrixType::Random(rows,cols);
QVector<MatrixType> v(10, MatrixType(rows,cols)), w(20, y);
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(w[i], y);
}
v[5] = x;
w[6] = v[5];
VERIFY_IS_APPROX(w[6], v[5]);
v = w;
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(w[i], v[i]);
}
v.resize(21);
v[20] = x;
VERIFY_IS_APPROX(v[20], x);
v.fill(y,22);
VERIFY_IS_APPROX(v[21], y);
v.push_back(x);
VERIFY_IS_APPROX(v[22], x);
VERIFY((size_t)&(v[22]) == (size_t)&(v[21]) + sizeof(MatrixType));
// do a lot of push_back such that the vector gets internally resized
// (with memory reallocation)
MatrixType* ref = &w[0];
for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i)
v.push_back(w[i%w.size()]);
for(int i=23; i<v.size(); ++i)
{
VERIFY(v[i]==w[(i-23)%w.size()]);
}
}
template<typename TransformType>
void check_qtvector_transform(const TransformType&)
{
typedef typename TransformType::MatrixType MatrixType;
TransformType x(MatrixType::Random()), y(MatrixType::Random());
QVector<TransformType> v(10), w(20, y);
v[5] = x;
w[6] = v[5];
VERIFY_IS_APPROX(w[6], v[5]);
v = w;
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(w[i], v[i]);
}
v.resize(21);
v[20] = x;
VERIFY_IS_APPROX(v[20], x);
v.fill(y,22);
VERIFY_IS_APPROX(v[21], y);
v.push_back(x);
VERIFY_IS_APPROX(v[22], x);
VERIFY((size_t)&(v[22]) == (size_t)&(v[21]) + sizeof(TransformType));
// do a lot of push_back such that the vector gets internally resized
// (with memory reallocation)
TransformType* ref = &w[0];
for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i)
v.push_back(w[i%w.size()]);
for(unsigned int i=23; int(i)<v.size(); ++i)
{
VERIFY(v[i].matrix()==w[(i-23)%w.size()].matrix());
}
}
template<typename QuaternionType>
void check_qtvector_quaternion(const QuaternionType&)
{
typedef typename QuaternionType::Coefficients Coefficients;
QuaternionType x(Coefficients::Random()), y(Coefficients::Random());
QVector<QuaternionType> v(10), w(20, y);
v[5] = x;
w[6] = v[5];
VERIFY_IS_APPROX(w[6], v[5]);
v = w;
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(w[i], v[i]);
}
v.resize(21);
v[20] = x;
VERIFY_IS_APPROX(v[20], x);
v.fill(y,22);
VERIFY_IS_APPROX(v[21], y);
v.push_back(x);
VERIFY_IS_APPROX(v[22], x);
VERIFY((size_t)&(v[22]) == (size_t)&(v[21]) + sizeof(QuaternionType));
// do a lot of push_back such that the vector gets internally resized
// (with memory reallocation)
QuaternionType* ref = &w[0];
for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i)
v.push_back(w[i%w.size()]);
for(unsigned int i=23; int(i)<v.size(); ++i)
{
VERIFY(v[i].coeffs()==w[(i-23)%w.size()].coeffs());
}
}
void test_qtvector()
{
// some non vectorizable fixed sizes
CALL_SUBTEST(check_qtvector_matrix(Vector2f()));
CALL_SUBTEST(check_qtvector_matrix(Matrix3f()));
CALL_SUBTEST(check_qtvector_matrix(Matrix3d()));
// some vectorizable fixed sizes
CALL_SUBTEST(check_qtvector_matrix(Matrix2f()));
CALL_SUBTEST(check_qtvector_matrix(Vector4f()));
CALL_SUBTEST(check_qtvector_matrix(Matrix4f()));
CALL_SUBTEST(check_qtvector_matrix(Matrix4d()));
// some dynamic sizes
CALL_SUBTEST(check_qtvector_matrix(MatrixXd(1,1)));
CALL_SUBTEST(check_qtvector_matrix(VectorXd(20)));
CALL_SUBTEST(check_qtvector_matrix(RowVectorXf(20)));
CALL_SUBTEST(check_qtvector_matrix(MatrixXcf(10,10)));
// some Transform
CALL_SUBTEST(check_qtvector_transform(Affine2f()));
CALL_SUBTEST(check_qtvector_transform(Affine3f()));
CALL_SUBTEST(check_qtvector_transform(Affine3d()));
//CALL_SUBTEST(check_qtvector_transform(Transform4d()));
// some Quaternion
CALL_SUBTEST(check_qtvector_quaternion(Quaternionf()));
CALL_SUBTEST(check_qtvector_quaternion(Quaternionf()));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/rvalue_types.cpp | .cpp | 4,306 | 111 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2013 Hauke Heibel <hauke.heibel@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_RUNTIME_NO_MALLOC
#include "main.h"
#include <Eigen/Core>
using internal::UIntPtr;
#if EIGEN_HAS_RVALUE_REFERENCES
template <typename MatrixType>
void rvalue_copyassign(const MatrixType& m)
{
typedef typename internal::traits<MatrixType>::Scalar Scalar;
// create a temporary which we are about to destroy by moving
MatrixType tmp = m;
UIntPtr src_address = reinterpret_cast<UIntPtr>(tmp.data());
Eigen::internal::set_is_malloc_allowed(false); // moving from an rvalue reference shall never allocate
// move the temporary to n
MatrixType n = std::move(tmp);
UIntPtr dst_address = reinterpret_cast<UIntPtr>(n.data());
if (MatrixType::RowsAtCompileTime==Dynamic|| MatrixType::ColsAtCompileTime==Dynamic)
{
// verify that we actually moved the guts
VERIFY_IS_EQUAL(src_address, dst_address);
VERIFY_IS_EQUAL(tmp.size(), 0);
VERIFY_IS_EQUAL(reinterpret_cast<UIntPtr>(tmp.data()), UIntPtr(0));
}
// verify that the content did not change
Scalar abs_diff = (m-n).array().abs().sum();
VERIFY_IS_EQUAL(abs_diff, Scalar(0));
Eigen::internal::set_is_malloc_allowed(true);
}
template<typename TranspositionsType>
void rvalue_transpositions(Index rows)
{
typedef typename TranspositionsType::IndicesType PermutationVectorType;
PermutationVectorType vec;
randomPermutationVector(vec, rows);
TranspositionsType t0(vec);
Eigen::internal::set_is_malloc_allowed(false); // moving from an rvalue reference shall never allocate
UIntPtr t0_address = reinterpret_cast<UIntPtr>(t0.indices().data());
// Move constructors:
TranspositionsType t1 = std::move(t0);
UIntPtr t1_address = reinterpret_cast<UIntPtr>(t1.indices().data());
VERIFY_IS_EQUAL(t0_address, t1_address);
// t0 must be de-allocated:
VERIFY_IS_EQUAL(t0.size(), 0);
VERIFY_IS_EQUAL(reinterpret_cast<UIntPtr>(t0.indices().data()), UIntPtr(0));
// Move assignment:
t0 = std::move(t1);
t0_address = reinterpret_cast<UIntPtr>(t0.indices().data());
VERIFY_IS_EQUAL(t0_address, t1_address);
// t1 must be de-allocated:
VERIFY_IS_EQUAL(t1.size(), 0);
VERIFY_IS_EQUAL(reinterpret_cast<UIntPtr>(t1.indices().data()), UIntPtr(0));
Eigen::internal::set_is_malloc_allowed(true);
}
#else
template <typename MatrixType>
void rvalue_copyassign(const MatrixType&) {}
template<typename TranspositionsType>
void rvalue_transpositions(Index) {}
#endif
void test_rvalue_types()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(rvalue_copyassign( MatrixXf::Random(50,50).eval() ));
CALL_SUBTEST_1(rvalue_copyassign( ArrayXXf::Random(50,50).eval() ));
CALL_SUBTEST_1(rvalue_copyassign( Matrix<float,1,Dynamic>::Random(50).eval() ));
CALL_SUBTEST_1(rvalue_copyassign( Array<float,1,Dynamic>::Random(50).eval() ));
CALL_SUBTEST_1(rvalue_copyassign( Matrix<float,Dynamic,1>::Random(50).eval() ));
CALL_SUBTEST_1(rvalue_copyassign( Array<float,Dynamic,1>::Random(50).eval() ));
CALL_SUBTEST_2(rvalue_copyassign( Array<float,2,1>::Random().eval() ));
CALL_SUBTEST_2(rvalue_copyassign( Array<float,3,1>::Random().eval() ));
CALL_SUBTEST_2(rvalue_copyassign( Array<float,4,1>::Random().eval() ));
CALL_SUBTEST_2(rvalue_copyassign( Array<float,2,2>::Random().eval() ));
CALL_SUBTEST_2(rvalue_copyassign( Array<float,3,3>::Random().eval() ));
CALL_SUBTEST_2(rvalue_copyassign( Array<float,4,4>::Random().eval() ));
CALL_SUBTEST_3((rvalue_transpositions<PermutationMatrix<Dynamic, Dynamic, int> >(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))));
CALL_SUBTEST_3((rvalue_transpositions<PermutationMatrix<Dynamic, Dynamic, Index> >(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))));
CALL_SUBTEST_4((rvalue_transpositions<Transpositions<Dynamic, Dynamic, int> >(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))));
CALL_SUBTEST_4((rvalue_transpositions<Transpositions<Dynamic, Dynamic, Index> >(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))));
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/product.h | .h | 11,826 | 258 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/QR>
template<typename Derived1, typename Derived2>
bool areNotApprox(const MatrixBase<Derived1>& m1, const MatrixBase<Derived2>& m2, typename Derived1::RealScalar epsilon = NumTraits<typename Derived1::RealScalar>::dummy_precision())
{
return !((m1-m2).cwiseAbs2().maxCoeff() < epsilon * epsilon
* (std::max)(m1.cwiseAbs2().maxCoeff(), m2.cwiseAbs2().maxCoeff()));
}
template<typename MatrixType> void product(const MatrixType& m)
{
/* this test covers the following files:
Identity.h Product.h
*/
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> RowVectorType;
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> ColVectorType;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> RowSquareMatrixType;
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> ColSquareMatrixType;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime,
MatrixType::Flags&RowMajorBit?ColMajor:RowMajor> OtherMajorMatrixType;
Index rows = m.rows();
Index cols = m.cols();
// this test relies a lot on Random.h, and there's not much more that we can do
// to test it, hence I consider that we will have tested Random.h
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols);
RowSquareMatrixType
identity = RowSquareMatrixType::Identity(rows, rows),
square = RowSquareMatrixType::Random(rows, rows),
res = RowSquareMatrixType::Random(rows, rows);
ColSquareMatrixType
square2 = ColSquareMatrixType::Random(cols, cols),
res2 = ColSquareMatrixType::Random(cols, cols);
RowVectorType v1 = RowVectorType::Random(rows);
ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols);
OtherMajorMatrixType tm1 = m1;
Scalar s1 = internal::random<Scalar>();
Index r = internal::random<Index>(0, rows-1),
c = internal::random<Index>(0, cols-1),
c2 = internal::random<Index>(0, cols-1);
// begin testing Product.h: only associativity for now
// (we use Transpose.h but this doesn't count as a test for it)
VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2));
m3 = m1;
m3 *= m1.transpose() * m2;
VERIFY_IS_APPROX(m3, m1 * (m1.transpose()*m2));
VERIFY_IS_APPROX(m3, m1 * (m1.transpose()*m2));
// continue testing Product.h: distributivity
VERIFY_IS_APPROX(square*(m1 + m2), square*m1+square*m2);
VERIFY_IS_APPROX(square*(m1 - m2), square*m1-square*m2);
// continue testing Product.h: compatibility with ScalarMultiple.h
VERIFY_IS_APPROX(s1*(square*m1), (s1*square)*m1);
VERIFY_IS_APPROX(s1*(square*m1), square*(m1*s1));
// test Product.h together with Identity.h
VERIFY_IS_APPROX(v1, identity*v1);
VERIFY_IS_APPROX(v1.transpose(), v1.transpose() * identity);
// again, test operator() to check const-qualification
VERIFY_IS_APPROX(MatrixType::Identity(rows, cols)(r,c), static_cast<Scalar>(r==c));
if (rows!=cols)
VERIFY_RAISES_ASSERT(m3 = m1*m1);
// test the previous tests were not screwed up because operator* returns 0
// (we use the more accurate default epsilon)
if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1)
{
VERIFY(areNotApprox(m1.transpose()*m2,m2.transpose()*m1));
}
// test optimized operator+= path
res = square;
res.noalias() += m1 * m2.transpose();
VERIFY_IS_APPROX(res, square + m1 * m2.transpose());
if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1)
{
VERIFY(areNotApprox(res,square + m2 * m1.transpose()));
}
vcres = vc2;
vcres.noalias() += m1.transpose() * v1;
VERIFY_IS_APPROX(vcres, vc2 + m1.transpose() * v1);
// test optimized operator-= path
res = square;
res.noalias() -= m1 * m2.transpose();
VERIFY_IS_APPROX(res, square - (m1 * m2.transpose()));
if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1)
{
VERIFY(areNotApprox(res,square - m2 * m1.transpose()));
}
vcres = vc2;
vcres.noalias() -= m1.transpose() * v1;
VERIFY_IS_APPROX(vcres, vc2 - m1.transpose() * v1);
// test scaled products
res = square;
res.noalias() = s1 * m1 * m2.transpose();
VERIFY_IS_APPROX(res, ((s1*m1).eval() * m2.transpose()));
res = square;
res.noalias() += s1 * m1 * m2.transpose();
VERIFY_IS_APPROX(res, square + ((s1*m1).eval() * m2.transpose()));
res = square;
res.noalias() -= s1 * m1 * m2.transpose();
VERIFY_IS_APPROX(res, square - ((s1*m1).eval() * m2.transpose()));
// test d ?= a+b*c rules
res.noalias() = square + m1 * m2.transpose();
VERIFY_IS_APPROX(res, square + m1 * m2.transpose());
res.noalias() += square + m1 * m2.transpose();
VERIFY_IS_APPROX(res, 2*(square + m1 * m2.transpose()));
res.noalias() -= square + m1 * m2.transpose();
VERIFY_IS_APPROX(res, square + m1 * m2.transpose());
// test d ?= a-b*c rules
res.noalias() = square - m1 * m2.transpose();
VERIFY_IS_APPROX(res, square - m1 * m2.transpose());
res.noalias() += square - m1 * m2.transpose();
VERIFY_IS_APPROX(res, 2*(square - m1 * m2.transpose()));
res.noalias() -= square - m1 * m2.transpose();
VERIFY_IS_APPROX(res, square - m1 * m2.transpose());
tm1 = m1;
VERIFY_IS_APPROX(tm1.transpose() * v1, m1.transpose() * v1);
VERIFY_IS_APPROX(v1.transpose() * tm1, v1.transpose() * m1);
// test submatrix and matrix/vector product
for (int i=0; i<rows; ++i)
res.row(i) = m1.row(i) * m2.transpose();
VERIFY_IS_APPROX(res, m1 * m2.transpose());
// the other way round:
for (int i=0; i<rows; ++i)
res.col(i) = m1 * m2.transpose().col(i);
VERIFY_IS_APPROX(res, m1 * m2.transpose());
res2 = square2;
res2.noalias() += m1.transpose() * m2;
VERIFY_IS_APPROX(res2, square2 + m1.transpose() * m2);
if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1)
{
VERIFY(areNotApprox(res2,square2 + m2.transpose() * m1));
}
VERIFY_IS_APPROX(res.col(r).noalias() = square.adjoint() * square.col(r), (square.adjoint() * square.col(r)).eval());
VERIFY_IS_APPROX(res.col(r).noalias() = square * square.col(r), (square * square.col(r)).eval());
// vector at runtime (see bug 1166)
{
RowSquareMatrixType ref(square);
ColSquareMatrixType ref2(square2);
ref = res = square;
VERIFY_IS_APPROX(res.block(0,0,1,rows).noalias() = m1.col(0).transpose() * square.transpose(), (ref.row(0) = m1.col(0).transpose() * square.transpose()));
VERIFY_IS_APPROX(res.block(0,0,1,rows).noalias() = m1.block(0,0,rows,1).transpose() * square.transpose(), (ref.row(0) = m1.col(0).transpose() * square.transpose()));
VERIFY_IS_APPROX(res.block(0,0,1,rows).noalias() = m1.col(0).transpose() * square, (ref.row(0) = m1.col(0).transpose() * square));
VERIFY_IS_APPROX(res.block(0,0,1,rows).noalias() = m1.block(0,0,rows,1).transpose() * square, (ref.row(0) = m1.col(0).transpose() * square));
ref2 = res2 = square2;
VERIFY_IS_APPROX(res2.block(0,0,1,cols).noalias() = m1.row(0) * square2.transpose(), (ref2.row(0) = m1.row(0) * square2.transpose()));
VERIFY_IS_APPROX(res2.block(0,0,1,cols).noalias() = m1.block(0,0,1,cols) * square2.transpose(), (ref2.row(0) = m1.row(0) * square2.transpose()));
VERIFY_IS_APPROX(res2.block(0,0,1,cols).noalias() = m1.row(0) * square2, (ref2.row(0) = m1.row(0) * square2));
VERIFY_IS_APPROX(res2.block(0,0,1,cols).noalias() = m1.block(0,0,1,cols) * square2, (ref2.row(0) = m1.row(0) * square2));
}
// vector.block() (see bug 1283)
{
RowVectorType w1(rows);
VERIFY_IS_APPROX(square * v1.block(0,0,rows,1), square * v1);
VERIFY_IS_APPROX(w1.noalias() = square * v1.block(0,0,rows,1), square * v1);
VERIFY_IS_APPROX(w1.block(0,0,rows,1).noalias() = square * v1.block(0,0,rows,1), square * v1);
Matrix<Scalar,1,MatrixType::ColsAtCompileTime> w2(cols);
VERIFY_IS_APPROX(vc2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2);
VERIFY_IS_APPROX(w2.noalias() = vc2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2);
VERIFY_IS_APPROX(w2.block(0,0,1,cols).noalias() = vc2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2);
vc2 = square2.block(0,0,1,cols).transpose();
VERIFY_IS_APPROX(square2.block(0,0,1,cols) * square2, vc2.transpose() * square2);
VERIFY_IS_APPROX(w2.noalias() = square2.block(0,0,1,cols) * square2, vc2.transpose() * square2);
VERIFY_IS_APPROX(w2.block(0,0,1,cols).noalias() = square2.block(0,0,1,cols) * square2, vc2.transpose() * square2);
vc2 = square2.block(0,0,cols,1);
VERIFY_IS_APPROX(square2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2);
VERIFY_IS_APPROX(w2.noalias() = square2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2);
VERIFY_IS_APPROX(w2.block(0,0,1,cols).noalias() = square2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2);
}
// inner product
{
Scalar x = square2.row(c) * square2.col(c2);
VERIFY_IS_APPROX(x, square2.row(c).transpose().cwiseProduct(square2.col(c2)).sum());
}
// outer product
{
VERIFY_IS_APPROX(m1.col(c) * m1.row(r), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
VERIFY_IS_APPROX(m1.row(r).transpose() * m1.col(c).transpose(), m1.block(r,0,1,cols).transpose() * m1.block(0,c,rows,1).transpose());
VERIFY_IS_APPROX(m1.block(0,c,rows,1) * m1.row(r), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
VERIFY_IS_APPROX(m1.col(c) * m1.block(r,0,1,cols), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
VERIFY_IS_APPROX(m1.leftCols(1) * m1.row(r), m1.block(0,0,rows,1) * m1.block(r,0,1,cols));
VERIFY_IS_APPROX(m1.col(c) * m1.topRows(1), m1.block(0,c,rows,1) * m1.block(0,0,1,cols));
}
// Aliasing
{
ColVectorType x(cols); x.setRandom();
ColVectorType z(x);
ColVectorType y(cols); y.setZero();
ColSquareMatrixType A(cols,cols); A.setRandom();
// CwiseBinaryOp
VERIFY_IS_APPROX(x = y + A*x, A*z);
x = z;
// CwiseUnaryOp
VERIFY_IS_APPROX(x = Scalar(1.)*(A*x), A*z);
}
// regression for blas_trais
{
VERIFY_IS_APPROX(square * (square*square).transpose(), square * square.transpose() * square.transpose());
VERIFY_IS_APPROX(square * (-(square*square)), -square * square * square);
VERIFY_IS_APPROX(square * (s1*(square*square)), s1 * square * square * square);
VERIFY_IS_APPROX(square * (square*square).conjugate(), square * square.conjugate() * square.conjugate());
}
// destination with a non-default inner-stride
// see bug 1741
if(!MatrixType::IsRowMajor)
{
typedef Matrix<Scalar,Dynamic,Dynamic> MatrixX;
MatrixX buffer(2*rows,2*rows);
Map<RowSquareMatrixType,0,Stride<Dynamic,2> > map1(buffer.data(),rows,rows,Stride<Dynamic,2>(2*rows,2));
buffer.setZero();
VERIFY_IS_APPROX(map1 = m1 * m2.transpose(), (m1 * m2.transpose()).eval());
buffer.setZero();
VERIFY_IS_APPROX(map1.noalias() = m1 * m2.transpose(), (m1 * m2.transpose()).eval());
buffer.setZero();
VERIFY_IS_APPROX(map1.noalias() += m1 * m2.transpose(), (m1 * m2.transpose()).eval());
}
}
| Unknown |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/nomalloc.cpp | .cpp | 8,713 | 229 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
// discard stack allocation as that too bypasses malloc
#define EIGEN_STACK_ALLOCATION_LIMIT 0
// heap allocation will raise an assert if enabled at runtime
#define EIGEN_RUNTIME_NO_MALLOC
#include "main.h"
#include <Eigen/Cholesky>
#include <Eigen/Eigenvalues>
#include <Eigen/LU>
#include <Eigen/QR>
#include <Eigen/SVD>
template<typename MatrixType> void nomalloc(const MatrixType& m)
{
/* this test check no dynamic memory allocation are issued with fixed-size matrices
*/
typedef typename MatrixType::Scalar Scalar;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols);
Scalar s1 = internal::random<Scalar>();
Index r = internal::random<Index>(0, rows-1),
c = internal::random<Index>(0, cols-1);
VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2);
VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c)));
VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), (m1.array()*m1.array()).matrix());
VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2));
m2.col(0).noalias() = m1 * m1.col(0);
m2.col(0).noalias() -= m1.adjoint() * m1.col(0);
m2.col(0).noalias() -= m1 * m1.row(0).adjoint();
m2.col(0).noalias() -= m1.adjoint() * m1.row(0).adjoint();
m2.row(0).noalias() = m1.row(0) * m1;
m2.row(0).noalias() -= m1.row(0) * m1.adjoint();
m2.row(0).noalias() -= m1.col(0).adjoint() * m1;
m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint();
VERIFY_IS_APPROX(m2,m2);
m2.col(0).noalias() = m1.template triangularView<Upper>() * m1.col(0);
m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.col(0);
m2.col(0).noalias() -= m1.template triangularView<Upper>() * m1.row(0).adjoint();
m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.row(0).adjoint();
m2.row(0).noalias() = m1.row(0) * m1.template triangularView<Upper>();
m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template triangularView<Upper>();
m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template triangularView<Upper>();
m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template triangularView<Upper>();
VERIFY_IS_APPROX(m2,m2);
m2.col(0).noalias() = m1.template selfadjointView<Upper>() * m1.col(0);
m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.col(0);
m2.col(0).noalias() -= m1.template selfadjointView<Upper>() * m1.row(0).adjoint();
m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.row(0).adjoint();
m2.row(0).noalias() = m1.row(0) * m1.template selfadjointView<Upper>();
m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template selfadjointView<Upper>();
m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template selfadjointView<Upper>();
m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template selfadjointView<Upper>();
VERIFY_IS_APPROX(m2,m2);
m2.template selfadjointView<Lower>().rankUpdate(m1.col(0),-1);
m2.template selfadjointView<Upper>().rankUpdate(m1.row(0),-1);
m2.template selfadjointView<Lower>().rankUpdate(m1.col(0), m1.col(0)); // rank-2
// The following fancy matrix-matrix products are not safe yet regarding static allocation
m2.template selfadjointView<Lower>().rankUpdate(m1);
m2 += m2.template triangularView<Upper>() * m1;
m2.template triangularView<Upper>() = m2 * m2;
m1 += m1.template selfadjointView<Lower>() * m2;
VERIFY_IS_APPROX(m2,m2);
}
template<typename Scalar>
void ctms_decompositions()
{
const int maxSize = 16;
const int size = 12;
typedef Eigen::Matrix<Scalar,
Eigen::Dynamic, Eigen::Dynamic,
0,
maxSize, maxSize> Matrix;
typedef Eigen::Matrix<Scalar,
Eigen::Dynamic, 1,
0,
maxSize, 1> Vector;
typedef Eigen::Matrix<std::complex<Scalar>,
Eigen::Dynamic, Eigen::Dynamic,
0,
maxSize, maxSize> ComplexMatrix;
const Matrix A(Matrix::Random(size, size)), B(Matrix::Random(size, size));
Matrix X(size,size);
const ComplexMatrix complexA(ComplexMatrix::Random(size, size));
const Matrix saA = A.adjoint() * A;
const Vector b(Vector::Random(size));
Vector x(size);
// Cholesky module
Eigen::LLT<Matrix> LLT; LLT.compute(A);
X = LLT.solve(B);
x = LLT.solve(b);
Eigen::LDLT<Matrix> LDLT; LDLT.compute(A);
X = LDLT.solve(B);
x = LDLT.solve(b);
// Eigenvalues module
Eigen::HessenbergDecomposition<ComplexMatrix> hessDecomp; hessDecomp.compute(complexA);
Eigen::ComplexSchur<ComplexMatrix> cSchur(size); cSchur.compute(complexA);
Eigen::ComplexEigenSolver<ComplexMatrix> cEigSolver; cEigSolver.compute(complexA);
Eigen::EigenSolver<Matrix> eigSolver; eigSolver.compute(A);
Eigen::SelfAdjointEigenSolver<Matrix> saEigSolver(size); saEigSolver.compute(saA);
Eigen::Tridiagonalization<Matrix> tridiag; tridiag.compute(saA);
// LU module
Eigen::PartialPivLU<Matrix> ppLU; ppLU.compute(A);
X = ppLU.solve(B);
x = ppLU.solve(b);
Eigen::FullPivLU<Matrix> fpLU; fpLU.compute(A);
X = fpLU.solve(B);
x = fpLU.solve(b);
// QR module
Eigen::HouseholderQR<Matrix> hQR; hQR.compute(A);
X = hQR.solve(B);
x = hQR.solve(b);
Eigen::ColPivHouseholderQR<Matrix> cpQR; cpQR.compute(A);
X = cpQR.solve(B);
x = cpQR.solve(b);
Eigen::FullPivHouseholderQR<Matrix> fpQR; fpQR.compute(A);
// FIXME X = fpQR.solve(B);
x = fpQR.solve(b);
// SVD module
Eigen::JacobiSVD<Matrix> jSVD; jSVD.compute(A, ComputeFullU | ComputeFullV);
}
void test_zerosized() {
// default constructors:
Eigen::MatrixXd A;
Eigen::VectorXd v;
// explicit zero-sized:
Eigen::ArrayXXd A0(0,0);
Eigen::ArrayXd v0(0);
// assigning empty objects to each other:
A=A0;
v=v0;
}
template<typename MatrixType> void test_reference(const MatrixType& m) {
typedef typename MatrixType::Scalar Scalar;
enum { Flag = MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor};
enum { TransposeFlag = !MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor};
typename MatrixType::Index rows = m.rows(), cols=m.cols();
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flag > MatrixX;
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, TransposeFlag> MatrixXT;
// Dynamic reference:
typedef Eigen::Ref<const MatrixX > Ref;
typedef Eigen::Ref<const MatrixXT > RefT;
Ref r1(m);
Ref r2(m.block(rows/3, cols/4, rows/2, cols/2));
RefT r3(m.transpose());
RefT r4(m.topLeftCorner(rows/2, cols/2).transpose());
VERIFY_RAISES_ASSERT(RefT r5(m));
VERIFY_RAISES_ASSERT(Ref r6(m.transpose()));
VERIFY_RAISES_ASSERT(Ref r7(Scalar(2) * m));
// Copy constructors shall also never malloc
Ref r8 = r1;
RefT r9 = r3;
// Initializing from a compatible Ref shall also never malloc
Eigen::Ref<const MatrixX, Unaligned, Stride<Dynamic, Dynamic> > r10=r8, r11=m;
// Initializing from an incompatible Ref will malloc:
typedef Eigen::Ref<const MatrixX, Aligned> RefAligned;
VERIFY_RAISES_ASSERT(RefAligned r12=r10);
VERIFY_RAISES_ASSERT(Ref r13=r10); // r10 has more dynamic strides
}
void test_nomalloc()
{
// create some dynamic objects
Eigen::MatrixXd M1 = MatrixXd::Random(3,3);
Ref<const MatrixXd> R1 = 2.0*M1; // Ref requires temporary
// from here on prohibit malloc:
Eigen::internal::set_is_malloc_allowed(false);
// check that our operator new is indeed called:
VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3,3)));
CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2(nomalloc(Matrix4d()) );
CALL_SUBTEST_3(nomalloc(Matrix<float,32,32>()) );
// Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms)
CALL_SUBTEST_4(ctms_decompositions<float>());
CALL_SUBTEST_5(test_zerosized());
CALL_SUBTEST_6(test_reference(Matrix<float,32,32>()));
CALL_SUBTEST_7(test_reference(R1));
CALL_SUBTEST_8(Ref<MatrixXd> R2 = M1.topRows<2>(); test_reference(R2));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/sparse_solvers.cpp | .cpp | 4,691 | 113 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "sparse.h"
template<typename Scalar> void
initSPD(double density,
Matrix<Scalar,Dynamic,Dynamic>& refMat,
SparseMatrix<Scalar>& sparseMat)
{
Matrix<Scalar,Dynamic,Dynamic> aux(refMat.rows(),refMat.cols());
initSparse(density,refMat,sparseMat);
refMat = refMat * refMat.adjoint();
for (int k=0; k<2; ++k)
{
initSparse(density,aux,sparseMat,ForceNonZeroDiag);
refMat += aux * aux.adjoint();
}
sparseMat.setZero();
for (int j=0 ; j<sparseMat.cols(); ++j)
for (int i=j ; i<sparseMat.rows(); ++i)
if (refMat(i,j)!=Scalar(0))
sparseMat.insert(i,j) = refMat(i,j);
sparseMat.finalize();
}
template<typename Scalar> void sparse_solvers(int rows, int cols)
{
double density = (std::max)(8./(rows*cols), 0.01);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
// Scalar eps = 1e-6;
DenseVector vec1 = DenseVector::Random(rows);
std::vector<Vector2i> zeroCoords;
std::vector<Vector2i> nonzeroCoords;
// test triangular solver
{
DenseVector vec2 = vec1, vec3 = vec1;
SparseMatrix<Scalar> m2(rows, cols);
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
// lower - dense
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, &zeroCoords, &nonzeroCoords);
VERIFY_IS_APPROX(refMat2.template triangularView<Lower>().solve(vec2),
m2.template triangularView<Lower>().solve(vec3));
// upper - dense
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular, &zeroCoords, &nonzeroCoords);
VERIFY_IS_APPROX(refMat2.template triangularView<Upper>().solve(vec2),
m2.template triangularView<Upper>().solve(vec3));
VERIFY_IS_APPROX(refMat2.conjugate().template triangularView<Upper>().solve(vec2),
m2.conjugate().template triangularView<Upper>().solve(vec3));
{
SparseMatrix<Scalar> cm2(m2);
//Index rows, Index cols, Index nnz, Index* outerIndexPtr, Index* innerIndexPtr, Scalar* valuePtr
MappedSparseMatrix<Scalar> mm2(rows, cols, cm2.nonZeros(), cm2.outerIndexPtr(), cm2.innerIndexPtr(), cm2.valuePtr());
VERIFY_IS_APPROX(refMat2.conjugate().template triangularView<Upper>().solve(vec2),
mm2.conjugate().template triangularView<Upper>().solve(vec3));
}
// lower - transpose
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, &zeroCoords, &nonzeroCoords);
VERIFY_IS_APPROX(refMat2.transpose().template triangularView<Upper>().solve(vec2),
m2.transpose().template triangularView<Upper>().solve(vec3));
// upper - transpose
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular, &zeroCoords, &nonzeroCoords);
VERIFY_IS_APPROX(refMat2.transpose().template triangularView<Lower>().solve(vec2),
m2.transpose().template triangularView<Lower>().solve(vec3));
SparseMatrix<Scalar> matB(rows, rows);
DenseMatrix refMatB = DenseMatrix::Zero(rows, rows);
// lower - sparse
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular);
initSparse<Scalar>(density, refMatB, matB);
refMat2.template triangularView<Lower>().solveInPlace(refMatB);
m2.template triangularView<Lower>().solveInPlace(matB);
VERIFY_IS_APPROX(matB.toDense(), refMatB);
// upper - sparse
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular);
initSparse<Scalar>(density, refMatB, matB);
refMat2.template triangularView<Upper>().solveInPlace(refMatB);
m2.template triangularView<Upper>().solveInPlace(matB);
VERIFY_IS_APPROX(matB, refMatB);
// test deprecated API
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, &zeroCoords, &nonzeroCoords);
VERIFY_IS_APPROX(refMat2.template triangularView<Lower>().solve(vec2),
m2.template triangularView<Lower>().solve(vec3));
}
}
void test_sparse_solvers()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(sparse_solvers<double>(8, 8) );
int s = internal::random<int>(1,300);
CALL_SUBTEST_2(sparse_solvers<std::complex<double> >(s,s) );
CALL_SUBTEST_1(sparse_solvers<double>(s,s) );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/sparse_vector.cpp | .cpp | 5,079 | 164 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "sparse.h"
template<typename Scalar,typename StorageIndex> void sparse_vector(int rows, int cols)
{
double densityMat = (std::max)(8./(rows*cols), 0.01);
double densityVec = (std::max)(8./(rows), 0.1);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
typedef SparseVector<Scalar,0,StorageIndex> SparseVectorType;
typedef SparseMatrix<Scalar,0,StorageIndex> SparseMatrixType;
Scalar eps = 1e-6;
SparseMatrixType m1(rows,rows);
SparseVectorType v1(rows), v2(rows), v3(rows);
DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
DenseVector refV1 = DenseVector::Random(rows),
refV2 = DenseVector::Random(rows),
refV3 = DenseVector::Random(rows);
std::vector<int> zerocoords, nonzerocoords;
initSparse<Scalar>(densityVec, refV1, v1, &zerocoords, &nonzerocoords);
initSparse<Scalar>(densityMat, refM1, m1);
initSparse<Scalar>(densityVec, refV2, v2);
initSparse<Scalar>(densityVec, refV3, v3);
Scalar s1 = internal::random<Scalar>();
// test coeff and coeffRef
for (unsigned int i=0; i<zerocoords.size(); ++i)
{
VERIFY_IS_MUCH_SMALLER_THAN( v1.coeff(zerocoords[i]), eps );
//VERIFY_RAISES_ASSERT( v1.coeffRef(zerocoords[i]) = 5 );
}
{
VERIFY(int(nonzerocoords.size()) == v1.nonZeros());
int j=0;
for (typename SparseVectorType::InnerIterator it(v1); it; ++it,++j)
{
VERIFY(nonzerocoords[j]==it.index());
VERIFY(it.value()==v1.coeff(it.index()));
VERIFY(it.value()==refV1.coeff(it.index()));
}
}
VERIFY_IS_APPROX(v1, refV1);
// test coeffRef with reallocation
{
SparseVectorType v4(rows);
DenseVector v5 = DenseVector::Zero(rows);
for(int k=0; k<rows; ++k)
{
int i = internal::random<int>(0,rows-1);
Scalar v = internal::random<Scalar>();
v4.coeffRef(i) += v;
v5.coeffRef(i) += v;
}
VERIFY_IS_APPROX(v4,v5);
}
v1.coeffRef(nonzerocoords[0]) = Scalar(5);
refV1.coeffRef(nonzerocoords[0]) = Scalar(5);
VERIFY_IS_APPROX(v1, refV1);
VERIFY_IS_APPROX(v1+v2, refV1+refV2);
VERIFY_IS_APPROX(v1+v2+v3, refV1+refV2+refV3);
VERIFY_IS_APPROX(v1*s1-v2, refV1*s1-refV2);
VERIFY_IS_APPROX(v1*=s1, refV1*=s1);
VERIFY_IS_APPROX(v1/=s1, refV1/=s1);
VERIFY_IS_APPROX(v1+=v2, refV1+=refV2);
VERIFY_IS_APPROX(v1-=v2, refV1-=refV2);
VERIFY_IS_APPROX(v1.dot(v2), refV1.dot(refV2));
VERIFY_IS_APPROX(v1.dot(refV2), refV1.dot(refV2));
VERIFY_IS_APPROX(m1*v2, refM1*refV2);
VERIFY_IS_APPROX(v1.dot(m1*v2), refV1.dot(refM1*refV2));
{
int i = internal::random<int>(0,rows-1);
VERIFY_IS_APPROX(v1.dot(m1.col(i)), refV1.dot(refM1.col(i)));
}
VERIFY_IS_APPROX(v1.squaredNorm(), refV1.squaredNorm());
VERIFY_IS_APPROX(v1.blueNorm(), refV1.blueNorm());
// test aliasing
VERIFY_IS_APPROX((v1 = -v1), (refV1 = -refV1));
VERIFY_IS_APPROX((v1 = v1.transpose()), (refV1 = refV1.transpose().eval()));
VERIFY_IS_APPROX((v1 += -v1), (refV1 += -refV1));
// sparse matrix to sparse vector
SparseMatrixType mv1;
VERIFY_IS_APPROX((mv1=v1),v1);
VERIFY_IS_APPROX(mv1,(v1=mv1));
VERIFY_IS_APPROX(mv1,(v1=mv1.transpose()));
// check copy to dense vector with transpose
refV3.resize(0);
VERIFY_IS_APPROX(refV3 = v1.transpose(),v1.toDense());
VERIFY_IS_APPROX(DenseVector(v1),v1.toDense());
// test conservative resize
{
std::vector<StorageIndex> inc;
if(rows > 3)
inc.push_back(-3);
inc.push_back(0);
inc.push_back(3);
inc.push_back(1);
inc.push_back(10);
for(std::size_t i = 0; i< inc.size(); i++) {
StorageIndex incRows = inc[i];
SparseVectorType vec1(rows);
DenseVector refVec1 = DenseVector::Zero(rows);
initSparse<Scalar>(densityVec, refVec1, vec1);
vec1.conservativeResize(rows+incRows);
refVec1.conservativeResize(rows+incRows);
if (incRows > 0) refVec1.tail(incRows).setZero();
VERIFY_IS_APPROX(vec1, refVec1);
// Insert new values
if (incRows > 0)
vec1.insert(vec1.rows()-1) = refVec1(refVec1.rows()-1) = 1;
VERIFY_IS_APPROX(vec1, refVec1);
}
}
}
void test_sparse_vector()
{
for(int i = 0; i < g_repeat; i++) {
int r = Eigen::internal::random<int>(1,500), c = Eigen::internal::random<int>(1,500);
if(Eigen::internal::random<int>(0,4) == 0) {
r = c; // check square matrices in 25% of tries
}
EIGEN_UNUSED_VARIABLE(r+c);
CALL_SUBTEST_1(( sparse_vector<double,int>(8, 8) ));
CALL_SUBTEST_2(( sparse_vector<std::complex<double>, int>(r, c) ));
CALL_SUBTEST_1(( sparse_vector<double,long int>(r, c) ));
CALL_SUBTEST_1(( sparse_vector<double,short>(r, c) ));
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/lscg.cpp | .cpp | 1,491 | 38 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "sparse_solver.h"
#include <Eigen/IterativeLinearSolvers>
template<typename T> void test_lscg_T()
{
LeastSquaresConjugateGradient<SparseMatrix<T> > lscg_colmajor_diag;
LeastSquaresConjugateGradient<SparseMatrix<T>, IdentityPreconditioner> lscg_colmajor_I;
LeastSquaresConjugateGradient<SparseMatrix<T,RowMajor> > lscg_rowmajor_diag;
LeastSquaresConjugateGradient<SparseMatrix<T,RowMajor>, IdentityPreconditioner> lscg_rowmajor_I;
CALL_SUBTEST( check_sparse_square_solving(lscg_colmajor_diag) );
CALL_SUBTEST( check_sparse_square_solving(lscg_colmajor_I) );
CALL_SUBTEST( check_sparse_leastsquare_solving(lscg_colmajor_diag) );
CALL_SUBTEST( check_sparse_leastsquare_solving(lscg_colmajor_I) );
CALL_SUBTEST( check_sparse_square_solving(lscg_rowmajor_diag) );
CALL_SUBTEST( check_sparse_square_solving(lscg_rowmajor_I) );
CALL_SUBTEST( check_sparse_leastsquare_solving(lscg_rowmajor_diag) );
CALL_SUBTEST( check_sparse_leastsquare_solving(lscg_rowmajor_I) );
}
void test_lscg()
{
CALL_SUBTEST_1(test_lscg_T<double>());
CALL_SUBTEST_2(test_lscg_T<std::complex<double> >());
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/fastmath.cpp | .cpp | 5,248 | 100 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
void check(bool b, bool ref)
{
std::cout << b;
if(b==ref)
std::cout << " OK ";
else
std::cout << " BAD ";
}
#if EIGEN_COMP_MSVC && EIGEN_COMP_MSVC < 1800
namespace std {
template<typename T> bool (isfinite)(T x) { return _finite(x); }
template<typename T> bool (isnan)(T x) { return _isnan(x); }
template<typename T> bool (isinf)(T x) { return _fpclass(x)==_FPCLASS_NINF || _fpclass(x)==_FPCLASS_PINF; }
}
#endif
template<typename T>
void check_inf_nan(bool dryrun) {
Matrix<T,Dynamic,1> m(10);
m.setRandom();
m(3) = std::numeric_limits<T>::quiet_NaN();
if(dryrun)
{
std::cout << "std::isfinite(" << m(3) << ") = "; check((std::isfinite)(m(3)),false); std::cout << " ; numext::isfinite = "; check((numext::isfinite)(m(3)), false); std::cout << "\n";
std::cout << "std::isinf(" << m(3) << ") = "; check((std::isinf)(m(3)),false); std::cout << " ; numext::isinf = "; check((numext::isinf)(m(3)), false); std::cout << "\n";
std::cout << "std::isnan(" << m(3) << ") = "; check((std::isnan)(m(3)),true); std::cout << " ; numext::isnan = "; check((numext::isnan)(m(3)), true); std::cout << "\n";
std::cout << "allFinite: "; check(m.allFinite(), 0); std::cout << "\n";
std::cout << "hasNaN: "; check(m.hasNaN(), 1); std::cout << "\n";
std::cout << "\n";
}
else
{
if( (std::isfinite)(m(3))) g_test_level=1; VERIFY( !(numext::isfinite)(m(3)) ); g_test_level=0;
if( (std::isinf) (m(3))) g_test_level=1; VERIFY( !(numext::isinf)(m(3)) ); g_test_level=0;
if(!(std::isnan) (m(3))) g_test_level=1; VERIFY( (numext::isnan)(m(3)) ); g_test_level=0;
if( (std::isfinite)(m(3))) g_test_level=1; VERIFY( !m.allFinite() ); g_test_level=0;
if(!(std::isnan) (m(3))) g_test_level=1; VERIFY( m.hasNaN() ); g_test_level=0;
}
T hidden_zero = (std::numeric_limits<T>::min)()*(std::numeric_limits<T>::min)();
m(4) /= hidden_zero;
if(dryrun)
{
std::cout << "std::isfinite(" << m(4) << ") = "; check((std::isfinite)(m(4)),false); std::cout << " ; numext::isfinite = "; check((numext::isfinite)(m(4)), false); std::cout << "\n";
std::cout << "std::isinf(" << m(4) << ") = "; check((std::isinf)(m(4)),true); std::cout << " ; numext::isinf = "; check((numext::isinf)(m(4)), true); std::cout << "\n";
std::cout << "std::isnan(" << m(4) << ") = "; check((std::isnan)(m(4)),false); std::cout << " ; numext::isnan = "; check((numext::isnan)(m(4)), false); std::cout << "\n";
std::cout << "allFinite: "; check(m.allFinite(), 0); std::cout << "\n";
std::cout << "hasNaN: "; check(m.hasNaN(), 1); std::cout << "\n";
std::cout << "\n";
}
else
{
if( (std::isfinite)(m(3))) g_test_level=1; VERIFY( !(numext::isfinite)(m(4)) ); g_test_level=0;
if(!(std::isinf) (m(3))) g_test_level=1; VERIFY( (numext::isinf)(m(4)) ); g_test_level=0;
if( (std::isnan) (m(3))) g_test_level=1; VERIFY( !(numext::isnan)(m(4)) ); g_test_level=0;
if( (std::isfinite)(m(3))) g_test_level=1; VERIFY( !m.allFinite() ); g_test_level=0;
if(!(std::isnan) (m(3))) g_test_level=1; VERIFY( m.hasNaN() ); g_test_level=0;
}
m(3) = 0;
if(dryrun)
{
std::cout << "std::isfinite(" << m(3) << ") = "; check((std::isfinite)(m(3)),true); std::cout << " ; numext::isfinite = "; check((numext::isfinite)(m(3)), true); std::cout << "\n";
std::cout << "std::isinf(" << m(3) << ") = "; check((std::isinf)(m(3)),false); std::cout << " ; numext::isinf = "; check((numext::isinf)(m(3)), false); std::cout << "\n";
std::cout << "std::isnan(" << m(3) << ") = "; check((std::isnan)(m(3)),false); std::cout << " ; numext::isnan = "; check((numext::isnan)(m(3)), false); std::cout << "\n";
std::cout << "allFinite: "; check(m.allFinite(), 0); std::cout << "\n";
std::cout << "hasNaN: "; check(m.hasNaN(), 0); std::cout << "\n";
std::cout << "\n\n";
}
else
{
if(!(std::isfinite)(m(3))) g_test_level=1; VERIFY( (numext::isfinite)(m(3)) ); g_test_level=0;
if( (std::isinf) (m(3))) g_test_level=1; VERIFY( !(numext::isinf)(m(3)) ); g_test_level=0;
if( (std::isnan) (m(3))) g_test_level=1; VERIFY( !(numext::isnan)(m(3)) ); g_test_level=0;
if( (std::isfinite)(m(3))) g_test_level=1; VERIFY( !m.allFinite() ); g_test_level=0;
if( (std::isnan) (m(3))) g_test_level=1; VERIFY( !m.hasNaN() ); g_test_level=0;
}
}
void test_fastmath() {
std::cout << "*** float *** \n\n"; check_inf_nan<float>(true);
std::cout << "*** double ***\n\n"; check_inf_nan<double>(true);
std::cout << "*** long double *** \n\n"; check_inf_nan<long double>(true);
check_inf_nan<float>(false);
check_inf_nan<double>(false);
check_inf_nan<long double>(false);
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/upperbidiagonalization.cpp | .cpp | 1,750 | 44 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/SVD>
template<typename MatrixType> void upperbidiag(const MatrixType& m)
{
const typename MatrixType::Index rows = m.rows();
const typename MatrixType::Index cols = m.cols();
typedef Matrix<typename MatrixType::RealScalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime> RealMatrixType;
typedef Matrix<typename MatrixType::Scalar, MatrixType::ColsAtCompileTime, MatrixType::RowsAtCompileTime> TransposeMatrixType;
MatrixType a = MatrixType::Random(rows,cols);
internal::UpperBidiagonalization<MatrixType> ubd(a);
RealMatrixType b(rows, cols);
b.setZero();
b.block(0,0,cols,cols) = ubd.bidiagonal();
MatrixType c = ubd.householderU() * b * ubd.householderV().adjoint();
VERIFY_IS_APPROX(a,c);
TransposeMatrixType d = ubd.householderV() * b.adjoint() * ubd.householderU().adjoint();
VERIFY_IS_APPROX(a.adjoint(),d);
}
void test_upperbidiagonalization()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( upperbidiag(MatrixXf(3,3)) );
CALL_SUBTEST_2( upperbidiag(MatrixXd(17,12)) );
CALL_SUBTEST_3( upperbidiag(MatrixXcf(20,20)) );
CALL_SUBTEST_4( upperbidiag(Matrix<std::complex<double>,Dynamic,Dynamic,RowMajor>(16,15)) );
CALL_SUBTEST_5( upperbidiag(Matrix<float,6,4>()) );
CALL_SUBTEST_6( upperbidiag(Matrix<float,5,5>()) );
CALL_SUBTEST_7( upperbidiag(Matrix<double,4,3>()) );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/bug1213.cpp | .cpp | 174 | 14 |
// This anonymous enum is essential to trigger the linking issue
enum {
Foo
};
#include "bug1213.h"
bool bug1213_1(const Eigen::Vector3f& x)
{
return bug1213_2(x);
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/constructor.cpp | .cpp | 2,554 | 99 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define TEST_ENABLE_TEMPORARY_TRACKING
#include "main.h"
template<typename MatrixType> struct Wrapper
{
MatrixType m_mat;
inline Wrapper(const MatrixType &x) : m_mat(x) {}
inline operator const MatrixType& () const { return m_mat; }
inline operator MatrixType& () { return m_mat; }
};
enum my_sizes { M = 12, N = 7};
template<typename MatrixType> void ctor_init1(const MatrixType& m)
{
// Check logic in PlainObjectBase::_init1
Index rows = m.rows();
Index cols = m.cols();
MatrixType m0 = MatrixType::Random(rows,cols);
VERIFY_EVALUATION_COUNT( MatrixType m1(m0), 1);
VERIFY_EVALUATION_COUNT( MatrixType m2(m0+m0), 1);
VERIFY_EVALUATION_COUNT( MatrixType m2(m0.block(0,0,rows,cols)) , 1);
Wrapper<MatrixType> wrapper(m0);
VERIFY_EVALUATION_COUNT( MatrixType m3(wrapper) , 1);
}
void test_constructor()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( ctor_init1(Matrix<float, 1, 1>()) );
CALL_SUBTEST_1( ctor_init1(Matrix4d()) );
CALL_SUBTEST_1( ctor_init1(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_1( ctor_init1(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
{
Matrix<Index,1,1> a(123);
VERIFY_IS_EQUAL(a[0], 123);
}
{
Matrix<Index,1,1> a(123.0);
VERIFY_IS_EQUAL(a[0], 123);
}
{
Matrix<float,1,1> a(123);
VERIFY_IS_EQUAL(a[0], 123.f);
}
{
Array<Index,1,1> a(123);
VERIFY_IS_EQUAL(a[0], 123);
}
{
Array<Index,1,1> a(123.0);
VERIFY_IS_EQUAL(a[0], 123);
}
{
Array<float,1,1> a(123);
VERIFY_IS_EQUAL(a[0], 123.f);
}
{
Array<Index,3,3> a(123);
VERIFY_IS_EQUAL(a(4), 123);
}
{
Array<Index,3,3> a(123.0);
VERIFY_IS_EQUAL(a(4), 123);
}
{
Array<float,3,3> a(123);
VERIFY_IS_EQUAL(a(4), 123.f);
}
{
MatrixXi m1(M,N);
VERIFY_IS_EQUAL(m1.rows(),M);
VERIFY_IS_EQUAL(m1.cols(),N);
ArrayXXi a1(M,N);
VERIFY_IS_EQUAL(a1.rows(),M);
VERIFY_IS_EQUAL(a1.cols(),N);
VectorXi v1(M);
VERIFY_IS_EQUAL(v1.size(),M);
ArrayXi a2(M);
VERIFY_IS_EQUAL(a2.size(),M);
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/schur_complex.cpp | .cpp | 3,561 | 92 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <limits>
#include <Eigen/Eigenvalues>
template<typename MatrixType> void schur(int size = MatrixType::ColsAtCompileTime)
{
typedef typename ComplexSchur<MatrixType>::ComplexScalar ComplexScalar;
typedef typename ComplexSchur<MatrixType>::ComplexMatrixType ComplexMatrixType;
// Test basic functionality: T is triangular and A = U T U*
for(int counter = 0; counter < g_repeat; ++counter) {
MatrixType A = MatrixType::Random(size, size);
ComplexSchur<MatrixType> schurOfA(A);
VERIFY_IS_EQUAL(schurOfA.info(), Success);
ComplexMatrixType U = schurOfA.matrixU();
ComplexMatrixType T = schurOfA.matrixT();
for(int row = 1; row < size; ++row) {
for(int col = 0; col < row; ++col) {
VERIFY(T(row,col) == (typename MatrixType::Scalar)0);
}
}
VERIFY_IS_APPROX(A.template cast<ComplexScalar>(), U * T * U.adjoint());
}
// Test asserts when not initialized
ComplexSchur<MatrixType> csUninitialized;
VERIFY_RAISES_ASSERT(csUninitialized.matrixT());
VERIFY_RAISES_ASSERT(csUninitialized.matrixU());
VERIFY_RAISES_ASSERT(csUninitialized.info());
// Test whether compute() and constructor returns same result
MatrixType A = MatrixType::Random(size, size);
ComplexSchur<MatrixType> cs1;
cs1.compute(A);
ComplexSchur<MatrixType> cs2(A);
VERIFY_IS_EQUAL(cs1.info(), Success);
VERIFY_IS_EQUAL(cs2.info(), Success);
VERIFY_IS_EQUAL(cs1.matrixT(), cs2.matrixT());
VERIFY_IS_EQUAL(cs1.matrixU(), cs2.matrixU());
// Test maximum number of iterations
ComplexSchur<MatrixType> cs3;
cs3.setMaxIterations(ComplexSchur<MatrixType>::m_maxIterationsPerRow * size).compute(A);
VERIFY_IS_EQUAL(cs3.info(), Success);
VERIFY_IS_EQUAL(cs3.matrixT(), cs1.matrixT());
VERIFY_IS_EQUAL(cs3.matrixU(), cs1.matrixU());
cs3.setMaxIterations(1).compute(A);
VERIFY_IS_EQUAL(cs3.info(), size > 1 ? NoConvergence : Success);
VERIFY_IS_EQUAL(cs3.getMaxIterations(), 1);
MatrixType Atriangular = A;
Atriangular.template triangularView<StrictlyLower>().setZero();
cs3.setMaxIterations(1).compute(Atriangular); // triangular matrices do not need any iterations
VERIFY_IS_EQUAL(cs3.info(), Success);
VERIFY_IS_EQUAL(cs3.matrixT(), Atriangular.template cast<ComplexScalar>());
VERIFY_IS_EQUAL(cs3.matrixU(), ComplexMatrixType::Identity(size, size));
// Test computation of only T, not U
ComplexSchur<MatrixType> csOnlyT(A, false);
VERIFY_IS_EQUAL(csOnlyT.info(), Success);
VERIFY_IS_EQUAL(cs1.matrixT(), csOnlyT.matrixT());
VERIFY_RAISES_ASSERT(csOnlyT.matrixU());
if (size > 1 && size < 20)
{
// Test matrix with NaN
A(0,0) = std::numeric_limits<typename MatrixType::RealScalar>::quiet_NaN();
ComplexSchur<MatrixType> csNaN(A);
VERIFY_IS_EQUAL(csNaN.info(), NoConvergence);
}
}
void test_schur_complex()
{
CALL_SUBTEST_1(( schur<Matrix4cd>() ));
CALL_SUBTEST_2(( schur<MatrixXcf>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4)) ));
CALL_SUBTEST_3(( schur<Matrix<std::complex<float>, 1, 1> >() ));
CALL_SUBTEST_4(( schur<Matrix<float, 3, 3, Eigen::RowMajor> >() ));
// Test problem size constructors
CALL_SUBTEST_5(ComplexSchur<MatrixXf>(10));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/svd_common.h | .h | 17,971 | 479 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef SVD_DEFAULT
#error a macro SVD_DEFAULT(MatrixType) must be defined prior to including svd_common.h
#endif
#ifndef SVD_FOR_MIN_NORM
#error a macro SVD_FOR_MIN_NORM(MatrixType) must be defined prior to including svd_common.h
#endif
#include "svd_fill.h"
// Check that the matrix m is properly reconstructed and that the U and V factors are unitary
// The SVD must have already been computed.
template<typename SvdType, typename MatrixType>
void svd_check_full(const MatrixType& m, const SvdType& svd)
{
Index rows = m.rows();
Index cols = m.cols();
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime
};
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime> MatrixUType;
typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime> MatrixVType;
MatrixType sigma = MatrixType::Zero(rows,cols);
sigma.diagonal() = svd.singularValues().template cast<Scalar>();
MatrixUType u = svd.matrixU();
MatrixVType v = svd.matrixV();
RealScalar scaling = m.cwiseAbs().maxCoeff();
if(scaling<(std::numeric_limits<RealScalar>::min)())
{
VERIFY(sigma.cwiseAbs().maxCoeff() <= (std::numeric_limits<RealScalar>::min)());
}
else
{
VERIFY_IS_APPROX(m/scaling, u * (sigma/scaling) * v.adjoint());
}
VERIFY_IS_UNITARY(u);
VERIFY_IS_UNITARY(v);
}
// Compare partial SVD defined by computationOptions to a full SVD referenceSvd
template<typename SvdType, typename MatrixType>
void svd_compare_to_full(const MatrixType& m,
unsigned int computationOptions,
const SvdType& referenceSvd)
{
typedef typename MatrixType::RealScalar RealScalar;
Index rows = m.rows();
Index cols = m.cols();
Index diagSize = (std::min)(rows, cols);
RealScalar prec = test_precision<RealScalar>();
SvdType svd(m, computationOptions);
VERIFY_IS_APPROX(svd.singularValues(), referenceSvd.singularValues());
if(computationOptions & (ComputeFullV|ComputeThinV))
{
VERIFY( (svd.matrixV().adjoint()*svd.matrixV()).isIdentity(prec) );
VERIFY_IS_APPROX( svd.matrixV().leftCols(diagSize) * svd.singularValues().asDiagonal() * svd.matrixV().leftCols(diagSize).adjoint(),
referenceSvd.matrixV().leftCols(diagSize) * referenceSvd.singularValues().asDiagonal() * referenceSvd.matrixV().leftCols(diagSize).adjoint());
}
if(computationOptions & (ComputeFullU|ComputeThinU))
{
VERIFY( (svd.matrixU().adjoint()*svd.matrixU()).isIdentity(prec) );
VERIFY_IS_APPROX( svd.matrixU().leftCols(diagSize) * svd.singularValues().cwiseAbs2().asDiagonal() * svd.matrixU().leftCols(diagSize).adjoint(),
referenceSvd.matrixU().leftCols(diagSize) * referenceSvd.singularValues().cwiseAbs2().asDiagonal() * referenceSvd.matrixU().leftCols(diagSize).adjoint());
}
// The following checks are not critical.
// For instance, with Dived&Conquer SVD, if only the factor 'V' is computedt then different matrix-matrix product implementation will be used
// and the resulting 'V' factor might be significantly different when the SVD decomposition is not unique, especially with single precision float.
++g_test_level;
if(computationOptions & ComputeFullU) VERIFY_IS_APPROX(svd.matrixU(), referenceSvd.matrixU());
if(computationOptions & ComputeThinU) VERIFY_IS_APPROX(svd.matrixU(), referenceSvd.matrixU().leftCols(diagSize));
if(computationOptions & ComputeFullV) VERIFY_IS_APPROX(svd.matrixV().cwiseAbs(), referenceSvd.matrixV().cwiseAbs());
if(computationOptions & ComputeThinV) VERIFY_IS_APPROX(svd.matrixV(), referenceSvd.matrixV().leftCols(diagSize));
--g_test_level;
}
//
template<typename SvdType, typename MatrixType>
void svd_least_square(const MatrixType& m, unsigned int computationOptions)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
Index rows = m.rows();
Index cols = m.cols();
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime
};
typedef Matrix<Scalar, RowsAtCompileTime, Dynamic> RhsType;
typedef Matrix<Scalar, ColsAtCompileTime, Dynamic> SolutionType;
RhsType rhs = RhsType::Random(rows, internal::random<Index>(1, cols));
SvdType svd(m, computationOptions);
if(internal::is_same<RealScalar,double>::value) svd.setThreshold(1e-8);
else if(internal::is_same<RealScalar,float>::value) svd.setThreshold(2e-4);
SolutionType x = svd.solve(rhs);
RealScalar residual = (m*x-rhs).norm();
RealScalar rhs_norm = rhs.norm();
if(!test_isMuchSmallerThan(residual,rhs.norm()))
{
// ^^^ If the residual is very small, then we have an exact solution, so we are already good.
// evaluate normal equation which works also for least-squares solutions
if(internal::is_same<RealScalar,double>::value || svd.rank()==m.diagonal().size())
{
using std::sqrt;
// This test is not stable with single precision.
// This is probably because squaring m signicantly affects the precision.
if(internal::is_same<RealScalar,float>::value) ++g_test_level;
VERIFY_IS_APPROX(m.adjoint()*(m*x),m.adjoint()*rhs);
if(internal::is_same<RealScalar,float>::value) --g_test_level;
}
// Check that there is no significantly better solution in the neighborhood of x
for(Index k=0;k<x.rows();++k)
{
using std::abs;
SolutionType y(x);
y.row(k) = (RealScalar(1)+2*NumTraits<RealScalar>::epsilon())*x.row(k);
RealScalar residual_y = (m*y-rhs).norm();
VERIFY( test_isMuchSmallerThan(abs(residual_y-residual), rhs_norm) || residual < residual_y );
if(internal::is_same<RealScalar,float>::value) ++g_test_level;
VERIFY( test_isApprox(residual_y,residual) || residual < residual_y );
if(internal::is_same<RealScalar,float>::value) --g_test_level;
y.row(k) = (RealScalar(1)-2*NumTraits<RealScalar>::epsilon())*x.row(k);
residual_y = (m*y-rhs).norm();
VERIFY( test_isMuchSmallerThan(abs(residual_y-residual), rhs_norm) || residual < residual_y );
if(internal::is_same<RealScalar,float>::value) ++g_test_level;
VERIFY( test_isApprox(residual_y,residual) || residual < residual_y );
if(internal::is_same<RealScalar,float>::value) --g_test_level;
}
}
}
// check minimal norm solutions, the inoput matrix m is only used to recover problem size
template<typename MatrixType>
void svd_min_norm(const MatrixType& m, unsigned int computationOptions)
{
typedef typename MatrixType::Scalar Scalar;
Index cols = m.cols();
enum {
ColsAtCompileTime = MatrixType::ColsAtCompileTime
};
typedef Matrix<Scalar, ColsAtCompileTime, Dynamic> SolutionType;
// generate a full-rank m x n problem with m<n
enum {
RankAtCompileTime2 = ColsAtCompileTime==Dynamic ? Dynamic : (ColsAtCompileTime)/2+1,
RowsAtCompileTime3 = ColsAtCompileTime==Dynamic ? Dynamic : ColsAtCompileTime+1
};
typedef Matrix<Scalar, RankAtCompileTime2, ColsAtCompileTime> MatrixType2;
typedef Matrix<Scalar, RankAtCompileTime2, 1> RhsType2;
typedef Matrix<Scalar, ColsAtCompileTime, RankAtCompileTime2> MatrixType2T;
Index rank = RankAtCompileTime2==Dynamic ? internal::random<Index>(1,cols) : Index(RankAtCompileTime2);
MatrixType2 m2(rank,cols);
int guard = 0;
do {
m2.setRandom();
} while(SVD_FOR_MIN_NORM(MatrixType2)(m2).setThreshold(test_precision<Scalar>()).rank()!=rank && (++guard)<10);
VERIFY(guard<10);
RhsType2 rhs2 = RhsType2::Random(rank);
// use QR to find a reference minimal norm solution
HouseholderQR<MatrixType2T> qr(m2.adjoint());
Matrix<Scalar,Dynamic,1> tmp = qr.matrixQR().topLeftCorner(rank,rank).template triangularView<Upper>().adjoint().solve(rhs2);
tmp.conservativeResize(cols);
tmp.tail(cols-rank).setZero();
SolutionType x21 = qr.householderQ() * tmp;
// now check with SVD
SVD_FOR_MIN_NORM(MatrixType2) svd2(m2, computationOptions);
SolutionType x22 = svd2.solve(rhs2);
VERIFY_IS_APPROX(m2*x21, rhs2);
VERIFY_IS_APPROX(m2*x22, rhs2);
VERIFY_IS_APPROX(x21, x22);
// Now check with a rank deficient matrix
typedef Matrix<Scalar, RowsAtCompileTime3, ColsAtCompileTime> MatrixType3;
typedef Matrix<Scalar, RowsAtCompileTime3, 1> RhsType3;
Index rows3 = RowsAtCompileTime3==Dynamic ? internal::random<Index>(rank+1,2*cols) : Index(RowsAtCompileTime3);
Matrix<Scalar,RowsAtCompileTime3,Dynamic> C = Matrix<Scalar,RowsAtCompileTime3,Dynamic>::Random(rows3,rank);
MatrixType3 m3 = C * m2;
RhsType3 rhs3 = C * rhs2;
SVD_FOR_MIN_NORM(MatrixType3) svd3(m3, computationOptions);
SolutionType x3 = svd3.solve(rhs3);
VERIFY_IS_APPROX(m3*x3, rhs3);
VERIFY_IS_APPROX(m3*x21, rhs3);
VERIFY_IS_APPROX(m2*x3, rhs2);
VERIFY_IS_APPROX(x21, x3);
}
// Check full, compare_to_full, least_square, and min_norm for all possible compute-options
template<typename SvdType, typename MatrixType>
void svd_test_all_computation_options(const MatrixType& m, bool full_only)
{
// if (QRPreconditioner == NoQRPreconditioner && m.rows() != m.cols())
// return;
SvdType fullSvd(m, ComputeFullU|ComputeFullV);
CALL_SUBTEST(( svd_check_full(m, fullSvd) ));
CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeFullU | ComputeFullV) ));
CALL_SUBTEST(( svd_min_norm(m, ComputeFullU | ComputeFullV) ));
#if defined __INTEL_COMPILER
// remark #111: statement is unreachable
#pragma warning disable 111
#endif
if(full_only)
return;
CALL_SUBTEST(( svd_compare_to_full(m, ComputeFullU, fullSvd) ));
CALL_SUBTEST(( svd_compare_to_full(m, ComputeFullV, fullSvd) ));
CALL_SUBTEST(( svd_compare_to_full(m, 0, fullSvd) ));
if (MatrixType::ColsAtCompileTime == Dynamic) {
// thin U/V are only available with dynamic number of columns
CALL_SUBTEST(( svd_compare_to_full(m, ComputeFullU|ComputeThinV, fullSvd) ));
CALL_SUBTEST(( svd_compare_to_full(m, ComputeThinV, fullSvd) ));
CALL_SUBTEST(( svd_compare_to_full(m, ComputeThinU|ComputeFullV, fullSvd) ));
CALL_SUBTEST(( svd_compare_to_full(m, ComputeThinU , fullSvd) ));
CALL_SUBTEST(( svd_compare_to_full(m, ComputeThinU|ComputeThinV, fullSvd) ));
CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeFullU | ComputeThinV) ));
CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeThinU | ComputeFullV) ));
CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeThinU | ComputeThinV) ));
CALL_SUBTEST(( svd_min_norm(m, ComputeFullU | ComputeThinV) ));
CALL_SUBTEST(( svd_min_norm(m, ComputeThinU | ComputeFullV) ));
CALL_SUBTEST(( svd_min_norm(m, ComputeThinU | ComputeThinV) ));
// test reconstruction
Index diagSize = (std::min)(m.rows(), m.cols());
SvdType svd(m, ComputeThinU | ComputeThinV);
VERIFY_IS_APPROX(m, svd.matrixU().leftCols(diagSize) * svd.singularValues().asDiagonal() * svd.matrixV().leftCols(diagSize).adjoint());
}
}
// work around stupid msvc error when constructing at compile time an expression that involves
// a division by zero, even if the numeric type has floating point
template<typename Scalar>
EIGEN_DONT_INLINE Scalar zero() { return Scalar(0); }
// workaround aggressive optimization in ICC
template<typename T> EIGEN_DONT_INLINE T sub(T a, T b) { return a - b; }
// all this function does is verify we don't iterate infinitely on nan/inf values
template<typename SvdType, typename MatrixType>
void svd_inf_nan()
{
SvdType svd;
typedef typename MatrixType::Scalar Scalar;
Scalar some_inf = Scalar(1) / zero<Scalar>();
VERIFY(sub(some_inf, some_inf) != sub(some_inf, some_inf));
svd.compute(MatrixType::Constant(10,10,some_inf), ComputeFullU | ComputeFullV);
Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
VERIFY(nan != nan);
svd.compute(MatrixType::Constant(10,10,nan), ComputeFullU | ComputeFullV);
MatrixType m = MatrixType::Zero(10,10);
m(internal::random<int>(0,9), internal::random<int>(0,9)) = some_inf;
svd.compute(m, ComputeFullU | ComputeFullV);
m = MatrixType::Zero(10,10);
m(internal::random<int>(0,9), internal::random<int>(0,9)) = nan;
svd.compute(m, ComputeFullU | ComputeFullV);
// regression test for bug 791
m.resize(3,3);
m << 0, 2*NumTraits<Scalar>::epsilon(), 0.5,
0, -0.5, 0,
nan, 0, 0;
svd.compute(m, ComputeFullU | ComputeFullV);
m.resize(4,4);
m << 1, 0, 0, 0,
0, 3, 1, 2e-308,
1, 0, 1, nan,
0, nan, nan, 0;
svd.compute(m, ComputeFullU | ComputeFullV);
}
// Regression test for bug 286: JacobiSVD loops indefinitely with some
// matrices containing denormal numbers.
template<typename>
void svd_underoverflow()
{
#if defined __INTEL_COMPILER
// shut up warning #239: floating point underflow
#pragma warning push
#pragma warning disable 239
#endif
Matrix2d M;
M << -7.90884e-313, -4.94e-324,
0, 5.60844e-313;
SVD_DEFAULT(Matrix2d) svd;
svd.compute(M,ComputeFullU|ComputeFullV);
CALL_SUBTEST( svd_check_full(M,svd) );
// Check all 2x2 matrices made with the following coefficients:
VectorXd value_set(9);
value_set << 0, 1, -1, 5.60844e-313, -5.60844e-313, 4.94e-324, -4.94e-324, -4.94e-223, 4.94e-223;
Array4i id(0,0,0,0);
int k = 0;
do
{
M << value_set(id(0)), value_set(id(1)), value_set(id(2)), value_set(id(3));
svd.compute(M,ComputeFullU|ComputeFullV);
CALL_SUBTEST( svd_check_full(M,svd) );
id(k)++;
if(id(k)>=value_set.size())
{
while(k<3 && id(k)>=value_set.size()) id(++k)++;
id.head(k).setZero();
k=0;
}
} while((id<int(value_set.size())).all());
#if defined __INTEL_COMPILER
#pragma warning pop
#endif
// Check for overflow:
Matrix3d M3;
M3 << 4.4331978442502944e+307, -5.8585363752028680e+307, 6.4527017443412964e+307,
3.7841695601406358e+307, 2.4331702789740617e+306, -3.5235707140272905e+307,
-8.7190887618028355e+307, -7.3453213709232193e+307, -2.4367363684472105e+307;
SVD_DEFAULT(Matrix3d) svd3;
svd3.compute(M3,ComputeFullU|ComputeFullV); // just check we don't loop indefinitely
CALL_SUBTEST( svd_check_full(M3,svd3) );
}
// void jacobisvd(const MatrixType& a = MatrixType(), bool pickrandom = true)
template<typename MatrixType>
void svd_all_trivial_2x2( void (*cb)(const MatrixType&,bool) )
{
MatrixType M;
VectorXd value_set(3);
value_set << 0, 1, -1;
Array4i id(0,0,0,0);
int k = 0;
do
{
M << value_set(id(0)), value_set(id(1)), value_set(id(2)), value_set(id(3));
cb(M,false);
id(k)++;
if(id(k)>=value_set.size())
{
while(k<3 && id(k)>=value_set.size()) id(++k)++;
id.head(k).setZero();
k=0;
}
} while((id<int(value_set.size())).all());
}
template<typename>
void svd_preallocate()
{
Vector3f v(3.f, 2.f, 1.f);
MatrixXf m = v.asDiagonal();
internal::set_is_malloc_allowed(false);
VERIFY_RAISES_ASSERT(VectorXf tmp(10);)
SVD_DEFAULT(MatrixXf) svd;
internal::set_is_malloc_allowed(true);
svd.compute(m);
VERIFY_IS_APPROX(svd.singularValues(), v);
SVD_DEFAULT(MatrixXf) svd2(3,3);
internal::set_is_malloc_allowed(false);
svd2.compute(m);
internal::set_is_malloc_allowed(true);
VERIFY_IS_APPROX(svd2.singularValues(), v);
VERIFY_RAISES_ASSERT(svd2.matrixU());
VERIFY_RAISES_ASSERT(svd2.matrixV());
svd2.compute(m, ComputeFullU | ComputeFullV);
VERIFY_IS_APPROX(svd2.matrixU(), Matrix3f::Identity());
VERIFY_IS_APPROX(svd2.matrixV(), Matrix3f::Identity());
internal::set_is_malloc_allowed(false);
svd2.compute(m);
internal::set_is_malloc_allowed(true);
SVD_DEFAULT(MatrixXf) svd3(3,3,ComputeFullU|ComputeFullV);
internal::set_is_malloc_allowed(false);
svd2.compute(m);
internal::set_is_malloc_allowed(true);
VERIFY_IS_APPROX(svd2.singularValues(), v);
VERIFY_IS_APPROX(svd2.matrixU(), Matrix3f::Identity());
VERIFY_IS_APPROX(svd2.matrixV(), Matrix3f::Identity());
internal::set_is_malloc_allowed(false);
svd2.compute(m, ComputeFullU|ComputeFullV);
internal::set_is_malloc_allowed(true);
}
template<typename SvdType,typename MatrixType>
void svd_verify_assert(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
Index rows = m.rows();
Index cols = m.cols();
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime
};
typedef Matrix<Scalar, RowsAtCompileTime, 1> RhsType;
RhsType rhs(rows);
SvdType svd;
VERIFY_RAISES_ASSERT(svd.matrixU())
VERIFY_RAISES_ASSERT(svd.singularValues())
VERIFY_RAISES_ASSERT(svd.matrixV())
VERIFY_RAISES_ASSERT(svd.solve(rhs))
MatrixType a = MatrixType::Zero(rows, cols);
a.setZero();
svd.compute(a, 0);
VERIFY_RAISES_ASSERT(svd.matrixU())
VERIFY_RAISES_ASSERT(svd.matrixV())
svd.singularValues();
VERIFY_RAISES_ASSERT(svd.solve(rhs))
if (ColsAtCompileTime == Dynamic)
{
svd.compute(a, ComputeThinU);
svd.matrixU();
VERIFY_RAISES_ASSERT(svd.matrixV())
VERIFY_RAISES_ASSERT(svd.solve(rhs))
svd.compute(a, ComputeThinV);
svd.matrixV();
VERIFY_RAISES_ASSERT(svd.matrixU())
VERIFY_RAISES_ASSERT(svd.solve(rhs))
}
else
{
VERIFY_RAISES_ASSERT(svd.compute(a, ComputeThinU))
VERIFY_RAISES_ASSERT(svd.compute(a, ComputeThinV))
}
}
#undef SVD_DEFAULT
#undef SVD_FOR_MIN_NORM
| Unknown |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/bug1213_main.cpp | .cpp | 279 | 19 |
// This is a regression unit regarding a weird linking issue with gcc.
#include "bug1213.h"
int main()
{
return 0;
}
template<typename T, int dim>
bool bug1213_2(const Eigen::Matrix<T,dim,1>& )
{
return true;
}
template bool bug1213_2<float,3>(const Eigen::Vector3f&);
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/array_reverse.cpp | .cpp | 4,877 | 146 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2009 Ricard Marxer <email@ricardmarxer.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <iostream>
using namespace std;
template<typename MatrixType> void reverse(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
Index rows = m.rows();
Index cols = m.cols();
// this test relies a lot on Random.h, and there's not much more that we can do
// to test it, hence I consider that we will have tested Random.h
MatrixType m1 = MatrixType::Random(rows, cols), m2;
VectorType v1 = VectorType::Random(rows);
MatrixType m1_r = m1.reverse();
// Verify that MatrixBase::reverse() works
for ( int i = 0; i < rows; i++ ) {
for ( int j = 0; j < cols; j++ ) {
VERIFY_IS_APPROX(m1_r(i, j), m1(rows - 1 - i, cols - 1 - j));
}
}
Reverse<MatrixType> m1_rd(m1);
// Verify that a Reverse default (in both directions) of an expression works
for ( int i = 0; i < rows; i++ ) {
for ( int j = 0; j < cols; j++ ) {
VERIFY_IS_APPROX(m1_rd(i, j), m1(rows - 1 - i, cols - 1 - j));
}
}
Reverse<MatrixType, BothDirections> m1_rb(m1);
// Verify that a Reverse in both directions of an expression works
for ( int i = 0; i < rows; i++ ) {
for ( int j = 0; j < cols; j++ ) {
VERIFY_IS_APPROX(m1_rb(i, j), m1(rows - 1 - i, cols - 1 - j));
}
}
Reverse<MatrixType, Vertical> m1_rv(m1);
// Verify that a Reverse in the vertical directions of an expression works
for ( int i = 0; i < rows; i++ ) {
for ( int j = 0; j < cols; j++ ) {
VERIFY_IS_APPROX(m1_rv(i, j), m1(rows - 1 - i, j));
}
}
Reverse<MatrixType, Horizontal> m1_rh(m1);
// Verify that a Reverse in the horizontal directions of an expression works
for ( int i = 0; i < rows; i++ ) {
for ( int j = 0; j < cols; j++ ) {
VERIFY_IS_APPROX(m1_rh(i, j), m1(i, cols - 1 - j));
}
}
VectorType v1_r = v1.reverse();
// Verify that a VectorType::reverse() of an expression works
for ( int i = 0; i < rows; i++ ) {
VERIFY_IS_APPROX(v1_r(i), v1(rows - 1 - i));
}
MatrixType m1_cr = m1.colwise().reverse();
// Verify that PartialRedux::reverse() works (for colwise())
for ( int i = 0; i < rows; i++ ) {
for ( int j = 0; j < cols; j++ ) {
VERIFY_IS_APPROX(m1_cr(i, j), m1(rows - 1 - i, j));
}
}
MatrixType m1_rr = m1.rowwise().reverse();
// Verify that PartialRedux::reverse() works (for rowwise())
for ( int i = 0; i < rows; i++ ) {
for ( int j = 0; j < cols; j++ ) {
VERIFY_IS_APPROX(m1_rr(i, j), m1(i, cols - 1 - j));
}
}
Scalar x = internal::random<Scalar>();
Index r = internal::random<Index>(0, rows-1),
c = internal::random<Index>(0, cols-1);
m1.reverse()(r, c) = x;
VERIFY_IS_APPROX(x, m1(rows - 1 - r, cols - 1 - c));
m2 = m1;
m2.reverseInPlace();
VERIFY_IS_APPROX(m2,m1.reverse().eval());
m2 = m1;
m2.col(0).reverseInPlace();
VERIFY_IS_APPROX(m2.col(0),m1.col(0).reverse().eval());
m2 = m1;
m2.row(0).reverseInPlace();
VERIFY_IS_APPROX(m2.row(0),m1.row(0).reverse().eval());
m2 = m1;
m2.rowwise().reverseInPlace();
VERIFY_IS_APPROX(m2,m1.rowwise().reverse().eval());
m2 = m1;
m2.colwise().reverseInPlace();
VERIFY_IS_APPROX(m2,m1.colwise().reverse().eval());
m1.colwise().reverse()(r, c) = x;
VERIFY_IS_APPROX(x, m1(rows - 1 - r, c));
m1.rowwise().reverse()(r, c) = x;
VERIFY_IS_APPROX(x, m1(r, cols - 1 - c));
}
void test_array_reverse()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( reverse(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( reverse(Matrix2f()) );
CALL_SUBTEST_3( reverse(Matrix4f()) );
CALL_SUBTEST_4( reverse(Matrix4d()) );
CALL_SUBTEST_5( reverse(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_6( reverse(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_7( reverse(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_8( reverse(Matrix<float, 100, 100>()) );
CALL_SUBTEST_9( reverse(Matrix<float,Dynamic,Dynamic,RowMajor>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
#ifdef EIGEN_TEST_PART_3
Vector4f x; x << 1, 2, 3, 4;
Vector4f y; y << 4, 3, 2, 1;
VERIFY(x.reverse()[1] == 3);
VERIFY(x.reverse() == y);
#endif
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/eigen2support.cpp | .cpp | 2,188 | 66 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN2_SUPPORT
#include "main.h"
template<typename MatrixType> void eigen2support(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m3(rows, cols);
Scalar s1 = internal::random<Scalar>(),
s2 = internal::random<Scalar>();
// scalar addition
VERIFY_IS_APPROX(m1.cwise() + s1, s1 + m1.cwise());
VERIFY_IS_APPROX(m1.cwise() + s1, MatrixType::Constant(rows,cols,s1) + m1);
VERIFY_IS_APPROX((m1*Scalar(2)).cwise() - s2, (m1+m1) - MatrixType::Constant(rows,cols,s2) );
m3 = m1;
m3.cwise() += s2;
VERIFY_IS_APPROX(m3, m1.cwise() + s2);
m3 = m1;
m3.cwise() -= s1;
VERIFY_IS_APPROX(m3, m1.cwise() - s1);
VERIFY_IS_EQUAL((m1.corner(TopLeft,1,1)), (m1.block(0,0,1,1)));
VERIFY_IS_EQUAL((m1.template corner<1,1>(TopLeft)), (m1.template block<1,1>(0,0)));
VERIFY_IS_EQUAL((m1.col(0).start(1)), (m1.col(0).segment(0,1)));
VERIFY_IS_EQUAL((m1.col(0).template start<1>()), (m1.col(0).segment(0,1)));
VERIFY_IS_EQUAL((m1.col(0).end(1)), (m1.col(0).segment(rows-1,1)));
VERIFY_IS_EQUAL((m1.col(0).template end<1>()), (m1.col(0).segment(rows-1,1)));
using std::cos;
using numext::real;
using numext::abs2;
VERIFY_IS_EQUAL(ei_cos(s1), cos(s1));
VERIFY_IS_EQUAL(ei_real(s1), real(s1));
VERIFY_IS_EQUAL(ei_abs2(s1), abs2(s1));
m1.minor(0,0);
}
void test_eigen2support()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( eigen2support(Matrix<double,1,1>()) );
CALL_SUBTEST_2( eigen2support(MatrixXd(1,1)) );
CALL_SUBTEST_4( eigen2support(Matrix3f()) );
CALL_SUBTEST_5( eigen2support(Matrix4d()) );
CALL_SUBTEST_2( eigen2support(MatrixXf(200,200)) );
CALL_SUBTEST_6( eigen2support(MatrixXcd(100,100)) );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/cholesky.cpp | .cpp | 17,757 | 530 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_NO_ASSERTION_CHECKING
#define EIGEN_NO_ASSERTION_CHECKING
#endif
#define TEST_ENABLE_TEMPORARY_TRACKING
#include "main.h"
#include <Eigen/Cholesky>
#include <Eigen/QR>
template<typename MatrixType, int UpLo>
typename MatrixType::RealScalar matrix_l1_norm(const MatrixType& m) {
if(m.cols()==0) return typename MatrixType::RealScalar(0);
MatrixType symm = m.template selfadjointView<UpLo>();
return symm.cwiseAbs().colwise().sum().maxCoeff();
}
template<typename MatrixType,template <typename,int> class CholType> void test_chol_update(const MatrixType& symm)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
MatrixType symmLo = symm.template triangularView<Lower>();
MatrixType symmUp = symm.template triangularView<Upper>();
MatrixType symmCpy = symm;
CholType<MatrixType,Lower> chollo(symmLo);
CholType<MatrixType,Upper> cholup(symmUp);
for (int k=0; k<10; ++k)
{
VectorType vec = VectorType::Random(symm.rows());
RealScalar sigma = internal::random<RealScalar>();
symmCpy += sigma * vec * vec.adjoint();
// we are doing some downdates, so it might be the case that the matrix is not SPD anymore
CholType<MatrixType,Lower> chol(symmCpy);
if(chol.info()!=Success)
break;
chollo.rankUpdate(vec, sigma);
VERIFY_IS_APPROX(symmCpy, chollo.reconstructedMatrix());
cholup.rankUpdate(vec, sigma);
VERIFY_IS_APPROX(symmCpy, cholup.reconstructedMatrix());
}
}
template<typename MatrixType> void cholesky(const MatrixType& m)
{
/* this test covers the following files:
LLT.h LDLT.h
*/
Index rows = m.rows();
Index cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
MatrixType a0 = MatrixType::Random(rows,cols);
VectorType vecB = VectorType::Random(rows), vecX(rows);
MatrixType matB = MatrixType::Random(rows,cols), matX(rows,cols);
SquareMatrixType symm = a0 * a0.adjoint();
// let's make sure the matrix is not singular or near singular
for (int k=0; k<3; ++k)
{
MatrixType a1 = MatrixType::Random(rows,cols);
symm += a1 * a1.adjoint();
}
{
SquareMatrixType symmUp = symm.template triangularView<Upper>();
SquareMatrixType symmLo = symm.template triangularView<Lower>();
LLT<SquareMatrixType,Lower> chollo(symmLo);
VERIFY_IS_APPROX(symm, chollo.reconstructedMatrix());
vecX = chollo.solve(vecB);
VERIFY_IS_APPROX(symm * vecX, vecB);
matX = chollo.solve(matB);
VERIFY_IS_APPROX(symm * matX, matB);
const MatrixType symmLo_inverse = chollo.solve(MatrixType::Identity(rows,cols));
RealScalar rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Lower>(symmLo)) /
matrix_l1_norm<MatrixType, Lower>(symmLo_inverse);
RealScalar rcond_est = chollo.rcond();
// Verify that the estimated condition number is within a factor of 10 of the
// truth.
VERIFY(rcond_est >= rcond / 10 && rcond_est <= rcond * 10);
// test the upper mode
LLT<SquareMatrixType,Upper> cholup(symmUp);
VERIFY_IS_APPROX(symm, cholup.reconstructedMatrix());
vecX = cholup.solve(vecB);
VERIFY_IS_APPROX(symm * vecX, vecB);
matX = cholup.solve(matB);
VERIFY_IS_APPROX(symm * matX, matB);
// Verify that the estimated condition number is within a factor of 10 of the
// truth.
const MatrixType symmUp_inverse = cholup.solve(MatrixType::Identity(rows,cols));
rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Upper>(symmUp)) /
matrix_l1_norm<MatrixType, Upper>(symmUp_inverse);
rcond_est = cholup.rcond();
VERIFY(rcond_est >= rcond / 10 && rcond_est <= rcond * 10);
MatrixType neg = -symmLo;
chollo.compute(neg);
VERIFY(neg.size()==0 || chollo.info()==NumericalIssue);
VERIFY_IS_APPROX(MatrixType(chollo.matrixL().transpose().conjugate()), MatrixType(chollo.matrixU()));
VERIFY_IS_APPROX(MatrixType(chollo.matrixU().transpose().conjugate()), MatrixType(chollo.matrixL()));
VERIFY_IS_APPROX(MatrixType(cholup.matrixL().transpose().conjugate()), MatrixType(cholup.matrixU()));
VERIFY_IS_APPROX(MatrixType(cholup.matrixU().transpose().conjugate()), MatrixType(cholup.matrixL()));
// test some special use cases of SelfCwiseBinaryOp:
MatrixType m1 = MatrixType::Random(rows,cols), m2(rows,cols);
m2 = m1;
m2 += symmLo.template selfadjointView<Lower>().llt().solve(matB);
VERIFY_IS_APPROX(m2, m1 + symmLo.template selfadjointView<Lower>().llt().solve(matB));
m2 = m1;
m2 -= symmLo.template selfadjointView<Lower>().llt().solve(matB);
VERIFY_IS_APPROX(m2, m1 - symmLo.template selfadjointView<Lower>().llt().solve(matB));
m2 = m1;
m2.noalias() += symmLo.template selfadjointView<Lower>().llt().solve(matB);
VERIFY_IS_APPROX(m2, m1 + symmLo.template selfadjointView<Lower>().llt().solve(matB));
m2 = m1;
m2.noalias() -= symmLo.template selfadjointView<Lower>().llt().solve(matB);
VERIFY_IS_APPROX(m2, m1 - symmLo.template selfadjointView<Lower>().llt().solve(matB));
}
// LDLT
{
int sign = internal::random<int>()%2 ? 1 : -1;
if(sign == -1)
{
symm = -symm; // test a negative matrix
}
SquareMatrixType symmUp = symm.template triangularView<Upper>();
SquareMatrixType symmLo = symm.template triangularView<Lower>();
LDLT<SquareMatrixType,Lower> ldltlo(symmLo);
VERIFY(ldltlo.info()==Success);
VERIFY_IS_APPROX(symm, ldltlo.reconstructedMatrix());
vecX = ldltlo.solve(vecB);
VERIFY_IS_APPROX(symm * vecX, vecB);
matX = ldltlo.solve(matB);
VERIFY_IS_APPROX(symm * matX, matB);
const MatrixType symmLo_inverse = ldltlo.solve(MatrixType::Identity(rows,cols));
RealScalar rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Lower>(symmLo)) /
matrix_l1_norm<MatrixType, Lower>(symmLo_inverse);
RealScalar rcond_est = ldltlo.rcond();
// Verify that the estimated condition number is within a factor of 10 of the
// truth.
VERIFY(rcond_est >= rcond / 10 && rcond_est <= rcond * 10);
LDLT<SquareMatrixType,Upper> ldltup(symmUp);
VERIFY(ldltup.info()==Success);
VERIFY_IS_APPROX(symm, ldltup.reconstructedMatrix());
vecX = ldltup.solve(vecB);
VERIFY_IS_APPROX(symm * vecX, vecB);
matX = ldltup.solve(matB);
VERIFY_IS_APPROX(symm * matX, matB);
// Verify that the estimated condition number is within a factor of 10 of the
// truth.
const MatrixType symmUp_inverse = ldltup.solve(MatrixType::Identity(rows,cols));
rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Upper>(symmUp)) /
matrix_l1_norm<MatrixType, Upper>(symmUp_inverse);
rcond_est = ldltup.rcond();
VERIFY(rcond_est >= rcond / 10 && rcond_est <= rcond * 10);
VERIFY_IS_APPROX(MatrixType(ldltlo.matrixL().transpose().conjugate()), MatrixType(ldltlo.matrixU()));
VERIFY_IS_APPROX(MatrixType(ldltlo.matrixU().transpose().conjugate()), MatrixType(ldltlo.matrixL()));
VERIFY_IS_APPROX(MatrixType(ldltup.matrixL().transpose().conjugate()), MatrixType(ldltup.matrixU()));
VERIFY_IS_APPROX(MatrixType(ldltup.matrixU().transpose().conjugate()), MatrixType(ldltup.matrixL()));
if(MatrixType::RowsAtCompileTime==Dynamic)
{
// note : each inplace permutation requires a small temporary vector (mask)
// check inplace solve
matX = matB;
VERIFY_EVALUATION_COUNT(matX = ldltlo.solve(matX), 0);
VERIFY_IS_APPROX(matX, ldltlo.solve(matB).eval());
matX = matB;
VERIFY_EVALUATION_COUNT(matX = ldltup.solve(matX), 0);
VERIFY_IS_APPROX(matX, ldltup.solve(matB).eval());
}
// restore
if(sign == -1)
symm = -symm;
// check matrices coming from linear constraints with Lagrange multipliers
if(rows>=3)
{
SquareMatrixType A = symm;
Index c = internal::random<Index>(0,rows-2);
A.bottomRightCorner(c,c).setZero();
// Make sure a solution exists:
vecX.setRandom();
vecB = A * vecX;
vecX.setZero();
ldltlo.compute(A);
VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix());
vecX = ldltlo.solve(vecB);
VERIFY_IS_APPROX(A * vecX, vecB);
}
// check non-full rank matrices
if(rows>=3)
{
Index r = internal::random<Index>(1,rows-1);
Matrix<Scalar,Dynamic,Dynamic> a = Matrix<Scalar,Dynamic,Dynamic>::Random(rows,r);
SquareMatrixType A = a * a.adjoint();
// Make sure a solution exists:
vecX.setRandom();
vecB = A * vecX;
vecX.setZero();
ldltlo.compute(A);
VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix());
vecX = ldltlo.solve(vecB);
VERIFY_IS_APPROX(A * vecX, vecB);
}
// check matrices with a wide spectrum
if(rows>=3)
{
using std::pow;
using std::sqrt;
RealScalar s = (std::min)(16,std::numeric_limits<RealScalar>::max_exponent10/8);
Matrix<Scalar,Dynamic,Dynamic> a = Matrix<Scalar,Dynamic,Dynamic>::Random(rows,rows);
Matrix<RealScalar,Dynamic,1> d = Matrix<RealScalar,Dynamic,1>::Random(rows);
for(Index k=0; k<rows; ++k)
d(k) = d(k)*pow(RealScalar(10),internal::random<RealScalar>(-s,s));
SquareMatrixType A = a * d.asDiagonal() * a.adjoint();
// Make sure a solution exists:
vecX.setRandom();
vecB = A * vecX;
vecX.setZero();
ldltlo.compute(A);
VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix());
vecX = ldltlo.solve(vecB);
if(ldltlo.vectorD().real().cwiseAbs().minCoeff()>RealScalar(0))
{
VERIFY_IS_APPROX(A * vecX,vecB);
}
else
{
RealScalar large_tol = sqrt(test_precision<RealScalar>());
VERIFY((A * vecX).isApprox(vecB, large_tol));
++g_test_level;
VERIFY_IS_APPROX(A * vecX,vecB);
--g_test_level;
}
}
}
// update/downdate
CALL_SUBTEST(( test_chol_update<SquareMatrixType,LLT>(symm) ));
CALL_SUBTEST(( test_chol_update<SquareMatrixType,LDLT>(symm) ));
}
template<typename MatrixType> void cholesky_cplx(const MatrixType& m)
{
// classic test
cholesky(m);
// test mixing real/scalar types
Index rows = m.rows();
Index cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> RealMatrixType;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
RealMatrixType a0 = RealMatrixType::Random(rows,cols);
VectorType vecB = VectorType::Random(rows), vecX(rows);
MatrixType matB = MatrixType::Random(rows,cols), matX(rows,cols);
RealMatrixType symm = a0 * a0.adjoint();
// let's make sure the matrix is not singular or near singular
for (int k=0; k<3; ++k)
{
RealMatrixType a1 = RealMatrixType::Random(rows,cols);
symm += a1 * a1.adjoint();
}
{
RealMatrixType symmLo = symm.template triangularView<Lower>();
LLT<RealMatrixType,Lower> chollo(symmLo);
VERIFY_IS_APPROX(symm, chollo.reconstructedMatrix());
vecX = chollo.solve(vecB);
VERIFY_IS_APPROX(symm * vecX, vecB);
// matX = chollo.solve(matB);
// VERIFY_IS_APPROX(symm * matX, matB);
}
// LDLT
{
int sign = internal::random<int>()%2 ? 1 : -1;
if(sign == -1)
{
symm = -symm; // test a negative matrix
}
RealMatrixType symmLo = symm.template triangularView<Lower>();
LDLT<RealMatrixType,Lower> ldltlo(symmLo);
VERIFY(ldltlo.info()==Success);
VERIFY_IS_APPROX(symm, ldltlo.reconstructedMatrix());
vecX = ldltlo.solve(vecB);
VERIFY_IS_APPROX(symm * vecX, vecB);
// matX = ldltlo.solve(matB);
// VERIFY_IS_APPROX(symm * matX, matB);
}
}
// regression test for bug 241
template<typename MatrixType> void cholesky_bug241(const MatrixType& m)
{
eigen_assert(m.rows() == 2 && m.cols() == 2);
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
MatrixType matA;
matA << 1, 1, 1, 1;
VectorType vecB;
vecB << 1, 1;
VectorType vecX = matA.ldlt().solve(vecB);
VERIFY_IS_APPROX(matA * vecX, vecB);
}
// LDLT is not guaranteed to work for indefinite matrices, but happens to work fine if matrix is diagonal.
// This test checks that LDLT reports correctly that matrix is indefinite.
// See http://forum.kde.org/viewtopic.php?f=74&t=106942 and bug 736
template<typename MatrixType> void cholesky_definiteness(const MatrixType& m)
{
eigen_assert(m.rows() == 2 && m.cols() == 2);
MatrixType mat;
LDLT<MatrixType> ldlt(2);
{
mat << 1, 0, 0, -1;
ldlt.compute(mat);
VERIFY(ldlt.info()==Success);
VERIFY(!ldlt.isNegative());
VERIFY(!ldlt.isPositive());
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
}
{
mat << 1, 2, 2, 1;
ldlt.compute(mat);
VERIFY(ldlt.info()==Success);
VERIFY(!ldlt.isNegative());
VERIFY(!ldlt.isPositive());
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
}
{
mat << 0, 0, 0, 0;
ldlt.compute(mat);
VERIFY(ldlt.info()==Success);
VERIFY(ldlt.isNegative());
VERIFY(ldlt.isPositive());
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
}
{
mat << 0, 0, 0, 1;
ldlt.compute(mat);
VERIFY(ldlt.info()==Success);
VERIFY(!ldlt.isNegative());
VERIFY(ldlt.isPositive());
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
}
{
mat << -1, 0, 0, 0;
ldlt.compute(mat);
VERIFY(ldlt.info()==Success);
VERIFY(ldlt.isNegative());
VERIFY(!ldlt.isPositive());
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
}
}
template<typename>
void cholesky_faillure_cases()
{
MatrixXd mat;
LDLT<MatrixXd> ldlt;
{
mat.resize(2,2);
mat << 0, 1, 1, 0;
ldlt.compute(mat);
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
VERIFY(ldlt.info()==NumericalIssue);
}
#if (!EIGEN_ARCH_i386) || defined(EIGEN_VECTORIZE_SSE2)
{
mat.resize(3,3);
mat << -1, -3, 3,
-3, -8.9999999999999999999, 1,
3, 1, 0;
ldlt.compute(mat);
VERIFY(ldlt.info()==NumericalIssue);
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
}
#endif
{
mat.resize(3,3);
mat << 1, 2, 3,
2, 4, 1,
3, 1, 0;
ldlt.compute(mat);
VERIFY(ldlt.info()==NumericalIssue);
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
}
{
mat.resize(8,8);
mat << 0.1, 0, -0.1, 0, 0, 0, 1, 0,
0, 4.24667, 0, 2.00333, 0, 0, 0, 0,
-0.1, 0, 0.2, 0, -0.1, 0, 0, 0,
0, 2.00333, 0, 8.49333, 0, 2.00333, 0, 0,
0, 0, -0.1, 0, 0.1, 0, 0, 1,
0, 0, 0, 2.00333, 0, 4.24667, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0;
ldlt.compute(mat);
VERIFY(ldlt.info()==NumericalIssue);
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
}
// bug 1479
{
mat.resize(4,4);
mat << 1, 2, 0, 1,
2, 4, 0, 2,
0, 0, 0, 1,
1, 2, 1, 1;
ldlt.compute(mat);
VERIFY(ldlt.info()==NumericalIssue);
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
}
}
template<typename MatrixType> void cholesky_verify_assert()
{
MatrixType tmp;
LLT<MatrixType> llt;
VERIFY_RAISES_ASSERT(llt.matrixL())
VERIFY_RAISES_ASSERT(llt.matrixU())
VERIFY_RAISES_ASSERT(llt.solve(tmp))
VERIFY_RAISES_ASSERT(llt.solveInPlace(&tmp))
LDLT<MatrixType> ldlt;
VERIFY_RAISES_ASSERT(ldlt.matrixL())
VERIFY_RAISES_ASSERT(ldlt.permutationP())
VERIFY_RAISES_ASSERT(ldlt.vectorD())
VERIFY_RAISES_ASSERT(ldlt.isPositive())
VERIFY_RAISES_ASSERT(ldlt.isNegative())
VERIFY_RAISES_ASSERT(ldlt.solve(tmp))
VERIFY_RAISES_ASSERT(ldlt.solveInPlace(&tmp))
}
void test_cholesky()
{
int s = 0;
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( cholesky(Matrix<double,1,1>()) );
CALL_SUBTEST_3( cholesky(Matrix2d()) );
CALL_SUBTEST_3( cholesky_bug241(Matrix2d()) );
CALL_SUBTEST_3( cholesky_definiteness(Matrix2d()) );
CALL_SUBTEST_4( cholesky(Matrix3f()) );
CALL_SUBTEST_5( cholesky(Matrix4d()) );
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
CALL_SUBTEST_2( cholesky(MatrixXd(s,s)) );
TEST_SET_BUT_UNUSED_VARIABLE(s)
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
CALL_SUBTEST_6( cholesky_cplx(MatrixXcd(s,s)) );
TEST_SET_BUT_UNUSED_VARIABLE(s)
}
// empty matrix, regression test for Bug 785:
CALL_SUBTEST_2( cholesky(MatrixXd(0,0)) );
// This does not work yet:
// CALL_SUBTEST_2( cholesky(Matrix<double,0,0>()) );
CALL_SUBTEST_4( cholesky_verify_assert<Matrix3f>() );
CALL_SUBTEST_7( cholesky_verify_assert<Matrix3d>() );
CALL_SUBTEST_8( cholesky_verify_assert<MatrixXf>() );
CALL_SUBTEST_2( cholesky_verify_assert<MatrixXd>() );
// Test problem size constructors
CALL_SUBTEST_9( LLT<MatrixXf>(10) );
CALL_SUBTEST_9( LDLT<MatrixXf>(10) );
CALL_SUBTEST_2( cholesky_faillure_cases<void>() );
TEST_SET_BUT_UNUSED_VARIABLE(nb_temporaries)
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/conjugate_gradient.cpp | .cpp | 1,533 | 35 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "sparse_solver.h"
#include <Eigen/IterativeLinearSolvers>
template<typename T, typename I> void test_conjugate_gradient_T()
{
typedef SparseMatrix<T,0,I> SparseMatrixType;
ConjugateGradient<SparseMatrixType, Lower > cg_colmajor_lower_diag;
ConjugateGradient<SparseMatrixType, Upper > cg_colmajor_upper_diag;
ConjugateGradient<SparseMatrixType, Lower|Upper> cg_colmajor_loup_diag;
ConjugateGradient<SparseMatrixType, Lower, IdentityPreconditioner> cg_colmajor_lower_I;
ConjugateGradient<SparseMatrixType, Upper, IdentityPreconditioner> cg_colmajor_upper_I;
CALL_SUBTEST( check_sparse_spd_solving(cg_colmajor_lower_diag) );
CALL_SUBTEST( check_sparse_spd_solving(cg_colmajor_upper_diag) );
CALL_SUBTEST( check_sparse_spd_solving(cg_colmajor_loup_diag) );
CALL_SUBTEST( check_sparse_spd_solving(cg_colmajor_lower_I) );
CALL_SUBTEST( check_sparse_spd_solving(cg_colmajor_upper_I) );
}
void test_conjugate_gradient()
{
CALL_SUBTEST_1(( test_conjugate_gradient_T<double,int>() ));
CALL_SUBTEST_2(( test_conjugate_gradient_T<std::complex<double>, int>() ));
CALL_SUBTEST_3(( test_conjugate_gradient_T<double,long int>() ));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/product_mmtr.cpp | .cpp | 4,439 | 107 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010-2017 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#define CHECK_MMTR(DEST, TRI, OP) { \
ref3 = DEST; \
ref2 = ref1 = DEST; \
DEST.template triangularView<TRI>() OP; \
ref1 OP; \
ref2.template triangularView<TRI>() \
= ref1.template triangularView<TRI>(); \
VERIFY_IS_APPROX(DEST,ref2); \
\
DEST = ref3; \
ref3 = ref2; \
ref3.diagonal() = DEST.diagonal(); \
DEST.template triangularView<TRI|ZeroDiag>() OP; \
VERIFY_IS_APPROX(DEST,ref3); \
}
template<typename Scalar> void mmtr(int size)
{
typedef Matrix<Scalar,Dynamic,Dynamic,ColMajor> MatrixColMaj;
typedef Matrix<Scalar,Dynamic,Dynamic,RowMajor> MatrixRowMaj;
DenseIndex othersize = internal::random<DenseIndex>(1,200);
MatrixColMaj matc = MatrixColMaj::Zero(size, size);
MatrixRowMaj matr = MatrixRowMaj::Zero(size, size);
MatrixColMaj ref1(size, size), ref2(size, size), ref3(size,size);
MatrixColMaj soc(size,othersize); soc.setRandom();
MatrixColMaj osc(othersize,size); osc.setRandom();
MatrixRowMaj sor(size,othersize); sor.setRandom();
MatrixRowMaj osr(othersize,size); osr.setRandom();
MatrixColMaj sqc(size,size); sqc.setRandom();
MatrixRowMaj sqr(size,size); sqr.setRandom();
Scalar s = internal::random<Scalar>();
CHECK_MMTR(matc, Lower, = s*soc*sor.adjoint());
CHECK_MMTR(matc, Upper, = s*(soc*soc.adjoint()));
CHECK_MMTR(matr, Lower, = s*soc*soc.adjoint());
CHECK_MMTR(matr, Upper, = soc*(s*sor.adjoint()));
CHECK_MMTR(matc, Lower, += s*soc*soc.adjoint());
CHECK_MMTR(matc, Upper, += s*(soc*sor.transpose()));
CHECK_MMTR(matr, Lower, += s*sor*soc.adjoint());
CHECK_MMTR(matr, Upper, += soc*(s*soc.adjoint()));
CHECK_MMTR(matc, Lower, -= s*soc*soc.adjoint());
CHECK_MMTR(matc, Upper, -= s*(osc.transpose()*osc.conjugate()));
CHECK_MMTR(matr, Lower, -= s*soc*soc.adjoint());
CHECK_MMTR(matr, Upper, -= soc*(s*soc.adjoint()));
CHECK_MMTR(matc, Lower, -= s*sqr*sqc.template triangularView<Upper>());
CHECK_MMTR(matc, Upper, = s*sqc*sqr.template triangularView<Upper>());
CHECK_MMTR(matc, Lower, += s*sqr*sqc.template triangularView<Lower>());
CHECK_MMTR(matc, Upper, = s*sqc*sqc.template triangularView<Lower>());
CHECK_MMTR(matc, Lower, = (s*sqr).template triangularView<Upper>()*sqc);
CHECK_MMTR(matc, Upper, -= (s*sqc).template triangularView<Upper>()*sqc);
CHECK_MMTR(matc, Lower, = (s*sqr).template triangularView<Lower>()*sqc);
CHECK_MMTR(matc, Upper, += (s*sqc).template triangularView<Lower>()*sqc);
// check aliasing
ref2 = ref1 = matc;
ref1 = sqc.adjoint() * matc * sqc;
ref2.template triangularView<Upper>() = ref1.template triangularView<Upper>();
matc.template triangularView<Upper>() = sqc.adjoint() * matc * sqc;
VERIFY_IS_APPROX(matc, ref2);
ref2 = ref1 = matc;
ref1 = sqc * matc * sqc.adjoint();
ref2.template triangularView<Lower>() = ref1.template triangularView<Lower>();
matc.template triangularView<Lower>() = sqc * matc * sqc.adjoint();
VERIFY_IS_APPROX(matc, ref2);
// destination with a non-default inner-stride
// see bug 1741
{
typedef Matrix<Scalar,Dynamic,Dynamic> MatrixX;
MatrixX buffer(2*size,2*size);
Map<MatrixColMaj,0,Stride<Dynamic,Dynamic> > map1(buffer.data(),size,size,Stride<Dynamic,Dynamic>(2*size,2));
buffer.setZero();
CHECK_MMTR(map1, Lower, = s*soc*sor.adjoint());
}
}
void test_product_mmtr()
{
for(int i = 0; i < g_repeat ; i++)
{
CALL_SUBTEST_1((mmtr<float>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))));
CALL_SUBTEST_2((mmtr<double>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))));
CALL_SUBTEST_3((mmtr<std::complex<float> >(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))));
CALL_SUBTEST_4((mmtr<std::complex<double> >(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))));
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/half_float.cpp | .cpp | 10,819 | 269 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include <sstream>
#include "main.h"
#include <Eigen/src/Core/arch/CUDA/Half.h>
#ifdef EIGEN_HAS_CUDA_FP16
#error "EIGEN_HAS_CUDA_FP16 should not be defined in this CPU unit test"
#endif
// Make sure it's possible to forward declare Eigen::half
namespace Eigen {
struct half;
}
using Eigen::half;
void test_conversion()
{
using Eigen::half_impl::__half_raw;
// Conversion from float.
VERIFY_IS_EQUAL(half(1.0f).x, 0x3c00);
VERIFY_IS_EQUAL(half(0.5f).x, 0x3800);
VERIFY_IS_EQUAL(half(0.33333f).x, 0x3555);
VERIFY_IS_EQUAL(half(0.0f).x, 0x0000);
VERIFY_IS_EQUAL(half(-0.0f).x, 0x8000);
VERIFY_IS_EQUAL(half(65504.0f).x, 0x7bff);
VERIFY_IS_EQUAL(half(65536.0f).x, 0x7c00); // Becomes infinity.
// Denormals.
VERIFY_IS_EQUAL(half(-5.96046e-08f).x, 0x8001);
VERIFY_IS_EQUAL(half(5.96046e-08f).x, 0x0001);
VERIFY_IS_EQUAL(half(1.19209e-07f).x, 0x0002);
// Verify round-to-nearest-even behavior.
float val1 = float(half(__half_raw(0x3c00)));
float val2 = float(half(__half_raw(0x3c01)));
float val3 = float(half(__half_raw(0x3c02)));
VERIFY_IS_EQUAL(half(0.5f * (val1 + val2)).x, 0x3c00);
VERIFY_IS_EQUAL(half(0.5f * (val2 + val3)).x, 0x3c02);
// Conversion from int.
VERIFY_IS_EQUAL(half(-1).x, 0xbc00);
VERIFY_IS_EQUAL(half(0).x, 0x0000);
VERIFY_IS_EQUAL(half(1).x, 0x3c00);
VERIFY_IS_EQUAL(half(2).x, 0x4000);
VERIFY_IS_EQUAL(half(3).x, 0x4200);
// Conversion from bool.
VERIFY_IS_EQUAL(half(false).x, 0x0000);
VERIFY_IS_EQUAL(half(true).x, 0x3c00);
// Conversion to float.
VERIFY_IS_EQUAL(float(half(__half_raw(0x0000))), 0.0f);
VERIFY_IS_EQUAL(float(half(__half_raw(0x3c00))), 1.0f);
// Denormals.
VERIFY_IS_APPROX(float(half(__half_raw(0x8001))), -5.96046e-08f);
VERIFY_IS_APPROX(float(half(__half_raw(0x0001))), 5.96046e-08f);
VERIFY_IS_APPROX(float(half(__half_raw(0x0002))), 1.19209e-07f);
// NaNs and infinities.
VERIFY(!(numext::isinf)(float(half(65504.0f)))); // Largest finite number.
VERIFY(!(numext::isnan)(float(half(0.0f))));
VERIFY((numext::isinf)(float(half(__half_raw(0xfc00)))));
VERIFY((numext::isnan)(float(half(__half_raw(0xfc01)))));
VERIFY((numext::isinf)(float(half(__half_raw(0x7c00)))));
VERIFY((numext::isnan)(float(half(__half_raw(0x7c01)))));
#if !EIGEN_COMP_MSVC
// Visual Studio errors out on divisions by 0
VERIFY((numext::isnan)(float(half(0.0 / 0.0))));
VERIFY((numext::isinf)(float(half(1.0 / 0.0))));
VERIFY((numext::isinf)(float(half(-1.0 / 0.0))));
#endif
// Exactly same checks as above, just directly on the half representation.
VERIFY(!(numext::isinf)(half(__half_raw(0x7bff))));
VERIFY(!(numext::isnan)(half(__half_raw(0x0000))));
VERIFY((numext::isinf)(half(__half_raw(0xfc00))));
VERIFY((numext::isnan)(half(__half_raw(0xfc01))));
VERIFY((numext::isinf)(half(__half_raw(0x7c00))));
VERIFY((numext::isnan)(half(__half_raw(0x7c01))));
#if !EIGEN_COMP_MSVC
// Visual Studio errors out on divisions by 0
VERIFY((numext::isnan)(half(0.0 / 0.0)));
VERIFY((numext::isinf)(half(1.0 / 0.0)));
VERIFY((numext::isinf)(half(-1.0 / 0.0)));
#endif
}
void test_numtraits()
{
std::cout << "epsilon = " << NumTraits<half>::epsilon() << " (0x" << std::hex << NumTraits<half>::epsilon().x << ")" << std::endl;
std::cout << "highest = " << NumTraits<half>::highest() << " (0x" << std::hex << NumTraits<half>::highest().x << ")" << std::endl;
std::cout << "lowest = " << NumTraits<half>::lowest() << " (0x" << std::hex << NumTraits<half>::lowest().x << ")" << std::endl;
std::cout << "min = " << (std::numeric_limits<half>::min)() << " (0x" << std::hex << half((std::numeric_limits<half>::min)()).x << ")" << std::endl;
std::cout << "denorm min = " << (std::numeric_limits<half>::denorm_min)() << " (0x" << std::hex << half((std::numeric_limits<half>::denorm_min)()).x << ")" << std::endl;
std::cout << "infinity = " << NumTraits<half>::infinity() << " (0x" << std::hex << NumTraits<half>::infinity().x << ")" << std::endl;
std::cout << "quiet nan = " << NumTraits<half>::quiet_NaN() << " (0x" << std::hex << NumTraits<half>::quiet_NaN().x << ")" << std::endl;
std::cout << "signaling nan = " << std::numeric_limits<half>::signaling_NaN() << " (0x" << std::hex << std::numeric_limits<half>::signaling_NaN().x << ")" << std::endl;
VERIFY(NumTraits<half>::IsSigned);
VERIFY_IS_EQUAL( std::numeric_limits<half>::infinity().x, half(std::numeric_limits<float>::infinity()).x );
VERIFY_IS_EQUAL( std::numeric_limits<half>::quiet_NaN().x, half(std::numeric_limits<float>::quiet_NaN()).x );
VERIFY_IS_EQUAL( std::numeric_limits<half>::signaling_NaN().x, half(std::numeric_limits<float>::signaling_NaN()).x );
VERIFY( (std::numeric_limits<half>::min)() > half(0.f) );
VERIFY( (std::numeric_limits<half>::denorm_min)() > half(0.f) );
VERIFY( (std::numeric_limits<half>::min)()/half(2) > half(0.f) );
VERIFY_IS_EQUAL( (std::numeric_limits<half>::denorm_min)()/half(2), half(0.f) );
}
void test_arithmetic()
{
VERIFY_IS_EQUAL(float(half(2) + half(2)), 4);
VERIFY_IS_EQUAL(float(half(2) + half(-2)), 0);
VERIFY_IS_APPROX(float(half(0.33333f) + half(0.66667f)), 1.0f);
VERIFY_IS_EQUAL(float(half(2.0f) * half(-5.5f)), -11.0f);
VERIFY_IS_APPROX(float(half(1.0f) / half(3.0f)), 0.33333f);
VERIFY_IS_EQUAL(float(-half(4096.0f)), -4096.0f);
VERIFY_IS_EQUAL(float(-half(-4096.0f)), 4096.0f);
}
void test_comparison()
{
VERIFY(half(1.0f) > half(0.5f));
VERIFY(half(0.5f) < half(1.0f));
VERIFY(!(half(1.0f) < half(0.5f)));
VERIFY(!(half(0.5f) > half(1.0f)));
VERIFY(!(half(4.0f) > half(4.0f)));
VERIFY(!(half(4.0f) < half(4.0f)));
VERIFY(!(half(0.0f) < half(-0.0f)));
VERIFY(!(half(-0.0f) < half(0.0f)));
VERIFY(!(half(0.0f) > half(-0.0f)));
VERIFY(!(half(-0.0f) > half(0.0f)));
VERIFY(half(0.2f) > half(-1.0f));
VERIFY(half(-1.0f) < half(0.2f));
VERIFY(half(-16.0f) < half(-15.0f));
VERIFY(half(1.0f) == half(1.0f));
VERIFY(half(1.0f) != half(2.0f));
// Comparisons with NaNs and infinities.
#if !EIGEN_COMP_MSVC
// Visual Studio errors out on divisions by 0
VERIFY(!(half(0.0 / 0.0) == half(0.0 / 0.0)));
VERIFY(half(0.0 / 0.0) != half(0.0 / 0.0));
VERIFY(!(half(1.0) == half(0.0 / 0.0)));
VERIFY(!(half(1.0) < half(0.0 / 0.0)));
VERIFY(!(half(1.0) > half(0.0 / 0.0)));
VERIFY(half(1.0) != half(0.0 / 0.0));
VERIFY(half(1.0) < half(1.0 / 0.0));
VERIFY(half(1.0) > half(-1.0 / 0.0));
#endif
}
void test_basic_functions()
{
VERIFY_IS_EQUAL(float(numext::abs(half(3.5f))), 3.5f);
VERIFY_IS_EQUAL(float(abs(half(3.5f))), 3.5f);
VERIFY_IS_EQUAL(float(numext::abs(half(-3.5f))), 3.5f);
VERIFY_IS_EQUAL(float(abs(half(-3.5f))), 3.5f);
VERIFY_IS_EQUAL(float(numext::floor(half(3.5f))), 3.0f);
VERIFY_IS_EQUAL(float(floor(half(3.5f))), 3.0f);
VERIFY_IS_EQUAL(float(numext::floor(half(-3.5f))), -4.0f);
VERIFY_IS_EQUAL(float(floor(half(-3.5f))), -4.0f);
VERIFY_IS_EQUAL(float(numext::ceil(half(3.5f))), 4.0f);
VERIFY_IS_EQUAL(float(ceil(half(3.5f))), 4.0f);
VERIFY_IS_EQUAL(float(numext::ceil(half(-3.5f))), -3.0f);
VERIFY_IS_EQUAL(float(ceil(half(-3.5f))), -3.0f);
VERIFY_IS_APPROX(float(numext::sqrt(half(0.0f))), 0.0f);
VERIFY_IS_APPROX(float(sqrt(half(0.0f))), 0.0f);
VERIFY_IS_APPROX(float(numext::sqrt(half(4.0f))), 2.0f);
VERIFY_IS_APPROX(float(sqrt(half(4.0f))), 2.0f);
VERIFY_IS_APPROX(float(numext::pow(half(0.0f), half(1.0f))), 0.0f);
VERIFY_IS_APPROX(float(pow(half(0.0f), half(1.0f))), 0.0f);
VERIFY_IS_APPROX(float(numext::pow(half(2.0f), half(2.0f))), 4.0f);
VERIFY_IS_APPROX(float(pow(half(2.0f), half(2.0f))), 4.0f);
VERIFY_IS_EQUAL(float(numext::exp(half(0.0f))), 1.0f);
VERIFY_IS_EQUAL(float(exp(half(0.0f))), 1.0f);
VERIFY_IS_APPROX(float(numext::exp(half(EIGEN_PI))), 20.f + float(EIGEN_PI));
VERIFY_IS_APPROX(float(exp(half(EIGEN_PI))), 20.f + float(EIGEN_PI));
VERIFY_IS_EQUAL(float(numext::log(half(1.0f))), 0.0f);
VERIFY_IS_EQUAL(float(log(half(1.0f))), 0.0f);
VERIFY_IS_APPROX(float(numext::log(half(10.0f))), 2.30273f);
VERIFY_IS_APPROX(float(log(half(10.0f))), 2.30273f);
VERIFY_IS_EQUAL(float(numext::log1p(half(0.0f))), 0.0f);
VERIFY_IS_EQUAL(float(log1p(half(0.0f))), 0.0f);
VERIFY_IS_APPROX(float(numext::log1p(half(10.0f))), 2.3978953f);
VERIFY_IS_APPROX(float(log1p(half(10.0f))), 2.3978953f);
}
void test_trigonometric_functions()
{
VERIFY_IS_APPROX(numext::cos(half(0.0f)), half(cosf(0.0f)));
VERIFY_IS_APPROX(cos(half(0.0f)), half(cosf(0.0f)));
VERIFY_IS_APPROX(numext::cos(half(EIGEN_PI)), half(cosf(EIGEN_PI)));
//VERIFY_IS_APPROX(numext::cos(half(EIGEN_PI/2)), half(cosf(EIGEN_PI/2)));
//VERIFY_IS_APPROX(numext::cos(half(3*EIGEN_PI/2)), half(cosf(3*EIGEN_PI/2)));
VERIFY_IS_APPROX(numext::cos(half(3.5f)), half(cosf(3.5f)));
VERIFY_IS_APPROX(numext::sin(half(0.0f)), half(sinf(0.0f)));
VERIFY_IS_APPROX(sin(half(0.0f)), half(sinf(0.0f)));
// VERIFY_IS_APPROX(numext::sin(half(EIGEN_PI)), half(sinf(EIGEN_PI)));
VERIFY_IS_APPROX(numext::sin(half(EIGEN_PI/2)), half(sinf(EIGEN_PI/2)));
VERIFY_IS_APPROX(numext::sin(half(3*EIGEN_PI/2)), half(sinf(3*EIGEN_PI/2)));
VERIFY_IS_APPROX(numext::sin(half(3.5f)), half(sinf(3.5f)));
VERIFY_IS_APPROX(numext::tan(half(0.0f)), half(tanf(0.0f)));
VERIFY_IS_APPROX(tan(half(0.0f)), half(tanf(0.0f)));
// VERIFY_IS_APPROX(numext::tan(half(EIGEN_PI)), half(tanf(EIGEN_PI)));
// VERIFY_IS_APPROX(numext::tan(half(EIGEN_PI/2)), half(tanf(EIGEN_PI/2)));
//VERIFY_IS_APPROX(numext::tan(half(3*EIGEN_PI/2)), half(tanf(3*EIGEN_PI/2)));
VERIFY_IS_APPROX(numext::tan(half(3.5f)), half(tanf(3.5f)));
}
void test_array()
{
typedef Array<half,1,Dynamic> ArrayXh;
Index size = internal::random<Index>(1,10);
Index i = internal::random<Index>(0,size-1);
ArrayXh a1 = ArrayXh::Random(size), a2 = ArrayXh::Random(size);
VERIFY_IS_APPROX( a1+a1, half(2)*a1 );
VERIFY( (a1.abs() >= half(0)).all() );
VERIFY_IS_APPROX( (a1*a1).sqrt(), a1.abs() );
VERIFY( ((a1.min)(a2) <= (a1.max)(a2)).all() );
a1(i) = half(-10.);
VERIFY_IS_EQUAL( a1.minCoeff(), half(-10.) );
a1(i) = half(10.);
VERIFY_IS_EQUAL( a1.maxCoeff(), half(10.) );
std::stringstream ss;
ss << a1;
}
void test_half_float()
{
CALL_SUBTEST(test_conversion());
CALL_SUBTEST(test_numtraits());
CALL_SUBTEST(test_arithmetic());
CALL_SUBTEST(test_comparison());
CALL_SUBTEST(test_basic_functions());
CALL_SUBTEST(test_trigonometric_functions());
CALL_SUBTEST(test_array());
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/metis_support.cpp | .cpp | 743 | 26 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "sparse_solver.h"
#include <Eigen/SparseLU>
#include <Eigen/MetisSupport>
#include <unsupported/Eigen/SparseExtra>
template<typename T> void test_metis_T()
{
SparseLU<SparseMatrix<T, ColMajor>, MetisOrdering<int> > sparselu_metis;
check_sparse_square_solving(sparselu_metis);
}
void test_metis_support()
{
CALL_SUBTEST_1(test_metis_T<double>());
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/conservative_resize.cpp | .cpp | 4,280 | 134 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Hauke Heibel <hauke.heibel@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/Core>
using namespace Eigen;
template <typename Scalar, int Storage>
void run_matrix_tests()
{
typedef Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Storage> MatrixType;
MatrixType m, n;
// boundary cases ...
m = n = MatrixType::Random(50,50);
m.conservativeResize(1,50);
VERIFY_IS_APPROX(m, n.block(0,0,1,50));
m = n = MatrixType::Random(50,50);
m.conservativeResize(50,1);
VERIFY_IS_APPROX(m, n.block(0,0,50,1));
m = n = MatrixType::Random(50,50);
m.conservativeResize(50,50);
VERIFY_IS_APPROX(m, n.block(0,0,50,50));
// random shrinking ...
for (int i=0; i<25; ++i)
{
const Index rows = internal::random<Index>(1,50);
const Index cols = internal::random<Index>(1,50);
m = n = MatrixType::Random(50,50);
m.conservativeResize(rows,cols);
VERIFY_IS_APPROX(m, n.block(0,0,rows,cols));
}
// random growing with zeroing ...
for (int i=0; i<25; ++i)
{
const Index rows = internal::random<Index>(50,75);
const Index cols = internal::random<Index>(50,75);
m = n = MatrixType::Random(50,50);
m.conservativeResizeLike(MatrixType::Zero(rows,cols));
VERIFY_IS_APPROX(m.block(0,0,n.rows(),n.cols()), n);
VERIFY( rows<=50 || m.block(50,0,rows-50,cols).sum() == Scalar(0) );
VERIFY( cols<=50 || m.block(0,50,rows,cols-50).sum() == Scalar(0) );
}
}
template <typename Scalar>
void run_vector_tests()
{
typedef Matrix<Scalar, 1, Eigen::Dynamic> VectorType;
VectorType m, n;
// boundary cases ...
m = n = VectorType::Random(50);
m.conservativeResize(1);
VERIFY_IS_APPROX(m, n.segment(0,1));
m = n = VectorType::Random(50);
m.conservativeResize(50);
VERIFY_IS_APPROX(m, n.segment(0,50));
m = n = VectorType::Random(50);
m.conservativeResize(m.rows(),1);
VERIFY_IS_APPROX(m, n.segment(0,1));
m = n = VectorType::Random(50);
m.conservativeResize(m.rows(),50);
VERIFY_IS_APPROX(m, n.segment(0,50));
// random shrinking ...
for (int i=0; i<50; ++i)
{
const int size = internal::random<int>(1,50);
m = n = VectorType::Random(50);
m.conservativeResize(size);
VERIFY_IS_APPROX(m, n.segment(0,size));
m = n = VectorType::Random(50);
m.conservativeResize(m.rows(), size);
VERIFY_IS_APPROX(m, n.segment(0,size));
}
// random growing with zeroing ...
for (int i=0; i<50; ++i)
{
const int size = internal::random<int>(50,100);
m = n = VectorType::Random(50);
m.conservativeResizeLike(VectorType::Zero(size));
VERIFY_IS_APPROX(m.segment(0,50), n);
VERIFY( size<=50 || m.segment(50,size-50).sum() == Scalar(0) );
m = n = VectorType::Random(50);
m.conservativeResizeLike(Matrix<Scalar,Dynamic,Dynamic>::Zero(1,size));
VERIFY_IS_APPROX(m.segment(0,50), n);
VERIFY( size<=50 || m.segment(50,size-50).sum() == Scalar(0) );
}
}
void test_conservative_resize()
{
for(int i=0; i<g_repeat; ++i)
{
CALL_SUBTEST_1((run_matrix_tests<int, Eigen::RowMajor>()));
CALL_SUBTEST_1((run_matrix_tests<int, Eigen::ColMajor>()));
CALL_SUBTEST_2((run_matrix_tests<float, Eigen::RowMajor>()));
CALL_SUBTEST_2((run_matrix_tests<float, Eigen::ColMajor>()));
CALL_SUBTEST_3((run_matrix_tests<double, Eigen::RowMajor>()));
CALL_SUBTEST_3((run_matrix_tests<double, Eigen::ColMajor>()));
CALL_SUBTEST_4((run_matrix_tests<std::complex<float>, Eigen::RowMajor>()));
CALL_SUBTEST_4((run_matrix_tests<std::complex<float>, Eigen::ColMajor>()));
CALL_SUBTEST_5((run_matrix_tests<std::complex<double>, Eigen::RowMajor>()));
CALL_SUBTEST_6((run_matrix_tests<std::complex<double>, Eigen::ColMajor>()));
CALL_SUBTEST_1((run_vector_tests<int>()));
CALL_SUBTEST_2((run_vector_tests<float>()));
CALL_SUBTEST_3((run_vector_tests<double>()));
CALL_SUBTEST_4((run_vector_tests<std::complex<float> >()));
CALL_SUBTEST_5((run_vector_tests<std::complex<double> >()));
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/miscmatrices.cpp | .cpp | 1,781 | 47 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename MatrixType> void miscMatrices(const MatrixType& m)
{
/* this test covers the following files:
DiagonalMatrix.h Ones.h
*/
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
Index rows = m.rows();
Index cols = m.cols();
Index r = internal::random<Index>(0, rows-1), r2 = internal::random<Index>(0, rows-1), c = internal::random<Index>(0, cols-1);
VERIFY_IS_APPROX(MatrixType::Ones(rows,cols)(r,c), static_cast<Scalar>(1));
MatrixType m1 = MatrixType::Ones(rows,cols);
VERIFY_IS_APPROX(m1(r,c), static_cast<Scalar>(1));
VectorType v1 = VectorType::Random(rows);
v1[0];
Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
square(v1.asDiagonal());
if(r==r2) VERIFY_IS_APPROX(square(r,r2), v1[r]);
else VERIFY_IS_MUCH_SMALLER_THAN(square(r,r2), static_cast<Scalar>(1));
square = MatrixType::Zero(rows, rows);
square.diagonal() = VectorType::Ones(rows);
VERIFY_IS_APPROX(square, MatrixType::Identity(rows, rows));
}
void test_miscmatrices()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( miscMatrices(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( miscMatrices(Matrix4d()) );
CALL_SUBTEST_3( miscMatrices(MatrixXcf(3, 3)) );
CALL_SUBTEST_4( miscMatrices(MatrixXi(8, 12)) );
CALL_SUBTEST_5( miscMatrices(MatrixXcd(20, 20)) );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/unalignedassert.cpp | .cpp | 5,857 | 181 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#if defined(EIGEN_TEST_PART_1)
// default
#elif defined(EIGEN_TEST_PART_2)
#define EIGEN_MAX_STATIC_ALIGN_BYTES 16
#define EIGEN_MAX_ALIGN_BYTES 16
#elif defined(EIGEN_TEST_PART_3)
#define EIGEN_MAX_STATIC_ALIGN_BYTES 32
#define EIGEN_MAX_ALIGN_BYTES 32
#elif defined(EIGEN_TEST_PART_4)
#define EIGEN_MAX_STATIC_ALIGN_BYTES 64
#define EIGEN_MAX_ALIGN_BYTES 64
#endif
#include "main.h"
typedef Matrix<float, 6,1> Vector6f;
typedef Matrix<float, 8,1> Vector8f;
typedef Matrix<float, 12,1> Vector12f;
typedef Matrix<double, 5,1> Vector5d;
typedef Matrix<double, 6,1> Vector6d;
typedef Matrix<double, 7,1> Vector7d;
typedef Matrix<double, 8,1> Vector8d;
typedef Matrix<double, 9,1> Vector9d;
typedef Matrix<double,10,1> Vector10d;
typedef Matrix<double,12,1> Vector12d;
struct TestNew1
{
MatrixXd m; // good: m will allocate its own array, taking care of alignment.
TestNew1() : m(20,20) {}
};
struct TestNew2
{
Matrix3d m; // good: m's size isn't a multiple of 16 bytes, so m doesn't have to be 16-byte aligned,
// 8-byte alignment is good enough here, which we'll get automatically
};
struct TestNew3
{
Vector2f m; // good: m's size isn't a multiple of 16 bytes, so m doesn't have to be 16-byte aligned
};
struct TestNew4
{
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
Vector2d m;
float f; // make the struct have sizeof%16!=0 to make it a little more tricky when we allow an array of 2 such objects
};
struct TestNew5
{
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
float f; // try the f at first -- the EIGEN_ALIGN_MAX attribute of m should make that still work
Matrix4f m;
};
struct TestNew6
{
Matrix<float,2,2,DontAlign> m; // good: no alignment requested
float f;
};
template<bool Align> struct Depends
{
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(Align)
Vector2d m;
float f;
};
template<typename T>
void check_unalignedassert_good()
{
T *x, *y;
x = new T;
delete x;
y = new T[2];
delete[] y;
}
#if EIGEN_MAX_STATIC_ALIGN_BYTES>0
template<typename T>
void construct_at_boundary(int boundary)
{
char buf[sizeof(T)+256];
size_t _buf = reinterpret_cast<internal::UIntPtr>(buf);
_buf += (EIGEN_MAX_ALIGN_BYTES - (_buf % EIGEN_MAX_ALIGN_BYTES)); // make 16/32/...-byte aligned
_buf += boundary; // make exact boundary-aligned
T *x = ::new(reinterpret_cast<void*>(_buf)) T;
x[0].setZero(); // just in order to silence warnings
x->~T();
}
#endif
void unalignedassert()
{
#if EIGEN_MAX_STATIC_ALIGN_BYTES>0
construct_at_boundary<Vector2f>(4);
construct_at_boundary<Vector3f>(4);
construct_at_boundary<Vector4f>(16);
construct_at_boundary<Vector6f>(4);
construct_at_boundary<Vector8f>(EIGEN_MAX_ALIGN_BYTES);
construct_at_boundary<Vector12f>(16);
construct_at_boundary<Matrix2f>(16);
construct_at_boundary<Matrix3f>(4);
construct_at_boundary<Matrix4f>(EIGEN_MAX_ALIGN_BYTES);
construct_at_boundary<Vector2d>(16);
construct_at_boundary<Vector3d>(4);
construct_at_boundary<Vector4d>(EIGEN_MAX_ALIGN_BYTES);
construct_at_boundary<Vector5d>(4);
construct_at_boundary<Vector6d>(16);
construct_at_boundary<Vector7d>(4);
construct_at_boundary<Vector8d>(EIGEN_MAX_ALIGN_BYTES);
construct_at_boundary<Vector9d>(4);
construct_at_boundary<Vector10d>(16);
construct_at_boundary<Vector12d>(EIGEN_MAX_ALIGN_BYTES);
construct_at_boundary<Matrix2d>(EIGEN_MAX_ALIGN_BYTES);
construct_at_boundary<Matrix3d>(4);
construct_at_boundary<Matrix4d>(EIGEN_MAX_ALIGN_BYTES);
construct_at_boundary<Vector2cf>(16);
construct_at_boundary<Vector3cf>(4);
construct_at_boundary<Vector2cd>(EIGEN_MAX_ALIGN_BYTES);
construct_at_boundary<Vector3cd>(16);
#endif
check_unalignedassert_good<TestNew1>();
check_unalignedassert_good<TestNew2>();
check_unalignedassert_good<TestNew3>();
check_unalignedassert_good<TestNew4>();
check_unalignedassert_good<TestNew5>();
check_unalignedassert_good<TestNew6>();
check_unalignedassert_good<Depends<true> >();
#if EIGEN_MAX_STATIC_ALIGN_BYTES>0
if(EIGEN_MAX_ALIGN_BYTES>=16)
{
VERIFY_RAISES_ASSERT(construct_at_boundary<Vector4f>(8));
VERIFY_RAISES_ASSERT(construct_at_boundary<Vector8f>(8));
VERIFY_RAISES_ASSERT(construct_at_boundary<Vector12f>(8));
VERIFY_RAISES_ASSERT(construct_at_boundary<Vector2d>(8));
VERIFY_RAISES_ASSERT(construct_at_boundary<Vector4d>(8));
VERIFY_RAISES_ASSERT(construct_at_boundary<Vector6d>(8));
VERIFY_RAISES_ASSERT(construct_at_boundary<Vector8d>(8));
VERIFY_RAISES_ASSERT(construct_at_boundary<Vector10d>(8));
VERIFY_RAISES_ASSERT(construct_at_boundary<Vector12d>(8));
// Complexes are disabled because the compiler might aggressively vectorize
// the initialization of complex coeffs to 0 before we can check for alignedness
//VERIFY_RAISES_ASSERT(construct_at_boundary<Vector2cf>(8));
VERIFY_RAISES_ASSERT(construct_at_boundary<Vector4i>(8));
}
for(int b=8; b<EIGEN_MAX_ALIGN_BYTES; b+=8)
{
if(b<32) VERIFY_RAISES_ASSERT(construct_at_boundary<Vector8f>(b));
if(b<64) VERIFY_RAISES_ASSERT(construct_at_boundary<Matrix4f>(b));
if(b<32) VERIFY_RAISES_ASSERT(construct_at_boundary<Vector4d>(b));
if(b<32) VERIFY_RAISES_ASSERT(construct_at_boundary<Matrix2d>(b));
if(b<128) VERIFY_RAISES_ASSERT(construct_at_boundary<Matrix4d>(b));
//if(b<32) VERIFY_RAISES_ASSERT(construct_at_boundary<Vector2cd>(b));
}
#endif
}
void test_unalignedassert()
{
CALL_SUBTEST(unalignedassert());
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/sparse_solver.h | .h | 19,059 | 566 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "sparse.h"
#include <Eigen/SparseCore>
#include <sstream>
template<typename Solver, typename Rhs, typename Guess,typename Result>
void solve_with_guess(IterativeSolverBase<Solver>& solver, const MatrixBase<Rhs>& b, const Guess& g, Result &x) {
if(internal::random<bool>())
{
// With a temporary through evaluator<SolveWithGuess>
x = solver.derived().solveWithGuess(b,g) + Result::Zero(x.rows(), x.cols());
}
else
{
// direct evaluation within x through Assignment<Result,SolveWithGuess>
x = solver.derived().solveWithGuess(b.derived(),g);
}
}
template<typename Solver, typename Rhs, typename Guess,typename Result>
void solve_with_guess(SparseSolverBase<Solver>& solver, const MatrixBase<Rhs>& b, const Guess& , Result& x) {
if(internal::random<bool>())
x = solver.derived().solve(b) + Result::Zero(x.rows(), x.cols());
else
x = solver.derived().solve(b);
}
template<typename Solver, typename Rhs, typename Guess,typename Result>
void solve_with_guess(SparseSolverBase<Solver>& solver, const SparseMatrixBase<Rhs>& b, const Guess& , Result& x) {
x = solver.derived().solve(b);
}
template<typename Solver, typename Rhs, typename DenseMat, typename DenseRhs>
void check_sparse_solving(Solver& solver, const typename Solver::MatrixType& A, const Rhs& b, const DenseMat& dA, const DenseRhs& db)
{
typedef typename Solver::MatrixType Mat;
typedef typename Mat::Scalar Scalar;
typedef typename Mat::StorageIndex StorageIndex;
DenseRhs refX = dA.householderQr().solve(db);
{
Rhs x(A.cols(), b.cols());
Rhs oldb = b;
solver.compute(A);
if (solver.info() != Success)
{
std::cerr << "ERROR | sparse solver testing, factorization failed (" << typeid(Solver).name() << ")\n";
VERIFY(solver.info() == Success);
}
x = solver.solve(b);
if (solver.info() != Success)
{
std::cerr << "WARNING | sparse solver testing: solving failed (" << typeid(Solver).name() << ")\n";
return;
}
VERIFY(oldb.isApprox(b) && "sparse solver testing: the rhs should not be modified!");
VERIFY(x.isApprox(refX,test_precision<Scalar>()));
x.setZero();
solve_with_guess(solver, b, x, x);
VERIFY(solver.info() == Success && "solving failed when using analyzePattern/factorize API");
VERIFY(oldb.isApprox(b) && "sparse solver testing: the rhs should not be modified!");
VERIFY(x.isApprox(refX,test_precision<Scalar>()));
x.setZero();
// test the analyze/factorize API
solver.analyzePattern(A);
solver.factorize(A);
VERIFY(solver.info() == Success && "factorization failed when using analyzePattern/factorize API");
x = solver.solve(b);
VERIFY(solver.info() == Success && "solving failed when using analyzePattern/factorize API");
VERIFY(oldb.isApprox(b) && "sparse solver testing: the rhs should not be modified!");
VERIFY(x.isApprox(refX,test_precision<Scalar>()));
x.setZero();
// test with Map
MappedSparseMatrix<Scalar,Mat::Options,StorageIndex> Am(A.rows(), A.cols(), A.nonZeros(), const_cast<StorageIndex*>(A.outerIndexPtr()), const_cast<StorageIndex*>(A.innerIndexPtr()), const_cast<Scalar*>(A.valuePtr()));
solver.compute(Am);
VERIFY(solver.info() == Success && "factorization failed when using Map");
DenseRhs dx(refX);
dx.setZero();
Map<DenseRhs> xm(dx.data(), dx.rows(), dx.cols());
Map<const DenseRhs> bm(db.data(), db.rows(), db.cols());
xm = solver.solve(bm);
VERIFY(solver.info() == Success && "solving failed when using Map");
VERIFY(oldb.isApprox(bm) && "sparse solver testing: the rhs should not be modified!");
VERIFY(xm.isApprox(refX,test_precision<Scalar>()));
}
// if not too large, do some extra check:
if(A.rows()<2000)
{
// test initialization ctor
{
Rhs x(b.rows(), b.cols());
Solver solver2(A);
VERIFY(solver2.info() == Success);
x = solver2.solve(b);
VERIFY(x.isApprox(refX,test_precision<Scalar>()));
}
// test dense Block as the result and rhs:
{
DenseRhs x(refX.rows(), refX.cols());
DenseRhs oldb(db);
x.setZero();
x.block(0,0,x.rows(),x.cols()) = solver.solve(db.block(0,0,db.rows(),db.cols()));
VERIFY(oldb.isApprox(db) && "sparse solver testing: the rhs should not be modified!");
VERIFY(x.isApprox(refX,test_precision<Scalar>()));
}
// test uncompressed inputs
{
Mat A2 = A;
A2.reserve((ArrayXf::Random(A.outerSize())+2).template cast<typename Mat::StorageIndex>().eval());
solver.compute(A2);
Rhs x = solver.solve(b);
VERIFY(x.isApprox(refX,test_precision<Scalar>()));
}
// test expression as input
{
solver.compute(0.5*(A+A));
Rhs x = solver.solve(b);
VERIFY(x.isApprox(refX,test_precision<Scalar>()));
Solver solver2(0.5*(A+A));
Rhs x2 = solver2.solve(b);
VERIFY(x2.isApprox(refX,test_precision<Scalar>()));
}
}
}
template<typename Solver, typename Rhs>
void check_sparse_solving_real_cases(Solver& solver, const typename Solver::MatrixType& A, const Rhs& b, const typename Solver::MatrixType& fullA, const Rhs& refX)
{
typedef typename Solver::MatrixType Mat;
typedef typename Mat::Scalar Scalar;
typedef typename Mat::RealScalar RealScalar;
Rhs x(A.cols(), b.cols());
solver.compute(A);
if (solver.info() != Success)
{
std::cerr << "ERROR | sparse solver testing, factorization failed (" << typeid(Solver).name() << ")\n";
VERIFY(solver.info() == Success);
}
x = solver.solve(b);
if (solver.info() != Success)
{
std::cerr << "WARNING | sparse solver testing, solving failed (" << typeid(Solver).name() << ")\n";
return;
}
RealScalar res_error = (fullA*x-b).norm()/b.norm();
VERIFY( (res_error <= test_precision<Scalar>() ) && "sparse solver failed without noticing it");
if(refX.size() != 0 && (refX - x).norm()/refX.norm() > test_precision<Scalar>())
{
std::cerr << "WARNING | found solution is different from the provided reference one\n";
}
}
template<typename Solver, typename DenseMat>
void check_sparse_determinant(Solver& solver, const typename Solver::MatrixType& A, const DenseMat& dA)
{
typedef typename Solver::MatrixType Mat;
typedef typename Mat::Scalar Scalar;
solver.compute(A);
if (solver.info() != Success)
{
std::cerr << "WARNING | sparse solver testing: factorization failed (check_sparse_determinant)\n";
return;
}
Scalar refDet = dA.determinant();
VERIFY_IS_APPROX(refDet,solver.determinant());
}
template<typename Solver, typename DenseMat>
void check_sparse_abs_determinant(Solver& solver, const typename Solver::MatrixType& A, const DenseMat& dA)
{
using std::abs;
typedef typename Solver::MatrixType Mat;
typedef typename Mat::Scalar Scalar;
solver.compute(A);
if (solver.info() != Success)
{
std::cerr << "WARNING | sparse solver testing: factorization failed (check_sparse_abs_determinant)\n";
return;
}
Scalar refDet = abs(dA.determinant());
VERIFY_IS_APPROX(refDet,solver.absDeterminant());
}
template<typename Solver, typename DenseMat>
int generate_sparse_spd_problem(Solver& , typename Solver::MatrixType& A, typename Solver::MatrixType& halfA, DenseMat& dA, int maxSize = 300)
{
typedef typename Solver::MatrixType Mat;
typedef typename Mat::Scalar Scalar;
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
int size = internal::random<int>(1,maxSize);
double density = (std::max)(8./(size*size), 0.01);
Mat M(size, size);
DenseMatrix dM(size, size);
initSparse<Scalar>(density, dM, M, ForceNonZeroDiag);
A = M * M.adjoint();
dA = dM * dM.adjoint();
halfA.resize(size,size);
if(Solver::UpLo==(Lower|Upper))
halfA = A;
else
halfA.template selfadjointView<Solver::UpLo>().rankUpdate(M);
return size;
}
#ifdef TEST_REAL_CASES
template<typename Scalar>
inline std::string get_matrixfolder()
{
std::string mat_folder = TEST_REAL_CASES;
if( internal::is_same<Scalar, std::complex<float> >::value || internal::is_same<Scalar, std::complex<double> >::value )
mat_folder = mat_folder + static_cast<std::string>("/complex/");
else
mat_folder = mat_folder + static_cast<std::string>("/real/");
return mat_folder;
}
std::string sym_to_string(int sym)
{
if(sym==Symmetric) return "Symmetric ";
if(sym==SPD) return "SPD ";
return "";
}
template<typename Derived>
std::string solver_stats(const IterativeSolverBase<Derived> &solver)
{
std::stringstream ss;
ss << solver.iterations() << " iters, error: " << solver.error();
return ss.str();
}
template<typename Derived>
std::string solver_stats(const SparseSolverBase<Derived> &/*solver*/)
{
return "";
}
#endif
template<typename Solver> void check_sparse_spd_solving(Solver& solver, int maxSize = 300, int maxRealWorldSize = 100000)
{
typedef typename Solver::MatrixType Mat;
typedef typename Mat::Scalar Scalar;
typedef typename Mat::StorageIndex StorageIndex;
typedef SparseMatrix<Scalar,ColMajor, StorageIndex> SpMat;
typedef SparseVector<Scalar, 0, StorageIndex> SpVec;
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
// generate the problem
Mat A, halfA;
DenseMatrix dA;
for (int i = 0; i < g_repeat; i++) {
int size = generate_sparse_spd_problem(solver, A, halfA, dA, maxSize);
// generate the right hand sides
int rhsCols = internal::random<int>(1,16);
double density = (std::max)(8./(size*rhsCols), 0.1);
SpMat B(size,rhsCols);
DenseVector b = DenseVector::Random(size);
DenseMatrix dB(size,rhsCols);
initSparse<Scalar>(density, dB, B, ForceNonZeroDiag);
SpVec c = B.col(0);
DenseVector dc = dB.col(0);
CALL_SUBTEST( check_sparse_solving(solver, A, b, dA, b) );
CALL_SUBTEST( check_sparse_solving(solver, halfA, b, dA, b) );
CALL_SUBTEST( check_sparse_solving(solver, A, dB, dA, dB) );
CALL_SUBTEST( check_sparse_solving(solver, halfA, dB, dA, dB) );
CALL_SUBTEST( check_sparse_solving(solver, A, B, dA, dB) );
CALL_SUBTEST( check_sparse_solving(solver, halfA, B, dA, dB) );
CALL_SUBTEST( check_sparse_solving(solver, A, c, dA, dc) );
CALL_SUBTEST( check_sparse_solving(solver, halfA, c, dA, dc) );
// check only once
if(i==0)
{
b = DenseVector::Zero(size);
check_sparse_solving(solver, A, b, dA, b);
}
}
// First, get the folder
#ifdef TEST_REAL_CASES
// Test real problems with double precision only
if (internal::is_same<typename NumTraits<Scalar>::Real, double>::value)
{
std::string mat_folder = get_matrixfolder<Scalar>();
MatrixMarketIterator<Scalar> it(mat_folder);
for (; it; ++it)
{
if (it.sym() == SPD){
A = it.matrix();
if(A.diagonal().size() <= maxRealWorldSize)
{
DenseVector b = it.rhs();
DenseVector refX = it.refX();
PermutationMatrix<Dynamic, Dynamic, StorageIndex> pnull;
halfA.resize(A.rows(), A.cols());
if(Solver::UpLo == (Lower|Upper))
halfA = A;
else
halfA.template selfadjointView<Solver::UpLo>() = A.template triangularView<Eigen::Lower>().twistedBy(pnull);
std::cout << "INFO | Testing " << sym_to_string(it.sym()) << "sparse problem " << it.matname()
<< " (" << A.rows() << "x" << A.cols() << ") using " << typeid(Solver).name() << "..." << std::endl;
CALL_SUBTEST( check_sparse_solving_real_cases(solver, A, b, A, refX) );
std::string stats = solver_stats(solver);
if(stats.size()>0)
std::cout << "INFO | " << stats << std::endl;
CALL_SUBTEST( check_sparse_solving_real_cases(solver, halfA, b, A, refX) );
}
else
{
std::cout << "INFO | Skip sparse problem \"" << it.matname() << "\" (too large)" << std::endl;
}
}
}
}
#else
EIGEN_UNUSED_VARIABLE(maxRealWorldSize);
#endif
}
template<typename Solver> void check_sparse_spd_determinant(Solver& solver)
{
typedef typename Solver::MatrixType Mat;
typedef typename Mat::Scalar Scalar;
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
// generate the problem
Mat A, halfA;
DenseMatrix dA;
generate_sparse_spd_problem(solver, A, halfA, dA, 30);
for (int i = 0; i < g_repeat; i++) {
check_sparse_determinant(solver, A, dA);
check_sparse_determinant(solver, halfA, dA );
}
}
template<typename Solver, typename DenseMat>
Index generate_sparse_square_problem(Solver&, typename Solver::MatrixType& A, DenseMat& dA, int maxSize = 300, int options = ForceNonZeroDiag)
{
typedef typename Solver::MatrixType Mat;
typedef typename Mat::Scalar Scalar;
Index size = internal::random<int>(1,maxSize);
double density = (std::max)(8./(size*size), 0.01);
A.resize(size,size);
dA.resize(size,size);
initSparse<Scalar>(density, dA, A, options);
return size;
}
struct prune_column {
Index m_col;
prune_column(Index col) : m_col(col) {}
template<class Scalar>
bool operator()(Index, Index col, const Scalar&) const {
return col != m_col;
}
};
template<typename Solver> void check_sparse_square_solving(Solver& solver, int maxSize = 300, int maxRealWorldSize = 100000, bool checkDeficient = false)
{
typedef typename Solver::MatrixType Mat;
typedef typename Mat::Scalar Scalar;
typedef SparseMatrix<Scalar,ColMajor, typename Mat::StorageIndex> SpMat;
typedef SparseVector<Scalar, 0, typename Mat::StorageIndex> SpVec;
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
int rhsCols = internal::random<int>(1,16);
Mat A;
DenseMatrix dA;
for (int i = 0; i < g_repeat; i++) {
Index size = generate_sparse_square_problem(solver, A, dA, maxSize);
A.makeCompressed();
DenseVector b = DenseVector::Random(size);
DenseMatrix dB(size,rhsCols);
SpMat B(size,rhsCols);
double density = (std::max)(8./(size*rhsCols), 0.1);
initSparse<Scalar>(density, dB, B, ForceNonZeroDiag);
B.makeCompressed();
SpVec c = B.col(0);
DenseVector dc = dB.col(0);
CALL_SUBTEST(check_sparse_solving(solver, A, b, dA, b));
CALL_SUBTEST(check_sparse_solving(solver, A, dB, dA, dB));
CALL_SUBTEST(check_sparse_solving(solver, A, B, dA, dB));
CALL_SUBTEST(check_sparse_solving(solver, A, c, dA, dc));
// check only once
if(i==0)
{
b = DenseVector::Zero(size);
check_sparse_solving(solver, A, b, dA, b);
}
// regression test for Bug 792 (structurally rank deficient matrices):
if(checkDeficient && size>1) {
Index col = internal::random<int>(0,int(size-1));
A.prune(prune_column(col));
solver.compute(A);
VERIFY_IS_EQUAL(solver.info(), NumericalIssue);
}
}
// First, get the folder
#ifdef TEST_REAL_CASES
// Test real problems with double precision only
if (internal::is_same<typename NumTraits<Scalar>::Real, double>::value)
{
std::string mat_folder = get_matrixfolder<Scalar>();
MatrixMarketIterator<Scalar> it(mat_folder);
for (; it; ++it)
{
A = it.matrix();
if(A.diagonal().size() <= maxRealWorldSize)
{
DenseVector b = it.rhs();
DenseVector refX = it.refX();
std::cout << "INFO | Testing " << sym_to_string(it.sym()) << "sparse problem " << it.matname()
<< " (" << A.rows() << "x" << A.cols() << ") using " << typeid(Solver).name() << "..." << std::endl;
CALL_SUBTEST(check_sparse_solving_real_cases(solver, A, b, A, refX));
std::string stats = solver_stats(solver);
if(stats.size()>0)
std::cout << "INFO | " << stats << std::endl;
}
else
{
std::cout << "INFO | SKIP sparse problem \"" << it.matname() << "\" (too large)" << std::endl;
}
}
}
#else
EIGEN_UNUSED_VARIABLE(maxRealWorldSize);
#endif
}
template<typename Solver> void check_sparse_square_determinant(Solver& solver)
{
typedef typename Solver::MatrixType Mat;
typedef typename Mat::Scalar Scalar;
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
for (int i = 0; i < g_repeat; i++) {
// generate the problem
Mat A;
DenseMatrix dA;
int size = internal::random<int>(1,30);
dA.setRandom(size,size);
dA = (dA.array().abs()<0.3).select(0,dA);
dA.diagonal() = (dA.diagonal().array()==0).select(1,dA.diagonal());
A = dA.sparseView();
A.makeCompressed();
check_sparse_determinant(solver, A, dA);
}
}
template<typename Solver> void check_sparse_square_abs_determinant(Solver& solver)
{
typedef typename Solver::MatrixType Mat;
typedef typename Mat::Scalar Scalar;
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
for (int i = 0; i < g_repeat; i++) {
// generate the problem
Mat A;
DenseMatrix dA;
generate_sparse_square_problem(solver, A, dA, 30);
A.makeCompressed();
check_sparse_abs_determinant(solver, A, dA);
}
}
template<typename Solver, typename DenseMat>
void generate_sparse_leastsquare_problem(Solver&, typename Solver::MatrixType& A, DenseMat& dA, int maxSize = 300, int options = ForceNonZeroDiag)
{
typedef typename Solver::MatrixType Mat;
typedef typename Mat::Scalar Scalar;
int rows = internal::random<int>(1,maxSize);
int cols = internal::random<int>(1,rows);
double density = (std::max)(8./(rows*cols), 0.01);
A.resize(rows,cols);
dA.resize(rows,cols);
initSparse<Scalar>(density, dA, A, options);
}
template<typename Solver> void check_sparse_leastsquare_solving(Solver& solver)
{
typedef typename Solver::MatrixType Mat;
typedef typename Mat::Scalar Scalar;
typedef SparseMatrix<Scalar,ColMajor, typename Mat::StorageIndex> SpMat;
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
int rhsCols = internal::random<int>(1,16);
Mat A;
DenseMatrix dA;
for (int i = 0; i < g_repeat; i++) {
generate_sparse_leastsquare_problem(solver, A, dA);
A.makeCompressed();
DenseVector b = DenseVector::Random(A.rows());
DenseMatrix dB(A.rows(),rhsCols);
SpMat B(A.rows(),rhsCols);
double density = (std::max)(8./(A.rows()*rhsCols), 0.1);
initSparse<Scalar>(density, dB, B, ForceNonZeroDiag);
B.makeCompressed();
check_sparse_solving(solver, A, b, dA, b);
check_sparse_solving(solver, A, dB, dA, dB);
check_sparse_solving(solver, A, B, dA, dB);
// check only once
if(i==0)
{
b = DenseVector::Zero(A.rows());
check_sparse_solving(solver, A, b, dA, b);
}
}
}
| Unknown |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/adjoint.cpp | .cpp | 8,251 | 200 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_NO_STATIC_ASSERT
#include "main.h"
template<bool IsInteger> struct adjoint_specific;
template<> struct adjoint_specific<true> {
template<typename Vec, typename Mat, typename Scalar>
static void run(const Vec& v1, const Vec& v2, Vec& v3, const Mat& square, Scalar s1, Scalar s2) {
VERIFY(test_isApproxWithRef((s1 * v1 + s2 * v2).dot(v3), numext::conj(s1) * v1.dot(v3) + numext::conj(s2) * v2.dot(v3), 0));
VERIFY(test_isApproxWithRef(v3.dot(s1 * v1 + s2 * v2), s1*v3.dot(v1)+s2*v3.dot(v2), 0));
// check compatibility of dot and adjoint
VERIFY(test_isApproxWithRef(v1.dot(square * v2), (square.adjoint() * v1).dot(v2), 0));
}
};
template<> struct adjoint_specific<false> {
template<typename Vec, typename Mat, typename Scalar>
static void run(const Vec& v1, const Vec& v2, Vec& v3, const Mat& square, Scalar s1, Scalar s2) {
typedef typename NumTraits<Scalar>::Real RealScalar;
using std::abs;
RealScalar ref = NumTraits<Scalar>::IsInteger ? RealScalar(0) : (std::max)((s1 * v1 + s2 * v2).norm(),v3.norm());
VERIFY(test_isApproxWithRef((s1 * v1 + s2 * v2).dot(v3), numext::conj(s1) * v1.dot(v3) + numext::conj(s2) * v2.dot(v3), ref));
VERIFY(test_isApproxWithRef(v3.dot(s1 * v1 + s2 * v2), s1*v3.dot(v1)+s2*v3.dot(v2), ref));
VERIFY_IS_APPROX(v1.squaredNorm(), v1.norm() * v1.norm());
// check normalized() and normalize()
VERIFY_IS_APPROX(v1, v1.norm() * v1.normalized());
v3 = v1;
v3.normalize();
VERIFY_IS_APPROX(v1, v1.norm() * v3);
VERIFY_IS_APPROX(v3, v1.normalized());
VERIFY_IS_APPROX(v3.norm(), RealScalar(1));
// check null inputs
VERIFY_IS_APPROX((v1*0).normalized(), (v1*0));
#if (!EIGEN_ARCH_i386) || defined(EIGEN_VECTORIZE)
RealScalar very_small = (std::numeric_limits<RealScalar>::min)();
VERIFY( (v1*very_small).norm() == 0 );
VERIFY_IS_APPROX((v1*very_small).normalized(), (v1*very_small));
v3 = v1*very_small;
v3.normalize();
VERIFY_IS_APPROX(v3, (v1*very_small));
#endif
// check compatibility of dot and adjoint
ref = NumTraits<Scalar>::IsInteger ? 0 : (std::max)((std::max)(v1.norm(),v2.norm()),(std::max)((square * v2).norm(),(square.adjoint() * v1).norm()));
VERIFY(internal::isMuchSmallerThan(abs(v1.dot(square * v2) - (square.adjoint() * v1).dot(v2)), ref, test_precision<Scalar>()));
// check that Random().normalized() works: tricky as the random xpr must be evaluated by
// normalized() in order to produce a consistent result.
VERIFY_IS_APPROX(Vec::Random(v1.size()).normalized().norm(), RealScalar(1));
}
};
template<typename MatrixType> void adjoint(const MatrixType& m)
{
/* this test covers the following files:
Transpose.h Conjugate.h Dot.h
*/
using std::abs;
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
const Index PacketSize = internal::packet_traits<Scalar>::size;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols),
square = SquareMatrixType::Random(rows, rows);
VectorType v1 = VectorType::Random(rows),
v2 = VectorType::Random(rows),
v3 = VectorType::Random(rows),
vzero = VectorType::Zero(rows);
Scalar s1 = internal::random<Scalar>(),
s2 = internal::random<Scalar>();
// check basic compatibility of adjoint, transpose, conjugate
VERIFY_IS_APPROX(m1.transpose().conjugate().adjoint(), m1);
VERIFY_IS_APPROX(m1.adjoint().conjugate().transpose(), m1);
// check multiplicative behavior
VERIFY_IS_APPROX((m1.adjoint() * m2).adjoint(), m2.adjoint() * m1);
VERIFY_IS_APPROX((s1 * m1).adjoint(), numext::conj(s1) * m1.adjoint());
// check basic properties of dot, squaredNorm
VERIFY_IS_APPROX(numext::conj(v1.dot(v2)), v2.dot(v1));
VERIFY_IS_APPROX(numext::real(v1.dot(v1)), v1.squaredNorm());
adjoint_specific<NumTraits<Scalar>::IsInteger>::run(v1, v2, v3, square, s1, s2);
VERIFY_IS_MUCH_SMALLER_THAN(abs(vzero.dot(v1)), static_cast<RealScalar>(1));
// like in testBasicStuff, test operator() to check const-qualification
Index r = internal::random<Index>(0, rows-1),
c = internal::random<Index>(0, cols-1);
VERIFY_IS_APPROX(m1.conjugate()(r,c), numext::conj(m1(r,c)));
VERIFY_IS_APPROX(m1.adjoint()(c,r), numext::conj(m1(r,c)));
// check inplace transpose
m3 = m1;
m3.transposeInPlace();
VERIFY_IS_APPROX(m3,m1.transpose());
m3.transposeInPlace();
VERIFY_IS_APPROX(m3,m1);
if(PacketSize<m3.rows() && PacketSize<m3.cols())
{
m3 = m1;
Index i = internal::random<Index>(0,m3.rows()-PacketSize);
Index j = internal::random<Index>(0,m3.cols()-PacketSize);
m3.template block<PacketSize,PacketSize>(i,j).transposeInPlace();
VERIFY_IS_APPROX( (m3.template block<PacketSize,PacketSize>(i,j)), (m1.template block<PacketSize,PacketSize>(i,j).transpose()) );
m3.template block<PacketSize,PacketSize>(i,j).transposeInPlace();
VERIFY_IS_APPROX(m3,m1);
}
// check inplace adjoint
m3 = m1;
m3.adjointInPlace();
VERIFY_IS_APPROX(m3,m1.adjoint());
m3.transposeInPlace();
VERIFY_IS_APPROX(m3,m1.conjugate());
// check mixed dot product
typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealVectorType;
RealVectorType rv1 = RealVectorType::Random(rows);
VERIFY_IS_APPROX(v1.dot(rv1.template cast<Scalar>()), v1.dot(rv1));
VERIFY_IS_APPROX(rv1.template cast<Scalar>().dot(v1), rv1.dot(v1));
}
void test_adjoint()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( adjoint(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( adjoint(Matrix3d()) );
CALL_SUBTEST_3( adjoint(Matrix4f()) );
CALL_SUBTEST_4( adjoint(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
CALL_SUBTEST_5( adjoint(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_6( adjoint(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
// Complement for 128 bits vectorization:
CALL_SUBTEST_8( adjoint(Matrix2d()) );
CALL_SUBTEST_9( adjoint(Matrix<int,4,4>()) );
// 256 bits vectorization:
CALL_SUBTEST_10( adjoint(Matrix<float,8,8>()) );
CALL_SUBTEST_11( adjoint(Matrix<double,4,4>()) );
CALL_SUBTEST_12( adjoint(Matrix<int,8,8>()) );
}
// test a large static matrix only once
CALL_SUBTEST_7( adjoint(Matrix<float, 100, 100>()) );
#ifdef EIGEN_TEST_PART_13
{
MatrixXcf a(10,10), b(10,10);
VERIFY_RAISES_ASSERT(a = a.transpose());
VERIFY_RAISES_ASSERT(a = a.transpose() + b);
VERIFY_RAISES_ASSERT(a = b + a.transpose());
VERIFY_RAISES_ASSERT(a = a.conjugate().transpose());
VERIFY_RAISES_ASSERT(a = a.adjoint());
VERIFY_RAISES_ASSERT(a = a.adjoint() + b);
VERIFY_RAISES_ASSERT(a = b + a.adjoint());
// no assertion should be triggered for these cases:
a.transpose() = a.transpose();
a.transpose() += a.transpose();
a.transpose() += a.transpose() + b;
a.transpose() = a.adjoint();
a.transpose() += a.adjoint();
a.transpose() += a.adjoint() + b;
// regression tests for check_for_aliasing
MatrixXd c(10,10);
c = 1.0 * MatrixXd::Ones(10,10) + c;
c = MatrixXd::Ones(10,10) * 1.0 + c;
c = c + MatrixXd::Ones(10,10) .cwiseProduct( MatrixXd::Zero(10,10) );
c = MatrixXd::Ones(10,10) * MatrixXd::Zero(10,10);
}
#endif
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/stdlist.cpp | .cpp | 4,224 | 131 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/StdList>
#include <Eigen/Geometry>
template<typename MatrixType>
void check_stdlist_matrix(const MatrixType& m)
{
Index rows = m.rows();
Index cols = m.cols();
MatrixType x = MatrixType::Random(rows,cols), y = MatrixType::Random(rows,cols);
std::list<MatrixType,Eigen::aligned_allocator<MatrixType> > v(10, MatrixType::Zero(rows,cols)), w(20, y);
v.front() = x;
w.front() = w.back();
VERIFY_IS_APPROX(w.front(), w.back());
v = w;
typename std::list<MatrixType,Eigen::aligned_allocator<MatrixType> >::iterator vi = v.begin();
typename std::list<MatrixType,Eigen::aligned_allocator<MatrixType> >::iterator wi = w.begin();
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(*vi, *wi);
++vi;
++wi;
}
v.resize(21, MatrixType::Zero(rows,cols));
v.back() = x;
VERIFY_IS_APPROX(v.back(), x);
v.resize(22,y);
VERIFY_IS_APPROX(v.back(), y);
v.push_back(x);
VERIFY_IS_APPROX(v.back(), x);
}
template<typename TransformType>
void check_stdlist_transform(const TransformType&)
{
typedef typename TransformType::MatrixType MatrixType;
TransformType x(MatrixType::Random()), y(MatrixType::Random()), ti=TransformType::Identity();
std::list<TransformType,Eigen::aligned_allocator<TransformType> > v(10,ti), w(20, y);
v.front() = x;
w.front() = w.back();
VERIFY_IS_APPROX(w.front(), w.back());
v = w;
typename std::list<TransformType,Eigen::aligned_allocator<TransformType> >::iterator vi = v.begin();
typename std::list<TransformType,Eigen::aligned_allocator<TransformType> >::iterator wi = w.begin();
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(*vi, *wi);
++vi;
++wi;
}
v.resize(21, ti);
v.back() = x;
VERIFY_IS_APPROX(v.back(), x);
v.resize(22,y);
VERIFY_IS_APPROX(v.back(), y);
v.push_back(x);
VERIFY_IS_APPROX(v.back(), x);
}
template<typename QuaternionType>
void check_stdlist_quaternion(const QuaternionType&)
{
typedef typename QuaternionType::Coefficients Coefficients;
QuaternionType x(Coefficients::Random()), y(Coefficients::Random()), qi=QuaternionType::Identity();
std::list<QuaternionType,Eigen::aligned_allocator<QuaternionType> > v(10,qi), w(20, y);
v.front() = x;
w.front() = w.back();
VERIFY_IS_APPROX(w.front(), w.back());
v = w;
typename std::list<QuaternionType,Eigen::aligned_allocator<QuaternionType> >::iterator vi = v.begin();
typename std::list<QuaternionType,Eigen::aligned_allocator<QuaternionType> >::iterator wi = w.begin();
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(*vi, *wi);
++vi;
++wi;
}
v.resize(21,qi);
v.back() = x;
VERIFY_IS_APPROX(v.back(), x);
v.resize(22,y);
VERIFY_IS_APPROX(v.back(), y);
v.push_back(x);
VERIFY_IS_APPROX(v.back(), x);
}
void test_stdlist()
{
// some non vectorizable fixed sizes
CALL_SUBTEST_1(check_stdlist_matrix(Vector2f()));
CALL_SUBTEST_1(check_stdlist_matrix(Matrix3f()));
CALL_SUBTEST_2(check_stdlist_matrix(Matrix3d()));
// some vectorizable fixed sizes
CALL_SUBTEST_1(check_stdlist_matrix(Matrix2f()));
CALL_SUBTEST_1(check_stdlist_matrix(Vector4f()));
CALL_SUBTEST_1(check_stdlist_matrix(Matrix4f()));
CALL_SUBTEST_2(check_stdlist_matrix(Matrix4d()));
// some dynamic sizes
CALL_SUBTEST_3(check_stdlist_matrix(MatrixXd(1,1)));
CALL_SUBTEST_3(check_stdlist_matrix(VectorXd(20)));
CALL_SUBTEST_3(check_stdlist_matrix(RowVectorXf(20)));
CALL_SUBTEST_3(check_stdlist_matrix(MatrixXcf(10,10)));
// some Transform
CALL_SUBTEST_4(check_stdlist_transform(Affine2f()));
CALL_SUBTEST_4(check_stdlist_transform(Affine3f()));
CALL_SUBTEST_4(check_stdlist_transform(Affine3d()));
// some Quaternion
CALL_SUBTEST_5(check_stdlist_quaternion(Quaternionf()));
CALL_SUBTEST_5(check_stdlist_quaternion(Quaterniond()));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/cuda_common.h | .h | 3,368 | 102 |
#ifndef EIGEN_TEST_CUDA_COMMON_H
#define EIGEN_TEST_CUDA_COMMON_H
#include <cuda.h>
#include <cuda_runtime.h>
#include <cuda_runtime_api.h>
#include <iostream>
#ifndef __CUDACC__
dim3 threadIdx, blockDim, blockIdx;
#endif
template<typename Kernel, typename Input, typename Output>
void run_on_cpu(const Kernel& ker, int n, const Input& in, Output& out)
{
for(int i=0; i<n; i++)
ker(i, in.data(), out.data());
}
template<typename Kernel, typename Input, typename Output>
__global__
void run_on_cuda_meta_kernel(const Kernel ker, int n, const Input* in, Output* out)
{
int i = threadIdx.x + blockIdx.x*blockDim.x;
if(i<n) {
ker(i, in, out);
}
}
template<typename Kernel, typename Input, typename Output>
void run_on_cuda(const Kernel& ker, int n, const Input& in, Output& out)
{
typename Input::Scalar* d_in;
typename Output::Scalar* d_out;
std::ptrdiff_t in_bytes = in.size() * sizeof(typename Input::Scalar);
std::ptrdiff_t out_bytes = out.size() * sizeof(typename Output::Scalar);
cudaMalloc((void**)(&d_in), in_bytes);
cudaMalloc((void**)(&d_out), out_bytes);
cudaMemcpy(d_in, in.data(), in_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_out, out.data(), out_bytes, cudaMemcpyHostToDevice);
// Simple and non-optimal 1D mapping assuming n is not too large
// That's only for unit testing!
dim3 Blocks(128);
dim3 Grids( (n+int(Blocks.x)-1)/int(Blocks.x) );
cudaThreadSynchronize();
run_on_cuda_meta_kernel<<<Grids,Blocks>>>(ker, n, d_in, d_out);
cudaThreadSynchronize();
// check inputs have not been modified
cudaMemcpy(const_cast<typename Input::Scalar*>(in.data()), d_in, in_bytes, cudaMemcpyDeviceToHost);
cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost);
cudaFree(d_in);
cudaFree(d_out);
}
template<typename Kernel, typename Input, typename Output>
void run_and_compare_to_cuda(const Kernel& ker, int n, const Input& in, Output& out)
{
Input in_ref, in_cuda;
Output out_ref, out_cuda;
#ifndef __CUDA_ARCH__
in_ref = in_cuda = in;
out_ref = out_cuda = out;
#endif
run_on_cpu (ker, n, in_ref, out_ref);
run_on_cuda(ker, n, in_cuda, out_cuda);
#ifndef __CUDA_ARCH__
VERIFY_IS_APPROX(in_ref, in_cuda);
VERIFY_IS_APPROX(out_ref, out_cuda);
#endif
}
void ei_test_init_cuda()
{
int device = 0;
cudaDeviceProp deviceProp;
cudaGetDeviceProperties(&deviceProp, device);
std::cout << "CUDA device info:\n";
std::cout << " name: " << deviceProp.name << "\n";
std::cout << " capability: " << deviceProp.major << "." << deviceProp.minor << "\n";
std::cout << " multiProcessorCount: " << deviceProp.multiProcessorCount << "\n";
std::cout << " maxThreadsPerMultiProcessor: " << deviceProp.maxThreadsPerMultiProcessor << "\n";
std::cout << " warpSize: " << deviceProp.warpSize << "\n";
std::cout << " regsPerBlock: " << deviceProp.regsPerBlock << "\n";
std::cout << " concurrentKernels: " << deviceProp.concurrentKernels << "\n";
std::cout << " clockRate: " << deviceProp.clockRate << "\n";
std::cout << " canMapHostMemory: " << deviceProp.canMapHostMemory << "\n";
std::cout << " computeMode: " << deviceProp.computeMode << "\n";
}
#endif // EIGEN_TEST_CUDA_COMMON_H
| Unknown |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/geo_transformations.cpp | .cpp | 25,326 | 705 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/Geometry>
#include <Eigen/LU>
#include <Eigen/SVD>
template<typename T>
Matrix<T,2,1> angleToVec(T a)
{
return Matrix<T,2,1>(std::cos(a), std::sin(a));
}
// This permits to workaround a bug in clang/llvm code generation.
template<typename T>
EIGEN_DONT_INLINE
void dont_over_optimize(T& x) { volatile typename T::Scalar tmp = x(0); x(0) = tmp; }
template<typename Scalar, int Mode, int Options> void non_projective_only()
{
/* this test covers the following files:
Cross.h Quaternion.h, Transform.cpp
*/
typedef Matrix<Scalar,3,1> Vector3;
typedef Quaternion<Scalar> Quaternionx;
typedef AngleAxis<Scalar> AngleAxisx;
typedef Transform<Scalar,3,Mode,Options> Transform3;
typedef DiagonalMatrix<Scalar,3> AlignedScaling3;
typedef Translation<Scalar,3> Translation3;
Vector3 v0 = Vector3::Random(),
v1 = Vector3::Random();
Transform3 t0, t1, t2;
Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI));
Quaternionx q1, q2;
q1 = AngleAxisx(a, v0.normalized());
t0 = Transform3::Identity();
VERIFY_IS_APPROX(t0.matrix(), Transform3::MatrixType::Identity());
t0.linear() = q1.toRotationMatrix();
v0 << 50, 2, 1;
t0.scale(v0);
VERIFY_IS_APPROX( (t0 * Vector3(1,0,0)).template head<3>().norm(), v0.x());
t0.setIdentity();
t1.setIdentity();
v1 << 1, 2, 3;
t0.linear() = q1.toRotationMatrix();
t0.pretranslate(v0);
t0.scale(v1);
t1.linear() = q1.conjugate().toRotationMatrix();
t1.prescale(v1.cwiseInverse());
t1.translate(-v0);
VERIFY((t0 * t1).matrix().isIdentity(test_precision<Scalar>()));
t1.fromPositionOrientationScale(v0, q1, v1);
VERIFY_IS_APPROX(t1.matrix(), t0.matrix());
VERIFY_IS_APPROX(t1*v1, t0*v1);
// translation * vector
t0.setIdentity();
t0.translate(v0);
VERIFY_IS_APPROX((t0 * v1).template head<3>(), Translation3(v0) * v1);
// AlignedScaling * vector
t0.setIdentity();
t0.scale(v0);
VERIFY_IS_APPROX((t0 * v1).template head<3>(), AlignedScaling3(v0) * v1);
}
template<typename Scalar, int Mode, int Options> void transformations()
{
/* this test covers the following files:
Cross.h Quaternion.h, Transform.cpp
*/
using std::cos;
using std::abs;
typedef Matrix<Scalar,3,3> Matrix3;
typedef Matrix<Scalar,4,4> Matrix4;
typedef Matrix<Scalar,2,1> Vector2;
typedef Matrix<Scalar,3,1> Vector3;
typedef Matrix<Scalar,4,1> Vector4;
typedef Quaternion<Scalar> Quaternionx;
typedef AngleAxis<Scalar> AngleAxisx;
typedef Transform<Scalar,2,Mode,Options> Transform2;
typedef Transform<Scalar,3,Mode,Options> Transform3;
typedef typename Transform3::MatrixType MatrixType;
typedef DiagonalMatrix<Scalar,3> AlignedScaling3;
typedef Translation<Scalar,2> Translation2;
typedef Translation<Scalar,3> Translation3;
Vector3 v0 = Vector3::Random(),
v1 = Vector3::Random();
Matrix3 matrot1, m;
Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI));
Scalar s0 = internal::random<Scalar>(), s1 = internal::random<Scalar>();
while(v0.norm() < test_precision<Scalar>()) v0 = Vector3::Random();
while(v1.norm() < test_precision<Scalar>()) v1 = Vector3::Random();
VERIFY_IS_APPROX(v0, AngleAxisx(a, v0.normalized()) * v0);
VERIFY_IS_APPROX(-v0, AngleAxisx(Scalar(EIGEN_PI), v0.unitOrthogonal()) * v0);
if(abs(cos(a)) > test_precision<Scalar>())
{
VERIFY_IS_APPROX(cos(a)*v0.squaredNorm(), v0.dot(AngleAxisx(a, v0.unitOrthogonal()) * v0));
}
m = AngleAxisx(a, v0.normalized()).toRotationMatrix().adjoint();
VERIFY_IS_APPROX(Matrix3::Identity(), m * AngleAxisx(a, v0.normalized()));
VERIFY_IS_APPROX(Matrix3::Identity(), AngleAxisx(a, v0.normalized()) * m);
Quaternionx q1, q2;
q1 = AngleAxisx(a, v0.normalized());
q2 = AngleAxisx(a, v1.normalized());
// rotation matrix conversion
matrot1 = AngleAxisx(Scalar(0.1), Vector3::UnitX())
* AngleAxisx(Scalar(0.2), Vector3::UnitY())
* AngleAxisx(Scalar(0.3), Vector3::UnitZ());
VERIFY_IS_APPROX(matrot1 * v1,
AngleAxisx(Scalar(0.1), Vector3(1,0,0)).toRotationMatrix()
* (AngleAxisx(Scalar(0.2), Vector3(0,1,0)).toRotationMatrix()
* (AngleAxisx(Scalar(0.3), Vector3(0,0,1)).toRotationMatrix() * v1)));
// angle-axis conversion
AngleAxisx aa = AngleAxisx(q1);
VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1);
// The following test is stable only if 2*angle != angle and v1 is not colinear with axis
if( (abs(aa.angle()) > test_precision<Scalar>()) && (abs(aa.axis().dot(v1.normalized()))<(Scalar(1)-Scalar(4)*test_precision<Scalar>())) )
{
VERIFY( !(q1 * v1).isApprox(Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1) );
}
aa.fromRotationMatrix(aa.toRotationMatrix());
VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1);
// The following test is stable only if 2*angle != angle and v1 is not colinear with axis
if( (abs(aa.angle()) > test_precision<Scalar>()) && (abs(aa.axis().dot(v1.normalized()))<(Scalar(1)-Scalar(4)*test_precision<Scalar>())) )
{
VERIFY( !(q1 * v1).isApprox(Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1) );
}
// AngleAxis
VERIFY_IS_APPROX(AngleAxisx(a,v1.normalized()).toRotationMatrix(),
Quaternionx(AngleAxisx(a,v1.normalized())).toRotationMatrix());
AngleAxisx aa1;
m = q1.toRotationMatrix();
aa1 = m;
VERIFY_IS_APPROX(AngleAxisx(m).toRotationMatrix(),
Quaternionx(m).toRotationMatrix());
// Transform
// TODO complete the tests !
a = 0;
while (abs(a)<Scalar(0.1))
a = internal::random<Scalar>(-Scalar(0.4)*Scalar(EIGEN_PI), Scalar(0.4)*Scalar(EIGEN_PI));
q1 = AngleAxisx(a, v0.normalized());
Transform3 t0, t1, t2;
// first test setIdentity() and Identity()
t0.setIdentity();
VERIFY_IS_APPROX(t0.matrix(), Transform3::MatrixType::Identity());
t0.matrix().setZero();
t0 = Transform3::Identity();
VERIFY_IS_APPROX(t0.matrix(), Transform3::MatrixType::Identity());
t0.setIdentity();
t1.setIdentity();
v1 << 1, 2, 3;
t0.linear() = q1.toRotationMatrix();
t0.pretranslate(v0);
t0.scale(v1);
t1.linear() = q1.conjugate().toRotationMatrix();
t1.prescale(v1.cwiseInverse());
t1.translate(-v0);
VERIFY((t0 * t1).matrix().isIdentity(test_precision<Scalar>()));
t1.fromPositionOrientationScale(v0, q1, v1);
VERIFY_IS_APPROX(t1.matrix(), t0.matrix());
t0.setIdentity(); t0.scale(v0).rotate(q1.toRotationMatrix());
t1.setIdentity(); t1.scale(v0).rotate(q1);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0.setIdentity(); t0.scale(v0).rotate(AngleAxisx(q1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
VERIFY_IS_APPROX(t0.scale(a).matrix(), t1.scale(Vector3::Constant(a)).matrix());
VERIFY_IS_APPROX(t0.prescale(a).matrix(), t1.prescale(Vector3::Constant(a)).matrix());
// More transform constructors, operator=, operator*=
Matrix3 mat3 = Matrix3::Random();
Matrix4 mat4;
mat4 << mat3 , Vector3::Zero() , Vector4::Zero().transpose();
Transform3 tmat3(mat3), tmat4(mat4);
if(Mode!=int(AffineCompact))
tmat4.matrix()(3,3) = Scalar(1);
VERIFY_IS_APPROX(tmat3.matrix(), tmat4.matrix());
Scalar a3 = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI));
Vector3 v3 = Vector3::Random().normalized();
AngleAxisx aa3(a3, v3);
Transform3 t3(aa3);
Transform3 t4;
t4 = aa3;
VERIFY_IS_APPROX(t3.matrix(), t4.matrix());
t4.rotate(AngleAxisx(-a3,v3));
VERIFY_IS_APPROX(t4.matrix(), MatrixType::Identity());
t4 *= aa3;
VERIFY_IS_APPROX(t3.matrix(), t4.matrix());
do {
v3 = Vector3::Random();
dont_over_optimize(v3);
} while (v3.cwiseAbs().minCoeff()<NumTraits<Scalar>::epsilon());
Translation3 tv3(v3);
Transform3 t5(tv3);
t4 = tv3;
VERIFY_IS_APPROX(t5.matrix(), t4.matrix());
t4.translate((-v3).eval());
VERIFY_IS_APPROX(t4.matrix(), MatrixType::Identity());
t4 *= tv3;
VERIFY_IS_APPROX(t5.matrix(), t4.matrix());
AlignedScaling3 sv3(v3);
Transform3 t6(sv3);
t4 = sv3;
VERIFY_IS_APPROX(t6.matrix(), t4.matrix());
t4.scale(v3.cwiseInverse());
VERIFY_IS_APPROX(t4.matrix(), MatrixType::Identity());
t4 *= sv3;
VERIFY_IS_APPROX(t6.matrix(), t4.matrix());
// matrix * transform
VERIFY_IS_APPROX((t3.matrix()*t4).matrix(), (t3*t4).matrix());
// chained Transform product
VERIFY_IS_APPROX(((t3*t4)*t5).matrix(), (t3*(t4*t5)).matrix());
// check that Transform product doesn't have aliasing problems
t5 = t4;
t5 = t5*t5;
VERIFY_IS_APPROX(t5, t4*t4);
// 2D transformation
Transform2 t20, t21;
Vector2 v20 = Vector2::Random();
Vector2 v21 = Vector2::Random();
for (int k=0; k<2; ++k)
if (abs(v21[k])<Scalar(1e-3)) v21[k] = Scalar(1e-3);
t21.setIdentity();
t21.linear() = Rotation2D<Scalar>(a).toRotationMatrix();
VERIFY_IS_APPROX(t20.fromPositionOrientationScale(v20,a,v21).matrix(),
t21.pretranslate(v20).scale(v21).matrix());
t21.setIdentity();
t21.linear() = Rotation2D<Scalar>(-a).toRotationMatrix();
VERIFY( (t20.fromPositionOrientationScale(v20,a,v21)
* (t21.prescale(v21.cwiseInverse()).translate(-v20))).matrix().isIdentity(test_precision<Scalar>()) );
// Transform - new API
// 3D
t0.setIdentity();
t0.rotate(q1).scale(v0).translate(v0);
// mat * aligned scaling and mat * translation
t1 = (Matrix3(q1) * AlignedScaling3(v0)) * Translation3(v0);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t1 = (Matrix3(q1) * Eigen::Scaling(v0)) * Translation3(v0);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t1 = (q1 * Eigen::Scaling(v0)) * Translation3(v0);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// mat * transformation and aligned scaling * translation
t1 = Matrix3(q1) * (AlignedScaling3(v0) * Translation3(v0));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0.setIdentity();
t0.scale(s0).translate(v0);
t1 = Eigen::Scaling(s0) * Translation3(v0);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0.prescale(s0);
t1 = Eigen::Scaling(s0) * t1;
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0 = t3;
t0.scale(s0);
t1 = t3 * Eigen::Scaling(s0,s0,s0);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0.prescale(s0);
t1 = Eigen::Scaling(s0,s0,s0) * t1;
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0 = t3;
t0.scale(s0);
t1 = t3 * Eigen::Scaling(s0);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0.prescale(s0);
t1 = Eigen::Scaling(s0) * t1;
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0.setIdentity();
t0.prerotate(q1).prescale(v0).pretranslate(v0);
// translation * aligned scaling and transformation * mat
t1 = (Translation3(v0) * AlignedScaling3(v0)) * Transform3(q1);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// scaling * mat and translation * mat
t1 = Translation3(v0) * (AlignedScaling3(v0) * Transform3(q1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t0.setIdentity();
t0.scale(v0).translate(v0).rotate(q1);
// translation * mat and aligned scaling * transformation
t1 = AlignedScaling3(v0) * (Translation3(v0) * Transform3(q1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// transformation * aligned scaling
t0.scale(v0);
t1 *= AlignedScaling3(v0);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
t1 = AlignedScaling3(v0) * (Translation3(v0) * Transform3(q1));
t1 = t1 * v0.asDiagonal();
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// transformation * translation
t0.translate(v0);
t1 = t1 * Translation3(v0);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// translation * transformation
t0.pretranslate(v0);
t1 = Translation3(v0) * t1;
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// transform * quaternion
t0.rotate(q1);
t1 = t1 * q1;
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// translation * quaternion
t0.translate(v1).rotate(q1);
t1 = t1 * (Translation3(v1) * q1);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// aligned scaling * quaternion
t0.scale(v1).rotate(q1);
t1 = t1 * (AlignedScaling3(v1) * q1);
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// quaternion * transform
t0.prerotate(q1);
t1 = q1 * t1;
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// quaternion * translation
t0.rotate(q1).translate(v1);
t1 = t1 * (q1 * Translation3(v1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// quaternion * aligned scaling
t0.rotate(q1).scale(v1);
t1 = t1 * (q1 * AlignedScaling3(v1));
VERIFY_IS_APPROX(t0.matrix(), t1.matrix());
// test transform inversion
t0.setIdentity();
t0.translate(v0);
do {
t0.linear().setRandom();
} while(t0.linear().jacobiSvd().singularValues()(2)<test_precision<Scalar>());
Matrix4 t044 = Matrix4::Zero();
t044(3,3) = 1;
t044.block(0,0,t0.matrix().rows(),4) = t0.matrix();
VERIFY_IS_APPROX(t0.inverse(Affine).matrix(), t044.inverse().block(0,0,t0.matrix().rows(),4));
t0.setIdentity();
t0.translate(v0).rotate(q1);
t044 = Matrix4::Zero();
t044(3,3) = 1;
t044.block(0,0,t0.matrix().rows(),4) = t0.matrix();
VERIFY_IS_APPROX(t0.inverse(Isometry).matrix(), t044.inverse().block(0,0,t0.matrix().rows(),4));
Matrix3 mat_rotation, mat_scaling;
t0.setIdentity();
t0.translate(v0).rotate(q1).scale(v1);
t0.computeRotationScaling(&mat_rotation, &mat_scaling);
VERIFY_IS_APPROX(t0.linear(), mat_rotation * mat_scaling);
VERIFY_IS_APPROX(mat_rotation*mat_rotation.adjoint(), Matrix3::Identity());
VERIFY_IS_APPROX(mat_rotation.determinant(), Scalar(1));
t0.computeScalingRotation(&mat_scaling, &mat_rotation);
VERIFY_IS_APPROX(t0.linear(), mat_scaling * mat_rotation);
VERIFY_IS_APPROX(mat_rotation*mat_rotation.adjoint(), Matrix3::Identity());
VERIFY_IS_APPROX(mat_rotation.determinant(), Scalar(1));
// test casting
Transform<float,3,Mode> t1f = t1.template cast<float>();
VERIFY_IS_APPROX(t1f.template cast<Scalar>(),t1);
Transform<double,3,Mode> t1d = t1.template cast<double>();
VERIFY_IS_APPROX(t1d.template cast<Scalar>(),t1);
Translation3 tr1(v0);
Translation<float,3> tr1f = tr1.template cast<float>();
VERIFY_IS_APPROX(tr1f.template cast<Scalar>(),tr1);
Translation<double,3> tr1d = tr1.template cast<double>();
VERIFY_IS_APPROX(tr1d.template cast<Scalar>(),tr1);
AngleAxis<float> aa1f = aa1.template cast<float>();
VERIFY_IS_APPROX(aa1f.template cast<Scalar>(),aa1);
AngleAxis<double> aa1d = aa1.template cast<double>();
VERIFY_IS_APPROX(aa1d.template cast<Scalar>(),aa1);
Rotation2D<Scalar> r2d1(internal::random<Scalar>());
Rotation2D<float> r2d1f = r2d1.template cast<float>();
VERIFY_IS_APPROX(r2d1f.template cast<Scalar>(),r2d1);
Rotation2D<double> r2d1d = r2d1.template cast<double>();
VERIFY_IS_APPROX(r2d1d.template cast<Scalar>(),r2d1);
for(int k=0; k<100; ++k)
{
Scalar angle = internal::random<Scalar>(-100,100);
Rotation2D<Scalar> rot2(angle);
VERIFY( rot2.smallestPositiveAngle() >= 0 );
VERIFY( rot2.smallestPositiveAngle() <= Scalar(2)*Scalar(EIGEN_PI) );
VERIFY_IS_APPROX( angleToVec(rot2.smallestPositiveAngle()), angleToVec(rot2.angle()) );
VERIFY( rot2.smallestAngle() >= -Scalar(EIGEN_PI) );
VERIFY( rot2.smallestAngle() <= Scalar(EIGEN_PI) );
VERIFY_IS_APPROX( angleToVec(rot2.smallestAngle()), angleToVec(rot2.angle()) );
Matrix<Scalar,2,2> rot2_as_mat(rot2);
Rotation2D<Scalar> rot3(rot2_as_mat);
VERIFY_IS_APPROX( angleToVec(rot2.smallestAngle()), angleToVec(rot3.angle()) );
}
s0 = internal::random<Scalar>(-100,100);
s1 = internal::random<Scalar>(-100,100);
Rotation2D<Scalar> R0(s0), R1(s1);
t20 = Translation2(v20) * (R0 * Eigen::Scaling(s0));
t21 = Translation2(v20) * R0 * Eigen::Scaling(s0);
VERIFY_IS_APPROX(t20,t21);
t20 = Translation2(v20) * (R0 * R0.inverse() * Eigen::Scaling(s0));
t21 = Translation2(v20) * Eigen::Scaling(s0);
VERIFY_IS_APPROX(t20,t21);
VERIFY_IS_APPROX(s0, (R0.slerp(0, R1)).angle());
VERIFY_IS_APPROX( angleToVec(R1.smallestPositiveAngle()), angleToVec((R0.slerp(1, R1)).smallestPositiveAngle()) );
VERIFY_IS_APPROX(R0.smallestPositiveAngle(), (R0.slerp(0.5, R0)).smallestPositiveAngle());
if(std::cos(s0)>0)
VERIFY_IS_MUCH_SMALLER_THAN((R0.slerp(0.5, R0.inverse())).smallestAngle(), Scalar(1));
else
VERIFY_IS_APPROX(Scalar(EIGEN_PI), (R0.slerp(0.5, R0.inverse())).smallestPositiveAngle());
// Check path length
Scalar l = 0;
int path_steps = 100;
for(int k=0; k<path_steps; ++k)
{
Scalar a1 = R0.slerp(Scalar(k)/Scalar(path_steps), R1).angle();
Scalar a2 = R0.slerp(Scalar(k+1)/Scalar(path_steps), R1).angle();
l += std::abs(a2-a1);
}
VERIFY(l<=Scalar(EIGEN_PI)*(Scalar(1)+NumTraits<Scalar>::epsilon()*Scalar(path_steps/2)));
// check basic features
{
Rotation2D<Scalar> r1; // default ctor
r1 = Rotation2D<Scalar>(s0); // copy assignment
VERIFY_IS_APPROX(r1.angle(),s0);
Rotation2D<Scalar> r2(r1); // copy ctor
VERIFY_IS_APPROX(r2.angle(),s0);
}
{
Transform3 t32(Matrix4::Random()), t33, t34;
t34 = t33 = t32;
t32.scale(v0);
t33*=AlignedScaling3(v0);
VERIFY_IS_APPROX(t32.matrix(), t33.matrix());
t33 = t34 * AlignedScaling3(v0);
VERIFY_IS_APPROX(t32.matrix(), t33.matrix());
}
}
template<typename A1, typename A2, typename P, typename Q, typename V, typename H>
void transform_associativity_left(const A1& a1, const A2& a2, const P& p, const Q& q, const V& v, const H& h)
{
VERIFY_IS_APPROX( q*(a1*v), (q*a1)*v );
VERIFY_IS_APPROX( q*(a2*v), (q*a2)*v );
VERIFY_IS_APPROX( q*(p*h).hnormalized(), ((q*p)*h).hnormalized() );
}
template<typename A1, typename A2, typename P, typename Q, typename V, typename H>
void transform_associativity2(const A1& a1, const A2& a2, const P& p, const Q& q, const V& v, const H& h)
{
VERIFY_IS_APPROX( a1*(q*v), (a1*q)*v );
VERIFY_IS_APPROX( a2*(q*v), (a2*q)*v );
VERIFY_IS_APPROX( p *(q*v).homogeneous(), (p *q)*v.homogeneous() );
transform_associativity_left(a1, a2,p, q, v, h);
}
template<typename Scalar, int Dim, int Options,typename RotationType>
void transform_associativity(const RotationType& R)
{
typedef Matrix<Scalar,Dim,1> VectorType;
typedef Matrix<Scalar,Dim+1,1> HVectorType;
typedef Matrix<Scalar,Dim,Dim> LinearType;
typedef Matrix<Scalar,Dim+1,Dim+1> MatrixType;
typedef Transform<Scalar,Dim,AffineCompact,Options> AffineCompactType;
typedef Transform<Scalar,Dim,Affine,Options> AffineType;
typedef Transform<Scalar,Dim,Projective,Options> ProjectiveType;
typedef DiagonalMatrix<Scalar,Dim> ScalingType;
typedef Translation<Scalar,Dim> TranslationType;
AffineCompactType A1c; A1c.matrix().setRandom();
AffineCompactType A2c; A2c.matrix().setRandom();
AffineType A1(A1c);
AffineType A2(A2c);
ProjectiveType P1; P1.matrix().setRandom();
VectorType v1 = VectorType::Random();
VectorType v2 = VectorType::Random();
HVectorType h1 = HVectorType::Random();
Scalar s1 = internal::random<Scalar>();
LinearType L = LinearType::Random();
MatrixType M = MatrixType::Random();
CALL_SUBTEST( transform_associativity2(A1c, A1, P1, A2, v2, h1) );
CALL_SUBTEST( transform_associativity2(A1c, A1, P1, A2c, v2, h1) );
CALL_SUBTEST( transform_associativity2(A1c, A1, P1, v1.asDiagonal(), v2, h1) );
CALL_SUBTEST( transform_associativity2(A1c, A1, P1, ScalingType(v1), v2, h1) );
CALL_SUBTEST( transform_associativity2(A1c, A1, P1, Scaling(v1), v2, h1) );
CALL_SUBTEST( transform_associativity2(A1c, A1, P1, Scaling(s1), v2, h1) );
CALL_SUBTEST( transform_associativity2(A1c, A1, P1, TranslationType(v1), v2, h1) );
CALL_SUBTEST( transform_associativity_left(A1c, A1, P1, L, v2, h1) );
CALL_SUBTEST( transform_associativity2(A1c, A1, P1, R, v2, h1) );
VERIFY_IS_APPROX( A1*(M*h1), (A1*M)*h1 );
VERIFY_IS_APPROX( A1c*(M*h1), (A1c*M)*h1 );
VERIFY_IS_APPROX( P1*(M*h1), (P1*M)*h1 );
VERIFY_IS_APPROX( M*(A1*h1), (M*A1)*h1 );
VERIFY_IS_APPROX( M*(A1c*h1), (M*A1c)*h1 );
VERIFY_IS_APPROX( M*(P1*h1), ((M*P1)*h1) );
}
template<typename Scalar> void transform_alignment()
{
typedef Transform<Scalar,3,Projective,AutoAlign> Projective3a;
typedef Transform<Scalar,3,Projective,DontAlign> Projective3u;
EIGEN_ALIGN_MAX Scalar array1[16];
EIGEN_ALIGN_MAX Scalar array2[16];
EIGEN_ALIGN_MAX Scalar array3[16+1];
Scalar* array3u = array3+1;
Projective3a *p1 = ::new(reinterpret_cast<void*>(array1)) Projective3a;
Projective3u *p2 = ::new(reinterpret_cast<void*>(array2)) Projective3u;
Projective3u *p3 = ::new(reinterpret_cast<void*>(array3u)) Projective3u;
p1->matrix().setRandom();
*p2 = *p1;
*p3 = *p1;
VERIFY_IS_APPROX(p1->matrix(), p2->matrix());
VERIFY_IS_APPROX(p1->matrix(), p3->matrix());
VERIFY_IS_APPROX( (*p1) * (*p1), (*p2)*(*p3));
#if defined(EIGEN_VECTORIZE) && EIGEN_MAX_STATIC_ALIGN_BYTES>0
if(internal::packet_traits<Scalar>::Vectorizable)
VERIFY_RAISES_ASSERT((::new(reinterpret_cast<void*>(array3u)) Projective3a));
#endif
}
template<typename Scalar, int Dim, int Options> void transform_products()
{
typedef Matrix<Scalar,Dim+1,Dim+1> Mat;
typedef Transform<Scalar,Dim,Projective,Options> Proj;
typedef Transform<Scalar,Dim,Affine,Options> Aff;
typedef Transform<Scalar,Dim,AffineCompact,Options> AffC;
Proj p; p.matrix().setRandom();
Aff a; a.linear().setRandom(); a.translation().setRandom();
AffC ac = a;
Mat p_m(p.matrix()), a_m(a.matrix());
VERIFY_IS_APPROX((p*p).matrix(), p_m*p_m);
VERIFY_IS_APPROX((a*a).matrix(), a_m*a_m);
VERIFY_IS_APPROX((p*a).matrix(), p_m*a_m);
VERIFY_IS_APPROX((a*p).matrix(), a_m*p_m);
VERIFY_IS_APPROX((ac*a).matrix(), a_m*a_m);
VERIFY_IS_APPROX((a*ac).matrix(), a_m*a_m);
VERIFY_IS_APPROX((p*ac).matrix(), p_m*a_m);
VERIFY_IS_APPROX((ac*p).matrix(), a_m*p_m);
}
template<typename Scalar, int Mode, int Options> void transformations_no_scale()
{
/* this test covers the following files:
Cross.h Quaternion.h, Transform.h
*/
typedef Matrix<Scalar,3,1> Vector3;
typedef Matrix<Scalar,4,1> Vector4;
typedef Quaternion<Scalar> Quaternionx;
typedef AngleAxis<Scalar> AngleAxisx;
typedef Transform<Scalar,3,Mode,Options> Transform3;
typedef Translation<Scalar,3> Translation3;
typedef Matrix<Scalar,4,4> Matrix4;
Vector3 v0 = Vector3::Random(),
v1 = Vector3::Random();
Transform3 t0, t1, t2;
Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI));
Quaternionx q1, q2;
q1 = AngleAxisx(a, v0.normalized());
t0 = Transform3::Identity();
VERIFY_IS_APPROX(t0.matrix(), Transform3::MatrixType::Identity());
t0.setIdentity();
t1.setIdentity();
v1 = Vector3::Ones();
t0.linear() = q1.toRotationMatrix();
t0.pretranslate(v0);
t1.linear() = q1.conjugate().toRotationMatrix();
t1.translate(-v0);
VERIFY((t0 * t1).matrix().isIdentity(test_precision<Scalar>()));
t1.fromPositionOrientationScale(v0, q1, v1);
VERIFY_IS_APPROX(t1.matrix(), t0.matrix());
VERIFY_IS_APPROX(t1*v1, t0*v1);
// translation * vector
t0.setIdentity();
t0.translate(v0);
VERIFY_IS_APPROX((t0 * v1).template head<3>(), Translation3(v0) * v1);
// Conversion to matrix.
Transform3 t3;
t3.linear() = q1.toRotationMatrix();
t3.translation() = v1;
Matrix4 m3 = t3.matrix();
VERIFY((m3 * m3.inverse()).isIdentity(test_precision<Scalar>()));
// Verify implicit last row is initialized.
VERIFY_IS_APPROX(Vector4(m3.row(3)), Vector4(0.0, 0.0, 0.0, 1.0));
}
void test_geo_transformations()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(( transformations<double,Affine,AutoAlign>() ));
CALL_SUBTEST_1(( non_projective_only<double,Affine,AutoAlign>() ));
CALL_SUBTEST_2(( transformations<float,AffineCompact,AutoAlign>() ));
CALL_SUBTEST_2(( non_projective_only<float,AffineCompact,AutoAlign>() ));
CALL_SUBTEST_2(( transform_alignment<float>() ));
CALL_SUBTEST_3(( transformations<double,Projective,AutoAlign>() ));
CALL_SUBTEST_3(( transformations<double,Projective,DontAlign>() ));
CALL_SUBTEST_3(( transform_alignment<double>() ));
CALL_SUBTEST_4(( transformations<float,Affine,RowMajor|AutoAlign>() ));
CALL_SUBTEST_4(( non_projective_only<float,Affine,RowMajor>() ));
CALL_SUBTEST_5(( transformations<double,AffineCompact,RowMajor|AutoAlign>() ));
CALL_SUBTEST_5(( non_projective_only<double,AffineCompact,RowMajor>() ));
CALL_SUBTEST_6(( transformations<double,Projective,RowMajor|AutoAlign>() ));
CALL_SUBTEST_6(( transformations<double,Projective,RowMajor|DontAlign>() ));
CALL_SUBTEST_7(( transform_products<double,3,RowMajor|AutoAlign>() ));
CALL_SUBTEST_7(( transform_products<float,2,AutoAlign>() ));
CALL_SUBTEST_8(( transform_associativity<double,2,ColMajor>(Rotation2D<double>(internal::random<double>()*double(EIGEN_PI))) ));
CALL_SUBTEST_8(( transform_associativity<double,3,ColMajor>(Quaterniond::UnitRandom()) ));
CALL_SUBTEST_9(( transformations_no_scale<double,Affine,AutoAlign>() ));
CALL_SUBTEST_9(( transformations_no_scale<double,Isometry,AutoAlign>() ));
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/nullary.cpp | .cpp | 11,724 | 305 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010-2011 Jitse Niesen <jitse@maths.leeds.ac.uk>
// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename MatrixType>
bool equalsIdentity(const MatrixType& A)
{
typedef typename MatrixType::Scalar Scalar;
Scalar zero = static_cast<Scalar>(0);
bool offDiagOK = true;
for (Index i = 0; i < A.rows(); ++i) {
for (Index j = i+1; j < A.cols(); ++j) {
offDiagOK = offDiagOK && (A(i,j) == zero);
}
}
for (Index i = 0; i < A.rows(); ++i) {
for (Index j = 0; j < (std::min)(i, A.cols()); ++j) {
offDiagOK = offDiagOK && (A(i,j) == zero);
}
}
bool diagOK = (A.diagonal().array() == 1).all();
return offDiagOK && diagOK;
}
template<typename VectorType>
void check_extremity_accuracy(const VectorType &v, const typename VectorType::Scalar &low, const typename VectorType::Scalar &high)
{
typedef typename VectorType::Scalar Scalar;
typedef typename VectorType::RealScalar RealScalar;
RealScalar prec = internal::is_same<RealScalar,float>::value ? NumTraits<RealScalar>::dummy_precision()*10 : NumTraits<RealScalar>::dummy_precision()/10;
Index size = v.size();
if(size<20)
return;
for (int i=0; i<size; ++i)
{
if(i<5 || i>size-6)
{
Scalar ref = (low*RealScalar(size-i-1))/RealScalar(size-1) + (high*RealScalar(i))/RealScalar(size-1);
if(std::abs(ref)>1)
{
if(!internal::isApprox(v(i), ref, prec))
std::cout << v(i) << " != " << ref << " ; relative error: " << std::abs((v(i)-ref)/ref) << " ; required precision: " << prec << " ; range: " << low << "," << high << " ; i: " << i << "\n";
VERIFY(internal::isApprox(v(i), (low*RealScalar(size-i-1))/RealScalar(size-1) + (high*RealScalar(i))/RealScalar(size-1), prec));
}
}
}
}
template<typename VectorType>
void testVectorType(const VectorType& base)
{
typedef typename VectorType::Scalar Scalar;
typedef typename VectorType::RealScalar RealScalar;
const Index size = base.size();
Scalar high = internal::random<Scalar>(-500,500);
Scalar low = (size == 1 ? high : internal::random<Scalar>(-500,500));
if (low>high) std::swap(low,high);
// check low==high
if(internal::random<float>(0.f,1.f)<0.05f)
low = high;
// check abs(low) >> abs(high)
else if(size>2 && std::numeric_limits<RealScalar>::max_exponent10>0 && internal::random<float>(0.f,1.f)<0.1f)
low = -internal::random<Scalar>(1,2) * RealScalar(std::pow(RealScalar(10),std::numeric_limits<RealScalar>::max_exponent10/2));
const Scalar step = ((size == 1) ? 1 : (high-low)/(size-1));
// check whether the result yields what we expect it to do
VectorType m(base);
m.setLinSpaced(size,low,high);
if(!NumTraits<Scalar>::IsInteger)
{
VectorType n(size);
for (int i=0; i<size; ++i)
n(i) = low+i*step;
VERIFY_IS_APPROX(m,n);
CALL_SUBTEST( check_extremity_accuracy(m, low, high) );
}
if((!NumTraits<Scalar>::IsInteger) || ((high-low)>=size && (Index(high-low)%(size-1))==0) || (Index(high-low+1)<size && (size%Index(high-low+1))==0))
{
VectorType n(size);
if((!NumTraits<Scalar>::IsInteger) || (high-low>=size))
for (int i=0; i<size; ++i)
n(i) = size==1 ? low : (low + ((high-low)*Scalar(i))/(size-1));
else
for (int i=0; i<size; ++i)
n(i) = size==1 ? low : low + Scalar((double(high-low+1)*double(i))/double(size));
VERIFY_IS_APPROX(m,n);
// random access version
m = VectorType::LinSpaced(size,low,high);
VERIFY_IS_APPROX(m,n);
VERIFY( internal::isApprox(m(m.size()-1),high) );
VERIFY( size==1 || internal::isApprox(m(0),low) );
VERIFY_IS_EQUAL(m(m.size()-1) , high);
if(!NumTraits<Scalar>::IsInteger)
CALL_SUBTEST( check_extremity_accuracy(m, low, high) );
}
VERIFY( m(m.size()-1) <= high );
VERIFY( (m.array() <= high).all() );
VERIFY( (m.array() >= low).all() );
VERIFY( m(m.size()-1) >= low );
if(size>=1)
{
VERIFY( internal::isApprox(m(0),low) );
VERIFY_IS_EQUAL(m(0) , low);
}
// check whether everything works with row and col major vectors
Matrix<Scalar,Dynamic,1> row_vector(size);
Matrix<Scalar,1,Dynamic> col_vector(size);
row_vector.setLinSpaced(size,low,high);
col_vector.setLinSpaced(size,low,high);
// when using the extended precision (e.g., FPU) the relative error might exceed 1 bit
// when computing the squared sum in isApprox, thus the 2x factor.
VERIFY( row_vector.isApprox(col_vector.transpose(), Scalar(2)*NumTraits<Scalar>::epsilon()));
Matrix<Scalar,Dynamic,1> size_changer(size+50);
size_changer.setLinSpaced(size,low,high);
VERIFY( size_changer.size() == size );
typedef Matrix<Scalar,1,1> ScalarMatrix;
ScalarMatrix scalar;
scalar.setLinSpaced(1,low,high);
VERIFY_IS_APPROX( scalar, ScalarMatrix::Constant(high) );
VERIFY_IS_APPROX( ScalarMatrix::LinSpaced(1,low,high), ScalarMatrix::Constant(high) );
// regression test for bug 526 (linear vectorized transversal)
if (size > 1 && (!NumTraits<Scalar>::IsInteger)) {
m.tail(size-1).setLinSpaced(low, high);
VERIFY_IS_APPROX(m(size-1), high);
}
// regression test for bug 1383 (LinSpaced with empty size/range)
{
Index n0 = VectorType::SizeAtCompileTime==Dynamic ? 0 : VectorType::SizeAtCompileTime;
low = internal::random<Scalar>();
m = VectorType::LinSpaced(n0,low,low-1);
VERIFY(m.size()==n0);
if(VectorType::SizeAtCompileTime==Dynamic)
{
VERIFY_IS_EQUAL(VectorType::LinSpaced(n0,0,Scalar(n0-1)).sum(),Scalar(0));
VERIFY_IS_EQUAL(VectorType::LinSpaced(n0,low,low-1).sum(),Scalar(0));
}
m.setLinSpaced(n0,0,Scalar(n0-1));
VERIFY(m.size()==n0);
m.setLinSpaced(n0,low,low-1);
VERIFY(m.size()==n0);
// empty range only:
VERIFY_IS_APPROX(VectorType::LinSpaced(size,low,low),VectorType::Constant(size,low));
m.setLinSpaced(size,low,low);
VERIFY_IS_APPROX(m,VectorType::Constant(size,low));
if(NumTraits<Scalar>::IsInteger)
{
VERIFY_IS_APPROX( VectorType::LinSpaced(size,low,Scalar(low+size-1)), VectorType::LinSpaced(size,Scalar(low+size-1),low).reverse() );
if(VectorType::SizeAtCompileTime==Dynamic)
{
// Check negative multiplicator path:
for(Index k=1; k<5; ++k)
VERIFY_IS_APPROX( VectorType::LinSpaced(size,low,Scalar(low+(size-1)*k)), VectorType::LinSpaced(size,Scalar(low+(size-1)*k),low).reverse() );
// Check negative divisor path:
for(Index k=1; k<5; ++k)
VERIFY_IS_APPROX( VectorType::LinSpaced(size*k,low,Scalar(low+size-1)), VectorType::LinSpaced(size*k,Scalar(low+size-1),low).reverse() );
}
}
}
}
template<typename MatrixType>
void testMatrixType(const MatrixType& m)
{
using std::abs;
const Index rows = m.rows();
const Index cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
Scalar s1;
do {
s1 = internal::random<Scalar>();
} while(abs(s1)<RealScalar(1e-5) && (!NumTraits<Scalar>::IsInteger));
MatrixType A;
A.setIdentity(rows, cols);
VERIFY(equalsIdentity(A));
VERIFY(equalsIdentity(MatrixType::Identity(rows, cols)));
A = MatrixType::Constant(rows,cols,s1);
Index i = internal::random<Index>(0,rows-1);
Index j = internal::random<Index>(0,cols-1);
VERIFY_IS_APPROX( MatrixType::Constant(rows,cols,s1)(i,j), s1 );
VERIFY_IS_APPROX( MatrixType::Constant(rows,cols,s1).coeff(i,j), s1 );
VERIFY_IS_APPROX( A(i,j), s1 );
}
void test_nullary()
{
CALL_SUBTEST_1( testMatrixType(Matrix2d()) );
CALL_SUBTEST_2( testMatrixType(MatrixXcf(internal::random<int>(1,300),internal::random<int>(1,300))) );
CALL_SUBTEST_3( testMatrixType(MatrixXf(internal::random<int>(1,300),internal::random<int>(1,300))) );
for(int i = 0; i < g_repeat*10; i++) {
CALL_SUBTEST_4( testVectorType(VectorXd(internal::random<int>(1,30000))) );
CALL_SUBTEST_5( testVectorType(Vector4d()) ); // regression test for bug 232
CALL_SUBTEST_6( testVectorType(Vector3d()) );
CALL_SUBTEST_7( testVectorType(VectorXf(internal::random<int>(1,30000))) );
CALL_SUBTEST_8( testVectorType(Vector3f()) );
CALL_SUBTEST_8( testVectorType(Vector4f()) );
CALL_SUBTEST_8( testVectorType(Matrix<float,8,1>()) );
CALL_SUBTEST_8( testVectorType(Matrix<float,1,1>()) );
CALL_SUBTEST_9( testVectorType(VectorXi(internal::random<int>(1,10))) );
CALL_SUBTEST_9( testVectorType(VectorXi(internal::random<int>(9,300))) );
CALL_SUBTEST_9( testVectorType(Matrix<int,1,1>()) );
}
#ifdef EIGEN_TEST_PART_6
// Assignment of a RowVectorXd to a MatrixXd (regression test for bug #79).
VERIFY( (MatrixXd(RowVectorXd::LinSpaced(3, 0, 1)) - RowVector3d(0, 0.5, 1)).norm() < std::numeric_limits<double>::epsilon() );
#endif
#ifdef EIGEN_TEST_PART_9
// Check possible overflow issue
{
int n = 60000;
ArrayXi a1(n), a2(n);
a1.setLinSpaced(n, 0, n-1);
for(int i=0; i<n; ++i)
a2(i) = i;
VERIFY_IS_APPROX(a1,a2);
}
#endif
#ifdef EIGEN_TEST_PART_10
// check some internal logic
VERIFY(( internal::has_nullary_operator<internal::scalar_constant_op<double> >::value ));
VERIFY(( !internal::has_unary_operator<internal::scalar_constant_op<double> >::value ));
VERIFY(( !internal::has_binary_operator<internal::scalar_constant_op<double> >::value ));
VERIFY(( internal::functor_has_linear_access<internal::scalar_constant_op<double> >::ret ));
VERIFY(( !internal::has_nullary_operator<internal::scalar_identity_op<double> >::value ));
VERIFY(( !internal::has_unary_operator<internal::scalar_identity_op<double> >::value ));
VERIFY(( internal::has_binary_operator<internal::scalar_identity_op<double> >::value ));
VERIFY(( !internal::functor_has_linear_access<internal::scalar_identity_op<double> >::ret ));
VERIFY(( !internal::has_nullary_operator<internal::linspaced_op<float,float> >::value ));
VERIFY(( internal::has_unary_operator<internal::linspaced_op<float,float> >::value ));
VERIFY(( !internal::has_binary_operator<internal::linspaced_op<float,float> >::value ));
VERIFY(( internal::functor_has_linear_access<internal::linspaced_op<float,float> >::ret ));
// Regression unit test for a weird MSVC bug.
// Search "nullary_wrapper_workaround_msvc" in CoreEvaluators.h for the details.
// See also traits<Ref>::match.
{
MatrixXf A = MatrixXf::Random(3,3);
Ref<const MatrixXf> R = 2.0*A;
VERIFY_IS_APPROX(R, A+A);
Ref<const MatrixXf> R1 = MatrixXf::Random(3,3)+A;
VectorXi V = VectorXi::Random(3);
Ref<const VectorXi> R2 = VectorXi::LinSpaced(3,1,3)+V;
VERIFY_IS_APPROX(R2, V+Vector3i(1,2,3));
VERIFY(( internal::has_nullary_operator<internal::scalar_constant_op<float> >::value ));
VERIFY(( !internal::has_unary_operator<internal::scalar_constant_op<float> >::value ));
VERIFY(( !internal::has_binary_operator<internal::scalar_constant_op<float> >::value ));
VERIFY(( internal::functor_has_linear_access<internal::scalar_constant_op<float> >::ret ));
VERIFY(( !internal::has_nullary_operator<internal::linspaced_op<int,int> >::value ));
VERIFY(( internal::has_unary_operator<internal::linspaced_op<int,int> >::value ));
VERIFY(( !internal::has_binary_operator<internal::linspaced_op<int,int> >::value ));
VERIFY(( internal::functor_has_linear_access<internal::linspaced_op<int,int> >::ret ));
}
#endif
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/product_large.cpp | .cpp | 4,352 | 110 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "product.h"
template<typename T>
void test_aliasing()
{
int rows = internal::random<int>(1,12);
int cols = internal::random<int>(1,12);
typedef Matrix<T,Dynamic,Dynamic> MatrixType;
typedef Matrix<T,Dynamic,1> VectorType;
VectorType x(cols); x.setRandom();
VectorType z(x);
VectorType y(rows); y.setZero();
MatrixType A(rows,cols); A.setRandom();
// CwiseBinaryOp
VERIFY_IS_APPROX(x = y + A*x, A*z); // OK because "y + A*x" is marked as "assume-aliasing"
x = z;
// CwiseUnaryOp
VERIFY_IS_APPROX(x = T(1.)*(A*x), A*z); // OK because 1*(A*x) is replaced by (1*A*x) which is a Product<> expression
x = z;
// VERIFY_IS_APPROX(x = y-A*x, -A*z); // Not OK in 3.3 because x is resized before A*x gets evaluated
x = z;
}
void test_product_large()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( product(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_2( product(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_2( product(MatrixXd(internal::random<int>(1,10), internal::random<int>(1,10))) );
CALL_SUBTEST_3( product(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_4( product(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
CALL_SUBTEST_5( product(Matrix<float,Dynamic,Dynamic,RowMajor>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_1( test_aliasing<float>() );
}
#if defined EIGEN_TEST_PART_6
{
// test a specific issue in DiagonalProduct
int N = 1000000;
VectorXf v = VectorXf::Ones(N);
MatrixXf m = MatrixXf::Ones(N,3);
m = (v+v).asDiagonal() * m;
VERIFY_IS_APPROX(m, MatrixXf::Constant(N,3,2));
}
{
// test deferred resizing in Matrix::operator=
MatrixXf a = MatrixXf::Random(10,4), b = MatrixXf::Random(4,10), c = a;
VERIFY_IS_APPROX((a = a * b), (c * b).eval());
}
{
// check the functions to setup blocking sizes compile and do not segfault
// FIXME check they do what they are supposed to do !!
std::ptrdiff_t l1 = internal::random<int>(10000,20000);
std::ptrdiff_t l2 = internal::random<int>(100000,200000);
std::ptrdiff_t l3 = internal::random<int>(1000000,2000000);
setCpuCacheSizes(l1,l2,l3);
VERIFY(l1==l1CacheSize());
VERIFY(l2==l2CacheSize());
std::ptrdiff_t k1 = internal::random<int>(10,100)*16;
std::ptrdiff_t m1 = internal::random<int>(10,100)*16;
std::ptrdiff_t n1 = internal::random<int>(10,100)*16;
// only makes sure it compiles fine
internal::computeProductBlockingSizes<float,float,std::ptrdiff_t>(k1,m1,n1,1);
}
{
// test regression in row-vector by matrix (bad Map type)
MatrixXf mat1(10,32); mat1.setRandom();
MatrixXf mat2(32,32); mat2.setRandom();
MatrixXf r1 = mat1.row(2)*mat2.transpose();
VERIFY_IS_APPROX(r1, (mat1.row(2)*mat2.transpose()).eval());
MatrixXf r2 = mat1.row(2)*mat2;
VERIFY_IS_APPROX(r2, (mat1.row(2)*mat2).eval());
}
{
Eigen::MatrixXd A(10,10), B, C;
A.setRandom();
C = A;
for(int k=0; k<79; ++k)
C = C * A;
B.noalias() = (((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)) * ((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)))
* (((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)) * ((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)));
VERIFY_IS_APPROX(B,C);
}
#endif
// Regression test for bug 714:
#if defined EIGEN_HAS_OPENMP
omp_set_dynamic(1);
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_6( product(Matrix<float,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
#endif
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/packetmath.cpp | .cpp | 24,039 | 637 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include "unsupported/Eigen/SpecialFunctions"
#if defined __GNUC__ && __GNUC__>=6
#pragma GCC diagnostic ignored "-Wignored-attributes"
#endif
// using namespace Eigen;
namespace Eigen {
namespace internal {
template<typename T> T negate(const T& x) { return -x; }
}
}
// NOTE: we disbale inlining for this function to workaround a GCC issue when using -O3 and the i387 FPU.
template<typename Scalar> EIGEN_DONT_INLINE
bool isApproxAbs(const Scalar& a, const Scalar& b, const typename NumTraits<Scalar>::Real& refvalue)
{
return internal::isMuchSmallerThan(a-b, refvalue);
}
template<typename Scalar> bool areApproxAbs(const Scalar* a, const Scalar* b, int size, const typename NumTraits<Scalar>::Real& refvalue)
{
for (int i=0; i<size; ++i)
{
if (!isApproxAbs(a[i],b[i],refvalue))
{
std::cout << "ref: [" << Map<const Matrix<Scalar,1,Dynamic> >(a,size) << "]" << " != vec: [" << Map<const Matrix<Scalar,1,Dynamic> >(b,size) << "]\n";
return false;
}
}
return true;
}
template<typename Scalar> bool areApprox(const Scalar* a, const Scalar* b, int size)
{
for (int i=0; i<size; ++i)
{
if (a[i]!=b[i] && !internal::isApprox(a[i],b[i]))
{
std::cout << "ref: [" << Map<const Matrix<Scalar,1,Dynamic> >(a,size) << "]" << " != vec: [" << Map<const Matrix<Scalar,1,Dynamic> >(b,size) << "]\n";
return false;
}
}
return true;
}
#define CHECK_CWISE1(REFOP, POP) { \
for (int i=0; i<PacketSize; ++i) \
ref[i] = REFOP(data1[i]); \
internal::pstore(data2, POP(internal::pload<Packet>(data1))); \
VERIFY(areApprox(ref, data2, PacketSize) && #POP); \
}
template<bool Cond,typename Packet>
struct packet_helper
{
template<typename T>
inline Packet load(const T* from) const { return internal::pload<Packet>(from); }
template<typename T>
inline void store(T* to, const Packet& x) const { internal::pstore(to,x); }
};
template<typename Packet>
struct packet_helper<false,Packet>
{
template<typename T>
inline T load(const T* from) const { return *from; }
template<typename T>
inline void store(T* to, const T& x) const { *to = x; }
};
#define CHECK_CWISE1_IF(COND, REFOP, POP) if(COND) { \
packet_helper<COND,Packet> h; \
for (int i=0; i<PacketSize; ++i) \
ref[i] = REFOP(data1[i]); \
h.store(data2, POP(h.load(data1))); \
VERIFY(areApprox(ref, data2, PacketSize) && #POP); \
}
#define CHECK_CWISE2_IF(COND, REFOP, POP) if(COND) { \
packet_helper<COND,Packet> h; \
for (int i=0; i<PacketSize; ++i) \
ref[i] = REFOP(data1[i], data1[i+PacketSize]); \
h.store(data2, POP(h.load(data1),h.load(data1+PacketSize))); \
VERIFY(areApprox(ref, data2, PacketSize) && #POP); \
}
#define REF_ADD(a,b) ((a)+(b))
#define REF_SUB(a,b) ((a)-(b))
#define REF_MUL(a,b) ((a)*(b))
#define REF_DIV(a,b) ((a)/(b))
template<typename Scalar> void packetmath()
{
using std::abs;
typedef internal::packet_traits<Scalar> PacketTraits;
typedef typename PacketTraits::type Packet;
const int PacketSize = PacketTraits::size;
typedef typename NumTraits<Scalar>::Real RealScalar;
const int max_size = PacketSize > 4 ? PacketSize : 4;
const int size = PacketSize*max_size;
EIGEN_ALIGN_MAX Scalar data1[size];
EIGEN_ALIGN_MAX Scalar data2[size];
EIGEN_ALIGN_MAX Packet packets[PacketSize*2];
EIGEN_ALIGN_MAX Scalar ref[size];
RealScalar refvalue = 0;
for (int i=0; i<size; ++i)
{
data1[i] = internal::random<Scalar>()/RealScalar(PacketSize);
data2[i] = internal::random<Scalar>()/RealScalar(PacketSize);
refvalue = (std::max)(refvalue,abs(data1[i]));
}
internal::pstore(data2, internal::pload<Packet>(data1));
VERIFY(areApprox(data1, data2, PacketSize) && "aligned load/store");
for (int offset=0; offset<PacketSize; ++offset)
{
internal::pstore(data2, internal::ploadu<Packet>(data1+offset));
VERIFY(areApprox(data1+offset, data2, PacketSize) && "internal::ploadu");
}
for (int offset=0; offset<PacketSize; ++offset)
{
internal::pstoreu(data2+offset, internal::pload<Packet>(data1));
VERIFY(areApprox(data1, data2+offset, PacketSize) && "internal::pstoreu");
}
for (int offset=0; offset<PacketSize; ++offset)
{
packets[0] = internal::pload<Packet>(data1);
packets[1] = internal::pload<Packet>(data1+PacketSize);
if (offset==0) internal::palign<0>(packets[0], packets[1]);
else if (offset==1) internal::palign<1>(packets[0], packets[1]);
else if (offset==2) internal::palign<2>(packets[0], packets[1]);
else if (offset==3) internal::palign<3>(packets[0], packets[1]);
else if (offset==4) internal::palign<4>(packets[0], packets[1]);
else if (offset==5) internal::palign<5>(packets[0], packets[1]);
else if (offset==6) internal::palign<6>(packets[0], packets[1]);
else if (offset==7) internal::palign<7>(packets[0], packets[1]);
else if (offset==8) internal::palign<8>(packets[0], packets[1]);
else if (offset==9) internal::palign<9>(packets[0], packets[1]);
else if (offset==10) internal::palign<10>(packets[0], packets[1]);
else if (offset==11) internal::palign<11>(packets[0], packets[1]);
else if (offset==12) internal::palign<12>(packets[0], packets[1]);
else if (offset==13) internal::palign<13>(packets[0], packets[1]);
else if (offset==14) internal::palign<14>(packets[0], packets[1]);
else if (offset==15) internal::palign<15>(packets[0], packets[1]);
internal::pstore(data2, packets[0]);
for (int i=0; i<PacketSize; ++i)
ref[i] = data1[i+offset];
VERIFY(areApprox(ref, data2, PacketSize) && "internal::palign");
}
VERIFY((!PacketTraits::Vectorizable) || PacketTraits::HasAdd);
VERIFY((!PacketTraits::Vectorizable) || PacketTraits::HasSub);
VERIFY((!PacketTraits::Vectorizable) || PacketTraits::HasMul);
VERIFY((!PacketTraits::Vectorizable) || PacketTraits::HasNegate);
VERIFY((internal::is_same<Scalar,int>::value) || (!PacketTraits::Vectorizable) || PacketTraits::HasDiv);
CHECK_CWISE2_IF(PacketTraits::HasAdd, REF_ADD, internal::padd);
CHECK_CWISE2_IF(PacketTraits::HasSub, REF_SUB, internal::psub);
CHECK_CWISE2_IF(PacketTraits::HasMul, REF_MUL, internal::pmul);
CHECK_CWISE2_IF(PacketTraits::HasDiv, REF_DIV, internal::pdiv);
CHECK_CWISE1(internal::negate, internal::pnegate);
CHECK_CWISE1(numext::conj, internal::pconj);
for(int offset=0;offset<3;++offset)
{
for (int i=0; i<PacketSize; ++i)
ref[i] = data1[offset];
internal::pstore(data2, internal::pset1<Packet>(data1[offset]));
VERIFY(areApprox(ref, data2, PacketSize) && "internal::pset1");
}
{
for (int i=0; i<PacketSize*4; ++i)
ref[i] = data1[i/PacketSize];
Packet A0, A1, A2, A3;
internal::pbroadcast4<Packet>(data1, A0, A1, A2, A3);
internal::pstore(data2+0*PacketSize, A0);
internal::pstore(data2+1*PacketSize, A1);
internal::pstore(data2+2*PacketSize, A2);
internal::pstore(data2+3*PacketSize, A3);
VERIFY(areApprox(ref, data2, 4*PacketSize) && "internal::pbroadcast4");
}
{
for (int i=0; i<PacketSize*2; ++i)
ref[i] = data1[i/PacketSize];
Packet A0, A1;
internal::pbroadcast2<Packet>(data1, A0, A1);
internal::pstore(data2+0*PacketSize, A0);
internal::pstore(data2+1*PacketSize, A1);
VERIFY(areApprox(ref, data2, 2*PacketSize) && "internal::pbroadcast2");
}
VERIFY(internal::isApprox(data1[0], internal::pfirst(internal::pload<Packet>(data1))) && "internal::pfirst");
if(PacketSize>1)
{
for(int offset=0;offset<4;++offset)
{
for(int i=0;i<PacketSize/2;++i)
ref[2*i+0] = ref[2*i+1] = data1[offset+i];
internal::pstore(data2,internal::ploaddup<Packet>(data1+offset));
VERIFY(areApprox(ref, data2, PacketSize) && "ploaddup");
}
}
if(PacketSize>2)
{
for(int offset=0;offset<4;++offset)
{
for(int i=0;i<PacketSize/4;++i)
ref[4*i+0] = ref[4*i+1] = ref[4*i+2] = ref[4*i+3] = data1[offset+i];
internal::pstore(data2,internal::ploadquad<Packet>(data1+offset));
VERIFY(areApprox(ref, data2, PacketSize) && "ploadquad");
}
}
ref[0] = 0;
for (int i=0; i<PacketSize; ++i)
ref[0] += data1[i];
VERIFY(isApproxAbs(ref[0], internal::predux(internal::pload<Packet>(data1)), refvalue) && "internal::predux");
{
int newsize = PacketSize>4?PacketSize/2:PacketSize;
for (int i=0; i<newsize; ++i)
ref[i] = 0;
for (int i=0; i<PacketSize; ++i)
ref[i%newsize] += data1[i];
internal::pstore(data2, internal::predux_downto4(internal::pload<Packet>(data1)));
VERIFY(areApprox(ref, data2, newsize) && "internal::predux_downto4");
}
ref[0] = 1;
for (int i=0; i<PacketSize; ++i)
ref[0] *= data1[i];
VERIFY(internal::isApprox(ref[0], internal::predux_mul(internal::pload<Packet>(data1))) && "internal::predux_mul");
for (int j=0; j<PacketSize; ++j)
{
ref[j] = 0;
for (int i=0; i<PacketSize; ++i)
ref[j] += data1[i+j*PacketSize];
packets[j] = internal::pload<Packet>(data1+j*PacketSize);
}
internal::pstore(data2, internal::preduxp(packets));
VERIFY(areApproxAbs(ref, data2, PacketSize, refvalue) && "internal::preduxp");
for (int i=0; i<PacketSize; ++i)
ref[i] = data1[PacketSize-i-1];
internal::pstore(data2, internal::preverse(internal::pload<Packet>(data1)));
VERIFY(areApprox(ref, data2, PacketSize) && "internal::preverse");
internal::PacketBlock<Packet> kernel;
for (int i=0; i<PacketSize; ++i) {
kernel.packet[i] = internal::pload<Packet>(data1+i*PacketSize);
}
ptranspose(kernel);
for (int i=0; i<PacketSize; ++i) {
internal::pstore(data2, kernel.packet[i]);
for (int j = 0; j < PacketSize; ++j) {
VERIFY(isApproxAbs(data2[j], data1[i+j*PacketSize], refvalue) && "ptranspose");
}
}
if (PacketTraits::HasBlend) {
Packet thenPacket = internal::pload<Packet>(data1);
Packet elsePacket = internal::pload<Packet>(data2);
EIGEN_ALIGN_MAX internal::Selector<PacketSize> selector;
for (int i = 0; i < PacketSize; ++i) {
selector.select[i] = i;
}
Packet blend = internal::pblend(selector, thenPacket, elsePacket);
EIGEN_ALIGN_MAX Scalar result[size];
internal::pstore(result, blend);
for (int i = 0; i < PacketSize; ++i) {
VERIFY(isApproxAbs(result[i], (selector.select[i] ? data1[i] : data2[i]), refvalue));
}
}
if (PacketTraits::HasBlend) {
// pinsertfirst
for (int i=0; i<PacketSize; ++i)
ref[i] = data1[i];
Scalar s = internal::random<Scalar>();
ref[0] = s;
internal::pstore(data2, internal::pinsertfirst(internal::pload<Packet>(data1),s));
VERIFY(areApprox(ref, data2, PacketSize) && "internal::pinsertfirst");
}
if (PacketTraits::HasBlend) {
// pinsertlast
for (int i=0; i<PacketSize; ++i)
ref[i] = data1[i];
Scalar s = internal::random<Scalar>();
ref[PacketSize-1] = s;
internal::pstore(data2, internal::pinsertlast(internal::pload<Packet>(data1),s));
VERIFY(areApprox(ref, data2, PacketSize) && "internal::pinsertlast");
}
}
template<typename Scalar> void packetmath_real()
{
using std::abs;
typedef internal::packet_traits<Scalar> PacketTraits;
typedef typename PacketTraits::type Packet;
const int PacketSize = PacketTraits::size;
const int size = PacketSize*4;
EIGEN_ALIGN_MAX Scalar data1[PacketTraits::size*4];
EIGEN_ALIGN_MAX Scalar data2[PacketTraits::size*4];
EIGEN_ALIGN_MAX Scalar ref[PacketTraits::size*4];
for (int i=0; i<size; ++i)
{
data1[i] = internal::random<Scalar>(-1,1) * std::pow(Scalar(10), internal::random<Scalar>(-3,3));
data2[i] = internal::random<Scalar>(-1,1) * std::pow(Scalar(10), internal::random<Scalar>(-3,3));
}
CHECK_CWISE1_IF(PacketTraits::HasSin, std::sin, internal::psin);
CHECK_CWISE1_IF(PacketTraits::HasCos, std::cos, internal::pcos);
CHECK_CWISE1_IF(PacketTraits::HasTan, std::tan, internal::ptan);
CHECK_CWISE1_IF(PacketTraits::HasRound, numext::round, internal::pround);
CHECK_CWISE1_IF(PacketTraits::HasCeil, numext::ceil, internal::pceil);
CHECK_CWISE1_IF(PacketTraits::HasFloor, numext::floor, internal::pfloor);
for (int i=0; i<size; ++i)
{
data1[i] = internal::random<Scalar>(-1,1);
data2[i] = internal::random<Scalar>(-1,1);
}
CHECK_CWISE1_IF(PacketTraits::HasASin, std::asin, internal::pasin);
CHECK_CWISE1_IF(PacketTraits::HasACos, std::acos, internal::pacos);
for (int i=0; i<size; ++i)
{
data1[i] = internal::random<Scalar>(-87,88);
data2[i] = internal::random<Scalar>(-87,88);
}
CHECK_CWISE1_IF(PacketTraits::HasExp, std::exp, internal::pexp);
for (int i=0; i<size; ++i)
{
data1[i] = internal::random<Scalar>(-1,1) * std::pow(Scalar(10), internal::random<Scalar>(-6,6));
data2[i] = internal::random<Scalar>(-1,1) * std::pow(Scalar(10), internal::random<Scalar>(-6,6));
}
CHECK_CWISE1_IF(PacketTraits::HasTanh, std::tanh, internal::ptanh);
if(PacketTraits::HasExp && PacketTraits::size>=2)
{
data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
data1[1] = std::numeric_limits<Scalar>::epsilon();
packet_helper<PacketTraits::HasExp,Packet> h;
h.store(data2, internal::pexp(h.load(data1)));
VERIFY((numext::isnan)(data2[0]));
VERIFY_IS_EQUAL(std::exp(std::numeric_limits<Scalar>::epsilon()), data2[1]);
data1[0] = -std::numeric_limits<Scalar>::epsilon();
data1[1] = 0;
h.store(data2, internal::pexp(h.load(data1)));
VERIFY_IS_EQUAL(std::exp(-std::numeric_limits<Scalar>::epsilon()), data2[0]);
VERIFY_IS_EQUAL(std::exp(Scalar(0)), data2[1]);
data1[0] = (std::numeric_limits<Scalar>::min)();
data1[1] = -(std::numeric_limits<Scalar>::min)();
h.store(data2, internal::pexp(h.load(data1)));
VERIFY_IS_EQUAL(std::exp((std::numeric_limits<Scalar>::min)()), data2[0]);
VERIFY_IS_EQUAL(std::exp(-(std::numeric_limits<Scalar>::min)()), data2[1]);
data1[0] = std::numeric_limits<Scalar>::denorm_min();
data1[1] = -std::numeric_limits<Scalar>::denorm_min();
h.store(data2, internal::pexp(h.load(data1)));
VERIFY_IS_EQUAL(std::exp(std::numeric_limits<Scalar>::denorm_min()), data2[0]);
VERIFY_IS_EQUAL(std::exp(-std::numeric_limits<Scalar>::denorm_min()), data2[1]);
}
if (PacketTraits::HasTanh) {
// NOTE this test migh fail with GCC prior to 6.3, see MathFunctionsImpl.h for details.
data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
packet_helper<internal::packet_traits<Scalar>::HasTanh,Packet> h;
h.store(data2, internal::ptanh(h.load(data1)));
VERIFY((numext::isnan)(data2[0]));
}
#if EIGEN_HAS_C99_MATH
{
data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
packet_helper<internal::packet_traits<Scalar>::HasLGamma,Packet> h;
h.store(data2, internal::plgamma(h.load(data1)));
VERIFY((numext::isnan)(data2[0]));
}
{
data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
packet_helper<internal::packet_traits<Scalar>::HasErf,Packet> h;
h.store(data2, internal::perf(h.load(data1)));
VERIFY((numext::isnan)(data2[0]));
}
{
data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
packet_helper<internal::packet_traits<Scalar>::HasErfc,Packet> h;
h.store(data2, internal::perfc(h.load(data1)));
VERIFY((numext::isnan)(data2[0]));
}
#endif // EIGEN_HAS_C99_MATH
for (int i=0; i<size; ++i)
{
data1[i] = internal::random<Scalar>(0,1) * std::pow(Scalar(10), internal::random<Scalar>(-6,6));
data2[i] = internal::random<Scalar>(0,1) * std::pow(Scalar(10), internal::random<Scalar>(-6,6));
}
if(internal::random<float>(0,1)<0.1f)
data1[internal::random<int>(0, PacketSize)] = 0;
CHECK_CWISE1_IF(PacketTraits::HasSqrt, std::sqrt, internal::psqrt);
CHECK_CWISE1_IF(PacketTraits::HasLog, std::log, internal::plog);
#if EIGEN_HAS_C99_MATH && (__cplusplus > 199711L)
CHECK_CWISE1_IF(PacketTraits::HasLog1p, std::log1p, internal::plog1p);
CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasLGamma, std::lgamma, internal::plgamma);
CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasErf, std::erf, internal::perf);
CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasErfc, std::erfc, internal::perfc);
#endif
if(PacketTraits::HasLog && PacketTraits::size>=2)
{
data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
data1[1] = std::numeric_limits<Scalar>::epsilon();
packet_helper<PacketTraits::HasLog,Packet> h;
h.store(data2, internal::plog(h.load(data1)));
VERIFY((numext::isnan)(data2[0]));
VERIFY_IS_EQUAL(std::log(std::numeric_limits<Scalar>::epsilon()), data2[1]);
data1[0] = -std::numeric_limits<Scalar>::epsilon();
data1[1] = 0;
h.store(data2, internal::plog(h.load(data1)));
VERIFY((numext::isnan)(data2[0]));
VERIFY_IS_EQUAL(std::log(Scalar(0)), data2[1]);
data1[0] = (std::numeric_limits<Scalar>::min)();
data1[1] = -(std::numeric_limits<Scalar>::min)();
h.store(data2, internal::plog(h.load(data1)));
VERIFY_IS_EQUAL(std::log((std::numeric_limits<Scalar>::min)()), data2[0]);
VERIFY((numext::isnan)(data2[1]));
data1[0] = std::numeric_limits<Scalar>::denorm_min();
data1[1] = -std::numeric_limits<Scalar>::denorm_min();
h.store(data2, internal::plog(h.load(data1)));
// VERIFY_IS_EQUAL(std::log(std::numeric_limits<Scalar>::denorm_min()), data2[0]);
VERIFY((numext::isnan)(data2[1]));
data1[0] = Scalar(-1.0f);
h.store(data2, internal::plog(h.load(data1)));
VERIFY((numext::isnan)(data2[0]));
h.store(data2, internal::psqrt(h.load(data1)));
VERIFY((numext::isnan)(data2[0]));
VERIFY((numext::isnan)(data2[1]));
}
}
template<typename Scalar> void packetmath_notcomplex()
{
using std::abs;
typedef internal::packet_traits<Scalar> PacketTraits;
typedef typename PacketTraits::type Packet;
const int PacketSize = PacketTraits::size;
EIGEN_ALIGN_MAX Scalar data1[PacketTraits::size*4];
EIGEN_ALIGN_MAX Scalar data2[PacketTraits::size*4];
EIGEN_ALIGN_MAX Scalar ref[PacketTraits::size*4];
Array<Scalar,Dynamic,1>::Map(data1, PacketTraits::size*4).setRandom();
ref[0] = data1[0];
for (int i=0; i<PacketSize; ++i)
ref[0] = (std::min)(ref[0],data1[i]);
VERIFY(internal::isApprox(ref[0], internal::predux_min(internal::pload<Packet>(data1))) && "internal::predux_min");
VERIFY((!PacketTraits::Vectorizable) || PacketTraits::HasMin);
VERIFY((!PacketTraits::Vectorizable) || PacketTraits::HasMax);
CHECK_CWISE2_IF(PacketTraits::HasMin, (std::min), internal::pmin);
CHECK_CWISE2_IF(PacketTraits::HasMax, (std::max), internal::pmax);
CHECK_CWISE1(abs, internal::pabs);
ref[0] = data1[0];
for (int i=0; i<PacketSize; ++i)
ref[0] = (std::max)(ref[0],data1[i]);
VERIFY(internal::isApprox(ref[0], internal::predux_max(internal::pload<Packet>(data1))) && "internal::predux_max");
for (int i=0; i<PacketSize; ++i)
ref[i] = data1[0]+Scalar(i);
internal::pstore(data2, internal::plset<Packet>(data1[0]));
VERIFY(areApprox(ref, data2, PacketSize) && "internal::plset");
}
template<typename Scalar,bool ConjLhs,bool ConjRhs> void test_conj_helper(Scalar* data1, Scalar* data2, Scalar* ref, Scalar* pval)
{
typedef internal::packet_traits<Scalar> PacketTraits;
typedef typename PacketTraits::type Packet;
const int PacketSize = PacketTraits::size;
internal::conj_if<ConjLhs> cj0;
internal::conj_if<ConjRhs> cj1;
internal::conj_helper<Scalar,Scalar,ConjLhs,ConjRhs> cj;
internal::conj_helper<Packet,Packet,ConjLhs,ConjRhs> pcj;
for(int i=0;i<PacketSize;++i)
{
ref[i] = cj0(data1[i]) * cj1(data2[i]);
VERIFY(internal::isApprox(ref[i], cj.pmul(data1[i],data2[i])) && "conj_helper pmul");
}
internal::pstore(pval,pcj.pmul(internal::pload<Packet>(data1),internal::pload<Packet>(data2)));
VERIFY(areApprox(ref, pval, PacketSize) && "conj_helper pmul");
for(int i=0;i<PacketSize;++i)
{
Scalar tmp = ref[i];
ref[i] += cj0(data1[i]) * cj1(data2[i]);
VERIFY(internal::isApprox(ref[i], cj.pmadd(data1[i],data2[i],tmp)) && "conj_helper pmadd");
}
internal::pstore(pval,pcj.pmadd(internal::pload<Packet>(data1),internal::pload<Packet>(data2),internal::pload<Packet>(pval)));
VERIFY(areApprox(ref, pval, PacketSize) && "conj_helper pmadd");
}
template<typename Scalar> void packetmath_complex()
{
typedef internal::packet_traits<Scalar> PacketTraits;
typedef typename PacketTraits::type Packet;
const int PacketSize = PacketTraits::size;
const int size = PacketSize*4;
EIGEN_ALIGN_MAX Scalar data1[PacketSize*4];
EIGEN_ALIGN_MAX Scalar data2[PacketSize*4];
EIGEN_ALIGN_MAX Scalar ref[PacketSize*4];
EIGEN_ALIGN_MAX Scalar pval[PacketSize*4];
for (int i=0; i<size; ++i)
{
data1[i] = internal::random<Scalar>() * Scalar(1e2);
data2[i] = internal::random<Scalar>() * Scalar(1e2);
}
test_conj_helper<Scalar,false,false> (data1,data2,ref,pval);
test_conj_helper<Scalar,false,true> (data1,data2,ref,pval);
test_conj_helper<Scalar,true,false> (data1,data2,ref,pval);
test_conj_helper<Scalar,true,true> (data1,data2,ref,pval);
{
for(int i=0;i<PacketSize;++i)
ref[i] = Scalar(std::imag(data1[i]),std::real(data1[i]));
internal::pstore(pval,internal::pcplxflip(internal::pload<Packet>(data1)));
VERIFY(areApprox(ref, pval, PacketSize) && "pcplxflip");
}
}
template<typename Scalar> void packetmath_scatter_gather()
{
typedef internal::packet_traits<Scalar> PacketTraits;
typedef typename PacketTraits::type Packet;
typedef typename NumTraits<Scalar>::Real RealScalar;
const int PacketSize = PacketTraits::size;
EIGEN_ALIGN_MAX Scalar data1[PacketSize];
RealScalar refvalue = 0;
for (int i=0; i<PacketSize; ++i) {
data1[i] = internal::random<Scalar>()/RealScalar(PacketSize);
}
int stride = internal::random<int>(1,20);
EIGEN_ALIGN_MAX Scalar buffer[PacketSize*20];
memset(buffer, 0, 20*PacketSize*sizeof(Scalar));
Packet packet = internal::pload<Packet>(data1);
internal::pscatter<Scalar, Packet>(buffer, packet, stride);
for (int i = 0; i < PacketSize*20; ++i) {
if ((i%stride) == 0 && i<stride*PacketSize) {
VERIFY(isApproxAbs(buffer[i], data1[i/stride], refvalue) && "pscatter");
} else {
VERIFY(isApproxAbs(buffer[i], Scalar(0), refvalue) && "pscatter");
}
}
for (int i=0; i<PacketSize*7; ++i) {
buffer[i] = internal::random<Scalar>()/RealScalar(PacketSize);
}
packet = internal::pgather<Scalar, Packet>(buffer, 7);
internal::pstore(data1, packet);
for (int i = 0; i < PacketSize; ++i) {
VERIFY(isApproxAbs(data1[i], buffer[i*7], refvalue) && "pgather");
}
}
void test_packetmath()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( packetmath<float>() );
CALL_SUBTEST_2( packetmath<double>() );
CALL_SUBTEST_3( packetmath<int>() );
CALL_SUBTEST_4( packetmath<std::complex<float> >() );
CALL_SUBTEST_5( packetmath<std::complex<double> >() );
CALL_SUBTEST_1( packetmath_notcomplex<float>() );
CALL_SUBTEST_2( packetmath_notcomplex<double>() );
CALL_SUBTEST_3( packetmath_notcomplex<int>() );
CALL_SUBTEST_1( packetmath_real<float>() );
CALL_SUBTEST_2( packetmath_real<double>() );
CALL_SUBTEST_4( packetmath_complex<std::complex<float> >() );
CALL_SUBTEST_5( packetmath_complex<std::complex<double> >() );
CALL_SUBTEST_1( packetmath_scatter_gather<float>() );
CALL_SUBTEST_2( packetmath_scatter_gather<double>() );
CALL_SUBTEST_3( packetmath_scatter_gather<int>() );
CALL_SUBTEST_4( packetmath_scatter_gather<std::complex<float> >() );
CALL_SUBTEST_5( packetmath_scatter_gather<std::complex<double> >() );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/superlu_support.cpp | .cpp | 948 | 24 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_NO_DEBUG_SMALL_PRODUCT_BLOCKS
#include "sparse_solver.h"
#include <Eigen/SuperLUSupport>
void test_superlu_support()
{
SuperLU<SparseMatrix<double> > superlu_double_colmajor;
SuperLU<SparseMatrix<std::complex<double> > > superlu_cplxdouble_colmajor;
CALL_SUBTEST_1( check_sparse_square_solving(superlu_double_colmajor) );
CALL_SUBTEST_2( check_sparse_square_solving(superlu_cplxdouble_colmajor) );
CALL_SUBTEST_1( check_sparse_square_determinant(superlu_double_colmajor) );
CALL_SUBTEST_2( check_sparse_square_determinant(superlu_cplxdouble_colmajor) );
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/geo_alignedbox.cpp | .cpp | 5,584 | 189 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/Geometry>
#include <Eigen/LU>
#include <Eigen/QR>
#include<iostream>
using namespace std;
// TODO not sure if this is actually still necessary anywhere ...
template<typename T> EIGEN_DONT_INLINE
void kill_extra_precision(T& ) { }
template<typename BoxType> void alignedbox(const BoxType& _box)
{
/* this test covers the following files:
AlignedBox.h
*/
typedef typename BoxType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, BoxType::AmbientDimAtCompileTime, 1> VectorType;
const Index dim = _box.dim();
VectorType p0 = VectorType::Random(dim);
VectorType p1 = VectorType::Random(dim);
while( p1 == p0 ){
p1 = VectorType::Random(dim); }
RealScalar s1 = internal::random<RealScalar>(0,1);
BoxType b0(dim);
BoxType b1(VectorType::Random(dim),VectorType::Random(dim));
BoxType b2;
kill_extra_precision(b1);
kill_extra_precision(p0);
kill_extra_precision(p1);
b0.extend(p0);
b0.extend(p1);
VERIFY(b0.contains(p0*s1+(Scalar(1)-s1)*p1));
VERIFY(b0.contains(b0.center()));
VERIFY_IS_APPROX(b0.center(),(p0+p1)/Scalar(2));
(b2 = b0).extend(b1);
VERIFY(b2.contains(b0));
VERIFY(b2.contains(b1));
VERIFY_IS_APPROX(b2.clamp(b0), b0);
// intersection
BoxType box1(VectorType::Random(dim));
box1.extend(VectorType::Random(dim));
BoxType box2(VectorType::Random(dim));
box2.extend(VectorType::Random(dim));
VERIFY(box1.intersects(box2) == !box1.intersection(box2).isEmpty());
// alignment -- make sure there is no memory alignment assertion
BoxType *bp0 = new BoxType(dim);
BoxType *bp1 = new BoxType(dim);
bp0->extend(*bp1);
delete bp0;
delete bp1;
// sampling
for( int i=0; i<10; ++i )
{
VectorType r = b0.sample();
VERIFY(b0.contains(r));
}
}
template<typename BoxType>
void alignedboxCastTests(const BoxType& _box)
{
// casting
typedef typename BoxType::Scalar Scalar;
typedef Matrix<Scalar, BoxType::AmbientDimAtCompileTime, 1> VectorType;
const Index dim = _box.dim();
VectorType p0 = VectorType::Random(dim);
VectorType p1 = VectorType::Random(dim);
BoxType b0(dim);
b0.extend(p0);
b0.extend(p1);
const int Dim = BoxType::AmbientDimAtCompileTime;
typedef typename GetDifferentType<Scalar>::type OtherScalar;
AlignedBox<OtherScalar,Dim> hp1f = b0.template cast<OtherScalar>();
VERIFY_IS_APPROX(hp1f.template cast<Scalar>(),b0);
AlignedBox<Scalar,Dim> hp1d = b0.template cast<Scalar>();
VERIFY_IS_APPROX(hp1d.template cast<Scalar>(),b0);
}
void specificTest1()
{
Vector2f m; m << -1.0f, -2.0f;
Vector2f M; M << 1.0f, 5.0f;
typedef AlignedBox2f BoxType;
BoxType box( m, M );
Vector2f sides = M-m;
VERIFY_IS_APPROX(sides, box.sizes() );
VERIFY_IS_APPROX(sides[1], box.sizes()[1] );
VERIFY_IS_APPROX(sides[1], box.sizes().maxCoeff() );
VERIFY_IS_APPROX(sides[0], box.sizes().minCoeff() );
VERIFY_IS_APPROX( 14.0f, box.volume() );
VERIFY_IS_APPROX( 53.0f, box.diagonal().squaredNorm() );
VERIFY_IS_APPROX( std::sqrt( 53.0f ), box.diagonal().norm() );
VERIFY_IS_APPROX( m, box.corner( BoxType::BottomLeft ) );
VERIFY_IS_APPROX( M, box.corner( BoxType::TopRight ) );
Vector2f bottomRight; bottomRight << M[0], m[1];
Vector2f topLeft; topLeft << m[0], M[1];
VERIFY_IS_APPROX( bottomRight, box.corner( BoxType::BottomRight ) );
VERIFY_IS_APPROX( topLeft, box.corner( BoxType::TopLeft ) );
}
void specificTest2()
{
Vector3i m; m << -1, -2, 0;
Vector3i M; M << 1, 5, 3;
typedef AlignedBox3i BoxType;
BoxType box( m, M );
Vector3i sides = M-m;
VERIFY_IS_APPROX(sides, box.sizes() );
VERIFY_IS_APPROX(sides[1], box.sizes()[1] );
VERIFY_IS_APPROX(sides[1], box.sizes().maxCoeff() );
VERIFY_IS_APPROX(sides[0], box.sizes().minCoeff() );
VERIFY_IS_APPROX( 42, box.volume() );
VERIFY_IS_APPROX( 62, box.diagonal().squaredNorm() );
VERIFY_IS_APPROX( m, box.corner( BoxType::BottomLeftFloor ) );
VERIFY_IS_APPROX( M, box.corner( BoxType::TopRightCeil ) );
Vector3i bottomRightFloor; bottomRightFloor << M[0], m[1], m[2];
Vector3i topLeftFloor; topLeftFloor << m[0], M[1], m[2];
VERIFY_IS_APPROX( bottomRightFloor, box.corner( BoxType::BottomRightFloor ) );
VERIFY_IS_APPROX( topLeftFloor, box.corner( BoxType::TopLeftFloor ) );
}
void test_geo_alignedbox()
{
for(int i = 0; i < g_repeat; i++)
{
CALL_SUBTEST_1( alignedbox(AlignedBox2f()) );
CALL_SUBTEST_2( alignedboxCastTests(AlignedBox2f()) );
CALL_SUBTEST_3( alignedbox(AlignedBox3f()) );
CALL_SUBTEST_4( alignedboxCastTests(AlignedBox3f()) );
CALL_SUBTEST_5( alignedbox(AlignedBox4d()) );
CALL_SUBTEST_6( alignedboxCastTests(AlignedBox4d()) );
CALL_SUBTEST_7( alignedbox(AlignedBox1d()) );
CALL_SUBTEST_8( alignedboxCastTests(AlignedBox1d()) );
CALL_SUBTEST_9( alignedbox(AlignedBox1i()) );
CALL_SUBTEST_10( alignedbox(AlignedBox2i()) );
CALL_SUBTEST_11( alignedbox(AlignedBox3i()) );
CALL_SUBTEST_14( alignedbox(AlignedBox<double,Dynamic>(4)) );
}
CALL_SUBTEST_12( specificTest1() );
CALL_SUBTEST_13( specificTest2() );
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/prec_inverse_4x4.cpp | .cpp | 3,196 | 84 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/LU>
#include <algorithm>
template<typename MatrixType> void inverse_permutation_4x4()
{
typedef typename MatrixType::Scalar Scalar;
Vector4i indices(0,1,2,3);
for(int i = 0; i < 24; ++i)
{
MatrixType m = PermutationMatrix<4>(indices);
MatrixType inv = m.inverse();
double error = double( (m*inv-MatrixType::Identity()).norm() / NumTraits<Scalar>::epsilon() );
EIGEN_DEBUG_VAR(error)
VERIFY(error == 0.0);
std::next_permutation(indices.data(),indices.data()+4);
}
}
template<typename MatrixType> void inverse_general_4x4(int repeat)
{
using std::abs;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
double error_sum = 0., error_max = 0.;
for(int i = 0; i < repeat; ++i)
{
MatrixType m;
RealScalar absdet;
do {
m = MatrixType::Random();
absdet = abs(m.determinant());
} while(absdet < NumTraits<Scalar>::epsilon());
MatrixType inv = m.inverse();
double error = double( (m*inv-MatrixType::Identity()).norm() * absdet / NumTraits<Scalar>::epsilon() );
error_sum += error;
error_max = (std::max)(error_max, error);
}
std::cerr << "inverse_general_4x4, Scalar = " << type_name<Scalar>() << std::endl;
double error_avg = error_sum / repeat;
EIGEN_DEBUG_VAR(error_avg);
EIGEN_DEBUG_VAR(error_max);
// FIXME that 1.25 used to be a 1.0 until the NumTraits changes on 28 April 2010, what's going wrong??
// FIXME that 1.25 used to be 1.2 until we tested gcc 4.1 on 30 June 2010 and got 1.21.
VERIFY(error_avg < (NumTraits<Scalar>::IsComplex ? 8.0 : 1.25));
VERIFY(error_max < (NumTraits<Scalar>::IsComplex ? 64.0 : 20.0));
{
int s = 5;//internal::random<int>(4,10);
int i = 0;//internal::random<int>(0,s-4);
int j = 0;//internal::random<int>(0,s-4);
Matrix<Scalar,5,5> mat(s,s);
mat.setRandom();
MatrixType submat = mat.template block<4,4>(i,j);
MatrixType mat_inv = mat.template block<4,4>(i,j).inverse();
VERIFY_IS_APPROX(mat_inv, submat.inverse());
mat.template block<4,4>(i,j) = submat.inverse();
VERIFY_IS_APPROX(mat_inv, (mat.template block<4,4>(i,j)));
}
}
void test_prec_inverse_4x4()
{
CALL_SUBTEST_1((inverse_permutation_4x4<Matrix4f>()));
CALL_SUBTEST_1(( inverse_general_4x4<Matrix4f>(200000 * g_repeat) ));
CALL_SUBTEST_1(( inverse_general_4x4<Matrix<float,4,4,RowMajor> >(200000 * g_repeat) ));
CALL_SUBTEST_2((inverse_permutation_4x4<Matrix<double,4,4,RowMajor> >()));
CALL_SUBTEST_2(( inverse_general_4x4<Matrix<double,4,4,ColMajor> >(200000 * g_repeat) ));
CALL_SUBTEST_2(( inverse_general_4x4<Matrix<double,4,4,RowMajor> >(200000 * g_repeat) ));
CALL_SUBTEST_3((inverse_permutation_4x4<Matrix4cf>()));
CALL_SUBTEST_3((inverse_general_4x4<Matrix4cf>(50000 * g_repeat)));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/swap.cpp | .cpp | 2,974 | 95 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_NO_STATIC_ASSERT
#include "main.h"
template<typename T>
struct other_matrix_type
{
typedef int type;
};
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct other_matrix_type<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
typedef Matrix<_Scalar, _Rows, _Cols, _Options^RowMajor, _MaxRows, _MaxCols> type;
};
template<typename MatrixType> void swap(const MatrixType& m)
{
typedef typename other_matrix_type<MatrixType>::type OtherMatrixType;
typedef typename MatrixType::Scalar Scalar;
eigen_assert((!internal::is_same<MatrixType,OtherMatrixType>::value));
typename MatrixType::Index rows = m.rows();
typename MatrixType::Index cols = m.cols();
// construct 3 matrix guaranteed to be distinct
MatrixType m1 = MatrixType::Random(rows,cols);
MatrixType m2 = MatrixType::Random(rows,cols) + Scalar(100) * MatrixType::Identity(rows,cols);
OtherMatrixType m3 = OtherMatrixType::Random(rows,cols) + Scalar(200) * OtherMatrixType::Identity(rows,cols);
MatrixType m1_copy = m1;
MatrixType m2_copy = m2;
OtherMatrixType m3_copy = m3;
// test swapping 2 matrices of same type
Scalar *d1=m1.data(), *d2=m2.data();
m1.swap(m2);
VERIFY_IS_APPROX(m1,m2_copy);
VERIFY_IS_APPROX(m2,m1_copy);
if(MatrixType::SizeAtCompileTime==Dynamic)
{
VERIFY(m1.data()==d2);
VERIFY(m2.data()==d1);
}
m1 = m1_copy;
m2 = m2_copy;
// test swapping 2 matrices of different types
m1.swap(m3);
VERIFY_IS_APPROX(m1,m3_copy);
VERIFY_IS_APPROX(m3,m1_copy);
m1 = m1_copy;
m3 = m3_copy;
// test swapping matrix with expression
m1.swap(m2.block(0,0,rows,cols));
VERIFY_IS_APPROX(m1,m2_copy);
VERIFY_IS_APPROX(m2,m1_copy);
m1 = m1_copy;
m2 = m2_copy;
// test swapping two expressions of different types
m1.transpose().swap(m3.transpose());
VERIFY_IS_APPROX(m1,m3_copy);
VERIFY_IS_APPROX(m3,m1_copy);
m1 = m1_copy;
m3 = m3_copy;
if(m1.rows()>1)
{
// test assertion on mismatching size -- matrix case
VERIFY_RAISES_ASSERT(m1.swap(m1.row(0)));
// test assertion on mismatching size -- xpr case
VERIFY_RAISES_ASSERT(m1.row(0).swap(m1));
}
}
void test_swap()
{
int s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
CALL_SUBTEST_1( swap(Matrix3f()) ); // fixed size, no vectorization
CALL_SUBTEST_2( swap(Matrix4d()) ); // fixed size, possible vectorization
CALL_SUBTEST_3( swap(MatrixXd(s,s)) ); // dyn size, no vectorization
CALL_SUBTEST_4( swap(MatrixXf(s,s)) ); // dyn size, possible vectorization
TEST_SET_BUT_UNUSED_VARIABLE(s)
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/sparse_product.cpp | .cpp | 25,566 | 476 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#if defined(_MSC_VER) && (_MSC_VER==1800)
// This unit test takes forever to compile in Release mode with MSVC 2013,
// multiple hours. So let's switch off optimization for this one.
#pragma optimize("",off)
#endif
static long int nb_temporaries;
inline void on_temporary_creation() {
// here's a great place to set a breakpoint when debugging failures in this test!
nb_temporaries++;
}
#define EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN { on_temporary_creation(); }
#include "sparse.h"
#define VERIFY_EVALUATION_COUNT(XPR,N) {\
nb_temporaries = 0; \
CALL_SUBTEST( XPR ); \
if(nb_temporaries!=N) std::cerr << "nb_temporaries == " << nb_temporaries << "\n"; \
VERIFY( (#XPR) && nb_temporaries==N ); \
}
template<typename SparseMatrixType> void sparse_product()
{
typedef typename SparseMatrixType::StorageIndex StorageIndex;
Index n = 100;
const Index rows = internal::random<Index>(1,n);
const Index cols = internal::random<Index>(1,n);
const Index depth = internal::random<Index>(1,n);
typedef typename SparseMatrixType::Scalar Scalar;
enum { Flags = SparseMatrixType::Flags };
double density = (std::max)(8./(rows*cols), 0.2);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
typedef Matrix<Scalar,1,Dynamic> RowDenseVector;
typedef SparseVector<Scalar,0,StorageIndex> ColSpVector;
typedef SparseVector<Scalar,RowMajor,StorageIndex> RowSpVector;
Scalar s1 = internal::random<Scalar>();
Scalar s2 = internal::random<Scalar>();
// test matrix-matrix product
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, depth);
DenseMatrix refMat2t = DenseMatrix::Zero(depth, rows);
DenseMatrix refMat3 = DenseMatrix::Zero(depth, cols);
DenseMatrix refMat3t = DenseMatrix::Zero(cols, depth);
DenseMatrix refMat4 = DenseMatrix::Zero(rows, cols);
DenseMatrix refMat4t = DenseMatrix::Zero(cols, rows);
DenseMatrix refMat5 = DenseMatrix::Random(depth, cols);
DenseMatrix refMat6 = DenseMatrix::Random(rows, rows);
DenseMatrix dm4 = DenseMatrix::Zero(rows, rows);
// DenseVector dv1 = DenseVector::Random(rows);
SparseMatrixType m2 (rows, depth);
SparseMatrixType m2t(depth, rows);
SparseMatrixType m3 (depth, cols);
SparseMatrixType m3t(cols, depth);
SparseMatrixType m4 (rows, cols);
SparseMatrixType m4t(cols, rows);
SparseMatrixType m6(rows, rows);
initSparse(density, refMat2, m2);
initSparse(density, refMat2t, m2t);
initSparse(density, refMat3, m3);
initSparse(density, refMat3t, m3t);
initSparse(density, refMat4, m4);
initSparse(density, refMat4t, m4t);
initSparse(density, refMat6, m6);
// int c = internal::random<int>(0,depth-1);
// sparse * sparse
VERIFY_IS_APPROX(m4=m2*m3, refMat4=refMat2*refMat3);
VERIFY_IS_APPROX(m4=m2t.transpose()*m3, refMat4=refMat2t.transpose()*refMat3);
VERIFY_IS_APPROX(m4=m2t.transpose()*m3t.transpose(), refMat4=refMat2t.transpose()*refMat3t.transpose());
VERIFY_IS_APPROX(m4=m2*m3t.transpose(), refMat4=refMat2*refMat3t.transpose());
VERIFY_IS_APPROX(m4 = m2*m3/s1, refMat4 = refMat2*refMat3/s1);
VERIFY_IS_APPROX(m4 = m2*m3*s1, refMat4 = refMat2*refMat3*s1);
VERIFY_IS_APPROX(m4 = s2*m2*m3*s1, refMat4 = s2*refMat2*refMat3*s1);
VERIFY_IS_APPROX(m4 = (m2+m2)*m3, refMat4 = (refMat2+refMat2)*refMat3);
VERIFY_IS_APPROX(m4 = m2*m3.leftCols(cols/2), refMat4 = refMat2*refMat3.leftCols(cols/2));
VERIFY_IS_APPROX(m4 = m2*(m3+m3).leftCols(cols/2), refMat4 = refMat2*(refMat3+refMat3).leftCols(cols/2));
VERIFY_IS_APPROX(m4=(m2*m3).pruned(0), refMat4=refMat2*refMat3);
VERIFY_IS_APPROX(m4=(m2t.transpose()*m3).pruned(0), refMat4=refMat2t.transpose()*refMat3);
VERIFY_IS_APPROX(m4=(m2t.transpose()*m3t.transpose()).pruned(0), refMat4=refMat2t.transpose()*refMat3t.transpose());
VERIFY_IS_APPROX(m4=(m2*m3t.transpose()).pruned(0), refMat4=refMat2*refMat3t.transpose());
// make sure the right product implementation is called:
if((!SparseMatrixType::IsRowMajor) && m2.rows()<=m3.cols())
{
VERIFY_EVALUATION_COUNT(m4 = m2*m3, 3); // 1 temp for the result + 2 for transposing and get a sorted result.
VERIFY_EVALUATION_COUNT(m4 = (m2*m3).pruned(0), 1);
VERIFY_EVALUATION_COUNT(m4 = (m2*m3).eval().pruned(0), 4);
}
// and that pruning is effective:
{
DenseMatrix Ad(2,2);
Ad << -1, 1, 1, 1;
SparseMatrixType As(Ad.sparseView()), B(2,2);
VERIFY_IS_EQUAL( (As*As.transpose()).eval().nonZeros(), 4);
VERIFY_IS_EQUAL( (Ad*Ad.transpose()).eval().sparseView().eval().nonZeros(), 2);
VERIFY_IS_EQUAL( (As*As.transpose()).pruned(1e-6).eval().nonZeros(), 2);
}
// dense ?= sparse * sparse
VERIFY_IS_APPROX(dm4 =m2*m3, refMat4 =refMat2*refMat3);
VERIFY_IS_APPROX(dm4+=m2*m3, refMat4+=refMat2*refMat3);
VERIFY_IS_APPROX(dm4-=m2*m3, refMat4-=refMat2*refMat3);
VERIFY_IS_APPROX(dm4 =m2t.transpose()*m3, refMat4 =refMat2t.transpose()*refMat3);
VERIFY_IS_APPROX(dm4+=m2t.transpose()*m3, refMat4+=refMat2t.transpose()*refMat3);
VERIFY_IS_APPROX(dm4-=m2t.transpose()*m3, refMat4-=refMat2t.transpose()*refMat3);
VERIFY_IS_APPROX(dm4 =m2t.transpose()*m3t.transpose(), refMat4 =refMat2t.transpose()*refMat3t.transpose());
VERIFY_IS_APPROX(dm4+=m2t.transpose()*m3t.transpose(), refMat4+=refMat2t.transpose()*refMat3t.transpose());
VERIFY_IS_APPROX(dm4-=m2t.transpose()*m3t.transpose(), refMat4-=refMat2t.transpose()*refMat3t.transpose());
VERIFY_IS_APPROX(dm4 =m2*m3t.transpose(), refMat4 =refMat2*refMat3t.transpose());
VERIFY_IS_APPROX(dm4+=m2*m3t.transpose(), refMat4+=refMat2*refMat3t.transpose());
VERIFY_IS_APPROX(dm4-=m2*m3t.transpose(), refMat4-=refMat2*refMat3t.transpose());
VERIFY_IS_APPROX(dm4 = m2*m3*s1, refMat4 = refMat2*refMat3*s1);
// test aliasing
m4 = m2; refMat4 = refMat2;
VERIFY_IS_APPROX(m4=m4*m3, refMat4=refMat4*refMat3);
// sparse * dense matrix
VERIFY_IS_APPROX(dm4=m2*refMat3, refMat4=refMat2*refMat3);
VERIFY_IS_APPROX(dm4=m2*refMat3t.transpose(), refMat4=refMat2*refMat3t.transpose());
VERIFY_IS_APPROX(dm4=m2t.transpose()*refMat3, refMat4=refMat2t.transpose()*refMat3);
VERIFY_IS_APPROX(dm4=m2t.transpose()*refMat3t.transpose(), refMat4=refMat2t.transpose()*refMat3t.transpose());
VERIFY_IS_APPROX(dm4=m2*refMat3, refMat4=refMat2*refMat3);
VERIFY_IS_APPROX(dm4=dm4+m2*refMat3, refMat4=refMat4+refMat2*refMat3);
VERIFY_IS_APPROX(dm4+=m2*refMat3, refMat4+=refMat2*refMat3);
VERIFY_IS_APPROX(dm4-=m2*refMat3, refMat4-=refMat2*refMat3);
VERIFY_IS_APPROX(dm4.noalias()+=m2*refMat3, refMat4+=refMat2*refMat3);
VERIFY_IS_APPROX(dm4.noalias()-=m2*refMat3, refMat4-=refMat2*refMat3);
VERIFY_IS_APPROX(dm4=m2*(refMat3+refMat3), refMat4=refMat2*(refMat3+refMat3));
VERIFY_IS_APPROX(dm4=m2t.transpose()*(refMat3+refMat5)*0.5, refMat4=refMat2t.transpose()*(refMat3+refMat5)*0.5);
// sparse * dense vector
VERIFY_IS_APPROX(dm4.col(0)=m2*refMat3.col(0), refMat4.col(0)=refMat2*refMat3.col(0));
VERIFY_IS_APPROX(dm4.col(0)=m2*refMat3t.transpose().col(0), refMat4.col(0)=refMat2*refMat3t.transpose().col(0));
VERIFY_IS_APPROX(dm4.col(0)=m2t.transpose()*refMat3.col(0), refMat4.col(0)=refMat2t.transpose()*refMat3.col(0));
VERIFY_IS_APPROX(dm4.col(0)=m2t.transpose()*refMat3t.transpose().col(0), refMat4.col(0)=refMat2t.transpose()*refMat3t.transpose().col(0));
// dense * sparse
VERIFY_IS_APPROX(dm4=refMat2*m3, refMat4=refMat2*refMat3);
VERIFY_IS_APPROX(dm4=dm4+refMat2*m3, refMat4=refMat4+refMat2*refMat3);
VERIFY_IS_APPROX(dm4+=refMat2*m3, refMat4+=refMat2*refMat3);
VERIFY_IS_APPROX(dm4-=refMat2*m3, refMat4-=refMat2*refMat3);
VERIFY_IS_APPROX(dm4.noalias()+=refMat2*m3, refMat4+=refMat2*refMat3);
VERIFY_IS_APPROX(dm4.noalias()-=refMat2*m3, refMat4-=refMat2*refMat3);
VERIFY_IS_APPROX(dm4=refMat2*m3t.transpose(), refMat4=refMat2*refMat3t.transpose());
VERIFY_IS_APPROX(dm4=refMat2t.transpose()*m3, refMat4=refMat2t.transpose()*refMat3);
VERIFY_IS_APPROX(dm4=refMat2t.transpose()*m3t.transpose(), refMat4=refMat2t.transpose()*refMat3t.transpose());
// sparse * dense and dense * sparse outer product
{
Index c = internal::random<Index>(0,depth-1);
Index r = internal::random<Index>(0,rows-1);
Index c1 = internal::random<Index>(0,cols-1);
Index r1 = internal::random<Index>(0,depth-1);
DenseMatrix dm5 = DenseMatrix::Random(depth, cols);
VERIFY_IS_APPROX( m4=m2.col(c)*dm5.col(c1).transpose(), refMat4=refMat2.col(c)*dm5.col(c1).transpose());
VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count());
VERIFY_IS_APPROX( m4=m2.middleCols(c,1)*dm5.col(c1).transpose(), refMat4=refMat2.col(c)*dm5.col(c1).transpose());
VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count());
VERIFY_IS_APPROX(dm4=m2.col(c)*dm5.col(c1).transpose(), refMat4=refMat2.col(c)*dm5.col(c1).transpose());
VERIFY_IS_APPROX(m4=dm5.col(c1)*m2.col(c).transpose(), refMat4=dm5.col(c1)*refMat2.col(c).transpose());
VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count());
VERIFY_IS_APPROX(m4=dm5.col(c1)*m2.middleCols(c,1).transpose(), refMat4=dm5.col(c1)*refMat2.col(c).transpose());
VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count());
VERIFY_IS_APPROX(dm4=dm5.col(c1)*m2.col(c).transpose(), refMat4=dm5.col(c1)*refMat2.col(c).transpose());
VERIFY_IS_APPROX( m4=dm5.row(r1).transpose()*m2.col(c).transpose(), refMat4=dm5.row(r1).transpose()*refMat2.col(c).transpose());
VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count());
VERIFY_IS_APPROX(dm4=dm5.row(r1).transpose()*m2.col(c).transpose(), refMat4=dm5.row(r1).transpose()*refMat2.col(c).transpose());
VERIFY_IS_APPROX( m4=m2.row(r).transpose()*dm5.col(c1).transpose(), refMat4=refMat2.row(r).transpose()*dm5.col(c1).transpose());
VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count());
VERIFY_IS_APPROX( m4=m2.middleRows(r,1).transpose()*dm5.col(c1).transpose(), refMat4=refMat2.row(r).transpose()*dm5.col(c1).transpose());
VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count());
VERIFY_IS_APPROX(dm4=m2.row(r).transpose()*dm5.col(c1).transpose(), refMat4=refMat2.row(r).transpose()*dm5.col(c1).transpose());
VERIFY_IS_APPROX( m4=dm5.col(c1)*m2.row(r), refMat4=dm5.col(c1)*refMat2.row(r));
VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count());
VERIFY_IS_APPROX( m4=dm5.col(c1)*m2.middleRows(r,1), refMat4=dm5.col(c1)*refMat2.row(r));
VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count());
VERIFY_IS_APPROX(dm4=dm5.col(c1)*m2.row(r), refMat4=dm5.col(c1)*refMat2.row(r));
VERIFY_IS_APPROX( m4=dm5.row(r1).transpose()*m2.row(r), refMat4=dm5.row(r1).transpose()*refMat2.row(r));
VERIFY_IS_EQUAL(m4.nonZeros(), (refMat4.array()!=0).count());
VERIFY_IS_APPROX(dm4=dm5.row(r1).transpose()*m2.row(r), refMat4=dm5.row(r1).transpose()*refMat2.row(r));
}
VERIFY_IS_APPROX(m6=m6*m6, refMat6=refMat6*refMat6);
// sparse matrix * sparse vector
ColSpVector cv0(cols), cv1;
DenseVector dcv0(cols), dcv1;
initSparse(2*density,dcv0, cv0);
RowSpVector rv0(depth), rv1;
RowDenseVector drv0(depth), drv1(rv1);
initSparse(2*density,drv0, rv0);
VERIFY_IS_APPROX(cv1=m3*cv0, dcv1=refMat3*dcv0);
VERIFY_IS_APPROX(rv1=rv0*m3, drv1=drv0*refMat3);
VERIFY_IS_APPROX(cv1=m3t.adjoint()*cv0, dcv1=refMat3t.adjoint()*dcv0);
VERIFY_IS_APPROX(cv1=rv0*m3, dcv1=drv0*refMat3);
VERIFY_IS_APPROX(rv1=m3*cv0, drv1=refMat3*dcv0);
}
// test matrix - diagonal product
{
DenseMatrix refM2 = DenseMatrix::Zero(rows, cols);
DenseMatrix refM3 = DenseMatrix::Zero(rows, cols);
DenseMatrix d3 = DenseMatrix::Zero(rows, cols);
DiagonalMatrix<Scalar,Dynamic> d1(DenseVector::Random(cols));
DiagonalMatrix<Scalar,Dynamic> d2(DenseVector::Random(rows));
SparseMatrixType m2(rows, cols);
SparseMatrixType m3(rows, cols);
initSparse<Scalar>(density, refM2, m2);
initSparse<Scalar>(density, refM3, m3);
VERIFY_IS_APPROX(m3=m2*d1, refM3=refM2*d1);
VERIFY_IS_APPROX(m3=m2.transpose()*d2, refM3=refM2.transpose()*d2);
VERIFY_IS_APPROX(m3=d2*m2, refM3=d2*refM2);
VERIFY_IS_APPROX(m3=d1*m2.transpose(), refM3=d1*refM2.transpose());
// also check with a SparseWrapper:
DenseVector v1 = DenseVector::Random(cols);
DenseVector v2 = DenseVector::Random(rows);
DenseVector v3 = DenseVector::Random(rows);
VERIFY_IS_APPROX(m3=m2*v1.asDiagonal(), refM3=refM2*v1.asDiagonal());
VERIFY_IS_APPROX(m3=m2.transpose()*v2.asDiagonal(), refM3=refM2.transpose()*v2.asDiagonal());
VERIFY_IS_APPROX(m3=v2.asDiagonal()*m2, refM3=v2.asDiagonal()*refM2);
VERIFY_IS_APPROX(m3=v1.asDiagonal()*m2.transpose(), refM3=v1.asDiagonal()*refM2.transpose());
VERIFY_IS_APPROX(m3=v2.asDiagonal()*m2*v1.asDiagonal(), refM3=v2.asDiagonal()*refM2*v1.asDiagonal());
VERIFY_IS_APPROX(v2=m2*v1.asDiagonal()*v1, refM2*v1.asDiagonal()*v1);
VERIFY_IS_APPROX(v3=v2.asDiagonal()*m2*v1, v2.asDiagonal()*refM2*v1);
// evaluate to a dense matrix to check the .row() and .col() iterator functions
VERIFY_IS_APPROX(d3=m2*d1, refM3=refM2*d1);
VERIFY_IS_APPROX(d3=m2.transpose()*d2, refM3=refM2.transpose()*d2);
VERIFY_IS_APPROX(d3=d2*m2, refM3=d2*refM2);
VERIFY_IS_APPROX(d3=d1*m2.transpose(), refM3=d1*refM2.transpose());
}
// test self-adjoint and triangular-view products
{
DenseMatrix b = DenseMatrix::Random(rows, rows);
DenseMatrix x = DenseMatrix::Random(rows, rows);
DenseMatrix refX = DenseMatrix::Random(rows, rows);
DenseMatrix refUp = DenseMatrix::Zero(rows, rows);
DenseMatrix refLo = DenseMatrix::Zero(rows, rows);
DenseMatrix refS = DenseMatrix::Zero(rows, rows);
DenseMatrix refA = DenseMatrix::Zero(rows, rows);
SparseMatrixType mUp(rows, rows);
SparseMatrixType mLo(rows, rows);
SparseMatrixType mS(rows, rows);
SparseMatrixType mA(rows, rows);
initSparse<Scalar>(density, refA, mA);
do {
initSparse<Scalar>(density, refUp, mUp, ForceRealDiag|/*ForceNonZeroDiag|*/MakeUpperTriangular);
} while (refUp.isZero());
refLo = refUp.adjoint();
mLo = mUp.adjoint();
refS = refUp + refLo;
refS.diagonal() *= 0.5;
mS = mUp + mLo;
// TODO be able to address the diagonal....
for (int k=0; k<mS.outerSize(); ++k)
for (typename SparseMatrixType::InnerIterator it(mS,k); it; ++it)
if (it.index() == k)
it.valueRef() *= Scalar(0.5);
VERIFY_IS_APPROX(refS.adjoint(), refS);
VERIFY_IS_APPROX(mS.adjoint(), mS);
VERIFY_IS_APPROX(mS, refS);
VERIFY_IS_APPROX(x=mS*b, refX=refS*b);
// sparse selfadjointView with dense matrices
VERIFY_IS_APPROX(x=mUp.template selfadjointView<Upper>()*b, refX=refS*b);
VERIFY_IS_APPROX(x=mLo.template selfadjointView<Lower>()*b, refX=refS*b);
VERIFY_IS_APPROX(x=mS.template selfadjointView<Upper|Lower>()*b, refX=refS*b);
VERIFY_IS_APPROX(x=b * mUp.template selfadjointView<Upper>(), refX=b*refS);
VERIFY_IS_APPROX(x=b * mLo.template selfadjointView<Lower>(), refX=b*refS);
VERIFY_IS_APPROX(x=b * mS.template selfadjointView<Upper|Lower>(), refX=b*refS);
VERIFY_IS_APPROX(x.noalias()+=mUp.template selfadjointView<Upper>()*b, refX+=refS*b);
VERIFY_IS_APPROX(x.noalias()-=mLo.template selfadjointView<Lower>()*b, refX-=refS*b);
VERIFY_IS_APPROX(x.noalias()+=mS.template selfadjointView<Upper|Lower>()*b, refX+=refS*b);
// sparse selfadjointView with sparse matrices
SparseMatrixType mSres(rows,rows);
VERIFY_IS_APPROX(mSres = mLo.template selfadjointView<Lower>()*mS,
refX = refLo.template selfadjointView<Lower>()*refS);
VERIFY_IS_APPROX(mSres = mS * mLo.template selfadjointView<Lower>(),
refX = refS * refLo.template selfadjointView<Lower>());
// sparse triangularView with dense matrices
VERIFY_IS_APPROX(x=mA.template triangularView<Upper>()*b, refX=refA.template triangularView<Upper>()*b);
VERIFY_IS_APPROX(x=mA.template triangularView<Lower>()*b, refX=refA.template triangularView<Lower>()*b);
VERIFY_IS_APPROX(x=b*mA.template triangularView<Upper>(), refX=b*refA.template triangularView<Upper>());
VERIFY_IS_APPROX(x=b*mA.template triangularView<Lower>(), refX=b*refA.template triangularView<Lower>());
// sparse triangularView with sparse matrices
VERIFY_IS_APPROX(mSres = mA.template triangularView<Lower>()*mS, refX = refA.template triangularView<Lower>()*refS);
VERIFY_IS_APPROX(mSres = mS * mA.template triangularView<Lower>(), refX = refS * refA.template triangularView<Lower>());
VERIFY_IS_APPROX(mSres = mA.template triangularView<Upper>()*mS, refX = refA.template triangularView<Upper>()*refS);
VERIFY_IS_APPROX(mSres = mS * mA.template triangularView<Upper>(), refX = refS * refA.template triangularView<Upper>());
}
}
// New test for Bug in SparseTimeDenseProduct
template<typename SparseMatrixType, typename DenseMatrixType> void sparse_product_regression_test()
{
// This code does not compile with afflicted versions of the bug
SparseMatrixType sm1(3,2);
DenseMatrixType m2(2,2);
sm1.setZero();
m2.setZero();
DenseMatrixType m3 = sm1*m2;
// This code produces a segfault with afflicted versions of another SparseTimeDenseProduct
// bug
SparseMatrixType sm2(20000,2);
sm2.setZero();
DenseMatrixType m4(sm2*m2);
VERIFY_IS_APPROX( m4(0,0), 0.0 );
}
template<typename Scalar>
void bug_942()
{
typedef Matrix<Scalar, Dynamic, 1> Vector;
typedef SparseMatrix<Scalar, ColMajor> ColSpMat;
typedef SparseMatrix<Scalar, RowMajor> RowSpMat;
ColSpMat cmA(1,1);
cmA.insert(0,0) = 1;
RowSpMat rmA(1,1);
rmA.insert(0,0) = 1;
Vector d(1);
d[0] = 2;
double res = 2;
VERIFY_IS_APPROX( ( cmA*d.asDiagonal() ).eval().coeff(0,0), res );
VERIFY_IS_APPROX( ( d.asDiagonal()*rmA ).eval().coeff(0,0), res );
VERIFY_IS_APPROX( ( rmA*d.asDiagonal() ).eval().coeff(0,0), res );
VERIFY_IS_APPROX( ( d.asDiagonal()*cmA ).eval().coeff(0,0), res );
}
template<typename Real>
void test_mixing_types()
{
typedef std::complex<Real> Cplx;
typedef SparseMatrix<Real> SpMatReal;
typedef SparseMatrix<Cplx> SpMatCplx;
typedef SparseMatrix<Cplx,RowMajor> SpRowMatCplx;
typedef Matrix<Real,Dynamic,Dynamic> DenseMatReal;
typedef Matrix<Cplx,Dynamic,Dynamic> DenseMatCplx;
Index n = internal::random<Index>(1,100);
double density = (std::max)(8./(n*n), 0.2);
SpMatReal sR1(n,n);
SpMatCplx sC1(n,n), sC2(n,n), sC3(n,n);
SpRowMatCplx sCR(n,n);
DenseMatReal dR1(n,n);
DenseMatCplx dC1(n,n), dC2(n,n), dC3(n,n);
initSparse<Real>(density, dR1, sR1);
initSparse<Cplx>(density, dC1, sC1);
initSparse<Cplx>(density, dC2, sC2);
VERIFY_IS_APPROX( sC2 = (sR1 * sC1), dC3 = dR1.template cast<Cplx>() * dC1 );
VERIFY_IS_APPROX( sC2 = (sC1 * sR1), dC3 = dC1 * dR1.template cast<Cplx>() );
VERIFY_IS_APPROX( sC2 = (sR1.transpose() * sC1), dC3 = dR1.template cast<Cplx>().transpose() * dC1 );
VERIFY_IS_APPROX( sC2 = (sC1.transpose() * sR1), dC3 = dC1.transpose() * dR1.template cast<Cplx>() );
VERIFY_IS_APPROX( sC2 = (sR1 * sC1.transpose()), dC3 = dR1.template cast<Cplx>() * dC1.transpose() );
VERIFY_IS_APPROX( sC2 = (sC1 * sR1.transpose()), dC3 = dC1 * dR1.template cast<Cplx>().transpose() );
VERIFY_IS_APPROX( sC2 = (sR1.transpose() * sC1.transpose()), dC3 = dR1.template cast<Cplx>().transpose() * dC1.transpose() );
VERIFY_IS_APPROX( sC2 = (sC1.transpose() * sR1.transpose()), dC3 = dC1.transpose() * dR1.template cast<Cplx>().transpose() );
VERIFY_IS_APPROX( sCR = (sR1 * sC1), dC3 = dR1.template cast<Cplx>() * dC1 );
VERIFY_IS_APPROX( sCR = (sC1 * sR1), dC3 = dC1 * dR1.template cast<Cplx>() );
VERIFY_IS_APPROX( sCR = (sR1.transpose() * sC1), dC3 = dR1.template cast<Cplx>().transpose() * dC1 );
VERIFY_IS_APPROX( sCR = (sC1.transpose() * sR1), dC3 = dC1.transpose() * dR1.template cast<Cplx>() );
VERIFY_IS_APPROX( sCR = (sR1 * sC1.transpose()), dC3 = dR1.template cast<Cplx>() * dC1.transpose() );
VERIFY_IS_APPROX( sCR = (sC1 * sR1.transpose()), dC3 = dC1 * dR1.template cast<Cplx>().transpose() );
VERIFY_IS_APPROX( sCR = (sR1.transpose() * sC1.transpose()), dC3 = dR1.template cast<Cplx>().transpose() * dC1.transpose() );
VERIFY_IS_APPROX( sCR = (sC1.transpose() * sR1.transpose()), dC3 = dC1.transpose() * dR1.template cast<Cplx>().transpose() );
VERIFY_IS_APPROX( sC2 = (sR1 * sC1).pruned(), dC3 = dR1.template cast<Cplx>() * dC1 );
VERIFY_IS_APPROX( sC2 = (sC1 * sR1).pruned(), dC3 = dC1 * dR1.template cast<Cplx>() );
VERIFY_IS_APPROX( sC2 = (sR1.transpose() * sC1).pruned(), dC3 = dR1.template cast<Cplx>().transpose() * dC1 );
VERIFY_IS_APPROX( sC2 = (sC1.transpose() * sR1).pruned(), dC3 = dC1.transpose() * dR1.template cast<Cplx>() );
VERIFY_IS_APPROX( sC2 = (sR1 * sC1.transpose()).pruned(), dC3 = dR1.template cast<Cplx>() * dC1.transpose() );
VERIFY_IS_APPROX( sC2 = (sC1 * sR1.transpose()).pruned(), dC3 = dC1 * dR1.template cast<Cplx>().transpose() );
VERIFY_IS_APPROX( sC2 = (sR1.transpose() * sC1.transpose()).pruned(), dC3 = dR1.template cast<Cplx>().transpose() * dC1.transpose() );
VERIFY_IS_APPROX( sC2 = (sC1.transpose() * sR1.transpose()).pruned(), dC3 = dC1.transpose() * dR1.template cast<Cplx>().transpose() );
VERIFY_IS_APPROX( sCR = (sR1 * sC1).pruned(), dC3 = dR1.template cast<Cplx>() * dC1 );
VERIFY_IS_APPROX( sCR = (sC1 * sR1).pruned(), dC3 = dC1 * dR1.template cast<Cplx>() );
VERIFY_IS_APPROX( sCR = (sR1.transpose() * sC1).pruned(), dC3 = dR1.template cast<Cplx>().transpose() * dC1 );
VERIFY_IS_APPROX( sCR = (sC1.transpose() * sR1).pruned(), dC3 = dC1.transpose() * dR1.template cast<Cplx>() );
VERIFY_IS_APPROX( sCR = (sR1 * sC1.transpose()).pruned(), dC3 = dR1.template cast<Cplx>() * dC1.transpose() );
VERIFY_IS_APPROX( sCR = (sC1 * sR1.transpose()).pruned(), dC3 = dC1 * dR1.template cast<Cplx>().transpose() );
VERIFY_IS_APPROX( sCR = (sR1.transpose() * sC1.transpose()).pruned(), dC3 = dR1.template cast<Cplx>().transpose() * dC1.transpose() );
VERIFY_IS_APPROX( sCR = (sC1.transpose() * sR1.transpose()).pruned(), dC3 = dC1.transpose() * dR1.template cast<Cplx>().transpose() );
VERIFY_IS_APPROX( dC2 = (sR1 * sC1), dC3 = dR1.template cast<Cplx>() * dC1 );
VERIFY_IS_APPROX( dC2 = (sC1 * sR1), dC3 = dC1 * dR1.template cast<Cplx>() );
VERIFY_IS_APPROX( dC2 = (sR1.transpose() * sC1), dC3 = dR1.template cast<Cplx>().transpose() * dC1 );
VERIFY_IS_APPROX( dC2 = (sC1.transpose() * sR1), dC3 = dC1.transpose() * dR1.template cast<Cplx>() );
VERIFY_IS_APPROX( dC2 = (sR1 * sC1.transpose()), dC3 = dR1.template cast<Cplx>() * dC1.transpose() );
VERIFY_IS_APPROX( dC2 = (sC1 * sR1.transpose()), dC3 = dC1 * dR1.template cast<Cplx>().transpose() );
VERIFY_IS_APPROX( dC2 = (sR1.transpose() * sC1.transpose()), dC3 = dR1.template cast<Cplx>().transpose() * dC1.transpose() );
VERIFY_IS_APPROX( dC2 = (sC1.transpose() * sR1.transpose()), dC3 = dC1.transpose() * dR1.template cast<Cplx>().transpose() );
VERIFY_IS_APPROX( dC2 = dR1 * sC1, dC3 = dR1.template cast<Cplx>() * sC1 );
VERIFY_IS_APPROX( dC2 = sR1 * dC1, dC3 = sR1.template cast<Cplx>() * dC1 );
VERIFY_IS_APPROX( dC2 = dC1 * sR1, dC3 = dC1 * sR1.template cast<Cplx>() );
VERIFY_IS_APPROX( dC2 = sC1 * dR1, dC3 = sC1 * dR1.template cast<Cplx>() );
VERIFY_IS_APPROX( dC2 = dR1.row(0) * sC1, dC3 = dR1.template cast<Cplx>().row(0) * sC1 );
VERIFY_IS_APPROX( dC2 = sR1 * dC1.col(0), dC3 = sR1.template cast<Cplx>() * dC1.col(0) );
VERIFY_IS_APPROX( dC2 = dC1.row(0) * sR1, dC3 = dC1.row(0) * sR1.template cast<Cplx>() );
VERIFY_IS_APPROX( dC2 = sC1 * dR1.col(0), dC3 = sC1 * dR1.template cast<Cplx>().col(0) );
}
void test_sparse_product()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( (sparse_product<SparseMatrix<double,ColMajor> >()) );
CALL_SUBTEST_1( (sparse_product<SparseMatrix<double,RowMajor> >()) );
CALL_SUBTEST_1( (bug_942<double>()) );
CALL_SUBTEST_2( (sparse_product<SparseMatrix<std::complex<double>, ColMajor > >()) );
CALL_SUBTEST_2( (sparse_product<SparseMatrix<std::complex<double>, RowMajor > >()) );
CALL_SUBTEST_3( (sparse_product<SparseMatrix<float,ColMajor,long int> >()) );
CALL_SUBTEST_4( (sparse_product_regression_test<SparseMatrix<double,RowMajor>, Matrix<double, Dynamic, Dynamic, RowMajor> >()) );
CALL_SUBTEST_5( (test_mixing_types<float>()) );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/ctorleak.cpp | .cpp | 2,008 | 82 | #include "main.h"
#include <exception> // std::exception
struct Foo
{
static Index object_count;
static Index object_limit;
int dummy;
Foo() : dummy(0)
{
#ifdef EIGEN_EXCEPTIONS
// TODO: Is this the correct way to handle this?
if (Foo::object_count > Foo::object_limit) { std::cout << "\nThrow!\n"; throw Foo::Fail(); }
#endif
std::cout << '+';
++Foo::object_count;
}
~Foo()
{
std::cout << '-';
--Foo::object_count;
}
class Fail : public std::exception {};
};
Index Foo::object_count = 0;
Index Foo::object_limit = 0;
#undef EIGEN_TEST_MAX_SIZE
#define EIGEN_TEST_MAX_SIZE 3
void test_ctorleak()
{
typedef Matrix<Foo, Dynamic, Dynamic> MatrixX;
typedef Matrix<Foo, Dynamic, 1> VectorX;
Foo::object_count = 0;
for(int i = 0; i < g_repeat; i++) {
Index rows = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE), cols = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE);
Foo::object_limit = rows*cols;
{
MatrixX r(rows, cols);
Foo::object_limit = r.size()+internal::random<Index>(0, rows*cols - 2);
std::cout << "object_limit =" << Foo::object_limit << std::endl;
#ifdef EIGEN_EXCEPTIONS
try
{
#endif
if(internal::random<bool>()) {
std::cout << "\nMatrixX m(" << rows << ", " << cols << ");\n";
MatrixX m(rows, cols);
}
else {
std::cout << "\nMatrixX m(r);\n";
MatrixX m(r);
}
#ifdef EIGEN_EXCEPTIONS
VERIFY(false); // not reached if exceptions are enabled
}
catch (const Foo::Fail&) { /* ignore */ }
#endif
}
VERIFY_IS_EQUAL(Index(0), Foo::object_count);
{
Foo::object_limit = (rows+1)*(cols+1);
MatrixX A(rows, cols);
VERIFY_IS_EQUAL(Foo::object_count, rows*cols);
VectorX v=A.row(0);
VERIFY_IS_EQUAL(Foo::object_count, (rows+1)*cols);
v = A.col(0);
VERIFY_IS_EQUAL(Foo::object_count, rows*(cols+1));
}
VERIFY_IS_EQUAL(Index(0), Foo::object_count);
}
std::cout << "\n";
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/sizeof.cpp | .cpp | 2,051 | 48 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename MatrixType> void verifySizeOf(const MatrixType&)
{
typedef typename MatrixType::Scalar Scalar;
if (MatrixType::RowsAtCompileTime!=Dynamic && MatrixType::ColsAtCompileTime!=Dynamic)
VERIFY_IS_EQUAL(std::ptrdiff_t(sizeof(MatrixType)),std::ptrdiff_t(sizeof(Scalar))*std::ptrdiff_t(MatrixType::SizeAtCompileTime));
else
VERIFY_IS_EQUAL(sizeof(MatrixType),sizeof(Scalar*) + 2 * sizeof(typename MatrixType::Index));
}
void test_sizeof()
{
CALL_SUBTEST(verifySizeOf(Matrix<float, 1, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 2, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 3, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 4, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 5, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 6, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 7, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 8, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 9, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 10, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 11, 1>()) );
CALL_SUBTEST(verifySizeOf(Array<float, 12, 1>()) );
CALL_SUBTEST(verifySizeOf(Vector2d()) );
CALL_SUBTEST(verifySizeOf(Vector4f()) );
CALL_SUBTEST(verifySizeOf(Matrix4d()) );
CALL_SUBTEST(verifySizeOf(Matrix<double, 4, 2>()) );
CALL_SUBTEST(verifySizeOf(Matrix<bool, 7, 5>()) );
CALL_SUBTEST(verifySizeOf(MatrixXcf(3, 3)) );
CALL_SUBTEST(verifySizeOf(MatrixXi(8, 12)) );
CALL_SUBTEST(verifySizeOf(MatrixXcd(20, 20)) );
CALL_SUBTEST(verifySizeOf(Matrix<float, 100, 100>()) );
VERIFY(sizeof(std::complex<float>) == 2*sizeof(float));
VERIFY(sizeof(std::complex<double>) == 2*sizeof(double));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/denseLM.cpp | .cpp | 5,054 | 191 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include <iostream>
#include <fstream>
#include <iomanip>
#include "main.h"
#include <Eigen/LevenbergMarquardt>
using namespace std;
using namespace Eigen;
template<typename Scalar>
struct DenseLM : DenseFunctor<Scalar>
{
typedef DenseFunctor<Scalar> Base;
typedef typename Base::JacobianType JacobianType;
typedef Matrix<Scalar,Dynamic,1> VectorType;
DenseLM(int n, int m) : DenseFunctor<Scalar>(n,m)
{ }
VectorType model(const VectorType& uv, VectorType& x)
{
VectorType y; // Should change to use expression template
int m = Base::values();
int n = Base::inputs();
eigen_assert(uv.size()%2 == 0);
eigen_assert(uv.size() == n);
eigen_assert(x.size() == m);
y.setZero(m);
int half = n/2;
VectorBlock<const VectorType> u(uv, 0, half);
VectorBlock<const VectorType> v(uv, half, half);
for (int j = 0; j < m; j++)
{
for (int i = 0; i < half; i++)
y(j) += u(i)*std::exp(-(x(j)-i)*(x(j)-i)/(v(i)*v(i)));
}
return y;
}
void initPoints(VectorType& uv_ref, VectorType& x)
{
m_x = x;
m_y = this->model(uv_ref, x);
}
int operator()(const VectorType& uv, VectorType& fvec)
{
int m = Base::values();
int n = Base::inputs();
eigen_assert(uv.size()%2 == 0);
eigen_assert(uv.size() == n);
eigen_assert(fvec.size() == m);
int half = n/2;
VectorBlock<const VectorType> u(uv, 0, half);
VectorBlock<const VectorType> v(uv, half, half);
for (int j = 0; j < m; j++)
{
fvec(j) = m_y(j);
for (int i = 0; i < half; i++)
{
fvec(j) -= u(i) *std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
}
}
return 0;
}
int df(const VectorType& uv, JacobianType& fjac)
{
int m = Base::values();
int n = Base::inputs();
eigen_assert(n == uv.size());
eigen_assert(fjac.rows() == m);
eigen_assert(fjac.cols() == n);
int half = n/2;
VectorBlock<const VectorType> u(uv, 0, half);
VectorBlock<const VectorType> v(uv, half, half);
for (int j = 0; j < m; j++)
{
for (int i = 0; i < half; i++)
{
fjac.coeffRef(j,i) = -std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
fjac.coeffRef(j,i+half) = -2.*u(i)*(m_x(j)-i)*(m_x(j)-i)/(std::pow(v(i),3)) * std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
}
}
return 0;
}
VectorType m_x, m_y; //Data Points
};
template<typename FunctorType, typename VectorType>
int test_minimizeLM(FunctorType& functor, VectorType& uv)
{
LevenbergMarquardt<FunctorType> lm(functor);
LevenbergMarquardtSpace::Status info;
info = lm.minimize(uv);
VERIFY_IS_EQUAL(info, 1);
//FIXME Check other parameters
return info;
}
template<typename FunctorType, typename VectorType>
int test_lmder(FunctorType& functor, VectorType& uv)
{
typedef typename VectorType::Scalar Scalar;
LevenbergMarquardtSpace::Status info;
LevenbergMarquardt<FunctorType> lm(functor);
info = lm.lmder1(uv);
VERIFY_IS_EQUAL(info, 1);
//FIXME Check other parameters
return info;
}
template<typename FunctorType, typename VectorType>
int test_minimizeSteps(FunctorType& functor, VectorType& uv)
{
LevenbergMarquardtSpace::Status info;
LevenbergMarquardt<FunctorType> lm(functor);
info = lm.minimizeInit(uv);
if (info==LevenbergMarquardtSpace::ImproperInputParameters)
return info;
do
{
info = lm.minimizeOneStep(uv);
} while (info==LevenbergMarquardtSpace::Running);
VERIFY_IS_EQUAL(info, 1);
//FIXME Check other parameters
return info;
}
template<typename T>
void test_denseLM_T()
{
typedef Matrix<T,Dynamic,1> VectorType;
int inputs = 10;
int values = 1000;
DenseLM<T> dense_gaussian(inputs, values);
VectorType uv(inputs),uv_ref(inputs);
VectorType x(values);
// Generate the reference solution
uv_ref << -2, 1, 4 ,8, 6, 1.8, 1.2, 1.1, 1.9 , 3;
//Generate the reference data points
x.setRandom();
x = 10*x;
x.array() += 10;
dense_gaussian.initPoints(uv_ref, x);
// Generate the initial parameters
VectorBlock<VectorType> u(uv, 0, inputs/2);
VectorBlock<VectorType> v(uv, inputs/2, inputs/2);
// Solve the optimization problem
//Solve in one go
u.setOnes(); v.setOnes();
test_minimizeLM(dense_gaussian, uv);
//Solve until the machine precision
u.setOnes(); v.setOnes();
test_lmder(dense_gaussian, uv);
// Solve step by step
v.setOnes(); u.setOnes();
test_minimizeSteps(dense_gaussian, uv);
}
void test_denseLM()
{
CALL_SUBTEST_2(test_denseLM_T<double>());
// CALL_SUBTEST_2(test_sparseLM_T<std::complex<double>());
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/boostmultiprec.cpp | .cpp | 5,534 | 202 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include <sstream>
#ifdef EIGEN_TEST_MAX_SIZE
#undef EIGEN_TEST_MAX_SIZE
#endif
#define EIGEN_TEST_MAX_SIZE 50
#ifdef EIGEN_TEST_PART_1
#include "cholesky.cpp"
#endif
#ifdef EIGEN_TEST_PART_2
#include "lu.cpp"
#endif
#ifdef EIGEN_TEST_PART_3
#include "qr.cpp"
#endif
#ifdef EIGEN_TEST_PART_4
#include "qr_colpivoting.cpp"
#endif
#ifdef EIGEN_TEST_PART_5
#include "qr_fullpivoting.cpp"
#endif
#ifdef EIGEN_TEST_PART_6
#include "eigensolver_selfadjoint.cpp"
#endif
#ifdef EIGEN_TEST_PART_7
#include "eigensolver_generic.cpp"
#endif
#ifdef EIGEN_TEST_PART_8
#include "eigensolver_generalized_real.cpp"
#endif
#ifdef EIGEN_TEST_PART_9
#include "jacobisvd.cpp"
#endif
#ifdef EIGEN_TEST_PART_10
#include "bdcsvd.cpp"
#endif
#include <Eigen/Dense>
#undef min
#undef max
#undef isnan
#undef isinf
#undef isfinite
#include <boost/multiprecision/cpp_dec_float.hpp>
#include <boost/multiprecision/number.hpp>
#include <boost/math/special_functions.hpp>
#include <boost/math/complex.hpp>
namespace mp = boost::multiprecision;
typedef mp::number<mp::cpp_dec_float<100>, mp::et_on> Real;
namespace Eigen {
template<> struct NumTraits<Real> : GenericNumTraits<Real> {
static inline Real dummy_precision() { return 1e-50; }
};
template<typename T1,typename T2,typename T3,typename T4,typename T5>
struct NumTraits<boost::multiprecision::detail::expression<T1,T2,T3,T4,T5> > : NumTraits<Real> {};
template<>
Real test_precision<Real>() { return 1e-50; }
// needed in C++93 mode where number does not support explicit cast.
namespace internal {
template<typename NewType>
struct cast_impl<Real,NewType> {
static inline NewType run(const Real& x) {
return x.template convert_to<NewType>();
}
};
template<>
struct cast_impl<Real,std::complex<Real> > {
static inline std::complex<Real> run(const Real& x) {
return std::complex<Real>(x);
}
};
}
}
namespace boost {
namespace multiprecision {
// to make ADL works as expected:
using boost::math::isfinite;
using boost::math::isnan;
using boost::math::isinf;
using boost::math::copysign;
using boost::math::hypot;
// The following is needed for std::complex<Real>:
Real fabs(const Real& a) { return abs EIGEN_NOT_A_MACRO (a); }
Real fmax(const Real& a, const Real& b) { using std::max; return max(a,b); }
// some specialization for the unit tests:
inline bool test_isMuchSmallerThan(const Real& a, const Real& b) {
return internal::isMuchSmallerThan(a, b, test_precision<Real>());
}
inline bool test_isApprox(const Real& a, const Real& b) {
return internal::isApprox(a, b, test_precision<Real>());
}
inline bool test_isApproxOrLessThan(const Real& a, const Real& b) {
return internal::isApproxOrLessThan(a, b, test_precision<Real>());
}
Real get_test_precision(const Real&) {
return test_precision<Real>();
}
Real test_relative_error(const Real &a, const Real &b) {
using Eigen::numext::abs2;
return sqrt(abs2<Real>(a-b)/Eigen::numext::mini<Real>(abs2(a),abs2(b)));
}
}
}
namespace Eigen {
}
void test_boostmultiprec()
{
typedef Matrix<Real,Dynamic,Dynamic> Mat;
typedef Matrix<std::complex<Real>,Dynamic,Dynamic> MatC;
std::cout << "NumTraits<Real>::epsilon() = " << NumTraits<Real>::epsilon() << std::endl;
std::cout << "NumTraits<Real>::dummy_precision() = " << NumTraits<Real>::dummy_precision() << std::endl;
std::cout << "NumTraits<Real>::lowest() = " << NumTraits<Real>::lowest() << std::endl;
std::cout << "NumTraits<Real>::highest() = " << NumTraits<Real>::highest() << std::endl;
std::cout << "NumTraits<Real>::digits10() = " << NumTraits<Real>::digits10() << std::endl;
// chekc stream output
{
Mat A(10,10);
A.setRandom();
std::stringstream ss;
ss << A;
}
{
MatC A(10,10);
A.setRandom();
std::stringstream ss;
ss << A;
}
for(int i = 0; i < g_repeat; i++) {
int s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
CALL_SUBTEST_1( cholesky(Mat(s,s)) );
CALL_SUBTEST_2( lu_non_invertible<Mat>() );
CALL_SUBTEST_2( lu_invertible<Mat>() );
CALL_SUBTEST_2( lu_non_invertible<MatC>() );
CALL_SUBTEST_2( lu_invertible<MatC>() );
CALL_SUBTEST_3( qr(Mat(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_3( qr_invertible<Mat>() );
CALL_SUBTEST_4( qr<Mat>() );
CALL_SUBTEST_4( cod<Mat>() );
CALL_SUBTEST_4( qr_invertible<Mat>() );
CALL_SUBTEST_5( qr<Mat>() );
CALL_SUBTEST_5( qr_invertible<Mat>() );
CALL_SUBTEST_6( selfadjointeigensolver(Mat(s,s)) );
CALL_SUBTEST_7( eigensolver(Mat(s,s)) );
CALL_SUBTEST_8( generalized_eigensolver_real(Mat(s,s)) );
TEST_SET_BUT_UNUSED_VARIABLE(s)
}
CALL_SUBTEST_9(( jacobisvd(Mat(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) ));
CALL_SUBTEST_10(( bdcsvd(Mat(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) ));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/selfadjoint.cpp | .cpp | 2,411 | 76 | // This file is triangularView of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define TEST_CHECK_STATIC_ASSERTIONS
#include "main.h"
// This file tests the basic selfadjointView API,
// the related products and decompositions are tested in specific files.
template<typename MatrixType> void selfadjoint(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols),
m4(rows, cols);
m1.diagonal() = m1.diagonal().real().template cast<Scalar>();
// check selfadjoint to dense
m3 = m1.template selfadjointView<Upper>();
VERIFY_IS_APPROX(MatrixType(m3.template triangularView<Upper>()), MatrixType(m1.template triangularView<Upper>()));
VERIFY_IS_APPROX(m3, m3.adjoint());
m3 = m1.template selfadjointView<Lower>();
VERIFY_IS_APPROX(MatrixType(m3.template triangularView<Lower>()), MatrixType(m1.template triangularView<Lower>()));
VERIFY_IS_APPROX(m3, m3.adjoint());
m3 = m1.template selfadjointView<Upper>();
m4 = m2;
m4 += m1.template selfadjointView<Upper>();
VERIFY_IS_APPROX(m4, m2+m3);
m3 = m1.template selfadjointView<Lower>();
m4 = m2;
m4 -= m1.template selfadjointView<Lower>();
VERIFY_IS_APPROX(m4, m2-m3);
VERIFY_RAISES_STATIC_ASSERT(m2.template selfadjointView<StrictlyUpper>());
VERIFY_RAISES_STATIC_ASSERT(m2.template selfadjointView<UnitLower>());
}
void bug_159()
{
Matrix3d m = Matrix3d::Random().selfadjointView<Lower>();
EIGEN_UNUSED_VARIABLE(m)
}
void test_selfadjoint()
{
for(int i = 0; i < g_repeat ; i++)
{
int s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
CALL_SUBTEST_1( selfadjoint(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( selfadjoint(Matrix<float, 2, 2>()) );
CALL_SUBTEST_3( selfadjoint(Matrix3cf()) );
CALL_SUBTEST_4( selfadjoint(MatrixXcd(s,s)) );
CALL_SUBTEST_5( selfadjoint(Matrix<float,Dynamic,Dynamic,RowMajor>(s, s)) );
TEST_SET_BUT_UNUSED_VARIABLE(s)
}
CALL_SUBTEST_1( bug_159() );
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/mapstride.cpp | .cpp | 10,112 | 235 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<int Alignment,typename VectorType> void map_class_vector(const VectorType& m)
{
typedef typename VectorType::Scalar Scalar;
Index size = m.size();
VectorType v = VectorType::Random(size);
Index arraysize = 3*size;
Scalar* a_array = internal::aligned_new<Scalar>(arraysize+1);
Scalar* array = a_array;
if(Alignment!=Aligned)
array = (Scalar*)(internal::IntPtr(a_array) + (internal::packet_traits<Scalar>::AlignedOnScalar?sizeof(Scalar):sizeof(typename NumTraits<Scalar>::Real)));
{
Map<VectorType, Alignment, InnerStride<3> > map(array, size);
map = v;
for(int i = 0; i < size; ++i)
{
VERIFY(array[3*i] == v[i]);
VERIFY(map[i] == v[i]);
}
}
{
Map<VectorType, Unaligned, InnerStride<Dynamic> > map(array, size, InnerStride<Dynamic>(2));
map = v;
for(int i = 0; i < size; ++i)
{
VERIFY(array[2*i] == v[i]);
VERIFY(map[i] == v[i]);
}
}
internal::aligned_delete(a_array, arraysize+1);
}
template<int Alignment,typename MatrixType> void map_class_matrix(const MatrixType& _m)
{
typedef typename MatrixType::Scalar Scalar;
Index rows = _m.rows(), cols = _m.cols();
MatrixType m = MatrixType::Random(rows,cols);
Scalar s1 = internal::random<Scalar>();
Index arraysize = 4*(rows+4)*(cols+4);
Scalar* a_array1 = internal::aligned_new<Scalar>(arraysize+1);
Scalar* array1 = a_array1;
if(Alignment!=Aligned)
array1 = (Scalar*)(internal::IntPtr(a_array1) + (internal::packet_traits<Scalar>::AlignedOnScalar?sizeof(Scalar):sizeof(typename NumTraits<Scalar>::Real)));
Scalar a_array2[256];
Scalar* array2 = a_array2;
if(Alignment!=Aligned)
array2 = (Scalar*)(internal::IntPtr(a_array2) + (internal::packet_traits<Scalar>::AlignedOnScalar?sizeof(Scalar):sizeof(typename NumTraits<Scalar>::Real)));
else
array2 = (Scalar*)(((internal::UIntPtr(a_array2)+EIGEN_MAX_ALIGN_BYTES-1)/EIGEN_MAX_ALIGN_BYTES)*EIGEN_MAX_ALIGN_BYTES);
Index maxsize2 = a_array2 - array2 + 256;
// test no inner stride and some dynamic outer stride
for(int k=0; k<2; ++k)
{
if(k==1 && (m.innerSize()+1)*m.outerSize() > maxsize2)
break;
Scalar* array = (k==0 ? array1 : array2);
Map<MatrixType, Alignment, OuterStride<Dynamic> > map(array, rows, cols, OuterStride<Dynamic>(m.innerSize()+1));
map = m;
VERIFY(map.outerStride() == map.innerSize()+1);
for(int i = 0; i < m.outerSize(); ++i)
for(int j = 0; j < m.innerSize(); ++j)
{
VERIFY(array[map.outerStride()*i+j] == m.coeffByOuterInner(i,j));
VERIFY(map.coeffByOuterInner(i,j) == m.coeffByOuterInner(i,j));
}
VERIFY_IS_APPROX(s1*map,s1*m);
map *= s1;
VERIFY_IS_APPROX(map,s1*m);
}
// test no inner stride and an outer stride of +4. This is quite important as for fixed-size matrices,
// this allows to hit the special case where it's vectorizable.
for(int k=0; k<2; ++k)
{
if(k==1 && (m.innerSize()+4)*m.outerSize() > maxsize2)
break;
Scalar* array = (k==0 ? array1 : array2);
enum {
InnerSize = MatrixType::InnerSizeAtCompileTime,
OuterStrideAtCompileTime = InnerSize==Dynamic ? Dynamic : InnerSize+4
};
Map<MatrixType, Alignment, OuterStride<OuterStrideAtCompileTime> >
map(array, rows, cols, OuterStride<OuterStrideAtCompileTime>(m.innerSize()+4));
map = m;
VERIFY(map.outerStride() == map.innerSize()+4);
for(int i = 0; i < m.outerSize(); ++i)
for(int j = 0; j < m.innerSize(); ++j)
{
VERIFY(array[map.outerStride()*i+j] == m.coeffByOuterInner(i,j));
VERIFY(map.coeffByOuterInner(i,j) == m.coeffByOuterInner(i,j));
}
VERIFY_IS_APPROX(s1*map,s1*m);
map *= s1;
VERIFY_IS_APPROX(map,s1*m);
}
// test both inner stride and outer stride
for(int k=0; k<2; ++k)
{
if(k==1 && (2*m.innerSize()+1)*(m.outerSize()*2) > maxsize2)
break;
Scalar* array = (k==0 ? array1 : array2);
Map<MatrixType, Alignment, Stride<Dynamic,Dynamic> > map(array, rows, cols, Stride<Dynamic,Dynamic>(2*m.innerSize()+1, 2));
map = m;
VERIFY(map.outerStride() == 2*map.innerSize()+1);
VERIFY(map.innerStride() == 2);
for(int i = 0; i < m.outerSize(); ++i)
for(int j = 0; j < m.innerSize(); ++j)
{
VERIFY(array[map.outerStride()*i+map.innerStride()*j] == m.coeffByOuterInner(i,j));
VERIFY(map.coeffByOuterInner(i,j) == m.coeffByOuterInner(i,j));
}
VERIFY_IS_APPROX(s1*map,s1*m);
map *= s1;
VERIFY_IS_APPROX(map,s1*m);
}
// test inner stride and no outer stride
for(int k=0; k<2; ++k)
{
if(k==1 && (m.innerSize()*2)*m.outerSize() > maxsize2)
break;
Scalar* array = (k==0 ? array1 : array2);
Map<MatrixType, Alignment, InnerStride<Dynamic> > map(array, rows, cols, InnerStride<Dynamic>(2));
map = m;
VERIFY(map.outerStride() == map.innerSize()*2);
for(int i = 0; i < m.outerSize(); ++i)
for(int j = 0; j < m.innerSize(); ++j)
{
VERIFY(array[map.innerSize()*i*2+j*2] == m.coeffByOuterInner(i,j));
VERIFY(map.coeffByOuterInner(i,j) == m.coeffByOuterInner(i,j));
}
VERIFY_IS_APPROX(s1*map,s1*m);
map *= s1;
VERIFY_IS_APPROX(map,s1*m);
}
internal::aligned_delete(a_array1, arraysize+1);
}
// Additional tests for inner-stride but no outer-stride
template<int>
void bug1453()
{
const int data[] = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31};
typedef Matrix<int,Dynamic,Dynamic,RowMajor> RowMatrixXi;
typedef Matrix<int,2,3,ColMajor> ColMatrix23i;
typedef Matrix<int,3,2,ColMajor> ColMatrix32i;
typedef Matrix<int,2,3,RowMajor> RowMatrix23i;
typedef Matrix<int,3,2,RowMajor> RowMatrix32i;
VERIFY_IS_APPROX(MatrixXi::Map(data, 2, 3, InnerStride<2>()), MatrixXi::Map(data, 2, 3, Stride<4,2>()));
VERIFY_IS_APPROX(MatrixXi::Map(data, 2, 3, InnerStride<>(2)), MatrixXi::Map(data, 2, 3, Stride<4,2>()));
VERIFY_IS_APPROX(MatrixXi::Map(data, 3, 2, InnerStride<2>()), MatrixXi::Map(data, 3, 2, Stride<6,2>()));
VERIFY_IS_APPROX(MatrixXi::Map(data, 3, 2, InnerStride<>(2)), MatrixXi::Map(data, 3, 2, Stride<6,2>()));
VERIFY_IS_APPROX(RowMatrixXi::Map(data, 2, 3, InnerStride<2>()), RowMatrixXi::Map(data, 2, 3, Stride<6,2>()));
VERIFY_IS_APPROX(RowMatrixXi::Map(data, 2, 3, InnerStride<>(2)), RowMatrixXi::Map(data, 2, 3, Stride<6,2>()));
VERIFY_IS_APPROX(RowMatrixXi::Map(data, 3, 2, InnerStride<2>()), RowMatrixXi::Map(data, 3, 2, Stride<4,2>()));
VERIFY_IS_APPROX(RowMatrixXi::Map(data, 3, 2, InnerStride<>(2)), RowMatrixXi::Map(data, 3, 2, Stride<4,2>()));
VERIFY_IS_APPROX(ColMatrix23i::Map(data, InnerStride<2>()), MatrixXi::Map(data, 2, 3, Stride<4,2>()));
VERIFY_IS_APPROX(ColMatrix23i::Map(data, InnerStride<>(2)), MatrixXi::Map(data, 2, 3, Stride<4,2>()));
VERIFY_IS_APPROX(ColMatrix32i::Map(data, InnerStride<2>()), MatrixXi::Map(data, 3, 2, Stride<6,2>()));
VERIFY_IS_APPROX(ColMatrix32i::Map(data, InnerStride<>(2)), MatrixXi::Map(data, 3, 2, Stride<6,2>()));
VERIFY_IS_APPROX(RowMatrix23i::Map(data, InnerStride<2>()), RowMatrixXi::Map(data, 2, 3, Stride<6,2>()));
VERIFY_IS_APPROX(RowMatrix23i::Map(data, InnerStride<>(2)), RowMatrixXi::Map(data, 2, 3, Stride<6,2>()));
VERIFY_IS_APPROX(RowMatrix32i::Map(data, InnerStride<2>()), RowMatrixXi::Map(data, 3, 2, Stride<4,2>()));
VERIFY_IS_APPROX(RowMatrix32i::Map(data, InnerStride<>(2)), RowMatrixXi::Map(data, 3, 2, Stride<4,2>()));
}
void test_mapstride()
{
for(int i = 0; i < g_repeat; i++) {
int maxn = 30;
CALL_SUBTEST_1( map_class_vector<Aligned>(Matrix<float, 1, 1>()) );
CALL_SUBTEST_1( map_class_vector<Unaligned>(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( map_class_vector<Aligned>(Vector4d()) );
CALL_SUBTEST_2( map_class_vector<Unaligned>(Vector4d()) );
CALL_SUBTEST_3( map_class_vector<Aligned>(RowVector4f()) );
CALL_SUBTEST_3( map_class_vector<Unaligned>(RowVector4f()) );
CALL_SUBTEST_4( map_class_vector<Aligned>(VectorXcf(internal::random<int>(1,maxn))) );
CALL_SUBTEST_4( map_class_vector<Unaligned>(VectorXcf(internal::random<int>(1,maxn))) );
CALL_SUBTEST_5( map_class_vector<Aligned>(VectorXi(internal::random<int>(1,maxn))) );
CALL_SUBTEST_5( map_class_vector<Unaligned>(VectorXi(internal::random<int>(1,maxn))) );
CALL_SUBTEST_1( map_class_matrix<Aligned>(Matrix<float, 1, 1>()) );
CALL_SUBTEST_1( map_class_matrix<Unaligned>(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( map_class_matrix<Aligned>(Matrix4d()) );
CALL_SUBTEST_2( map_class_matrix<Unaligned>(Matrix4d()) );
CALL_SUBTEST_3( map_class_matrix<Aligned>(Matrix<float,3,5>()) );
CALL_SUBTEST_3( map_class_matrix<Unaligned>(Matrix<float,3,5>()) );
CALL_SUBTEST_3( map_class_matrix<Aligned>(Matrix<float,4,8>()) );
CALL_SUBTEST_3( map_class_matrix<Unaligned>(Matrix<float,4,8>()) );
CALL_SUBTEST_4( map_class_matrix<Aligned>(MatrixXcf(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
CALL_SUBTEST_4( map_class_matrix<Unaligned>(MatrixXcf(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
CALL_SUBTEST_5( map_class_matrix<Aligned>(MatrixXi(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
CALL_SUBTEST_5( map_class_matrix<Unaligned>(MatrixXi(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
CALL_SUBTEST_6( map_class_matrix<Aligned>(MatrixXcd(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
CALL_SUBTEST_6( map_class_matrix<Unaligned>(MatrixXcd(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
CALL_SUBTEST_5( bug1453<0>() );
TEST_SET_BUT_UNUSED_VARIABLE(maxn);
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/product_trmm.cpp | .cpp | 6,913 | 138 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename T>
int get_random_size()
{
const int factor = NumTraits<T>::ReadCost;
const int max_test_size = EIGEN_TEST_MAX_SIZE>2*factor ? EIGEN_TEST_MAX_SIZE/factor : EIGEN_TEST_MAX_SIZE;
return internal::random<int>(1,max_test_size);
}
template<typename Scalar, int Mode, int TriOrder, int OtherOrder, int ResOrder, int OtherCols>
void trmm(int rows=get_random_size<Scalar>(),
int cols=get_random_size<Scalar>(),
int otherCols = OtherCols==Dynamic?get_random_size<Scalar>():OtherCols)
{
typedef Matrix<Scalar,Dynamic,Dynamic,TriOrder> TriMatrix;
typedef Matrix<Scalar,Dynamic,OtherCols,OtherCols==1?ColMajor:OtherOrder> OnTheRight;
typedef Matrix<Scalar,OtherCols,Dynamic,OtherCols==1?RowMajor:OtherOrder> OnTheLeft;
typedef Matrix<Scalar,Dynamic,OtherCols,OtherCols==1?ColMajor:ResOrder> ResXS;
typedef Matrix<Scalar,OtherCols,Dynamic,OtherCols==1?RowMajor:ResOrder> ResSX;
TriMatrix mat(rows,cols), tri(rows,cols), triTr(cols,rows), s1tri(rows,cols), s1triTr(cols,rows);
OnTheRight ge_right(cols,otherCols);
OnTheLeft ge_left(otherCols,rows);
ResSX ge_sx, ge_sx_save;
ResXS ge_xs, ge_xs_save;
Scalar s1 = internal::random<Scalar>(),
s2 = internal::random<Scalar>();
mat.setRandom();
tri = mat.template triangularView<Mode>();
triTr = mat.transpose().template triangularView<Mode>();
s1tri = (s1*mat).template triangularView<Mode>();
s1triTr = (s1*mat).transpose().template triangularView<Mode>();
ge_right.setRandom();
ge_left.setRandom();
VERIFY_IS_APPROX( ge_xs = mat.template triangularView<Mode>() * ge_right, tri * ge_right);
VERIFY_IS_APPROX( ge_sx = ge_left * mat.template triangularView<Mode>(), ge_left * tri);
VERIFY_IS_APPROX( ge_xs.noalias() = mat.template triangularView<Mode>() * ge_right, tri * ge_right);
VERIFY_IS_APPROX( ge_sx.noalias() = ge_left * mat.template triangularView<Mode>(), ge_left * tri);
if((Mode&UnitDiag)==0)
VERIFY_IS_APPROX( ge_xs.noalias() = (s1*mat.adjoint()).template triangularView<Mode>() * (s2*ge_left.transpose()), s1*triTr.conjugate() * (s2*ge_left.transpose()));
VERIFY_IS_APPROX( ge_xs.noalias() = (s1*mat.transpose()).template triangularView<Mode>() * (s2*ge_left.transpose()), s1triTr * (s2*ge_left.transpose()));
VERIFY_IS_APPROX( ge_sx.noalias() = (s2*ge_left) * (s1*mat).template triangularView<Mode>(), (s2*ge_left)*s1tri);
VERIFY_IS_APPROX( ge_sx.noalias() = ge_right.transpose() * mat.adjoint().template triangularView<Mode>(), ge_right.transpose() * triTr.conjugate());
VERIFY_IS_APPROX( ge_sx.noalias() = ge_right.adjoint() * mat.adjoint().template triangularView<Mode>(), ge_right.adjoint() * triTr.conjugate());
ge_xs_save = ge_xs;
if((Mode&UnitDiag)==0)
VERIFY_IS_APPROX( (ge_xs_save + s1*triTr.conjugate() * (s2*ge_left.adjoint())).eval(), ge_xs.noalias() += (s1*mat.adjoint()).template triangularView<Mode>() * (s2*ge_left.adjoint()) );
ge_xs_save = ge_xs;
VERIFY_IS_APPROX( (ge_xs_save + s1triTr * (s2*ge_left.adjoint())).eval(), ge_xs.noalias() += (s1*mat.transpose()).template triangularView<Mode>() * (s2*ge_left.adjoint()) );
ge_sx.setRandom();
ge_sx_save = ge_sx;
if((Mode&UnitDiag)==0)
VERIFY_IS_APPROX( ge_sx_save - (ge_right.adjoint() * (-s1 * triTr).conjugate()).eval(), ge_sx.noalias() -= (ge_right.adjoint() * (-s1 * mat).adjoint().template triangularView<Mode>()).eval());
if((Mode&UnitDiag)==0)
VERIFY_IS_APPROX( ge_xs = (s1*mat).adjoint().template triangularView<Mode>() * ge_left.adjoint(), numext::conj(s1) * triTr.conjugate() * ge_left.adjoint());
VERIFY_IS_APPROX( ge_xs = (s1*mat).transpose().template triangularView<Mode>() * ge_left.adjoint(), s1triTr * ge_left.adjoint());
// TODO check with sub-matrix expressions ?
// destination with a non-default inner-stride
// see bug 1741
{
VERIFY_IS_APPROX( ge_xs.noalias() = mat.template triangularView<Mode>() * ge_right, tri * ge_right);
typedef Matrix<Scalar,Dynamic,Dynamic> MatrixX;
MatrixX buffer(2*ge_xs.rows(),2*ge_xs.cols());
Map<ResXS,0,Stride<Dynamic,2> > map1(buffer.data(),ge_xs.rows(),ge_xs.cols(),Stride<Dynamic,2>(2*ge_xs.outerStride(),2));
buffer.setZero();
VERIFY_IS_APPROX( map1.noalias() = mat.template triangularView<Mode>() * ge_right, tri * ge_right);
}
}
template<typename Scalar, int Mode, int TriOrder>
void trmv(int rows=get_random_size<Scalar>(), int cols=get_random_size<Scalar>())
{
trmm<Scalar,Mode,TriOrder,ColMajor,ColMajor,1>(rows,cols,1);
}
template<typename Scalar, int Mode, int TriOrder, int OtherOrder, int ResOrder>
void trmm(int rows=get_random_size<Scalar>(), int cols=get_random_size<Scalar>(), int otherCols = get_random_size<Scalar>())
{
trmm<Scalar,Mode,TriOrder,OtherOrder,ResOrder,Dynamic>(rows,cols,otherCols);
}
#define CALL_ALL_ORDERS(NB,SCALAR,MODE) \
EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, ColMajor,ColMajor,ColMajor>())); \
EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, ColMajor,ColMajor,RowMajor>())); \
EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, ColMajor,RowMajor,ColMajor>())); \
EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, ColMajor,RowMajor,RowMajor>())); \
EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, RowMajor,ColMajor,ColMajor>())); \
EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, RowMajor,ColMajor,RowMajor>())); \
EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, RowMajor,RowMajor,ColMajor>())); \
EIGEN_CAT(CALL_SUBTEST_,NB)((trmm<SCALAR, MODE, RowMajor,RowMajor,RowMajor>())); \
\
EIGEN_CAT(CALL_SUBTEST_1,NB)((trmv<SCALAR, MODE, ColMajor>())); \
EIGEN_CAT(CALL_SUBTEST_1,NB)((trmv<SCALAR, MODE, RowMajor>()));
#define CALL_ALL(NB,SCALAR) \
CALL_ALL_ORDERS(EIGEN_CAT(1,NB),SCALAR,Upper) \
CALL_ALL_ORDERS(EIGEN_CAT(2,NB),SCALAR,UnitUpper) \
CALL_ALL_ORDERS(EIGEN_CAT(3,NB),SCALAR,StrictlyUpper) \
CALL_ALL_ORDERS(EIGEN_CAT(1,NB),SCALAR,Lower) \
CALL_ALL_ORDERS(EIGEN_CAT(2,NB),SCALAR,UnitLower) \
CALL_ALL_ORDERS(EIGEN_CAT(3,NB),SCALAR,StrictlyLower)
void test_product_trmm()
{
for(int i = 0; i < g_repeat ; i++)
{
CALL_ALL(1,float); // EIGEN_SUFFIXES;11;111;21;121;31;131
CALL_ALL(2,double); // EIGEN_SUFFIXES;12;112;22;122;32;132
CALL_ALL(3,std::complex<float>); // EIGEN_SUFFIXES;13;113;23;123;33;133
CALL_ALL(4,std::complex<double>); // EIGEN_SUFFIXES;14;114;24;124;34;134
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/first_aligned.cpp | .cpp | 1,866 | 52 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename Scalar>
void test_first_aligned_helper(Scalar *array, int size)
{
const int packet_size = sizeof(Scalar) * internal::packet_traits<Scalar>::size;
VERIFY(((size_t(array) + sizeof(Scalar) * internal::first_default_aligned(array, size)) % packet_size) == 0);
}
template<typename Scalar>
void test_none_aligned_helper(Scalar *array, int size)
{
EIGEN_UNUSED_VARIABLE(array);
EIGEN_UNUSED_VARIABLE(size);
VERIFY(internal::packet_traits<Scalar>::size == 1 || internal::first_default_aligned(array, size) == size);
}
struct some_non_vectorizable_type { float x; };
void test_first_aligned()
{
EIGEN_ALIGN16 float array_float[100];
test_first_aligned_helper(array_float, 50);
test_first_aligned_helper(array_float+1, 50);
test_first_aligned_helper(array_float+2, 50);
test_first_aligned_helper(array_float+3, 50);
test_first_aligned_helper(array_float+4, 50);
test_first_aligned_helper(array_float+5, 50);
EIGEN_ALIGN16 double array_double[100];
test_first_aligned_helper(array_double, 50);
test_first_aligned_helper(array_double+1, 50);
test_first_aligned_helper(array_double+2, 50);
double *array_double_plus_4_bytes = (double*)(internal::UIntPtr(array_double)+4);
test_none_aligned_helper(array_double_plus_4_bytes, 50);
test_none_aligned_helper(array_double_plus_4_bytes+1, 50);
some_non_vectorizable_type array_nonvec[100];
test_first_aligned_helper(array_nonvec, 100);
test_none_aligned_helper(array_nonvec, 100);
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/exceptions.cpp | .cpp | 3,669 | 116 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
// Various sanity tests with exceptions:
// - no memory leak when a custom scalar type trow an exceptions
// - todo: complete the list of tests!
#define EIGEN_STACK_ALLOCATION_LIMIT 100000000
#include "main.h"
struct my_exception
{
my_exception() {}
~my_exception() {}
};
class ScalarWithExceptions
{
public:
ScalarWithExceptions() { init(); }
ScalarWithExceptions(const float& _v) { init(); *v = _v; }
ScalarWithExceptions(const ScalarWithExceptions& other) { init(); *v = *(other.v); }
~ScalarWithExceptions() {
delete v;
instances--;
}
void init() {
v = new float;
instances++;
}
ScalarWithExceptions operator+(const ScalarWithExceptions& other) const
{
countdown--;
if(countdown<=0)
throw my_exception();
return ScalarWithExceptions(*v+*other.v);
}
ScalarWithExceptions operator-(const ScalarWithExceptions& other) const
{ return ScalarWithExceptions(*v-*other.v); }
ScalarWithExceptions operator*(const ScalarWithExceptions& other) const
{ return ScalarWithExceptions((*v)*(*other.v)); }
ScalarWithExceptions& operator+=(const ScalarWithExceptions& other)
{ *v+=*other.v; return *this; }
ScalarWithExceptions& operator-=(const ScalarWithExceptions& other)
{ *v-=*other.v; return *this; }
ScalarWithExceptions& operator=(const ScalarWithExceptions& other)
{ *v = *(other.v); return *this; }
bool operator==(const ScalarWithExceptions& other) const
{ return *v==*other.v; }
bool operator!=(const ScalarWithExceptions& other) const
{ return *v!=*other.v; }
float* v;
static int instances;
static int countdown;
};
ScalarWithExceptions real(const ScalarWithExceptions &x) { return x; }
ScalarWithExceptions imag(const ScalarWithExceptions & ) { return 0; }
ScalarWithExceptions conj(const ScalarWithExceptions &x) { return x; }
int ScalarWithExceptions::instances = 0;
int ScalarWithExceptions::countdown = 0;
#define CHECK_MEMLEAK(OP) { \
ScalarWithExceptions::countdown = 100; \
int before = ScalarWithExceptions::instances; \
bool exception_thrown = false; \
try { OP; } \
catch (my_exception) { \
exception_thrown = true; \
VERIFY(ScalarWithExceptions::instances==before && "memory leak detected in " && EIGEN_MAKESTRING(OP)); \
} \
VERIFY(exception_thrown && " no exception thrown in " && EIGEN_MAKESTRING(OP)); \
}
void memoryleak()
{
typedef Eigen::Matrix<ScalarWithExceptions,Dynamic,1> VectorType;
typedef Eigen::Matrix<ScalarWithExceptions,Dynamic,Dynamic> MatrixType;
{
int n = 50;
VectorType v0(n), v1(n);
MatrixType m0(n,n), m1(n,n), m2(n,n);
v0.setOnes(); v1.setOnes();
m0.setOnes(); m1.setOnes(); m2.setOnes();
CHECK_MEMLEAK(v0 = m0 * m1 * v1);
CHECK_MEMLEAK(m2 = m0 * m1 * m2);
CHECK_MEMLEAK((v0+v1).dot(v0+v1));
}
VERIFY(ScalarWithExceptions::instances==0 && "global memory leak detected in " && EIGEN_MAKESTRING(OP)); \
}
void test_exceptions()
{
EIGEN_TRY {
CALL_SUBTEST( memoryleak() );
} EIGEN_CATCH(...) {}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/block.cpp | .cpp | 11,602 | 277 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_NO_STATIC_ASSERT // otherwise we fail at compile time on unused paths
#include "main.h"
template<typename MatrixType, typename Index, typename Scalar>
typename Eigen::internal::enable_if<!NumTraits<typename MatrixType::Scalar>::IsComplex,typename MatrixType::Scalar>::type
block_real_only(const MatrixType &m1, Index r1, Index r2, Index c1, Index c2, const Scalar& s1) {
// check cwise-Functions:
VERIFY_IS_APPROX(m1.row(r1).cwiseMax(s1), m1.cwiseMax(s1).row(r1));
VERIFY_IS_APPROX(m1.col(c1).cwiseMin(s1), m1.cwiseMin(s1).col(c1));
VERIFY_IS_APPROX(m1.block(r1,c1,r2-r1+1,c2-c1+1).cwiseMin(s1), m1.cwiseMin(s1).block(r1,c1,r2-r1+1,c2-c1+1));
VERIFY_IS_APPROX(m1.block(r1,c1,r2-r1+1,c2-c1+1).cwiseMax(s1), m1.cwiseMax(s1).block(r1,c1,r2-r1+1,c2-c1+1));
return Scalar(0);
}
template<typename MatrixType, typename Index, typename Scalar>
typename Eigen::internal::enable_if<NumTraits<typename MatrixType::Scalar>::IsComplex,typename MatrixType::Scalar>::type
block_real_only(const MatrixType &, Index, Index, Index, Index, const Scalar&) {
return Scalar(0);
}
template<typename MatrixType> void block(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
typedef Matrix<Scalar, Dynamic, Dynamic, MatrixType::IsRowMajor?RowMajor:ColMajor> DynamicMatrixType;
typedef Matrix<Scalar, Dynamic, 1> DynamicVectorType;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m1_copy = m1,
m2 = MatrixType::Random(rows, cols),
m3(rows, cols),
ones = MatrixType::Ones(rows, cols);
VectorType v1 = VectorType::Random(rows);
Scalar s1 = internal::random<Scalar>();
Index r1 = internal::random<Index>(0,rows-1);
Index r2 = internal::random<Index>(r1,rows-1);
Index c1 = internal::random<Index>(0,cols-1);
Index c2 = internal::random<Index>(c1,cols-1);
block_real_only(m1, r1, r2, c1, c1, s1);
//check row() and col()
VERIFY_IS_EQUAL(m1.col(c1).transpose(), m1.transpose().row(c1));
//check operator(), both constant and non-constant, on row() and col()
m1 = m1_copy;
m1.row(r1) += s1 * m1_copy.row(r2);
VERIFY_IS_APPROX(m1.row(r1), m1_copy.row(r1) + s1 * m1_copy.row(r2));
// check nested block xpr on lhs
m1.row(r1).row(0) += s1 * m1_copy.row(r2);
VERIFY_IS_APPROX(m1.row(r1), m1_copy.row(r1) + Scalar(2) * s1 * m1_copy.row(r2));
m1 = m1_copy;
m1.col(c1) += s1 * m1_copy.col(c2);
VERIFY_IS_APPROX(m1.col(c1), m1_copy.col(c1) + s1 * m1_copy.col(c2));
m1.col(c1).col(0) += s1 * m1_copy.col(c2);
VERIFY_IS_APPROX(m1.col(c1), m1_copy.col(c1) + Scalar(2) * s1 * m1_copy.col(c2));
//check block()
Matrix<Scalar,Dynamic,Dynamic> b1(1,1); b1(0,0) = m1(r1,c1);
RowVectorType br1(m1.block(r1,0,1,cols));
VectorType bc1(m1.block(0,c1,rows,1));
VERIFY_IS_EQUAL(b1, m1.block(r1,c1,1,1));
VERIFY_IS_EQUAL(m1.row(r1), br1);
VERIFY_IS_EQUAL(m1.col(c1), bc1);
//check operator(), both constant and non-constant, on block()
m1.block(r1,c1,r2-r1+1,c2-c1+1) = s1 * m2.block(0, 0, r2-r1+1,c2-c1+1);
m1.block(r1,c1,r2-r1+1,c2-c1+1)(r2-r1,c2-c1) = m2.block(0, 0, r2-r1+1,c2-c1+1)(0,0);
enum {
BlockRows = 2,
BlockCols = 5
};
if (rows>=5 && cols>=8)
{
// test fixed block() as lvalue
m1.template block<BlockRows,BlockCols>(1,1) *= s1;
// test operator() on fixed block() both as constant and non-constant
m1.template block<BlockRows,BlockCols>(1,1)(0, 3) = m1.template block<2,5>(1,1)(1,2);
// check that fixed block() and block() agree
Matrix<Scalar,Dynamic,Dynamic> b = m1.template block<BlockRows,BlockCols>(3,3);
VERIFY_IS_EQUAL(b, m1.block(3,3,BlockRows,BlockCols));
// same tests with mixed fixed/dynamic size
m1.template block<BlockRows,Dynamic>(1,1,BlockRows,BlockCols) *= s1;
m1.template block<BlockRows,Dynamic>(1,1,BlockRows,BlockCols)(0,3) = m1.template block<2,5>(1,1)(1,2);
Matrix<Scalar,Dynamic,Dynamic> b2 = m1.template block<Dynamic,BlockCols>(3,3,2,5);
VERIFY_IS_EQUAL(b2, m1.block(3,3,BlockRows,BlockCols));
}
if (rows>2)
{
// test sub vectors
VERIFY_IS_EQUAL(v1.template head<2>(), v1.block(0,0,2,1));
VERIFY_IS_EQUAL(v1.template head<2>(), v1.head(2));
VERIFY_IS_EQUAL(v1.template head<2>(), v1.segment(0,2));
VERIFY_IS_EQUAL(v1.template head<2>(), v1.template segment<2>(0));
Index i = rows-2;
VERIFY_IS_EQUAL(v1.template tail<2>(), v1.block(i,0,2,1));
VERIFY_IS_EQUAL(v1.template tail<2>(), v1.tail(2));
VERIFY_IS_EQUAL(v1.template tail<2>(), v1.segment(i,2));
VERIFY_IS_EQUAL(v1.template tail<2>(), v1.template segment<2>(i));
i = internal::random<Index>(0,rows-2);
VERIFY_IS_EQUAL(v1.segment(i,2), v1.template segment<2>(i));
}
// stress some basic stuffs with block matrices
VERIFY(numext::real(ones.col(c1).sum()) == RealScalar(rows));
VERIFY(numext::real(ones.row(r1).sum()) == RealScalar(cols));
VERIFY(numext::real(ones.col(c1).dot(ones.col(c2))) == RealScalar(rows));
VERIFY(numext::real(ones.row(r1).dot(ones.row(r2))) == RealScalar(cols));
// check that linear acccessors works on blocks
m1 = m1_copy;
if((MatrixType::Flags&RowMajorBit)==0)
VERIFY_IS_EQUAL(m1.leftCols(c1).coeff(r1+c1*rows), m1(r1,c1));
else
VERIFY_IS_EQUAL(m1.topRows(r1).coeff(c1+r1*cols), m1(r1,c1));
// now test some block-inside-of-block.
// expressions with direct access
VERIFY_IS_EQUAL( (m1.block(r1,c1,rows-r1,cols-c1).block(r2-r1,c2-c1,rows-r2,cols-c2)) , (m1.block(r2,c2,rows-r2,cols-c2)) );
VERIFY_IS_EQUAL( (m1.block(r1,c1,r2-r1+1,c2-c1+1).row(0)) , (m1.row(r1).segment(c1,c2-c1+1)) );
VERIFY_IS_EQUAL( (m1.block(r1,c1,r2-r1+1,c2-c1+1).col(0)) , (m1.col(c1).segment(r1,r2-r1+1)) );
VERIFY_IS_EQUAL( (m1.block(r1,c1,r2-r1+1,c2-c1+1).transpose().col(0)) , (m1.row(r1).segment(c1,c2-c1+1)).transpose() );
VERIFY_IS_EQUAL( (m1.transpose().block(c1,r1,c2-c1+1,r2-r1+1).col(0)) , (m1.row(r1).segment(c1,c2-c1+1)).transpose() );
// expressions without direct access
VERIFY_IS_APPROX( ((m1+m2).block(r1,c1,rows-r1,cols-c1).block(r2-r1,c2-c1,rows-r2,cols-c2)) , ((m1+m2).block(r2,c2,rows-r2,cols-c2)) );
VERIFY_IS_APPROX( ((m1+m2).block(r1,c1,r2-r1+1,c2-c1+1).row(0)) , ((m1+m2).row(r1).segment(c1,c2-c1+1)) );
VERIFY_IS_APPROX( ((m1+m2).block(r1,c1,r2-r1+1,c2-c1+1).col(0)) , ((m1+m2).col(c1).segment(r1,r2-r1+1)) );
VERIFY_IS_APPROX( ((m1+m2).block(r1,c1,r2-r1+1,c2-c1+1).transpose().col(0)) , ((m1+m2).row(r1).segment(c1,c2-c1+1)).transpose() );
VERIFY_IS_APPROX( ((m1+m2).transpose().block(c1,r1,c2-c1+1,r2-r1+1).col(0)) , ((m1+m2).row(r1).segment(c1,c2-c1+1)).transpose() );
VERIFY_IS_APPROX( (m1*1).topRows(r1), m1.topRows(r1) );
VERIFY_IS_APPROX( (m1*1).leftCols(c1), m1.leftCols(c1) );
VERIFY_IS_APPROX( (m1*1).transpose().topRows(c1), m1.transpose().topRows(c1) );
VERIFY_IS_APPROX( (m1*1).transpose().leftCols(r1), m1.transpose().leftCols(r1) );
VERIFY_IS_APPROX( (m1*1).transpose().middleRows(c1,c2-c1+1), m1.transpose().middleRows(c1,c2-c1+1) );
VERIFY_IS_APPROX( (m1*1).transpose().middleCols(r1,r2-r1+1), m1.transpose().middleCols(r1,r2-r1+1) );
// evaluation into plain matrices from expressions with direct access (stress MapBase)
DynamicMatrixType dm;
DynamicVectorType dv;
dm.setZero();
dm = m1.block(r1,c1,rows-r1,cols-c1).block(r2-r1,c2-c1,rows-r2,cols-c2);
VERIFY_IS_EQUAL(dm, (m1.block(r2,c2,rows-r2,cols-c2)));
dm.setZero();
dv.setZero();
dm = m1.block(r1,c1,r2-r1+1,c2-c1+1).row(0).transpose();
dv = m1.row(r1).segment(c1,c2-c1+1);
VERIFY_IS_EQUAL(dv, dm);
dm.setZero();
dv.setZero();
dm = m1.col(c1).segment(r1,r2-r1+1);
dv = m1.block(r1,c1,r2-r1+1,c2-c1+1).col(0);
VERIFY_IS_EQUAL(dv, dm);
dm.setZero();
dv.setZero();
dm = m1.block(r1,c1,r2-r1+1,c2-c1+1).transpose().col(0);
dv = m1.row(r1).segment(c1,c2-c1+1);
VERIFY_IS_EQUAL(dv, dm);
dm.setZero();
dv.setZero();
dm = m1.row(r1).segment(c1,c2-c1+1).transpose();
dv = m1.transpose().block(c1,r1,c2-c1+1,r2-r1+1).col(0);
VERIFY_IS_EQUAL(dv, dm);
VERIFY_IS_EQUAL( (m1.template block<Dynamic,1>(1,0,0,1)), m1.block(1,0,0,1));
VERIFY_IS_EQUAL( (m1.template block<1,Dynamic>(0,1,1,0)), m1.block(0,1,1,0));
VERIFY_IS_EQUAL( ((m1*1).template block<Dynamic,1>(1,0,0,1)), m1.block(1,0,0,1));
VERIFY_IS_EQUAL( ((m1*1).template block<1,Dynamic>(0,1,1,0)), m1.block(0,1,1,0));
if (rows>=2 && cols>=2)
{
VERIFY_RAISES_ASSERT( m1 += m1.col(0) );
VERIFY_RAISES_ASSERT( m1 -= m1.col(0) );
VERIFY_RAISES_ASSERT( m1.array() *= m1.col(0).array() );
VERIFY_RAISES_ASSERT( m1.array() /= m1.col(0).array() );
}
}
template<typename MatrixType>
void compare_using_data_and_stride(const MatrixType& m)
{
Index rows = m.rows();
Index cols = m.cols();
Index size = m.size();
Index innerStride = m.innerStride();
Index outerStride = m.outerStride();
Index rowStride = m.rowStride();
Index colStride = m.colStride();
const typename MatrixType::Scalar* data = m.data();
for(int j=0;j<cols;++j)
for(int i=0;i<rows;++i)
VERIFY(m.coeff(i,j) == data[i*rowStride + j*colStride]);
if(!MatrixType::IsVectorAtCompileTime)
{
for(int j=0;j<cols;++j)
for(int i=0;i<rows;++i)
VERIFY(m.coeff(i,j) == data[(MatrixType::Flags&RowMajorBit)
? i*outerStride + j*innerStride
: j*outerStride + i*innerStride]);
}
if(MatrixType::IsVectorAtCompileTime)
{
VERIFY(innerStride == int((&m.coeff(1))-(&m.coeff(0))));
for (int i=0;i<size;++i)
VERIFY(m.coeff(i) == data[i*innerStride]);
}
}
template<typename MatrixType>
void data_and_stride(const MatrixType& m)
{
Index rows = m.rows();
Index cols = m.cols();
Index r1 = internal::random<Index>(0,rows-1);
Index r2 = internal::random<Index>(r1,rows-1);
Index c1 = internal::random<Index>(0,cols-1);
Index c2 = internal::random<Index>(c1,cols-1);
MatrixType m1 = MatrixType::Random(rows, cols);
compare_using_data_and_stride(m1.block(r1, c1, r2-r1+1, c2-c1+1));
compare_using_data_and_stride(m1.transpose().block(c1, r1, c2-c1+1, r2-r1+1));
compare_using_data_and_stride(m1.row(r1));
compare_using_data_and_stride(m1.col(c1));
compare_using_data_and_stride(m1.row(r1).transpose());
compare_using_data_and_stride(m1.col(c1).transpose());
}
void test_block()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( block(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( block(Matrix4d()) );
CALL_SUBTEST_3( block(MatrixXcf(3, 3)) );
CALL_SUBTEST_4( block(MatrixXi(8, 12)) );
CALL_SUBTEST_5( block(MatrixXcd(20, 20)) );
CALL_SUBTEST_6( block(MatrixXf(20, 20)) );
CALL_SUBTEST_8( block(Matrix<float,Dynamic,4>(3, 4)) );
#ifndef EIGEN_DEFAULT_TO_ROW_MAJOR
CALL_SUBTEST_6( data_and_stride(MatrixXf(internal::random(5,50), internal::random(5,50))) );
CALL_SUBTEST_7( data_and_stride(Matrix<int,Dynamic,Dynamic,RowMajor>(internal::random(5,50), internal::random(5,50))) );
#endif
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/qr.cpp | .cpp | 4,586 | 131 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/QR>
template<typename MatrixType> void qr(const MatrixType& m)
{
Index rows = m.rows();
Index cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> MatrixQType;
MatrixType a = MatrixType::Random(rows,cols);
HouseholderQR<MatrixType> qrOfA(a);
MatrixQType q = qrOfA.householderQ();
VERIFY_IS_UNITARY(q);
MatrixType r = qrOfA.matrixQR().template triangularView<Upper>();
VERIFY_IS_APPROX(a, qrOfA.householderQ() * r);
}
template<typename MatrixType, int Cols2> void qr_fixedsize()
{
enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime };
typedef typename MatrixType::Scalar Scalar;
Matrix<Scalar,Rows,Cols> m1 = Matrix<Scalar,Rows,Cols>::Random();
HouseholderQR<Matrix<Scalar,Rows,Cols> > qr(m1);
Matrix<Scalar,Rows,Cols> r = qr.matrixQR();
// FIXME need better way to construct trapezoid
for(int i = 0; i < Rows; i++) for(int j = 0; j < Cols; j++) if(i>j) r(i,j) = Scalar(0);
VERIFY_IS_APPROX(m1, qr.householderQ() * r);
Matrix<Scalar,Cols,Cols2> m2 = Matrix<Scalar,Cols,Cols2>::Random(Cols,Cols2);
Matrix<Scalar,Rows,Cols2> m3 = m1*m2;
m2 = Matrix<Scalar,Cols,Cols2>::Random(Cols,Cols2);
m2 = qr.solve(m3);
VERIFY_IS_APPROX(m3, m1*m2);
}
template<typename MatrixType> void qr_invertible()
{
using std::log;
using std::abs;
using std::pow;
using std::max;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef typename MatrixType::Scalar Scalar;
int size = internal::random<int>(10,50);
MatrixType m1(size, size), m2(size, size), m3(size, size);
m1 = MatrixType::Random(size,size);
if (internal::is_same<RealScalar,float>::value)
{
// let's build a matrix more stable to inverse
MatrixType a = MatrixType::Random(size,size*4);
m1 += a * a.adjoint();
}
HouseholderQR<MatrixType> qr(m1);
m3 = MatrixType::Random(size,size);
m2 = qr.solve(m3);
VERIFY_IS_APPROX(m3, m1*m2);
// now construct a matrix with prescribed determinant
m1.setZero();
for(int i = 0; i < size; i++) m1(i,i) = internal::random<Scalar>();
RealScalar absdet = abs(m1.diagonal().prod());
m3 = qr.householderQ(); // get a unitary
m1 = m3 * m1 * m3;
qr.compute(m1);
VERIFY_IS_APPROX(log(absdet), qr.logAbsDeterminant());
// This test is tricky if the determinant becomes too small.
// Since we generate random numbers with magnitude rrange [0,1], the average determinant is 0.5^size
VERIFY_IS_MUCH_SMALLER_THAN( abs(absdet-qr.absDeterminant()), numext::maxi(RealScalar(pow(0.5,size)),numext::maxi<RealScalar>(abs(absdet),abs(qr.absDeterminant()))) );
}
template<typename MatrixType> void qr_verify_assert()
{
MatrixType tmp;
HouseholderQR<MatrixType> qr;
VERIFY_RAISES_ASSERT(qr.matrixQR())
VERIFY_RAISES_ASSERT(qr.solve(tmp))
VERIFY_RAISES_ASSERT(qr.householderQ())
VERIFY_RAISES_ASSERT(qr.absDeterminant())
VERIFY_RAISES_ASSERT(qr.logAbsDeterminant())
}
void test_qr()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( qr(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_2( qr(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2),internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
CALL_SUBTEST_3(( qr_fixedsize<Matrix<float,3,4>, 2 >() ));
CALL_SUBTEST_4(( qr_fixedsize<Matrix<double,6,2>, 4 >() ));
CALL_SUBTEST_5(( qr_fixedsize<Matrix<double,2,5>, 7 >() ));
CALL_SUBTEST_11( qr(Matrix<float,1,1>()) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( qr_invertible<MatrixXf>() );
CALL_SUBTEST_6( qr_invertible<MatrixXd>() );
CALL_SUBTEST_7( qr_invertible<MatrixXcf>() );
CALL_SUBTEST_8( qr_invertible<MatrixXcd>() );
}
CALL_SUBTEST_9(qr_verify_assert<Matrix3f>());
CALL_SUBTEST_10(qr_verify_assert<Matrix3d>());
CALL_SUBTEST_1(qr_verify_assert<MatrixXf>());
CALL_SUBTEST_6(qr_verify_assert<MatrixXd>());
CALL_SUBTEST_7(qr_verify_assert<MatrixXcf>());
CALL_SUBTEST_8(qr_verify_assert<MatrixXcd>());
// Test problem size constructors
CALL_SUBTEST_12(HouseholderQR<MatrixXf>(10, 20));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/pardiso_support.cpp | .cpp | 947 | 30 | /*
Intel Copyright (C) ....
*/
#include "sparse_solver.h"
#include <Eigen/PardisoSupport>
template<typename T> void test_pardiso_T()
{
PardisoLLT < SparseMatrix<T, RowMajor>, Lower> pardiso_llt_lower;
PardisoLLT < SparseMatrix<T, RowMajor>, Upper> pardiso_llt_upper;
PardisoLDLT < SparseMatrix<T, RowMajor>, Lower> pardiso_ldlt_lower;
PardisoLDLT < SparseMatrix<T, RowMajor>, Upper> pardiso_ldlt_upper;
PardisoLU < SparseMatrix<T, RowMajor> > pardiso_lu;
check_sparse_spd_solving(pardiso_llt_lower);
check_sparse_spd_solving(pardiso_llt_upper);
check_sparse_spd_solving(pardiso_ldlt_lower);
check_sparse_spd_solving(pardiso_ldlt_upper);
check_sparse_square_solving(pardiso_lu);
}
void test_pardiso_support()
{
CALL_SUBTEST_1(test_pardiso_T<float>());
CALL_SUBTEST_2(test_pardiso_T<double>());
CALL_SUBTEST_3(test_pardiso_T< std::complex<float> >());
CALL_SUBTEST_4(test_pardiso_T< std::complex<double> >());
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/stdvector.cpp | .cpp | 5,150 | 159 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/StdVector>
#include <Eigen/Geometry>
template<typename MatrixType>
void check_stdvector_matrix(const MatrixType& m)
{
typename MatrixType::Index rows = m.rows();
typename MatrixType::Index cols = m.cols();
MatrixType x = MatrixType::Random(rows,cols), y = MatrixType::Random(rows,cols);
std::vector<MatrixType,Eigen::aligned_allocator<MatrixType> > v(10, MatrixType::Zero(rows,cols)), w(20, y);
v[5] = x;
w[6] = v[5];
VERIFY_IS_APPROX(w[6], v[5]);
v = w;
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(w[i], v[i]);
}
v.resize(21);
v[20] = x;
VERIFY_IS_APPROX(v[20], x);
v.resize(22,y);
VERIFY_IS_APPROX(v[21], y);
v.push_back(x);
VERIFY_IS_APPROX(v[22], x);
VERIFY((internal::UIntPtr)&(v[22]) == (internal::UIntPtr)&(v[21]) + sizeof(MatrixType));
// do a lot of push_back such that the vector gets internally resized
// (with memory reallocation)
MatrixType* ref = &w[0];
for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i)
v.push_back(w[i%w.size()]);
for(unsigned int i=23; i<v.size(); ++i)
{
VERIFY(v[i]==w[(i-23)%w.size()]);
}
}
template<typename TransformType>
void check_stdvector_transform(const TransformType&)
{
typedef typename TransformType::MatrixType MatrixType;
TransformType x(MatrixType::Random()), y(MatrixType::Random());
std::vector<TransformType,Eigen::aligned_allocator<TransformType> > v(10), w(20, y);
v[5] = x;
w[6] = v[5];
VERIFY_IS_APPROX(w[6], v[5]);
v = w;
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(w[i], v[i]);
}
v.resize(21);
v[20] = x;
VERIFY_IS_APPROX(v[20], x);
v.resize(22,y);
VERIFY_IS_APPROX(v[21], y);
v.push_back(x);
VERIFY_IS_APPROX(v[22], x);
VERIFY((internal::UIntPtr)&(v[22]) == (internal::UIntPtr)&(v[21]) + sizeof(TransformType));
// do a lot of push_back such that the vector gets internally resized
// (with memory reallocation)
TransformType* ref = &w[0];
for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i)
v.push_back(w[i%w.size()]);
for(unsigned int i=23; i<v.size(); ++i)
{
VERIFY(v[i].matrix()==w[(i-23)%w.size()].matrix());
}
}
template<typename QuaternionType>
void check_stdvector_quaternion(const QuaternionType&)
{
typedef typename QuaternionType::Coefficients Coefficients;
QuaternionType x(Coefficients::Random()), y(Coefficients::Random()), qi=QuaternionType::Identity();
std::vector<QuaternionType,Eigen::aligned_allocator<QuaternionType> > v(10,qi), w(20, y);
v[5] = x;
w[6] = v[5];
VERIFY_IS_APPROX(w[6], v[5]);
v = w;
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(w[i], v[i]);
}
v.resize(21);
v[20] = x;
VERIFY_IS_APPROX(v[20], x);
v.resize(22,y);
VERIFY_IS_APPROX(v[21], y);
v.push_back(x);
VERIFY_IS_APPROX(v[22], x);
VERIFY((internal::UIntPtr)&(v[22]) == (internal::UIntPtr)&(v[21]) + sizeof(QuaternionType));
// do a lot of push_back such that the vector gets internally resized
// (with memory reallocation)
QuaternionType* ref = &w[0];
for(int i=0; i<30 || ((ref==&w[0]) && i<300); ++i)
v.push_back(w[i%w.size()]);
for(unsigned int i=23; i<v.size(); ++i)
{
VERIFY(v[i].coeffs()==w[(i-23)%w.size()].coeffs());
}
}
// the code below triggered an invalid warning with gcc >= 7
// eigen/Eigen/src/Core/util/Memory.h:189:12: warning: argument 1 value '18446744073709551612' exceeds maximum object size 9223372036854775807
// This has been reported to gcc there: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=87544
void std_vector_gcc_warning()
{
typedef Eigen::Vector3f T;
std::vector<T, Eigen::aligned_allocator<T> > v;
v.push_back(T());
}
void test_stdvector()
{
// some non vectorizable fixed sizes
CALL_SUBTEST_1(check_stdvector_matrix(Vector2f()));
CALL_SUBTEST_1(check_stdvector_matrix(Matrix3f()));
CALL_SUBTEST_2(check_stdvector_matrix(Matrix3d()));
// some vectorizable fixed sizes
CALL_SUBTEST_1(check_stdvector_matrix(Matrix2f()));
CALL_SUBTEST_1(check_stdvector_matrix(Vector4f()));
CALL_SUBTEST_1(check_stdvector_matrix(Matrix4f()));
CALL_SUBTEST_2(check_stdvector_matrix(Matrix4d()));
// some dynamic sizes
CALL_SUBTEST_3(check_stdvector_matrix(MatrixXd(1,1)));
CALL_SUBTEST_3(check_stdvector_matrix(VectorXd(20)));
CALL_SUBTEST_3(check_stdvector_matrix(RowVectorXf(20)));
CALL_SUBTEST_3(check_stdvector_matrix(MatrixXcf(10,10)));
// some Transform
CALL_SUBTEST_4(check_stdvector_transform(Projective2f()));
CALL_SUBTEST_4(check_stdvector_transform(Projective3f()));
CALL_SUBTEST_4(check_stdvector_transform(Projective3d()));
//CALL_SUBTEST(heck_stdvector_transform(Projective4d()));
// some Quaternion
CALL_SUBTEST_5(check_stdvector_quaternion(Quaternionf()));
CALL_SUBTEST_5(check_stdvector_quaternion(Quaterniond()));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/hessenberg.cpp | .cpp | 2,396 | 63 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/Eigenvalues>
template<typename Scalar,int Size> void hessenberg(int size = Size)
{
typedef Matrix<Scalar,Size,Size> MatrixType;
// Test basic functionality: A = U H U* and H is Hessenberg
for(int counter = 0; counter < g_repeat; ++counter) {
MatrixType m = MatrixType::Random(size,size);
HessenbergDecomposition<MatrixType> hess(m);
MatrixType Q = hess.matrixQ();
MatrixType H = hess.matrixH();
VERIFY_IS_APPROX(m, Q * H * Q.adjoint());
for(int row = 2; row < size; ++row) {
for(int col = 0; col < row-1; ++col) {
VERIFY(H(row,col) == (typename MatrixType::Scalar)0);
}
}
}
// Test whether compute() and constructor returns same result
MatrixType A = MatrixType::Random(size, size);
HessenbergDecomposition<MatrixType> cs1;
cs1.compute(A);
HessenbergDecomposition<MatrixType> cs2(A);
VERIFY_IS_EQUAL(cs1.matrixH().eval(), cs2.matrixH().eval());
MatrixType cs1Q = cs1.matrixQ();
MatrixType cs2Q = cs2.matrixQ();
VERIFY_IS_EQUAL(cs1Q, cs2Q);
// Test assertions for when used uninitialized
HessenbergDecomposition<MatrixType> hessUninitialized;
VERIFY_RAISES_ASSERT( hessUninitialized.matrixH() );
VERIFY_RAISES_ASSERT( hessUninitialized.matrixQ() );
VERIFY_RAISES_ASSERT( hessUninitialized.householderCoefficients() );
VERIFY_RAISES_ASSERT( hessUninitialized.packedMatrix() );
// TODO: Add tests for packedMatrix() and householderCoefficients()
}
void test_hessenberg()
{
CALL_SUBTEST_1(( hessenberg<std::complex<double>,1>() ));
CALL_SUBTEST_2(( hessenberg<std::complex<double>,2>() ));
CALL_SUBTEST_3(( hessenberg<std::complex<float>,4>() ));
CALL_SUBTEST_4(( hessenberg<float,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)) ));
CALL_SUBTEST_5(( hessenberg<std::complex<double>,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE)) ));
// Test problem size constructors
CALL_SUBTEST_6(HessenbergDecomposition<MatrixXf>(10));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/sparse.h | .h | 6,396 | 211 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_TESTSPARSE_H
#define EIGEN_TESTSPARSE_H
#define EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
#include "main.h"
#if EIGEN_GNUC_AT_LEAST(4,0) && !defined __ICC && !defined(__clang__)
#ifdef min
#undef min
#endif
#ifdef max
#undef max
#endif
#include <tr1/unordered_map>
#define EIGEN_UNORDERED_MAP_SUPPORT
namespace std {
using std::tr1::unordered_map;
}
#endif
#ifdef EIGEN_GOOGLEHASH_SUPPORT
#include <google/sparse_hash_map>
#endif
#include <Eigen/Cholesky>
#include <Eigen/LU>
#include <Eigen/Sparse>
enum {
ForceNonZeroDiag = 1,
MakeLowerTriangular = 2,
MakeUpperTriangular = 4,
ForceRealDiag = 8
};
/* Initializes both a sparse and dense matrix with same random values,
* and a ratio of \a density non zero entries.
* \param flags is a union of ForceNonZeroDiag, MakeLowerTriangular and MakeUpperTriangular
* allowing to control the shape of the matrix.
* \param zeroCoords and nonzeroCoords allows to get the coordinate lists of the non zero,
* and zero coefficients respectively.
*/
template<typename Scalar,int Opt1,int Opt2,typename StorageIndex> void
initSparse(double density,
Matrix<Scalar,Dynamic,Dynamic,Opt1>& refMat,
SparseMatrix<Scalar,Opt2,StorageIndex>& sparseMat,
int flags = 0,
std::vector<Matrix<StorageIndex,2,1> >* zeroCoords = 0,
std::vector<Matrix<StorageIndex,2,1> >* nonzeroCoords = 0)
{
enum { IsRowMajor = SparseMatrix<Scalar,Opt2,StorageIndex>::IsRowMajor };
sparseMat.setZero();
//sparseMat.reserve(int(refMat.rows()*refMat.cols()*density));
sparseMat.reserve(VectorXi::Constant(IsRowMajor ? refMat.rows() : refMat.cols(), int((1.5*density)*(IsRowMajor?refMat.cols():refMat.rows()))));
for(Index j=0; j<sparseMat.outerSize(); j++)
{
//sparseMat.startVec(j);
for(Index i=0; i<sparseMat.innerSize(); i++)
{
Index ai(i), aj(j);
if(IsRowMajor)
std::swap(ai,aj);
Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0);
if ((flags&ForceNonZeroDiag) && (i==j))
{
// FIXME: the following is too conservative
v = internal::random<Scalar>()*Scalar(3.);
v = v*v;
if(numext::real(v)>0) v += Scalar(5);
else v -= Scalar(5);
}
if ((flags & MakeLowerTriangular) && aj>ai)
v = Scalar(0);
else if ((flags & MakeUpperTriangular) && aj<ai)
v = Scalar(0);
if ((flags&ForceRealDiag) && (i==j))
v = numext::real(v);
if (v!=Scalar(0))
{
//sparseMat.insertBackByOuterInner(j,i) = v;
sparseMat.insertByOuterInner(j,i) = v;
if (nonzeroCoords)
nonzeroCoords->push_back(Matrix<StorageIndex,2,1> (ai,aj));
}
else if (zeroCoords)
{
zeroCoords->push_back(Matrix<StorageIndex,2,1> (ai,aj));
}
refMat(ai,aj) = v;
}
}
//sparseMat.finalize();
}
template<typename Scalar,int Opt1,int Opt2,typename Index> void
initSparse(double density,
Matrix<Scalar,Dynamic,Dynamic, Opt1>& refMat,
DynamicSparseMatrix<Scalar, Opt2, Index>& sparseMat,
int flags = 0,
std::vector<Matrix<Index,2,1> >* zeroCoords = 0,
std::vector<Matrix<Index,2,1> >* nonzeroCoords = 0)
{
enum { IsRowMajor = DynamicSparseMatrix<Scalar,Opt2,Index>::IsRowMajor };
sparseMat.setZero();
sparseMat.reserve(int(refMat.rows()*refMat.cols()*density));
for(int j=0; j<sparseMat.outerSize(); j++)
{
sparseMat.startVec(j); // not needed for DynamicSparseMatrix
for(int i=0; i<sparseMat.innerSize(); i++)
{
int ai(i), aj(j);
if(IsRowMajor)
std::swap(ai,aj);
Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0);
if ((flags&ForceNonZeroDiag) && (i==j))
{
v = internal::random<Scalar>()*Scalar(3.);
v = v*v + Scalar(5.);
}
if ((flags & MakeLowerTriangular) && aj>ai)
v = Scalar(0);
else if ((flags & MakeUpperTriangular) && aj<ai)
v = Scalar(0);
if ((flags&ForceRealDiag) && (i==j))
v = numext::real(v);
if (v!=Scalar(0))
{
sparseMat.insertBackByOuterInner(j,i) = v;
if (nonzeroCoords)
nonzeroCoords->push_back(Matrix<Index,2,1> (ai,aj));
}
else if (zeroCoords)
{
zeroCoords->push_back(Matrix<Index,2,1> (ai,aj));
}
refMat(ai,aj) = v;
}
}
sparseMat.finalize();
}
template<typename Scalar,int Options,typename Index> void
initSparse(double density,
Matrix<Scalar,Dynamic,1>& refVec,
SparseVector<Scalar,Options,Index>& sparseVec,
std::vector<int>* zeroCoords = 0,
std::vector<int>* nonzeroCoords = 0)
{
sparseVec.reserve(int(refVec.size()*density));
sparseVec.setZero();
for(int i=0; i<refVec.size(); i++)
{
Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0);
if (v!=Scalar(0))
{
sparseVec.insertBack(i) = v;
if (nonzeroCoords)
nonzeroCoords->push_back(i);
}
else if (zeroCoords)
zeroCoords->push_back(i);
refVec[i] = v;
}
}
template<typename Scalar,int Options,typename Index> void
initSparse(double density,
Matrix<Scalar,1,Dynamic>& refVec,
SparseVector<Scalar,Options,Index>& sparseVec,
std::vector<int>* zeroCoords = 0,
std::vector<int>* nonzeroCoords = 0)
{
sparseVec.reserve(int(refVec.size()*density));
sparseVec.setZero();
for(int i=0; i<refVec.size(); i++)
{
Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0);
if (v!=Scalar(0))
{
sparseVec.insertBack(i) = v;
if (nonzeroCoords)
nonzeroCoords->push_back(i);
}
else if (zeroCoords)
zeroCoords->push_back(i);
refVec[i] = v;
}
}
#include <unsupported/Eigen/SparseExtra>
#endif // EIGEN_TESTSPARSE_H
| Unknown |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/spqr_support.cpp | .cpp | 1,833 | 65 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Desire Nuentsa Wakam <desire.nuentsa_wakam@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
#define EIGEN_NO_DEBUG_SMALL_PRODUCT_BLOCKS
#include "sparse.h"
#include <Eigen/SPQRSupport>
template<typename MatrixType,typename DenseMat>
int generate_sparse_rectangular_problem(MatrixType& A, DenseMat& dA, int maxRows = 300, int maxCols = 300)
{
eigen_assert(maxRows >= maxCols);
typedef typename MatrixType::Scalar Scalar;
int rows = internal::random<int>(1,maxRows);
int cols = internal::random<int>(1,rows);
double density = (std::max)(8./(rows*cols), 0.01);
A.resize(rows,cols);
dA.resize(rows,cols);
initSparse<Scalar>(density, dA, A,ForceNonZeroDiag);
A.makeCompressed();
return rows;
}
template<typename Scalar> void test_spqr_scalar()
{
typedef SparseMatrix<Scalar,ColMajor> MatrixType;
MatrixType A;
Matrix<Scalar,Dynamic,Dynamic> dA;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
DenseVector refX,x,b;
SPQR<MatrixType> solver;
generate_sparse_rectangular_problem(A,dA);
Index m = A.rows();
b = DenseVector::Random(m);
solver.compute(A);
if (solver.info() != Success)
{
std::cerr << "sparse QR factorization failed\n";
exit(0);
return;
}
x = solver.solve(b);
if (solver.info() != Success)
{
std::cerr << "sparse QR factorization failed\n";
exit(0);
return;
}
//Compare with a dense solver
refX = dA.colPivHouseholderQr().solve(b);
VERIFY(x.isApprox(refX,test_precision<Scalar>()));
}
void test_spqr_support()
{
CALL_SUBTEST_1(test_spqr_scalar<double>());
CALL_SUBTEST_2(test_spqr_scalar<std::complex<double> >());
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/geo_quaternion.cpp | .cpp | 11,125 | 311 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Mathieu Gautier <mathieu.gautier@cea.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/Geometry>
#include <Eigen/LU>
#include <Eigen/SVD>
template<typename T> T bounded_acos(T v)
{
using std::acos;
using std::min;
using std::max;
return acos((max)(T(-1),(min)(v,T(1))));
}
template<typename QuatType> void check_slerp(const QuatType& q0, const QuatType& q1)
{
using std::abs;
typedef typename QuatType::Scalar Scalar;
typedef AngleAxis<Scalar> AA;
Scalar largeEps = test_precision<Scalar>();
Scalar theta_tot = AA(q1*q0.inverse()).angle();
if(theta_tot>Scalar(EIGEN_PI))
theta_tot = Scalar(2.)*Scalar(EIGEN_PI)-theta_tot;
for(Scalar t=0; t<=Scalar(1.001); t+=Scalar(0.1))
{
QuatType q = q0.slerp(t,q1);
Scalar theta = AA(q*q0.inverse()).angle();
VERIFY(abs(q.norm() - 1) < largeEps);
if(theta_tot==0) VERIFY(theta_tot==0);
else VERIFY(abs(theta - t * theta_tot) < largeEps);
}
}
template<typename Scalar, int Options> void quaternion(void)
{
/* this test covers the following files:
Quaternion.h
*/
using std::abs;
typedef Matrix<Scalar,3,1> Vector3;
typedef Matrix<Scalar,3,3> Matrix3;
typedef Quaternion<Scalar,Options> Quaternionx;
typedef AngleAxis<Scalar> AngleAxisx;
Scalar largeEps = test_precision<Scalar>();
if (internal::is_same<Scalar,float>::value)
largeEps = Scalar(1e-3);
Scalar eps = internal::random<Scalar>() * Scalar(1e-2);
Vector3 v0 = Vector3::Random(),
v1 = Vector3::Random(),
v2 = Vector3::Random(),
v3 = Vector3::Random();
Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI)),
b = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI));
// Quaternion: Identity(), setIdentity();
Quaternionx q1, q2;
q2.setIdentity();
VERIFY_IS_APPROX(Quaternionx(Quaternionx::Identity()).coeffs(), q2.coeffs());
q1.coeffs().setRandom();
VERIFY_IS_APPROX(q1.coeffs(), (q1*q2).coeffs());
// concatenation
q1 *= q2;
q1 = AngleAxisx(a, v0.normalized());
q2 = AngleAxisx(a, v1.normalized());
// angular distance
Scalar refangle = abs(AngleAxisx(q1.inverse()*q2).angle());
if (refangle>Scalar(EIGEN_PI))
refangle = Scalar(2)*Scalar(EIGEN_PI) - refangle;
if((q1.coeffs()-q2.coeffs()).norm() > 10*largeEps)
{
VERIFY_IS_MUCH_SMALLER_THAN(abs(q1.angularDistance(q2) - refangle), Scalar(1));
}
// rotation matrix conversion
VERIFY_IS_APPROX(q1 * v2, q1.toRotationMatrix() * v2);
VERIFY_IS_APPROX(q1 * q2 * v2,
q1.toRotationMatrix() * q2.toRotationMatrix() * v2);
VERIFY( (q2*q1).isApprox(q1*q2, largeEps)
|| !(q2 * q1 * v2).isApprox(q1.toRotationMatrix() * q2.toRotationMatrix() * v2));
q2 = q1.toRotationMatrix();
VERIFY_IS_APPROX(q1*v1,q2*v1);
Matrix3 rot1(q1);
VERIFY_IS_APPROX(q1*v1,rot1*v1);
Quaternionx q3(rot1.transpose()*rot1);
VERIFY_IS_APPROX(q3*v1,v1);
// angle-axis conversion
AngleAxisx aa = AngleAxisx(q1);
VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1);
// Do not execute the test if the rotation angle is almost zero, or
// the rotation axis and v1 are almost parallel.
if (abs(aa.angle()) > 5*test_precision<Scalar>()
&& (aa.axis() - v1.normalized()).norm() < Scalar(1.99)
&& (aa.axis() + v1.normalized()).norm() < Scalar(1.99))
{
VERIFY_IS_NOT_APPROX(q1 * v1, Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1);
}
// from two vector creation
VERIFY_IS_APPROX( v2.normalized(),(q2.setFromTwoVectors(v1, v2)*v1).normalized());
VERIFY_IS_APPROX( v1.normalized(),(q2.setFromTwoVectors(v1, v1)*v1).normalized());
VERIFY_IS_APPROX(-v1.normalized(),(q2.setFromTwoVectors(v1,-v1)*v1).normalized());
if (internal::is_same<Scalar,double>::value)
{
v3 = (v1.array()+eps).matrix();
VERIFY_IS_APPROX( v3.normalized(),(q2.setFromTwoVectors(v1, v3)*v1).normalized());
VERIFY_IS_APPROX(-v3.normalized(),(q2.setFromTwoVectors(v1,-v3)*v1).normalized());
}
// from two vector creation static function
VERIFY_IS_APPROX( v2.normalized(),(Quaternionx::FromTwoVectors(v1, v2)*v1).normalized());
VERIFY_IS_APPROX( v1.normalized(),(Quaternionx::FromTwoVectors(v1, v1)*v1).normalized());
VERIFY_IS_APPROX(-v1.normalized(),(Quaternionx::FromTwoVectors(v1,-v1)*v1).normalized());
if (internal::is_same<Scalar,double>::value)
{
v3 = (v1.array()+eps).matrix();
VERIFY_IS_APPROX( v3.normalized(),(Quaternionx::FromTwoVectors(v1, v3)*v1).normalized());
VERIFY_IS_APPROX(-v3.normalized(),(Quaternionx::FromTwoVectors(v1,-v3)*v1).normalized());
}
// inverse and conjugate
VERIFY_IS_APPROX(q1 * (q1.inverse() * v1), v1);
VERIFY_IS_APPROX(q1 * (q1.conjugate() * v1), v1);
// test casting
Quaternion<float> q1f = q1.template cast<float>();
VERIFY_IS_APPROX(q1f.template cast<Scalar>(),q1);
Quaternion<double> q1d = q1.template cast<double>();
VERIFY_IS_APPROX(q1d.template cast<Scalar>(),q1);
// test bug 369 - improper alignment.
Quaternionx *q = new Quaternionx;
delete q;
q1 = Quaternionx::UnitRandom();
q2 = Quaternionx::UnitRandom();
check_slerp(q1,q2);
q1 = AngleAxisx(b, v1.normalized());
q2 = AngleAxisx(b+Scalar(EIGEN_PI), v1.normalized());
check_slerp(q1,q2);
q1 = AngleAxisx(b, v1.normalized());
q2 = AngleAxisx(-b, -v1.normalized());
check_slerp(q1,q2);
q1 = Quaternionx::UnitRandom();
q2.coeffs() = -q1.coeffs();
check_slerp(q1,q2);
}
template<typename Scalar> void mapQuaternion(void){
typedef Map<Quaternion<Scalar>, Aligned> MQuaternionA;
typedef Map<const Quaternion<Scalar>, Aligned> MCQuaternionA;
typedef Map<Quaternion<Scalar> > MQuaternionUA;
typedef Map<const Quaternion<Scalar> > MCQuaternionUA;
typedef Quaternion<Scalar> Quaternionx;
typedef Matrix<Scalar,3,1> Vector3;
typedef AngleAxis<Scalar> AngleAxisx;
Vector3 v0 = Vector3::Random(),
v1 = Vector3::Random();
Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI));
EIGEN_ALIGN_MAX Scalar array1[4];
EIGEN_ALIGN_MAX Scalar array2[4];
EIGEN_ALIGN_MAX Scalar array3[4+1];
Scalar* array3unaligned = array3+1;
MQuaternionA mq1(array1);
MCQuaternionA mcq1(array1);
MQuaternionA mq2(array2);
MQuaternionUA mq3(array3unaligned);
MCQuaternionUA mcq3(array3unaligned);
// std::cerr << array1 << " " << array2 << " " << array3 << "\n";
mq1 = AngleAxisx(a, v0.normalized());
mq2 = mq1;
mq3 = mq1;
Quaternionx q1 = mq1;
Quaternionx q2 = mq2;
Quaternionx q3 = mq3;
Quaternionx q4 = MCQuaternionUA(array3unaligned);
VERIFY_IS_APPROX(q1.coeffs(), q2.coeffs());
VERIFY_IS_APPROX(q1.coeffs(), q3.coeffs());
VERIFY_IS_APPROX(q4.coeffs(), q3.coeffs());
#ifdef EIGEN_VECTORIZE
if(internal::packet_traits<Scalar>::Vectorizable)
VERIFY_RAISES_ASSERT((MQuaternionA(array3unaligned)));
#endif
VERIFY_IS_APPROX(mq1 * (mq1.inverse() * v1), v1);
VERIFY_IS_APPROX(mq1 * (mq1.conjugate() * v1), v1);
VERIFY_IS_APPROX(mcq1 * (mcq1.inverse() * v1), v1);
VERIFY_IS_APPROX(mcq1 * (mcq1.conjugate() * v1), v1);
VERIFY_IS_APPROX(mq3 * (mq3.inverse() * v1), v1);
VERIFY_IS_APPROX(mq3 * (mq3.conjugate() * v1), v1);
VERIFY_IS_APPROX(mcq3 * (mcq3.inverse() * v1), v1);
VERIFY_IS_APPROX(mcq3 * (mcq3.conjugate() * v1), v1);
VERIFY_IS_APPROX(mq1*mq2, q1*q2);
VERIFY_IS_APPROX(mq3*mq2, q3*q2);
VERIFY_IS_APPROX(mcq1*mq2, q1*q2);
VERIFY_IS_APPROX(mcq3*mq2, q3*q2);
// Bug 1461, compilation issue with Map<const Quat>::w(), and other reference/constness checks:
VERIFY_IS_APPROX(mcq3.coeffs().x() + mcq3.coeffs().y() + mcq3.coeffs().z() + mcq3.coeffs().w(), mcq3.coeffs().sum());
VERIFY_IS_APPROX(mcq3.x() + mcq3.y() + mcq3.z() + mcq3.w(), mcq3.coeffs().sum());
mq3.w() = 1;
const Quaternionx& cq3(q3);
VERIFY( &cq3.x() == &q3.x() );
const MQuaternionUA& cmq3(mq3);
VERIFY( &cmq3.x() == &mq3.x() );
// FIXME the following should be ok. The problem is that currently the LValueBit flag
// is used to determine wether we can return a coeff by reference or not, which is not enough for Map<const ...>.
//const MCQuaternionUA& cmcq3(mcq3);
//VERIFY( &cmcq3.x() == &mcq3.x() );
// test cast
{
Quaternion<float> q1f = mq1.template cast<float>();
VERIFY_IS_APPROX(q1f.template cast<Scalar>(),mq1);
Quaternion<double> q1d = mq1.template cast<double>();
VERIFY_IS_APPROX(q1d.template cast<Scalar>(),mq1);
}
}
template<typename Scalar> void quaternionAlignment(void){
typedef Quaternion<Scalar,AutoAlign> QuaternionA;
typedef Quaternion<Scalar,DontAlign> QuaternionUA;
EIGEN_ALIGN_MAX Scalar array1[4];
EIGEN_ALIGN_MAX Scalar array2[4];
EIGEN_ALIGN_MAX Scalar array3[4+1];
Scalar* arrayunaligned = array3+1;
QuaternionA *q1 = ::new(reinterpret_cast<void*>(array1)) QuaternionA;
QuaternionUA *q2 = ::new(reinterpret_cast<void*>(array2)) QuaternionUA;
QuaternionUA *q3 = ::new(reinterpret_cast<void*>(arrayunaligned)) QuaternionUA;
q1->coeffs().setRandom();
*q2 = *q1;
*q3 = *q1;
VERIFY_IS_APPROX(q1->coeffs(), q2->coeffs());
VERIFY_IS_APPROX(q1->coeffs(), q3->coeffs());
#if defined(EIGEN_VECTORIZE) && EIGEN_MAX_STATIC_ALIGN_BYTES>0
if(internal::packet_traits<Scalar>::Vectorizable && internal::packet_traits<Scalar>::size<=4)
VERIFY_RAISES_ASSERT((::new(reinterpret_cast<void*>(arrayunaligned)) QuaternionA));
#endif
}
template<typename PlainObjectType> void check_const_correctness(const PlainObjectType&)
{
// there's a lot that we can't test here while still having this test compile!
// the only possible approach would be to run a script trying to compile stuff and checking that it fails.
// CMake can help with that.
// verify that map-to-const don't have LvalueBit
typedef typename internal::add_const<PlainObjectType>::type ConstPlainObjectType;
VERIFY( !(internal::traits<Map<ConstPlainObjectType> >::Flags & LvalueBit) );
VERIFY( !(internal::traits<Map<ConstPlainObjectType, Aligned> >::Flags & LvalueBit) );
VERIFY( !(Map<ConstPlainObjectType>::Flags & LvalueBit) );
VERIFY( !(Map<ConstPlainObjectType, Aligned>::Flags & LvalueBit) );
}
void test_geo_quaternion()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(( quaternion<float,AutoAlign>() ));
CALL_SUBTEST_1( check_const_correctness(Quaternionf()) );
CALL_SUBTEST_2(( quaternion<double,AutoAlign>() ));
CALL_SUBTEST_2( check_const_correctness(Quaterniond()) );
CALL_SUBTEST_3(( quaternion<float,DontAlign>() ));
CALL_SUBTEST_4(( quaternion<double,DontAlign>() ));
CALL_SUBTEST_5(( quaternionAlignment<float>() ));
CALL_SUBTEST_6(( quaternionAlignment<double>() ));
CALL_SUBTEST_1( mapQuaternion<float>() );
CALL_SUBTEST_2( mapQuaternion<double>() );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/cholmod_support.cpp | .cpp | 2,686 | 58 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_NO_DEBUG_SMALL_PRODUCT_BLOCKS
#include "sparse_solver.h"
#include <Eigen/CholmodSupport>
template<typename T> void test_cholmod_T()
{
CholmodDecomposition<SparseMatrix<T>, Lower> g_chol_colmajor_lower; g_chol_colmajor_lower.setMode(CholmodSupernodalLLt);
CholmodDecomposition<SparseMatrix<T>, Upper> g_chol_colmajor_upper; g_chol_colmajor_upper.setMode(CholmodSupernodalLLt);
CholmodDecomposition<SparseMatrix<T>, Lower> g_llt_colmajor_lower; g_llt_colmajor_lower.setMode(CholmodSimplicialLLt);
CholmodDecomposition<SparseMatrix<T>, Upper> g_llt_colmajor_upper; g_llt_colmajor_upper.setMode(CholmodSimplicialLLt);
CholmodDecomposition<SparseMatrix<T>, Lower> g_ldlt_colmajor_lower; g_ldlt_colmajor_lower.setMode(CholmodLDLt);
CholmodDecomposition<SparseMatrix<T>, Upper> g_ldlt_colmajor_upper; g_ldlt_colmajor_upper.setMode(CholmodLDLt);
CholmodSupernodalLLT<SparseMatrix<T>, Lower> chol_colmajor_lower;
CholmodSupernodalLLT<SparseMatrix<T>, Upper> chol_colmajor_upper;
CholmodSimplicialLLT<SparseMatrix<T>, Lower> llt_colmajor_lower;
CholmodSimplicialLLT<SparseMatrix<T>, Upper> llt_colmajor_upper;
CholmodSimplicialLDLT<SparseMatrix<T>, Lower> ldlt_colmajor_lower;
CholmodSimplicialLDLT<SparseMatrix<T>, Upper> ldlt_colmajor_upper;
check_sparse_spd_solving(g_chol_colmajor_lower);
check_sparse_spd_solving(g_chol_colmajor_upper);
check_sparse_spd_solving(g_llt_colmajor_lower);
check_sparse_spd_solving(g_llt_colmajor_upper);
check_sparse_spd_solving(g_ldlt_colmajor_lower);
check_sparse_spd_solving(g_ldlt_colmajor_upper);
check_sparse_spd_solving(chol_colmajor_lower);
check_sparse_spd_solving(chol_colmajor_upper);
check_sparse_spd_solving(llt_colmajor_lower);
check_sparse_spd_solving(llt_colmajor_upper);
check_sparse_spd_solving(ldlt_colmajor_lower);
check_sparse_spd_solving(ldlt_colmajor_upper);
check_sparse_spd_determinant(chol_colmajor_lower);
check_sparse_spd_determinant(chol_colmajor_upper);
check_sparse_spd_determinant(llt_colmajor_lower);
check_sparse_spd_determinant(llt_colmajor_upper);
check_sparse_spd_determinant(ldlt_colmajor_lower);
check_sparse_spd_determinant(ldlt_colmajor_upper);
}
void test_cholmod_support()
{
CALL_SUBTEST_1(test_cholmod_T<double>());
CALL_SUBTEST_2(test_cholmod_T<std::complex<double> >());
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/geo_homogeneous.cpp | .cpp | 5,462 | 126 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/Geometry>
template<typename Scalar,int Size> void homogeneous(void)
{
/* this test covers the following files:
Homogeneous.h
*/
typedef Matrix<Scalar,Size,Size> MatrixType;
typedef Matrix<Scalar,Size,1, ColMajor> VectorType;
typedef Matrix<Scalar,Size+1,Size> HMatrixType;
typedef Matrix<Scalar,Size+1,1> HVectorType;
typedef Matrix<Scalar,Size,Size+1> T1MatrixType;
typedef Matrix<Scalar,Size+1,Size+1> T2MatrixType;
typedef Matrix<Scalar,Size+1,Size> T3MatrixType;
VectorType v0 = VectorType::Random(),
ones = VectorType::Ones();
HVectorType hv0 = HVectorType::Random();
MatrixType m0 = MatrixType::Random();
HMatrixType hm0 = HMatrixType::Random();
hv0 << v0, 1;
VERIFY_IS_APPROX(v0.homogeneous(), hv0);
VERIFY_IS_APPROX(v0, hv0.hnormalized());
VERIFY_IS_APPROX(v0.homogeneous().sum(), hv0.sum());
VERIFY_IS_APPROX(v0.homogeneous().minCoeff(), hv0.minCoeff());
VERIFY_IS_APPROX(v0.homogeneous().maxCoeff(), hv0.maxCoeff());
hm0 << m0, ones.transpose();
VERIFY_IS_APPROX(m0.colwise().homogeneous(), hm0);
VERIFY_IS_APPROX(m0, hm0.colwise().hnormalized());
hm0.row(Size-1).setRandom();
for(int j=0; j<Size; ++j)
m0.col(j) = hm0.col(j).head(Size) / hm0(Size,j);
VERIFY_IS_APPROX(m0, hm0.colwise().hnormalized());
T1MatrixType t1 = T1MatrixType::Random();
VERIFY_IS_APPROX(t1 * (v0.homogeneous().eval()), t1 * v0.homogeneous());
VERIFY_IS_APPROX(t1 * (m0.colwise().homogeneous().eval()), t1 * m0.colwise().homogeneous());
T2MatrixType t2 = T2MatrixType::Random();
VERIFY_IS_APPROX(t2 * (v0.homogeneous().eval()), t2 * v0.homogeneous());
VERIFY_IS_APPROX(t2 * (m0.colwise().homogeneous().eval()), t2 * m0.colwise().homogeneous());
VERIFY_IS_APPROX(t2 * (v0.homogeneous().asDiagonal()), t2 * hv0.asDiagonal());
VERIFY_IS_APPROX((v0.homogeneous().asDiagonal()) * t2, hv0.asDiagonal() * t2);
VERIFY_IS_APPROX((v0.transpose().rowwise().homogeneous().eval()) * t2,
v0.transpose().rowwise().homogeneous() * t2);
VERIFY_IS_APPROX((m0.transpose().rowwise().homogeneous().eval()) * t2,
m0.transpose().rowwise().homogeneous() * t2);
T3MatrixType t3 = T3MatrixType::Random();
VERIFY_IS_APPROX((v0.transpose().rowwise().homogeneous().eval()) * t3,
v0.transpose().rowwise().homogeneous() * t3);
VERIFY_IS_APPROX((m0.transpose().rowwise().homogeneous().eval()) * t3,
m0.transpose().rowwise().homogeneous() * t3);
// test product with a Transform object
Transform<Scalar, Size, Affine> aff;
Transform<Scalar, Size, AffineCompact> caff;
Transform<Scalar, Size, Projective> proj;
Matrix<Scalar, Size, Dynamic> pts;
Matrix<Scalar, Size+1, Dynamic> pts1, pts2;
aff.affine().setRandom();
proj = caff = aff;
pts.setRandom(Size,internal::random<int>(1,20));
pts1 = pts.colwise().homogeneous();
VERIFY_IS_APPROX(aff * pts.colwise().homogeneous(), (aff * pts1).colwise().hnormalized());
VERIFY_IS_APPROX(caff * pts.colwise().homogeneous(), (caff * pts1).colwise().hnormalized());
VERIFY_IS_APPROX(proj * pts.colwise().homogeneous(), (proj * pts1));
VERIFY_IS_APPROX((aff * pts1).colwise().hnormalized(), aff * pts);
VERIFY_IS_APPROX((caff * pts1).colwise().hnormalized(), caff * pts);
pts2 = pts1;
pts2.row(Size).setRandom();
VERIFY_IS_APPROX((aff * pts2).colwise().hnormalized(), aff * pts2.colwise().hnormalized());
VERIFY_IS_APPROX((caff * pts2).colwise().hnormalized(), caff * pts2.colwise().hnormalized());
VERIFY_IS_APPROX((proj * pts2).colwise().hnormalized(), (proj * pts2.colwise().hnormalized().colwise().homogeneous()).colwise().hnormalized());
// Test combination of homogeneous
VERIFY_IS_APPROX( (t2 * v0.homogeneous()).hnormalized(),
(t2.template topLeftCorner<Size,Size>() * v0 + t2.template topRightCorner<Size,1>())
/ ((t2.template bottomLeftCorner<1,Size>()*v0).value() + t2(Size,Size)) );
VERIFY_IS_APPROX( (t2 * pts.colwise().homogeneous()).colwise().hnormalized(),
(Matrix<Scalar, Size+1, Dynamic>(t2 * pts1).colwise().hnormalized()) );
VERIFY_IS_APPROX( (t2 .lazyProduct( v0.homogeneous() )).hnormalized(), (t2 * v0.homogeneous()).hnormalized() );
VERIFY_IS_APPROX( (t2 .lazyProduct ( pts.colwise().homogeneous() )).colwise().hnormalized(), (t2 * pts1).colwise().hnormalized() );
VERIFY_IS_APPROX( (v0.transpose().homogeneous() .lazyProduct( t2 )).hnormalized(), (v0.transpose().homogeneous()*t2).hnormalized() );
VERIFY_IS_APPROX( (pts.transpose().rowwise().homogeneous() .lazyProduct( t2 )).rowwise().hnormalized(), (pts1.transpose()*t2).rowwise().hnormalized() );
VERIFY_IS_APPROX( (t2.template triangularView<Lower>() * v0.homogeneous()).eval(), (t2.template triangularView<Lower>()*hv0) );
}
void test_geo_homogeneous()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(( homogeneous<float,1>() ));
CALL_SUBTEST_2(( homogeneous<double,3>() ));
CALL_SUBTEST_3(( homogeneous<double,8>() ));
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/resize.cpp | .cpp | 1,097 | 42 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Keir Mierle <mierle@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<DenseIndex rows, DenseIndex cols>
void resizeLikeTest()
{
MatrixXf A(rows, cols);
MatrixXf B;
Matrix<double, rows, cols> C;
B.resizeLike(A);
C.resizeLike(B); // Shouldn't crash.
VERIFY(B.rows() == rows && B.cols() == cols);
VectorXf x(rows);
RowVectorXf y;
y.resizeLike(x);
VERIFY(y.rows() == 1 && y.cols() == rows);
y.resize(cols);
x.resizeLike(y);
VERIFY(x.rows() == cols && x.cols() == 1);
}
void resizeLikeTest12() { resizeLikeTest<1,2>(); }
void resizeLikeTest1020() { resizeLikeTest<10,20>(); }
void resizeLikeTest31() { resizeLikeTest<3,1>(); }
void test_resize()
{
CALL_SUBTEST(resizeLikeTest12() );
CALL_SUBTEST(resizeLikeTest1020() );
CALL_SUBTEST(resizeLikeTest31() );
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/mapstaticmethods.cpp | .cpp | 7,300 | 174 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
float *ptr;
const float *const_ptr;
template<typename PlainObjectType,
bool IsDynamicSize = PlainObjectType::SizeAtCompileTime == Dynamic,
bool IsVector = PlainObjectType::IsVectorAtCompileTime
>
struct mapstaticmethods_impl {};
template<typename PlainObjectType, bool IsVector>
struct mapstaticmethods_impl<PlainObjectType, false, IsVector>
{
static void run(const PlainObjectType& m)
{
mapstaticmethods_impl<PlainObjectType, true, IsVector>::run(m);
int i = internal::random<int>(2,5), j = internal::random<int>(2,5);
PlainObjectType::Map(ptr).setZero();
PlainObjectType::MapAligned(ptr).setZero();
PlainObjectType::Map(const_ptr).sum();
PlainObjectType::MapAligned(const_ptr).sum();
PlainObjectType::Map(ptr, InnerStride<>(i)).setZero();
PlainObjectType::MapAligned(ptr, InnerStride<>(i)).setZero();
PlainObjectType::Map(const_ptr, InnerStride<>(i)).sum();
PlainObjectType::MapAligned(const_ptr, InnerStride<>(i)).sum();
PlainObjectType::Map(ptr, InnerStride<2>()).setZero();
PlainObjectType::MapAligned(ptr, InnerStride<3>()).setZero();
PlainObjectType::Map(const_ptr, InnerStride<4>()).sum();
PlainObjectType::MapAligned(const_ptr, InnerStride<5>()).sum();
PlainObjectType::Map(ptr, OuterStride<>(i)).setZero();
PlainObjectType::MapAligned(ptr, OuterStride<>(i)).setZero();
PlainObjectType::Map(const_ptr, OuterStride<>(i)).sum();
PlainObjectType::MapAligned(const_ptr, OuterStride<>(i)).sum();
PlainObjectType::Map(ptr, OuterStride<2>()).setZero();
PlainObjectType::MapAligned(ptr, OuterStride<3>()).setZero();
PlainObjectType::Map(const_ptr, OuterStride<4>()).sum();
PlainObjectType::MapAligned(const_ptr, OuterStride<5>()).sum();
PlainObjectType::Map(ptr, Stride<Dynamic, Dynamic>(i,j)).setZero();
PlainObjectType::MapAligned(ptr, Stride<2,Dynamic>(2,i)).setZero();
PlainObjectType::Map(const_ptr, Stride<Dynamic,3>(i,3)).sum();
PlainObjectType::MapAligned(const_ptr, Stride<Dynamic, Dynamic>(i,j)).sum();
PlainObjectType::Map(ptr, Stride<2,3>()).setZero();
PlainObjectType::MapAligned(ptr, Stride<3,4>()).setZero();
PlainObjectType::Map(const_ptr, Stride<2,4>()).sum();
PlainObjectType::MapAligned(const_ptr, Stride<5,3>()).sum();
}
};
template<typename PlainObjectType>
struct mapstaticmethods_impl<PlainObjectType, true, false>
{
static void run(const PlainObjectType& m)
{
Index rows = m.rows(), cols = m.cols();
int i = internal::random<int>(2,5), j = internal::random<int>(2,5);
PlainObjectType::Map(ptr, rows, cols).setZero();
PlainObjectType::MapAligned(ptr, rows, cols).setZero();
PlainObjectType::Map(const_ptr, rows, cols).sum();
PlainObjectType::MapAligned(const_ptr, rows, cols).sum();
PlainObjectType::Map(ptr, rows, cols, InnerStride<>(i)).setZero();
PlainObjectType::MapAligned(ptr, rows, cols, InnerStride<>(i)).setZero();
PlainObjectType::Map(const_ptr, rows, cols, InnerStride<>(i)).sum();
PlainObjectType::MapAligned(const_ptr, rows, cols, InnerStride<>(i)).sum();
PlainObjectType::Map(ptr, rows, cols, InnerStride<2>()).setZero();
PlainObjectType::MapAligned(ptr, rows, cols, InnerStride<3>()).setZero();
PlainObjectType::Map(const_ptr, rows, cols, InnerStride<4>()).sum();
PlainObjectType::MapAligned(const_ptr, rows, cols, InnerStride<5>()).sum();
PlainObjectType::Map(ptr, rows, cols, OuterStride<>(i)).setZero();
PlainObjectType::MapAligned(ptr, rows, cols, OuterStride<>(i)).setZero();
PlainObjectType::Map(const_ptr, rows, cols, OuterStride<>(i)).sum();
PlainObjectType::MapAligned(const_ptr, rows, cols, OuterStride<>(i)).sum();
PlainObjectType::Map(ptr, rows, cols, OuterStride<2>()).setZero();
PlainObjectType::MapAligned(ptr, rows, cols, OuterStride<3>()).setZero();
PlainObjectType::Map(const_ptr, rows, cols, OuterStride<4>()).sum();
PlainObjectType::MapAligned(const_ptr, rows, cols, OuterStride<5>()).sum();
PlainObjectType::Map(ptr, rows, cols, Stride<Dynamic, Dynamic>(i,j)).setZero();
PlainObjectType::MapAligned(ptr, rows, cols, Stride<2,Dynamic>(2,i)).setZero();
PlainObjectType::Map(const_ptr, rows, cols, Stride<Dynamic,3>(i,3)).sum();
PlainObjectType::MapAligned(const_ptr, rows, cols, Stride<Dynamic, Dynamic>(i,j)).sum();
PlainObjectType::Map(ptr, rows, cols, Stride<2,3>()).setZero();
PlainObjectType::MapAligned(ptr, rows, cols, Stride<3,4>()).setZero();
PlainObjectType::Map(const_ptr, rows, cols, Stride<2,4>()).sum();
PlainObjectType::MapAligned(const_ptr, rows, cols, Stride<5,3>()).sum();
}
};
template<typename PlainObjectType>
struct mapstaticmethods_impl<PlainObjectType, true, true>
{
static void run(const PlainObjectType& v)
{
Index size = v.size();
int i = internal::random<int>(2,5);
PlainObjectType::Map(ptr, size).setZero();
PlainObjectType::MapAligned(ptr, size).setZero();
PlainObjectType::Map(const_ptr, size).sum();
PlainObjectType::MapAligned(const_ptr, size).sum();
PlainObjectType::Map(ptr, size, InnerStride<>(i)).setZero();
PlainObjectType::MapAligned(ptr, size, InnerStride<>(i)).setZero();
PlainObjectType::Map(const_ptr, size, InnerStride<>(i)).sum();
PlainObjectType::MapAligned(const_ptr, size, InnerStride<>(i)).sum();
PlainObjectType::Map(ptr, size, InnerStride<2>()).setZero();
PlainObjectType::MapAligned(ptr, size, InnerStride<3>()).setZero();
PlainObjectType::Map(const_ptr, size, InnerStride<4>()).sum();
PlainObjectType::MapAligned(const_ptr, size, InnerStride<5>()).sum();
}
};
template<typename PlainObjectType>
void mapstaticmethods(const PlainObjectType& m)
{
mapstaticmethods_impl<PlainObjectType>::run(m);
VERIFY(true); // just to avoid 'unused function' warning
}
void test_mapstaticmethods()
{
ptr = internal::aligned_new<float>(1000);
for(int i = 0; i < 1000; i++) ptr[i] = float(i);
const_ptr = ptr;
CALL_SUBTEST_1(( mapstaticmethods(Matrix<float, 1, 1>()) ));
CALL_SUBTEST_1(( mapstaticmethods(Vector2f()) ));
CALL_SUBTEST_2(( mapstaticmethods(Vector3f()) ));
CALL_SUBTEST_2(( mapstaticmethods(Matrix2f()) ));
CALL_SUBTEST_3(( mapstaticmethods(Matrix4f()) ));
CALL_SUBTEST_3(( mapstaticmethods(Array4f()) ));
CALL_SUBTEST_4(( mapstaticmethods(Array3f()) ));
CALL_SUBTEST_4(( mapstaticmethods(Array33f()) ));
CALL_SUBTEST_5(( mapstaticmethods(Array44f()) ));
CALL_SUBTEST_5(( mapstaticmethods(VectorXf(1)) ));
CALL_SUBTEST_5(( mapstaticmethods(VectorXf(8)) ));
CALL_SUBTEST_6(( mapstaticmethods(MatrixXf(1,1)) ));
CALL_SUBTEST_6(( mapstaticmethods(MatrixXf(5,7)) ));
CALL_SUBTEST_7(( mapstaticmethods(ArrayXf(1)) ));
CALL_SUBTEST_7(( mapstaticmethods(ArrayXf(5)) ));
CALL_SUBTEST_8(( mapstaticmethods(ArrayXXf(1,1)) ));
CALL_SUBTEST_8(( mapstaticmethods(ArrayXXf(8,6)) ));
internal::aligned_delete(ptr, 1000);
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/stdlist_overload.cpp | .cpp | 5,886 | 193 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/StdList>
#include <Eigen/Geometry>
EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Vector4f)
EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Matrix2f)
EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Matrix4f)
EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Matrix4d)
EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Affine3f)
EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Affine3d)
EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Quaternionf)
EIGEN_DEFINE_STL_LIST_SPECIALIZATION(Quaterniond)
template <class Container, class Position>
typename Container::iterator get(Container & c, Position position)
{
typename Container::iterator it = c.begin();
std::advance(it, position);
return it;
}
template <class Container, class Position, class Value>
void set(Container & c, Position position, const Value & value)
{
typename Container::iterator it = c.begin();
std::advance(it, position);
*it = value;
}
template<typename MatrixType>
void check_stdlist_matrix(const MatrixType& m)
{
typename MatrixType::Index rows = m.rows();
typename MatrixType::Index cols = m.cols();
MatrixType x = MatrixType::Random(rows,cols), y = MatrixType::Random(rows,cols);
std::list<MatrixType> v(10, MatrixType::Zero(rows,cols)), w(20, y);
typename std::list<MatrixType>::iterator itv = get(v, 5);
typename std::list<MatrixType>::iterator itw = get(w, 6);
*itv = x;
*itw = *itv;
VERIFY_IS_APPROX(*itw, *itv);
v = w;
itv = v.begin();
itw = w.begin();
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(*itw, *itv);
++itv;
++itw;
}
v.resize(21);
set(v, 20, x);
VERIFY_IS_APPROX(*get(v, 20), x);
v.resize(22,y);
VERIFY_IS_APPROX(*get(v, 21), y);
v.push_back(x);
VERIFY_IS_APPROX(*get(v, 22), x);
// do a lot of push_back such that the list gets internally resized
// (with memory reallocation)
MatrixType* ref = &(*get(w, 0));
for(int i=0; i<30 || ((ref==&(*get(w, 0))) && i<300); ++i)
v.push_back(*get(w, i%w.size()));
for(unsigned int i=23; i<v.size(); ++i)
{
VERIFY((*get(v, i))==(*get(w, (i-23)%w.size())));
}
}
template<typename TransformType>
void check_stdlist_transform(const TransformType&)
{
typedef typename TransformType::MatrixType MatrixType;
TransformType x(MatrixType::Random()), y(MatrixType::Random()), ti=TransformType::Identity();
std::list<TransformType> v(10,ti), w(20, y);
typename std::list<TransformType>::iterator itv = get(v, 5);
typename std::list<TransformType>::iterator itw = get(w, 6);
*itv = x;
*itw = *itv;
VERIFY_IS_APPROX(*itw, *itv);
v = w;
itv = v.begin();
itw = w.begin();
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(*itw, *itv);
++itv;
++itw;
}
v.resize(21, ti);
set(v, 20, x);
VERIFY_IS_APPROX(*get(v, 20), x);
v.resize(22,y);
VERIFY_IS_APPROX(*get(v, 21), y);
v.push_back(x);
VERIFY_IS_APPROX(*get(v, 22), x);
// do a lot of push_back such that the list gets internally resized
// (with memory reallocation)
TransformType* ref = &(*get(w, 0));
for(int i=0; i<30 || ((ref==&(*get(w, 0))) && i<300); ++i)
v.push_back(*get(w, i%w.size()));
for(unsigned int i=23; i<v.size(); ++i)
{
VERIFY(get(v, i)->matrix()==get(w, (i-23)%w.size())->matrix());
}
}
template<typename QuaternionType>
void check_stdlist_quaternion(const QuaternionType&)
{
typedef typename QuaternionType::Coefficients Coefficients;
QuaternionType x(Coefficients::Random()), y(Coefficients::Random()), qi=QuaternionType::Identity();
std::list<QuaternionType> v(10,qi), w(20, y);
typename std::list<QuaternionType>::iterator itv = get(v, 5);
typename std::list<QuaternionType>::iterator itw = get(w, 6);
*itv = x;
*itw = *itv;
VERIFY_IS_APPROX(*itw, *itv);
v = w;
itv = v.begin();
itw = w.begin();
for(int i = 0; i < 20; i++)
{
VERIFY_IS_APPROX(*itw, *itv);
++itv;
++itw;
}
v.resize(21,qi);
set(v, 20, x);
VERIFY_IS_APPROX(*get(v, 20), x);
v.resize(22,y);
VERIFY_IS_APPROX(*get(v, 21), y);
v.push_back(x);
VERIFY_IS_APPROX(*get(v, 22), x);
// do a lot of push_back such that the list gets internally resized
// (with memory reallocation)
QuaternionType* ref = &(*get(w, 0));
for(int i=0; i<30 || ((ref==&(*get(w, 0))) && i<300); ++i)
v.push_back(*get(w, i%w.size()));
for(unsigned int i=23; i<v.size(); ++i)
{
VERIFY(get(v, i)->coeffs()==get(w, (i-23)%w.size())->coeffs());
}
}
void test_stdlist_overload()
{
// some non vectorizable fixed sizes
CALL_SUBTEST_1(check_stdlist_matrix(Vector2f()));
CALL_SUBTEST_1(check_stdlist_matrix(Matrix3f()));
CALL_SUBTEST_2(check_stdlist_matrix(Matrix3d()));
// some vectorizable fixed sizes
CALL_SUBTEST_1(check_stdlist_matrix(Matrix2f()));
CALL_SUBTEST_1(check_stdlist_matrix(Vector4f()));
CALL_SUBTEST_1(check_stdlist_matrix(Matrix4f()));
CALL_SUBTEST_2(check_stdlist_matrix(Matrix4d()));
// some dynamic sizes
CALL_SUBTEST_3(check_stdlist_matrix(MatrixXd(1,1)));
CALL_SUBTEST_3(check_stdlist_matrix(VectorXd(20)));
CALL_SUBTEST_3(check_stdlist_matrix(RowVectorXf(20)));
CALL_SUBTEST_3(check_stdlist_matrix(MatrixXcf(10,10)));
// some Transform
CALL_SUBTEST_4(check_stdlist_transform(Affine2f())); // does not need the specialization (2+1)^2 = 9
CALL_SUBTEST_4(check_stdlist_transform(Affine3f()));
CALL_SUBTEST_4(check_stdlist_transform(Affine3d()));
// some Quaternion
CALL_SUBTEST_5(check_stdlist_quaternion(Quaternionf()));
CALL_SUBTEST_5(check_stdlist_quaternion(Quaterniond()));
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/smallvectors.cpp | .cpp | 2,125 | 68 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_NO_STATIC_ASSERT
#include "main.h"
template<typename Scalar> void smallVectors()
{
typedef Matrix<Scalar, 1, 2> V2;
typedef Matrix<Scalar, 3, 1> V3;
typedef Matrix<Scalar, 1, 4> V4;
typedef Matrix<Scalar, Dynamic, 1> VX;
Scalar x1 = internal::random<Scalar>(),
x2 = internal::random<Scalar>(),
x3 = internal::random<Scalar>(),
x4 = internal::random<Scalar>();
V2 v2(x1, x2);
V3 v3(x1, x2, x3);
V4 v4(x1, x2, x3, x4);
VERIFY_IS_APPROX(x1, v2.x());
VERIFY_IS_APPROX(x1, v3.x());
VERIFY_IS_APPROX(x1, v4.x());
VERIFY_IS_APPROX(x2, v2.y());
VERIFY_IS_APPROX(x2, v3.y());
VERIFY_IS_APPROX(x2, v4.y());
VERIFY_IS_APPROX(x3, v3.z());
VERIFY_IS_APPROX(x3, v4.z());
VERIFY_IS_APPROX(x4, v4.w());
if (!NumTraits<Scalar>::IsInteger)
{
VERIFY_RAISES_ASSERT(V3(2, 1))
VERIFY_RAISES_ASSERT(V3(3, 2))
VERIFY_RAISES_ASSERT(V3(Scalar(3), 1))
VERIFY_RAISES_ASSERT(V3(3, Scalar(1)))
VERIFY_RAISES_ASSERT(V3(Scalar(3), Scalar(1)))
VERIFY_RAISES_ASSERT(V3(Scalar(123), Scalar(123)))
VERIFY_RAISES_ASSERT(V4(1, 3))
VERIFY_RAISES_ASSERT(V4(2, 4))
VERIFY_RAISES_ASSERT(V4(1, Scalar(4)))
VERIFY_RAISES_ASSERT(V4(Scalar(1), 4))
VERIFY_RAISES_ASSERT(V4(Scalar(1), Scalar(4)))
VERIFY_RAISES_ASSERT(V4(Scalar(123), Scalar(123)))
VERIFY_RAISES_ASSERT(VX(3, 2))
VERIFY_RAISES_ASSERT(VX(Scalar(3), 1))
VERIFY_RAISES_ASSERT(VX(3, Scalar(1)))
VERIFY_RAISES_ASSERT(VX(Scalar(3), Scalar(1)))
VERIFY_RAISES_ASSERT(VX(Scalar(123), Scalar(123)))
}
}
void test_smallvectors()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST(smallVectors<int>() );
CALL_SUBTEST(smallVectors<float>() );
CALL_SUBTEST(smallVectors<double>() );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/unalignedcount.cpp | .cpp | 2,134 | 54 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
static int nb_load;
static int nb_loadu;
static int nb_store;
static int nb_storeu;
#define EIGEN_DEBUG_ALIGNED_LOAD { nb_load++; }
#define EIGEN_DEBUG_UNALIGNED_LOAD { nb_loadu++; }
#define EIGEN_DEBUG_ALIGNED_STORE { nb_store++; }
#define EIGEN_DEBUG_UNALIGNED_STORE { nb_storeu++; }
#define VERIFY_ALIGNED_UNALIGNED_COUNT(XPR,AL,UL,AS,US) {\
nb_load = nb_loadu = nb_store = nb_storeu = 0; \
XPR; \
if(!(nb_load==AL && nb_loadu==UL && nb_store==AS && nb_storeu==US)) \
std::cerr << " >> " << nb_load << ", " << nb_loadu << ", " << nb_store << ", " << nb_storeu << "\n"; \
VERIFY( (#XPR) && nb_load==AL && nb_loadu==UL && nb_store==AS && nb_storeu==US ); \
}
#include "main.h"
void test_unalignedcount()
{
#if defined(EIGEN_VECTORIZE_AVX)
VectorXf a(40), b(40);
VERIFY_ALIGNED_UNALIGNED_COUNT(a += b, 10, 0, 5, 0);
VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) += b.segment(0,40), 5, 5, 5, 0);
VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) -= b.segment(0,40), 5, 5, 5, 0);
VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) *= 3.5, 5, 0, 5, 0);
VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) /= 3.5, 5, 0, 5, 0);
#elif defined(EIGEN_VECTORIZE_SSE)
VectorXf a(40), b(40);
VERIFY_ALIGNED_UNALIGNED_COUNT(a += b, 20, 0, 10, 0);
VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) += b.segment(0,40), 10, 10, 10, 0);
VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) -= b.segment(0,40), 10, 10, 10, 0);
VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) *= 3.5, 10, 0, 10, 0);
VERIFY_ALIGNED_UNALIGNED_COUNT(a.segment(0,40) /= 3.5, 10, 0, 10, 0);
#else
// The following line is to eliminate "variable not used" warnings
nb_load = nb_loadu = nb_store = nb_storeu = 0;
int a(0), b(0);
VERIFY(a==b);
#endif
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/sparselu.cpp | .cpp | 1,783 | 46 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
// SparseLU solve does not accept column major matrices for the destination.
// However, as expected, the generic check_sparse_square_solving routines produces row-major
// rhs and destination matrices when compiled with EIGEN_DEFAULT_TO_ROW_MAJOR
#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR
#undef EIGEN_DEFAULT_TO_ROW_MAJOR
#endif
#include "sparse_solver.h"
#include <Eigen/SparseLU>
#include <unsupported/Eigen/SparseExtra>
template<typename T> void test_sparselu_T()
{
SparseLU<SparseMatrix<T, ColMajor> /*, COLAMDOrdering<int>*/ > sparselu_colamd; // COLAMDOrdering is the default
SparseLU<SparseMatrix<T, ColMajor>, AMDOrdering<int> > sparselu_amd;
SparseLU<SparseMatrix<T, ColMajor, long int>, NaturalOrdering<long int> > sparselu_natural;
check_sparse_square_solving(sparselu_colamd, 300, 100000, true);
check_sparse_square_solving(sparselu_amd, 300, 10000, true);
check_sparse_square_solving(sparselu_natural, 300, 2000, true);
check_sparse_square_abs_determinant(sparselu_colamd);
check_sparse_square_abs_determinant(sparselu_amd);
check_sparse_square_determinant(sparselu_colamd);
check_sparse_square_determinant(sparselu_amd);
}
void test_sparselu()
{
CALL_SUBTEST_1(test_sparselu_T<float>());
CALL_SUBTEST_2(test_sparselu_T<double>());
CALL_SUBTEST_3(test_sparselu_T<std::complex<float> >());
CALL_SUBTEST_4(test_sparselu_T<std::complex<double> >());
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/array_for_matrix.cpp | .cpp | 12,295 | 301 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename MatrixType> void array_for_matrix(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType;
typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols);
ColVectorType cv1 = ColVectorType::Random(rows);
RowVectorType rv1 = RowVectorType::Random(cols);
Scalar s1 = internal::random<Scalar>(),
s2 = internal::random<Scalar>();
// scalar addition
VERIFY_IS_APPROX(m1.array() + s1, s1 + m1.array());
VERIFY_IS_APPROX((m1.array() + s1).matrix(), MatrixType::Constant(rows,cols,s1) + m1);
VERIFY_IS_APPROX(((m1*Scalar(2)).array() - s2).matrix(), (m1+m1) - MatrixType::Constant(rows,cols,s2) );
m3 = m1;
m3.array() += s2;
VERIFY_IS_APPROX(m3, (m1.array() + s2).matrix());
m3 = m1;
m3.array() -= s1;
VERIFY_IS_APPROX(m3, (m1.array() - s1).matrix());
// reductions
VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum().sum() - m1.sum(), m1.squaredNorm());
VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum().sum() - m1.sum(), m1.squaredNorm());
VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum() + m2.colwise().sum() - (m1+m2).colwise().sum(), (m1+m2).squaredNorm());
VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum() - m2.rowwise().sum() - (m1-m2).rowwise().sum(), (m1-m2).squaredNorm());
VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar,Scalar>()));
// vector-wise ops
m3 = m1;
VERIFY_IS_APPROX(m3.colwise() += cv1, m1.colwise() + cv1);
m3 = m1;
VERIFY_IS_APPROX(m3.colwise() -= cv1, m1.colwise() - cv1);
m3 = m1;
VERIFY_IS_APPROX(m3.rowwise() += rv1, m1.rowwise() + rv1);
m3 = m1;
VERIFY_IS_APPROX(m3.rowwise() -= rv1, m1.rowwise() - rv1);
// empty objects
VERIFY_IS_APPROX(m1.block(0,0,0,cols).colwise().sum(), RowVectorType::Zero(cols));
VERIFY_IS_APPROX(m1.block(0,0,rows,0).rowwise().prod(), ColVectorType::Ones(rows));
// verify the const accessors exist
const Scalar& ref_m1 = m.matrix().array().coeffRef(0);
const Scalar& ref_m2 = m.matrix().array().coeffRef(0,0);
const Scalar& ref_a1 = m.array().matrix().coeffRef(0);
const Scalar& ref_a2 = m.array().matrix().coeffRef(0,0);
VERIFY(&ref_a1 == &ref_m1);
VERIFY(&ref_a2 == &ref_m2);
// Check write accessors:
m1.array().coeffRef(0,0) = 1;
VERIFY_IS_APPROX(m1(0,0),Scalar(1));
m1.array()(0,0) = 2;
VERIFY_IS_APPROX(m1(0,0),Scalar(2));
m1.array().matrix().coeffRef(0,0) = 3;
VERIFY_IS_APPROX(m1(0,0),Scalar(3));
m1.array().matrix()(0,0) = 4;
VERIFY_IS_APPROX(m1(0,0),Scalar(4));
}
template<typename MatrixType> void comparisons(const MatrixType& m)
{
using std::abs;
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
Index rows = m.rows();
Index cols = m.cols();
Index r = internal::random<Index>(0, rows-1),
c = internal::random<Index>(0, cols-1);
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols);
VERIFY(((m1.array() + Scalar(1)) > m1.array()).all());
VERIFY(((m1.array() - Scalar(1)) < m1.array()).all());
if (rows*cols>1)
{
m3 = m1;
m3(r,c) += 1;
VERIFY(! (m1.array() < m3.array()).all() );
VERIFY(! (m1.array() > m3.array()).all() );
}
// comparisons to scalar
VERIFY( (m1.array() != (m1(r,c)+1) ).any() );
VERIFY( (m1.array() > (m1(r,c)-1) ).any() );
VERIFY( (m1.array() < (m1(r,c)+1) ).any() );
VERIFY( (m1.array() == m1(r,c) ).any() );
VERIFY( m1.cwiseEqual(m1(r,c)).any() );
// test Select
VERIFY_IS_APPROX( (m1.array()<m2.array()).select(m1,m2), m1.cwiseMin(m2) );
VERIFY_IS_APPROX( (m1.array()>m2.array()).select(m1,m2), m1.cwiseMax(m2) );
Scalar mid = (m1.cwiseAbs().minCoeff() + m1.cwiseAbs().maxCoeff())/Scalar(2);
for (int j=0; j<cols; ++j)
for (int i=0; i<rows; ++i)
m3(i,j) = abs(m1(i,j))<mid ? 0 : m1(i,j);
VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array())
.select(MatrixType::Zero(rows,cols),m1), m3);
// shorter versions:
VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array())
.select(0,m1), m3);
VERIFY_IS_APPROX( (m1.array().abs()>=MatrixType::Constant(rows,cols,mid).array())
.select(m1,0), m3);
// even shorter version:
VERIFY_IS_APPROX( (m1.array().abs()<mid).select(0,m1), m3);
// count
VERIFY(((m1.array().abs()+1)>RealScalar(0.1)).count() == rows*cols);
// and/or
VERIFY( ((m1.array()<RealScalar(0)).matrix() && (m1.array()>RealScalar(0)).matrix()).count() == 0);
VERIFY( ((m1.array()<RealScalar(0)).matrix() || (m1.array()>=RealScalar(0)).matrix()).count() == rows*cols);
RealScalar a = m1.cwiseAbs().mean();
VERIFY( ((m1.array()<-a).matrix() || (m1.array()>a).matrix()).count() == (m1.cwiseAbs().array()>a).count());
typedef Matrix<typename MatrixType::Index, Dynamic, 1> VectorOfIndices;
// TODO allows colwise/rowwise for array
VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().colwise().count(), VectorOfIndices::Constant(cols,rows).transpose());
VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().rowwise().count(), VectorOfIndices::Constant(rows, cols));
}
template<typename VectorType> void lpNorm(const VectorType& v)
{
using std::sqrt;
typedef typename VectorType::RealScalar RealScalar;
VectorType u = VectorType::Random(v.size());
if(v.size()==0)
{
VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), RealScalar(0));
VERIFY_IS_APPROX(u.template lpNorm<1>(), RealScalar(0));
VERIFY_IS_APPROX(u.template lpNorm<2>(), RealScalar(0));
VERIFY_IS_APPROX(u.template lpNorm<5>(), RealScalar(0));
}
else
{
VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwiseAbs().maxCoeff());
}
VERIFY_IS_APPROX(u.template lpNorm<1>(), u.cwiseAbs().sum());
VERIFY_IS_APPROX(u.template lpNorm<2>(), sqrt(u.array().abs().square().sum()));
VERIFY_IS_APPROX(numext::pow(u.template lpNorm<5>(), typename VectorType::RealScalar(5)), u.array().abs().pow(5).sum());
}
template<typename MatrixType> void cwise_min_max(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols);
// min/max with array
Scalar maxM1 = m1.maxCoeff();
Scalar minM1 = m1.minCoeff();
VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1), m1.cwiseMin(MatrixType::Constant(rows,cols, minM1)));
VERIFY_IS_APPROX(m1, m1.cwiseMin(MatrixType::Constant(rows,cols, maxM1)));
VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1), m1.cwiseMax(MatrixType::Constant(rows,cols, maxM1)));
VERIFY_IS_APPROX(m1, m1.cwiseMax(MatrixType::Constant(rows,cols, minM1)));
// min/max with scalar input
VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1), m1.cwiseMin( minM1));
VERIFY_IS_APPROX(m1, m1.cwiseMin(maxM1));
VERIFY_IS_APPROX(-m1, (-m1).cwiseMin(-minM1));
VERIFY_IS_APPROX(-m1.array(), ((-m1).array().min)( -minM1));
VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1), m1.cwiseMax( maxM1));
VERIFY_IS_APPROX(m1, m1.cwiseMax(minM1));
VERIFY_IS_APPROX(-m1, (-m1).cwiseMax(-maxM1));
VERIFY_IS_APPROX(-m1.array(), ((-m1).array().max)(-maxM1));
VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1).array(), (m1.array().min)( minM1));
VERIFY_IS_APPROX(m1.array(), (m1.array().min)( maxM1));
VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1).array(), (m1.array().max)( maxM1));
VERIFY_IS_APPROX(m1.array(), (m1.array().max)( minM1));
}
template<typename MatrixTraits> void resize(const MatrixTraits& t)
{
typedef typename MatrixTraits::Scalar Scalar;
typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType;
typedef Array<Scalar,Dynamic,Dynamic> Array2DType;
typedef Matrix<Scalar,Dynamic,1> VectorType;
typedef Array<Scalar,Dynamic,1> Array1DType;
Index rows = t.rows(), cols = t.cols();
MatrixType m(rows,cols);
VectorType v(rows);
Array2DType a2(rows,cols);
Array1DType a1(rows);
m.array().resize(rows+1,cols+1);
VERIFY(m.rows()==rows+1 && m.cols()==cols+1);
a2.matrix().resize(rows+1,cols+1);
VERIFY(a2.rows()==rows+1 && a2.cols()==cols+1);
v.array().resize(cols);
VERIFY(v.size()==cols);
a1.matrix().resize(cols);
VERIFY(a1.size()==cols);
}
template<int>
void regression_bug_654()
{
ArrayXf a = RowVectorXf(3);
VectorXf v = Array<float,1,Dynamic>(3);
}
// Check propagation of LvalueBit through Array/Matrix-Wrapper
template<int>
void regrrssion_bug_1410()
{
const Matrix4i M;
const Array4i A;
ArrayWrapper<const Matrix4i> MA = M.array();
MA.row(0);
MatrixWrapper<const Array4i> AM = A.matrix();
AM.row(0);
VERIFY((internal::traits<ArrayWrapper<const Matrix4i> >::Flags&LvalueBit)==0);
VERIFY((internal::traits<MatrixWrapper<const Array4i> >::Flags&LvalueBit)==0);
VERIFY((internal::traits<ArrayWrapper<Matrix4i> >::Flags&LvalueBit)==LvalueBit);
VERIFY((internal::traits<MatrixWrapper<Array4i> >::Flags&LvalueBit)==LvalueBit);
}
void test_array_for_matrix()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( array_for_matrix(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( array_for_matrix(Matrix2f()) );
CALL_SUBTEST_3( array_for_matrix(Matrix4d()) );
CALL_SUBTEST_4( array_for_matrix(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_5( array_for_matrix(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_6( array_for_matrix(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( comparisons(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( comparisons(Matrix2f()) );
CALL_SUBTEST_3( comparisons(Matrix4d()) );
CALL_SUBTEST_5( comparisons(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_6( comparisons(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( cwise_min_max(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( cwise_min_max(Matrix2f()) );
CALL_SUBTEST_3( cwise_min_max(Matrix4d()) );
CALL_SUBTEST_5( cwise_min_max(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_6( cwise_min_max(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( lpNorm(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( lpNorm(Vector2f()) );
CALL_SUBTEST_7( lpNorm(Vector3d()) );
CALL_SUBTEST_8( lpNorm(Vector4f()) );
CALL_SUBTEST_5( lpNorm(VectorXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_4( lpNorm(VectorXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
CALL_SUBTEST_5( lpNorm(VectorXf(0)) );
CALL_SUBTEST_4( lpNorm(VectorXcf(0)) );
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_4( resize(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_5( resize(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_6( resize(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
}
CALL_SUBTEST_6( regression_bug_654<0>() );
CALL_SUBTEST_6( regrrssion_bug_1410<0>() );
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/sizeoverflow.cpp | .cpp | 2,631 | 65 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#define VERIFY_THROWS_BADALLOC(a) { \
bool threw = false; \
try { \
a; \
} \
catch (std::bad_alloc&) { threw = true; } \
VERIFY(threw && "should have thrown bad_alloc: " #a); \
}
template<typename MatrixType>
void triggerMatrixBadAlloc(Index rows, Index cols)
{
VERIFY_THROWS_BADALLOC( MatrixType m(rows, cols) );
VERIFY_THROWS_BADALLOC( MatrixType m; m.resize(rows, cols) );
VERIFY_THROWS_BADALLOC( MatrixType m; m.conservativeResize(rows, cols) );
}
template<typename VectorType>
void triggerVectorBadAlloc(Index size)
{
VERIFY_THROWS_BADALLOC( VectorType v(size) );
VERIFY_THROWS_BADALLOC( VectorType v; v.resize(size) );
VERIFY_THROWS_BADALLOC( VectorType v; v.conservativeResize(size) );
}
void test_sizeoverflow()
{
// there are 2 levels of overflow checking. first in PlainObjectBase.h we check for overflow in rows*cols computations.
// this is tested in tests of the form times_itself_gives_0 * times_itself_gives_0
// Then in Memory.h we check for overflow in size * sizeof(T) computations.
// this is tested in tests of the form times_4_gives_0 * sizeof(float)
size_t times_itself_gives_0 = size_t(1) << (8 * sizeof(Index) / 2);
VERIFY(times_itself_gives_0 * times_itself_gives_0 == 0);
size_t times_4_gives_0 = size_t(1) << (8 * sizeof(Index) - 2);
VERIFY(times_4_gives_0 * 4 == 0);
size_t times_8_gives_0 = size_t(1) << (8 * sizeof(Index) - 3);
VERIFY(times_8_gives_0 * 8 == 0);
triggerMatrixBadAlloc<MatrixXf>(times_itself_gives_0, times_itself_gives_0);
triggerMatrixBadAlloc<MatrixXf>(times_itself_gives_0 / 4, times_itself_gives_0);
triggerMatrixBadAlloc<MatrixXf>(times_4_gives_0, 1);
triggerMatrixBadAlloc<MatrixXd>(times_itself_gives_0, times_itself_gives_0);
triggerMatrixBadAlloc<MatrixXd>(times_itself_gives_0 / 8, times_itself_gives_0);
triggerMatrixBadAlloc<MatrixXd>(times_8_gives_0, 1);
triggerVectorBadAlloc<VectorXf>(times_4_gives_0);
triggerVectorBadAlloc<VectorXd>(times_8_gives_0);
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/bandmatrix.cpp | .cpp | 2,424 | 72 | // This file is triangularView of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename MatrixType> void bandmatrix(const MatrixType& _m)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrixType;
Index rows = _m.rows();
Index cols = _m.cols();
Index supers = _m.supers();
Index subs = _m.subs();
MatrixType m(rows,cols,supers,subs);
DenseMatrixType dm1(rows,cols);
dm1.setZero();
m.diagonal().setConstant(123);
dm1.diagonal().setConstant(123);
for (int i=1; i<=m.supers();++i)
{
m.diagonal(i).setConstant(static_cast<RealScalar>(i));
dm1.diagonal(i).setConstant(static_cast<RealScalar>(i));
}
for (int i=1; i<=m.subs();++i)
{
m.diagonal(-i).setConstant(-static_cast<RealScalar>(i));
dm1.diagonal(-i).setConstant(-static_cast<RealScalar>(i));
}
//std::cerr << m.m_data << "\n\n" << m.toDense() << "\n\n" << dm1 << "\n\n\n\n";
VERIFY_IS_APPROX(dm1,m.toDenseMatrix());
for (int i=0; i<cols; ++i)
{
m.col(i).setConstant(static_cast<RealScalar>(i+1));
dm1.col(i).setConstant(static_cast<RealScalar>(i+1));
}
Index d = (std::min)(rows,cols);
Index a = std::max<Index>(0,cols-d-supers);
Index b = std::max<Index>(0,rows-d-subs);
if(a>0) dm1.block(0,d+supers,rows,a).setZero();
dm1.block(0,supers+1,cols-supers-1-a,cols-supers-1-a).template triangularView<Upper>().setZero();
dm1.block(subs+1,0,rows-subs-1-b,rows-subs-1-b).template triangularView<Lower>().setZero();
if(b>0) dm1.block(d+subs,0,b,cols).setZero();
//std::cerr << m.m_data << "\n\n" << m.toDense() << "\n\n" << dm1 << "\n\n";
VERIFY_IS_APPROX(dm1,m.toDenseMatrix());
}
using Eigen::internal::BandMatrix;
void test_bandmatrix()
{
for(int i = 0; i < 10*g_repeat ; i++) {
Index rows = internal::random<Index>(1,10);
Index cols = internal::random<Index>(1,10);
Index sups = internal::random<Index>(0,cols-1);
Index subs = internal::random<Index>(0,rows-1);
CALL_SUBTEST(bandmatrix(BandMatrix<float>(rows,cols,sups,subs)) );
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/product_syrk.cpp | .cpp | 7,848 | 147 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename MatrixType> void syrk(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime, RowMajor> RMatrixType;
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, Dynamic> Rhs1;
typedef Matrix<Scalar, Dynamic, MatrixType::RowsAtCompileTime> Rhs2;
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, Dynamic,RowMajor> Rhs3;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3 = MatrixType::Random(rows, cols);
RMatrixType rm2 = MatrixType::Random(rows, cols);
Rhs1 rhs1 = Rhs1::Random(internal::random<int>(1,320), cols); Rhs1 rhs11 = Rhs1::Random(rhs1.rows(), cols);
Rhs2 rhs2 = Rhs2::Random(rows, internal::random<int>(1,320)); Rhs2 rhs22 = Rhs2::Random(rows, rhs2.cols());
Rhs3 rhs3 = Rhs3::Random(internal::random<int>(1,320), rows);
Scalar s1 = internal::random<Scalar>();
Index c = internal::random<Index>(0,cols-1);
m2.setZero();
VERIFY_IS_APPROX((m2.template selfadjointView<Lower>().rankUpdate(rhs2,s1)._expression()),
((s1 * rhs2 * rhs2.adjoint()).eval().template triangularView<Lower>().toDenseMatrix()));
m2.setZero();
VERIFY_IS_APPROX(((m2.template triangularView<Lower>() += s1 * rhs2 * rhs22.adjoint()).nestedExpression()),
((s1 * rhs2 * rhs22.adjoint()).eval().template triangularView<Lower>().toDenseMatrix()));
m2.setZero();
VERIFY_IS_APPROX(m2.template selfadjointView<Upper>().rankUpdate(rhs2,s1)._expression(),
(s1 * rhs2 * rhs2.adjoint()).eval().template triangularView<Upper>().toDenseMatrix());
m2.setZero();
VERIFY_IS_APPROX((m2.template triangularView<Upper>() += s1 * rhs22 * rhs2.adjoint()).nestedExpression(),
(s1 * rhs22 * rhs2.adjoint()).eval().template triangularView<Upper>().toDenseMatrix());
m2.setZero();
VERIFY_IS_APPROX(m2.template selfadjointView<Lower>().rankUpdate(rhs1.adjoint(),s1)._expression(),
(s1 * rhs1.adjoint() * rhs1).eval().template triangularView<Lower>().toDenseMatrix());
m2.setZero();
VERIFY_IS_APPROX((m2.template triangularView<Lower>() += s1 * rhs11.adjoint() * rhs1).nestedExpression(),
(s1 * rhs11.adjoint() * rhs1).eval().template triangularView<Lower>().toDenseMatrix());
m2.setZero();
VERIFY_IS_APPROX(m2.template selfadjointView<Upper>().rankUpdate(rhs1.adjoint(),s1)._expression(),
(s1 * rhs1.adjoint() * rhs1).eval().template triangularView<Upper>().toDenseMatrix());
VERIFY_IS_APPROX((m2.template triangularView<Upper>() = s1 * rhs1.adjoint() * rhs11).nestedExpression(),
(s1 * rhs1.adjoint() * rhs11).eval().template triangularView<Upper>().toDenseMatrix());
m2.setZero();
VERIFY_IS_APPROX(m2.template selfadjointView<Lower>().rankUpdate(rhs3.adjoint(),s1)._expression(),
(s1 * rhs3.adjoint() * rhs3).eval().template triangularView<Lower>().toDenseMatrix());
m2.setZero();
VERIFY_IS_APPROX(m2.template selfadjointView<Upper>().rankUpdate(rhs3.adjoint(),s1)._expression(),
(s1 * rhs3.adjoint() * rhs3).eval().template triangularView<Upper>().toDenseMatrix());
m2.setZero();
VERIFY_IS_APPROX((m2.template selfadjointView<Lower>().rankUpdate(m1.col(c),s1)._expression()),
((s1 * m1.col(c) * m1.col(c).adjoint()).eval().template triangularView<Lower>().toDenseMatrix()));
m2.setZero();
VERIFY_IS_APPROX((m2.template selfadjointView<Upper>().rankUpdate(m1.col(c),s1)._expression()),
((s1 * m1.col(c) * m1.col(c).adjoint()).eval().template triangularView<Upper>().toDenseMatrix()));
rm2.setZero();
VERIFY_IS_APPROX((rm2.template selfadjointView<Upper>().rankUpdate(m1.col(c),s1)._expression()),
((s1 * m1.col(c) * m1.col(c).adjoint()).eval().template triangularView<Upper>().toDenseMatrix()));
m2.setZero();
VERIFY_IS_APPROX((m2.template triangularView<Upper>() += s1 * m3.col(c) * m1.col(c).adjoint()).nestedExpression(),
((s1 * m3.col(c) * m1.col(c).adjoint()).eval().template triangularView<Upper>().toDenseMatrix()));
rm2.setZero();
VERIFY_IS_APPROX((rm2.template triangularView<Upper>() += s1 * m1.col(c) * m3.col(c).adjoint()).nestedExpression(),
((s1 * m1.col(c) * m3.col(c).adjoint()).eval().template triangularView<Upper>().toDenseMatrix()));
m2.setZero();
VERIFY_IS_APPROX((m2.template selfadjointView<Lower>().rankUpdate(m1.col(c).conjugate(),s1)._expression()),
((s1 * m1.col(c).conjugate() * m1.col(c).conjugate().adjoint()).eval().template triangularView<Lower>().toDenseMatrix()));
m2.setZero();
VERIFY_IS_APPROX((m2.template selfadjointView<Upper>().rankUpdate(m1.col(c).conjugate(),s1)._expression()),
((s1 * m1.col(c).conjugate() * m1.col(c).conjugate().adjoint()).eval().template triangularView<Upper>().toDenseMatrix()));
m2.setZero();
VERIFY_IS_APPROX((m2.template selfadjointView<Lower>().rankUpdate(m1.row(c),s1)._expression()),
((s1 * m1.row(c).transpose() * m1.row(c).transpose().adjoint()).eval().template triangularView<Lower>().toDenseMatrix()));
rm2.setZero();
VERIFY_IS_APPROX((rm2.template selfadjointView<Lower>().rankUpdate(m1.row(c),s1)._expression()),
((s1 * m1.row(c).transpose() * m1.row(c).transpose().adjoint()).eval().template triangularView<Lower>().toDenseMatrix()));
m2.setZero();
VERIFY_IS_APPROX((m2.template triangularView<Lower>() += s1 * m3.row(c).transpose() * m1.row(c).transpose().adjoint()).nestedExpression(),
((s1 * m3.row(c).transpose() * m1.row(c).transpose().adjoint()).eval().template triangularView<Lower>().toDenseMatrix()));
rm2.setZero();
VERIFY_IS_APPROX((rm2.template triangularView<Lower>() += s1 * m3.row(c).transpose() * m1.row(c).transpose().adjoint()).nestedExpression(),
((s1 * m3.row(c).transpose() * m1.row(c).transpose().adjoint()).eval().template triangularView<Lower>().toDenseMatrix()));
m2.setZero();
VERIFY_IS_APPROX((m2.template selfadjointView<Upper>().rankUpdate(m1.row(c).adjoint(),s1)._expression()),
((s1 * m1.row(c).adjoint() * m1.row(c).adjoint().adjoint()).eval().template triangularView<Upper>().toDenseMatrix()));
// destination with a non-default inner-stride
// see bug 1741
{
typedef Matrix<Scalar,Dynamic,Dynamic> MatrixX;
MatrixX buffer(2*rows,2*cols);
Map<MatrixType,0,Stride<Dynamic,2> > map1(buffer.data(),rows,cols,Stride<Dynamic,2>(2*rows,2));
buffer.setZero();
VERIFY_IS_APPROX((map1.template selfadjointView<Lower>().rankUpdate(rhs2,s1)._expression()),
((s1 * rhs2 * rhs2.adjoint()).eval().template triangularView<Lower>().toDenseMatrix()));
}
}
void test_product_syrk()
{
for(int i = 0; i < g_repeat ; i++)
{
int s;
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
CALL_SUBTEST_1( syrk(MatrixXf(s, s)) );
CALL_SUBTEST_2( syrk(MatrixXd(s, s)) );
TEST_SET_BUT_UNUSED_VARIABLE(s)
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
CALL_SUBTEST_3( syrk(MatrixXcf(s, s)) );
CALL_SUBTEST_4( syrk(MatrixXcd(s, s)) );
TEST_SET_BUT_UNUSED_VARIABLE(s)
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/sparseLM.cpp | .cpp | 4,732 | 177 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include <iostream>
#include <fstream>
#include <iomanip>
#include "main.h"
#include <Eigen/LevenbergMarquardt>
using namespace std;
using namespace Eigen;
template <typename Scalar>
struct sparseGaussianTest : SparseFunctor<Scalar, int>
{
typedef Matrix<Scalar,Dynamic,1> VectorType;
typedef SparseFunctor<Scalar,int> Base;
typedef typename Base::JacobianType JacobianType;
sparseGaussianTest(int inputs, int values) : SparseFunctor<Scalar,int>(inputs,values)
{ }
VectorType model(const VectorType& uv, VectorType& x)
{
VectorType y; //Change this to use expression template
int m = Base::values();
int n = Base::inputs();
eigen_assert(uv.size()%2 == 0);
eigen_assert(uv.size() == n);
eigen_assert(x.size() == m);
y.setZero(m);
int half = n/2;
VectorBlock<const VectorType> u(uv, 0, half);
VectorBlock<const VectorType> v(uv, half, half);
Scalar coeff;
for (int j = 0; j < m; j++)
{
for (int i = 0; i < half; i++)
{
coeff = (x(j)-i)/v(i);
coeff *= coeff;
if (coeff < 1. && coeff > 0.)
y(j) += u(i)*std::pow((1-coeff), 2);
}
}
return y;
}
void initPoints(VectorType& uv_ref, VectorType& x)
{
m_x = x;
m_y = this->model(uv_ref,x);
}
int operator()(const VectorType& uv, VectorType& fvec)
{
int m = Base::values();
int n = Base::inputs();
eigen_assert(uv.size()%2 == 0);
eigen_assert(uv.size() == n);
int half = n/2;
VectorBlock<const VectorType> u(uv, 0, half);
VectorBlock<const VectorType> v(uv, half, half);
fvec = m_y;
Scalar coeff;
for (int j = 0; j < m; j++)
{
for (int i = 0; i < half; i++)
{
coeff = (m_x(j)-i)/v(i);
coeff *= coeff;
if (coeff < 1. && coeff > 0.)
fvec(j) -= u(i)*std::pow((1-coeff), 2);
}
}
return 0;
}
int df(const VectorType& uv, JacobianType& fjac)
{
int m = Base::values();
int n = Base::inputs();
eigen_assert(n == uv.size());
eigen_assert(fjac.rows() == m);
eigen_assert(fjac.cols() == n);
int half = n/2;
VectorBlock<const VectorType> u(uv, 0, half);
VectorBlock<const VectorType> v(uv, half, half);
Scalar coeff;
//Derivatives with respect to u
for (int col = 0; col < half; col++)
{
for (int row = 0; row < m; row++)
{
coeff = (m_x(row)-col)/v(col);
coeff = coeff*coeff;
if(coeff < 1. && coeff > 0.)
{
fjac.coeffRef(row,col) = -(1-coeff)*(1-coeff);
}
}
}
//Derivatives with respect to v
for (int col = 0; col < half; col++)
{
for (int row = 0; row < m; row++)
{
coeff = (m_x(row)-col)/v(col);
coeff = coeff*coeff;
if(coeff < 1. && coeff > 0.)
{
fjac.coeffRef(row,col+half) = -4 * (u(col)/v(col))*coeff*(1-coeff);
}
}
}
return 0;
}
VectorType m_x, m_y; //Data points
};
template<typename T>
void test_sparseLM_T()
{
typedef Matrix<T,Dynamic,1> VectorType;
int inputs = 10;
int values = 2000;
sparseGaussianTest<T> sparse_gaussian(inputs, values);
VectorType uv(inputs),uv_ref(inputs);
VectorType x(values);
// Generate the reference solution
uv_ref << -2, 1, 4 ,8, 6, 1.8, 1.2, 1.1, 1.9 , 3;
//Generate the reference data points
x.setRandom();
x = 10*x;
x.array() += 10;
sparse_gaussian.initPoints(uv_ref, x);
// Generate the initial parameters
VectorBlock<VectorType> u(uv, 0, inputs/2);
VectorBlock<VectorType> v(uv, inputs/2, inputs/2);
v.setOnes();
//Generate u or Solve for u from v
u.setOnes();
// Solve the optimization problem
LevenbergMarquardt<sparseGaussianTest<T> > lm(sparse_gaussian);
int info;
// info = lm.minimize(uv);
VERIFY_IS_EQUAL(info,1);
// Do a step by step solution and save the residual
int maxiter = 200;
int iter = 0;
MatrixXd Err(values, maxiter);
MatrixXd Mod(values, maxiter);
LevenbergMarquardtSpace::Status status;
status = lm.minimizeInit(uv);
if (status==LevenbergMarquardtSpace::ImproperInputParameters)
return ;
}
void test_sparseLM()
{
CALL_SUBTEST_1(test_sparseLM_T<double>());
// CALL_SUBTEST_2(test_sparseLM_T<std::complex<double>());
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/jacobisvd.cpp | .cpp | 5,453 | 143 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
// discard stack allocation as that too bypasses malloc
#define EIGEN_STACK_ALLOCATION_LIMIT 0
#define EIGEN_RUNTIME_NO_MALLOC
#include "main.h"
#include <Eigen/SVD>
#define SVD_DEFAULT(M) JacobiSVD<M>
#define SVD_FOR_MIN_NORM(M) JacobiSVD<M,ColPivHouseholderQRPreconditioner>
#include "svd_common.h"
// Check all variants of JacobiSVD
template<typename MatrixType>
void jacobisvd(const MatrixType& a = MatrixType(), bool pickrandom = true)
{
MatrixType m = a;
if(pickrandom)
svd_fill_random(m);
CALL_SUBTEST(( svd_test_all_computation_options<JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner> >(m, true) )); // check full only
CALL_SUBTEST(( svd_test_all_computation_options<JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner> >(m, false) ));
CALL_SUBTEST(( svd_test_all_computation_options<JacobiSVD<MatrixType, HouseholderQRPreconditioner> >(m, false) ));
if(m.rows()==m.cols())
CALL_SUBTEST(( svd_test_all_computation_options<JacobiSVD<MatrixType, NoQRPreconditioner> >(m, false) ));
}
template<typename MatrixType> void jacobisvd_verify_assert(const MatrixType& m)
{
svd_verify_assert<JacobiSVD<MatrixType> >(m);
Index rows = m.rows();
Index cols = m.cols();
enum {
ColsAtCompileTime = MatrixType::ColsAtCompileTime
};
MatrixType a = MatrixType::Zero(rows, cols);
a.setZero();
if (ColsAtCompileTime == Dynamic)
{
JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner> svd_fullqr;
VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeFullU|ComputeThinV))
VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeThinV))
VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeFullV))
}
}
template<typename MatrixType>
void jacobisvd_method()
{
enum { Size = MatrixType::RowsAtCompileTime };
typedef typename MatrixType::RealScalar RealScalar;
typedef Matrix<RealScalar, Size, 1> RealVecType;
MatrixType m = MatrixType::Identity();
VERIFY_IS_APPROX(m.jacobiSvd().singularValues(), RealVecType::Ones());
VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixU());
VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixV());
VERIFY_IS_APPROX(m.jacobiSvd(ComputeFullU|ComputeFullV).solve(m), m);
}
namespace Foo {
// older compiler require a default constructor for Bar
// cf: https://stackoverflow.com/questions/7411515/
class Bar {public: Bar() {}};
bool operator<(const Bar&, const Bar&) { return true; }
}
// regression test for a very strange MSVC issue for which simply
// including SVDBase.h messes up with std::max and custom scalar type
void msvc_workaround()
{
const Foo::Bar a;
const Foo::Bar b;
std::max EIGEN_NOT_A_MACRO (a,b);
}
void test_jacobisvd()
{
CALL_SUBTEST_3(( jacobisvd_verify_assert(Matrix3f()) ));
CALL_SUBTEST_4(( jacobisvd_verify_assert(Matrix4d()) ));
CALL_SUBTEST_7(( jacobisvd_verify_assert(MatrixXf(10,12)) ));
CALL_SUBTEST_8(( jacobisvd_verify_assert(MatrixXcd(7,5)) ));
CALL_SUBTEST_11(svd_all_trivial_2x2(jacobisvd<Matrix2cd>));
CALL_SUBTEST_12(svd_all_trivial_2x2(jacobisvd<Matrix2d>));
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_3(( jacobisvd<Matrix3f>() ));
CALL_SUBTEST_4(( jacobisvd<Matrix4d>() ));
CALL_SUBTEST_5(( jacobisvd<Matrix<float,3,5> >() ));
CALL_SUBTEST_6(( jacobisvd<Matrix<double,Dynamic,2> >(Matrix<double,Dynamic,2>(10,2)) ));
int r = internal::random<int>(1, 30),
c = internal::random<int>(1, 30);
TEST_SET_BUT_UNUSED_VARIABLE(r)
TEST_SET_BUT_UNUSED_VARIABLE(c)
CALL_SUBTEST_10(( jacobisvd<MatrixXd>(MatrixXd(r,c)) ));
CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(r,c)) ));
CALL_SUBTEST_8(( jacobisvd<MatrixXcd>(MatrixXcd(r,c)) ));
(void) r;
(void) c;
// Test on inf/nan matrix
CALL_SUBTEST_7( (svd_inf_nan<JacobiSVD<MatrixXf>, MatrixXf>()) );
CALL_SUBTEST_10( (svd_inf_nan<JacobiSVD<MatrixXd>, MatrixXd>()) );
// bug1395 test compile-time vectors as input
CALL_SUBTEST_13(( jacobisvd_verify_assert(Matrix<double,6,1>()) ));
CALL_SUBTEST_13(( jacobisvd_verify_assert(Matrix<double,1,6>()) ));
CALL_SUBTEST_13(( jacobisvd_verify_assert(Matrix<double,Dynamic,1>(r)) ));
CALL_SUBTEST_13(( jacobisvd_verify_assert(Matrix<double,1,Dynamic>(c)) ));
}
CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) ));
CALL_SUBTEST_8(( jacobisvd<MatrixXcd>(MatrixXcd(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/3), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/3))) ));
// test matrixbase method
CALL_SUBTEST_1(( jacobisvd_method<Matrix2cd>() ));
CALL_SUBTEST_3(( jacobisvd_method<Matrix3f>() ));
// Test problem size constructors
CALL_SUBTEST_7( JacobiSVD<MatrixXf>(10,10) );
// Check that preallocation avoids subsequent mallocs
CALL_SUBTEST_9( svd_preallocate<void>() );
CALL_SUBTEST_2( svd_underoverflow<void>() );
msvc_workaround();
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/real_qz.cpp | .cpp | 3,094 | 95 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Alexey Korepanov <kaikaikai@yandex.ru>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_RUNTIME_NO_MALLOC
#include "main.h"
#include <limits>
#include <Eigen/Eigenvalues>
template<typename MatrixType> void real_qz(const MatrixType& m)
{
/* this test covers the following files:
RealQZ.h
*/
using std::abs;
typedef typename MatrixType::Scalar Scalar;
Index dim = m.cols();
MatrixType A = MatrixType::Random(dim,dim),
B = MatrixType::Random(dim,dim);
// Regression test for bug 985: Randomly set rows or columns to zero
Index k=internal::random<Index>(0, dim-1);
switch(internal::random<int>(0,10)) {
case 0:
A.row(k).setZero(); break;
case 1:
A.col(k).setZero(); break;
case 2:
B.row(k).setZero(); break;
case 3:
B.col(k).setZero(); break;
default:
break;
}
RealQZ<MatrixType> qz(dim);
// TODO enable full-prealocation of required memory, this probably requires an in-place mode for HessenbergDecomposition
//Eigen::internal::set_is_malloc_allowed(false);
qz.compute(A,B);
//Eigen::internal::set_is_malloc_allowed(true);
VERIFY_IS_EQUAL(qz.info(), Success);
// check for zeros
bool all_zeros = true;
for (Index i=0; i<A.cols(); i++)
for (Index j=0; j<i; j++) {
if (abs(qz.matrixT()(i,j))!=Scalar(0.0))
{
std::cerr << "Error: T(" << i << "," << j << ") = " << qz.matrixT()(i,j) << std::endl;
all_zeros = false;
}
if (j<i-1 && abs(qz.matrixS()(i,j))!=Scalar(0.0))
{
std::cerr << "Error: S(" << i << "," << j << ") = " << qz.matrixS()(i,j) << std::endl;
all_zeros = false;
}
if (j==i-1 && j>0 && abs(qz.matrixS()(i,j))!=Scalar(0.0) && abs(qz.matrixS()(i-1,j-1))!=Scalar(0.0))
{
std::cerr << "Error: S(" << i << "," << j << ") = " << qz.matrixS()(i,j) << " && S(" << i-1 << "," << j-1 << ") = " << qz.matrixS()(i-1,j-1) << std::endl;
all_zeros = false;
}
}
VERIFY_IS_EQUAL(all_zeros, true);
VERIFY_IS_APPROX(qz.matrixQ()*qz.matrixS()*qz.matrixZ(), A);
VERIFY_IS_APPROX(qz.matrixQ()*qz.matrixT()*qz.matrixZ(), B);
VERIFY_IS_APPROX(qz.matrixQ()*qz.matrixQ().adjoint(), MatrixType::Identity(dim,dim));
VERIFY_IS_APPROX(qz.matrixZ()*qz.matrixZ().adjoint(), MatrixType::Identity(dim,dim));
}
void test_real_qz()
{
int s = 0;
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( real_qz(Matrix4f()) );
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4);
CALL_SUBTEST_2( real_qz(MatrixXd(s,s)) );
// some trivial but implementation-wise tricky cases
CALL_SUBTEST_2( real_qz(MatrixXd(1,1)) );
CALL_SUBTEST_2( real_qz(MatrixXd(2,2)) );
CALL_SUBTEST_3( real_qz(Matrix<double,1,1>()) );
CALL_SUBTEST_4( real_qz(Matrix2d()) );
}
TEST_SET_BUT_UNUSED_VARIABLE(s)
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/zerosized.cpp | .cpp | 3,384 | 103 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename MatrixType> void zeroReduction(const MatrixType& m) {
// Reductions that must hold for zero sized objects
VERIFY(m.all());
VERIFY(!m.any());
VERIFY(m.prod()==1);
VERIFY(m.sum()==0);
VERIFY(m.count()==0);
VERIFY(m.allFinite());
VERIFY(!m.hasNaN());
}
template<typename MatrixType> void zeroSizedMatrix()
{
MatrixType t1;
typedef typename MatrixType::Scalar Scalar;
if (MatrixType::SizeAtCompileTime == Dynamic || MatrixType::SizeAtCompileTime == 0)
{
zeroReduction(t1);
if (MatrixType::RowsAtCompileTime == Dynamic)
VERIFY(t1.rows() == 0);
if (MatrixType::ColsAtCompileTime == Dynamic)
VERIFY(t1.cols() == 0);
if (MatrixType::RowsAtCompileTime == Dynamic && MatrixType::ColsAtCompileTime == Dynamic)
{
MatrixType t2(0, 0), t3(t1);
VERIFY(t2.rows() == 0);
VERIFY(t2.cols() == 0);
zeroReduction(t2);
VERIFY(t1==t2);
}
}
if(MatrixType::MaxColsAtCompileTime!=0 && MatrixType::MaxRowsAtCompileTime!=0)
{
Index rows = MatrixType::RowsAtCompileTime==Dynamic ? internal::random<Index>(1,10) : Index(MatrixType::RowsAtCompileTime);
Index cols = MatrixType::ColsAtCompileTime==Dynamic ? internal::random<Index>(1,10) : Index(MatrixType::ColsAtCompileTime);
MatrixType m(rows,cols);
zeroReduction(m.template block<0,MatrixType::ColsAtCompileTime>(0,0,0,cols));
zeroReduction(m.template block<MatrixType::RowsAtCompileTime,0>(0,0,rows,0));
zeroReduction(m.template block<0,1>(0,0));
zeroReduction(m.template block<1,0>(0,0));
Matrix<Scalar,Dynamic,Dynamic> prod = m.template block<MatrixType::RowsAtCompileTime,0>(0,0,rows,0) * m.template block<0,MatrixType::ColsAtCompileTime>(0,0,0,cols);
VERIFY(prod.rows()==rows && prod.cols()==cols);
VERIFY(prod.isZero());
prod = m.template block<1,0>(0,0) * m.template block<0,1>(0,0);
VERIFY(prod.size()==1);
VERIFY(prod.isZero());
}
}
template<typename VectorType> void zeroSizedVector()
{
VectorType t1;
if (VectorType::SizeAtCompileTime == Dynamic || VectorType::SizeAtCompileTime==0)
{
zeroReduction(t1);
VERIFY(t1.size() == 0);
VectorType t2(DenseIndex(0)); // DenseIndex disambiguates with 0-the-null-pointer (error with gcc 4.4 and MSVC8)
VERIFY(t2.size() == 0);
zeroReduction(t2);
VERIFY(t1==t2);
}
}
void test_zerosized()
{
zeroSizedMatrix<Matrix2d>();
zeroSizedMatrix<Matrix3i>();
zeroSizedMatrix<Matrix<float, 2, Dynamic> >();
zeroSizedMatrix<MatrixXf>();
zeroSizedMatrix<Matrix<float, 0, 0> >();
zeroSizedMatrix<Matrix<float, Dynamic, 0, 0, 0, 0> >();
zeroSizedMatrix<Matrix<float, 0, Dynamic, 0, 0, 0> >();
zeroSizedMatrix<Matrix<float, Dynamic, Dynamic, 0, 0, 0> >();
zeroSizedMatrix<Matrix<float, 0, 4> >();
zeroSizedMatrix<Matrix<float, 4, 0> >();
zeroSizedVector<Vector2d>();
zeroSizedVector<Vector3i>();
zeroSizedVector<VectorXf>();
zeroSizedVector<Matrix<float, 0, 1> >();
zeroSizedVector<Matrix<float, 1, 0> >();
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/product_trsolve.cpp | .cpp | 6,094 | 128 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#define VERIFY_TRSM(TRI,XB) { \
(XB).setRandom(); ref = (XB); \
(TRI).solveInPlace(XB); \
VERIFY_IS_APPROX((TRI).toDenseMatrix() * (XB), ref); \
(XB).setRandom(); ref = (XB); \
(XB) = (TRI).solve(XB); \
VERIFY_IS_APPROX((TRI).toDenseMatrix() * (XB), ref); \
}
#define VERIFY_TRSM_ONTHERIGHT(TRI,XB) { \
(XB).setRandom(); ref = (XB); \
(TRI).transpose().template solveInPlace<OnTheRight>(XB.transpose()); \
VERIFY_IS_APPROX((XB).transpose() * (TRI).transpose().toDenseMatrix(), ref.transpose()); \
(XB).setRandom(); ref = (XB); \
(XB).transpose() = (TRI).transpose().template solve<OnTheRight>(XB.transpose()); \
VERIFY_IS_APPROX((XB).transpose() * (TRI).transpose().toDenseMatrix(), ref.transpose()); \
}
template<typename Scalar,int Size, int Cols> void trsolve(int size=Size,int cols=Cols)
{
typedef typename NumTraits<Scalar>::Real RealScalar;
Matrix<Scalar,Size,Size,ColMajor> cmLhs(size,size);
Matrix<Scalar,Size,Size,RowMajor> rmLhs(size,size);
enum { colmajor = Size==1 ? RowMajor : ColMajor,
rowmajor = Cols==1 ? ColMajor : RowMajor };
Matrix<Scalar,Size,Cols,colmajor> cmRhs(size,cols);
Matrix<Scalar,Size,Cols,rowmajor> rmRhs(size,cols);
Matrix<Scalar,Dynamic,Dynamic,colmajor> ref(size,cols);
cmLhs.setRandom(); cmLhs *= static_cast<RealScalar>(0.1); cmLhs.diagonal().array() += static_cast<RealScalar>(1);
rmLhs.setRandom(); rmLhs *= static_cast<RealScalar>(0.1); rmLhs.diagonal().array() += static_cast<RealScalar>(1);
VERIFY_TRSM(cmLhs.conjugate().template triangularView<Lower>(), cmRhs);
VERIFY_TRSM(cmLhs.adjoint() .template triangularView<Lower>(), cmRhs);
VERIFY_TRSM(cmLhs .template triangularView<Upper>(), cmRhs);
VERIFY_TRSM(cmLhs .template triangularView<Lower>(), rmRhs);
VERIFY_TRSM(cmLhs.conjugate().template triangularView<Upper>(), rmRhs);
VERIFY_TRSM(cmLhs.adjoint() .template triangularView<Upper>(), rmRhs);
VERIFY_TRSM(cmLhs.conjugate().template triangularView<UnitLower>(), cmRhs);
VERIFY_TRSM(cmLhs .template triangularView<UnitUpper>(), rmRhs);
VERIFY_TRSM(rmLhs .template triangularView<Lower>(), cmRhs);
VERIFY_TRSM(rmLhs.conjugate().template triangularView<UnitUpper>(), rmRhs);
VERIFY_TRSM_ONTHERIGHT(cmLhs.conjugate().template triangularView<Lower>(), cmRhs);
VERIFY_TRSM_ONTHERIGHT(cmLhs .template triangularView<Upper>(), cmRhs);
VERIFY_TRSM_ONTHERIGHT(cmLhs .template triangularView<Lower>(), rmRhs);
VERIFY_TRSM_ONTHERIGHT(cmLhs.conjugate().template triangularView<Upper>(), rmRhs);
VERIFY_TRSM_ONTHERIGHT(cmLhs.conjugate().template triangularView<UnitLower>(), cmRhs);
VERIFY_TRSM_ONTHERIGHT(cmLhs .template triangularView<UnitUpper>(), rmRhs);
VERIFY_TRSM_ONTHERIGHT(rmLhs .template triangularView<Lower>(), cmRhs);
VERIFY_TRSM_ONTHERIGHT(rmLhs.conjugate().template triangularView<UnitUpper>(), rmRhs);
int c = internal::random<int>(0,cols-1);
VERIFY_TRSM(rmLhs.template triangularView<Lower>(), rmRhs.col(c));
VERIFY_TRSM(cmLhs.template triangularView<Lower>(), rmRhs.col(c));
// destination with a non-default inner-stride
// see bug 1741
{
typedef Matrix<Scalar,Dynamic,Dynamic> MatrixX;
MatrixX buffer(2*cmRhs.rows(),2*cmRhs.cols());
Map<Matrix<Scalar,Size,Cols,colmajor>,0,Stride<Dynamic,2> > map1(buffer.data(),cmRhs.rows(),cmRhs.cols(),Stride<Dynamic,2>(2*cmRhs.outerStride(),2));
Map<Matrix<Scalar,Size,Cols,rowmajor>,0,Stride<Dynamic,2> > map2(buffer.data(),rmRhs.rows(),rmRhs.cols(),Stride<Dynamic,2>(2*rmRhs.outerStride(),2));
buffer.setZero();
VERIFY_TRSM(cmLhs.conjugate().template triangularView<Lower>(), map1);
buffer.setZero();
VERIFY_TRSM(cmLhs .template triangularView<Lower>(), map2);
}
if(Size==Dynamic)
{
cmLhs.resize(0,0);
cmRhs.resize(0,cmRhs.cols());
Matrix<Scalar,Size,Cols,colmajor> res = cmLhs.template triangularView<Lower>().solve(cmRhs);
VERIFY_IS_EQUAL(res.rows(),0);
VERIFY_IS_EQUAL(res.cols(),cmRhs.cols());
res = cmRhs;
cmLhs.template triangularView<Lower>().solveInPlace(res);
VERIFY_IS_EQUAL(res.rows(),0);
VERIFY_IS_EQUAL(res.cols(),cmRhs.cols());
}
}
void test_product_trsolve()
{
for(int i = 0; i < g_repeat ; i++)
{
// matrices
CALL_SUBTEST_1((trsolve<float,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE))));
CALL_SUBTEST_2((trsolve<double,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE))));
CALL_SUBTEST_3((trsolve<std::complex<float>,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2),internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))));
CALL_SUBTEST_4((trsolve<std::complex<double>,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2),internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))));
// vectors
CALL_SUBTEST_5((trsolve<float,Dynamic,1>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))));
CALL_SUBTEST_6((trsolve<double,Dynamic,1>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))));
CALL_SUBTEST_7((trsolve<std::complex<float>,Dynamic,1>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))));
CALL_SUBTEST_8((trsolve<std::complex<double>,Dynamic,1>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))));
// meta-unrollers
CALL_SUBTEST_9((trsolve<float,4,1>()));
CALL_SUBTEST_10((trsolve<double,4,1>()));
CALL_SUBTEST_11((trsolve<std::complex<float>,4,1>()));
CALL_SUBTEST_12((trsolve<float,1,1>()));
CALL_SUBTEST_13((trsolve<float,1,2>()));
CALL_SUBTEST_14((trsolve<float,3,1>()));
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/determinant.cpp | .cpp | 2,267 | 67 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/LU>
template<typename MatrixType> void determinant(const MatrixType& m)
{
/* this test covers the following files:
Determinant.h
*/
Index size = m.rows();
MatrixType m1(size, size), m2(size, size);
m1.setRandom();
m2.setRandom();
typedef typename MatrixType::Scalar Scalar;
Scalar x = internal::random<Scalar>();
VERIFY_IS_APPROX(MatrixType::Identity(size, size).determinant(), Scalar(1));
VERIFY_IS_APPROX((m1*m2).eval().determinant(), m1.determinant() * m2.determinant());
if(size==1) return;
Index i = internal::random<Index>(0, size-1);
Index j;
do {
j = internal::random<Index>(0, size-1);
} while(j==i);
m2 = m1;
m2.row(i).swap(m2.row(j));
VERIFY_IS_APPROX(m2.determinant(), -m1.determinant());
m2 = m1;
m2.col(i).swap(m2.col(j));
VERIFY_IS_APPROX(m2.determinant(), -m1.determinant());
VERIFY_IS_APPROX(m2.determinant(), m2.transpose().determinant());
VERIFY_IS_APPROX(numext::conj(m2.determinant()), m2.adjoint().determinant());
m2 = m1;
m2.row(i) += x*m2.row(j);
VERIFY_IS_APPROX(m2.determinant(), m1.determinant());
m2 = m1;
m2.row(i) *= x;
VERIFY_IS_APPROX(m2.determinant(), m1.determinant() * x);
// check empty matrix
VERIFY_IS_APPROX(m2.block(0,0,0,0).determinant(), Scalar(1));
}
void test_determinant()
{
for(int i = 0; i < g_repeat; i++) {
int s = 0;
CALL_SUBTEST_1( determinant(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( determinant(Matrix<double, 2, 2>()) );
CALL_SUBTEST_3( determinant(Matrix<double, 3, 3>()) );
CALL_SUBTEST_4( determinant(Matrix<double, 4, 4>()) );
CALL_SUBTEST_5( determinant(Matrix<std::complex<double>, 10, 10>()) );
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4);
CALL_SUBTEST_6( determinant(MatrixXd(s, s)) );
TEST_SET_BUT_UNUSED_VARIABLE(s)
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/evaluators.cpp | .cpp | 19,757 | 500 |
#include "main.h"
namespace Eigen {
template<typename Lhs,typename Rhs>
const Product<Lhs,Rhs>
prod(const Lhs& lhs, const Rhs& rhs)
{
return Product<Lhs,Rhs>(lhs,rhs);
}
template<typename Lhs,typename Rhs>
const Product<Lhs,Rhs,LazyProduct>
lazyprod(const Lhs& lhs, const Rhs& rhs)
{
return Product<Lhs,Rhs,LazyProduct>(lhs,rhs);
}
template<typename DstXprType, typename SrcXprType>
EIGEN_STRONG_INLINE
DstXprType& copy_using_evaluator(const EigenBase<DstXprType> &dst, const SrcXprType &src)
{
call_assignment(dst.const_cast_derived(), src.derived(), internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
return dst.const_cast_derived();
}
template<typename DstXprType, template <typename> class StorageBase, typename SrcXprType>
EIGEN_STRONG_INLINE
const DstXprType& copy_using_evaluator(const NoAlias<DstXprType, StorageBase>& dst, const SrcXprType &src)
{
call_assignment(dst, src.derived(), internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
return dst.expression();
}
template<typename DstXprType, typename SrcXprType>
EIGEN_STRONG_INLINE
DstXprType& copy_using_evaluator(const PlainObjectBase<DstXprType> &dst, const SrcXprType &src)
{
#ifdef EIGEN_NO_AUTOMATIC_RESIZING
eigen_assert((dst.size()==0 || (IsVectorAtCompileTime ? (dst.size() == src.size())
: (dst.rows() == src.rows() && dst.cols() == src.cols())))
&& "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined");
#else
dst.const_cast_derived().resizeLike(src.derived());
#endif
call_assignment(dst.const_cast_derived(), src.derived(), internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
return dst.const_cast_derived();
}
template<typename DstXprType, typename SrcXprType>
void add_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src)
{
typedef typename DstXprType::Scalar Scalar;
call_assignment(const_cast<DstXprType&>(dst), src.derived(), internal::add_assign_op<Scalar,typename SrcXprType::Scalar>());
}
template<typename DstXprType, typename SrcXprType>
void subtract_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src)
{
typedef typename DstXprType::Scalar Scalar;
call_assignment(const_cast<DstXprType&>(dst), src.derived(), internal::sub_assign_op<Scalar,typename SrcXprType::Scalar>());
}
template<typename DstXprType, typename SrcXprType>
void multiply_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src)
{
typedef typename DstXprType::Scalar Scalar;
call_assignment(dst.const_cast_derived(), src.derived(), internal::mul_assign_op<Scalar,typename SrcXprType::Scalar>());
}
template<typename DstXprType, typename SrcXprType>
void divide_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src)
{
typedef typename DstXprType::Scalar Scalar;
call_assignment(dst.const_cast_derived(), src.derived(), internal::div_assign_op<Scalar,typename SrcXprType::Scalar>());
}
template<typename DstXprType, typename SrcXprType>
void swap_using_evaluator(const DstXprType& dst, const SrcXprType& src)
{
typedef typename DstXprType::Scalar Scalar;
call_assignment(dst.const_cast_derived(), src.const_cast_derived(), internal::swap_assign_op<Scalar>());
}
namespace internal {
template<typename Dst, template <typename> class StorageBase, typename Src, typename Func>
EIGEN_DEVICE_FUNC void call_assignment(const NoAlias<Dst,StorageBase>& dst, const Src& src, const Func& func)
{
call_assignment_no_alias(dst.expression(), src, func);
}
}
}
template<typename XprType> long get_cost(const XprType& ) { return Eigen::internal::evaluator<XprType>::CoeffReadCost; }
using namespace std;
#define VERIFY_IS_APPROX_EVALUATOR(DEST,EXPR) VERIFY_IS_APPROX(copy_using_evaluator(DEST,(EXPR)), (EXPR).eval());
#define VERIFY_IS_APPROX_EVALUATOR2(DEST,EXPR,REF) VERIFY_IS_APPROX(copy_using_evaluator(DEST,(EXPR)), (REF).eval());
void test_evaluators()
{
// Testing Matrix evaluator and Transpose
Vector2d v = Vector2d::Random();
const Vector2d v_const(v);
Vector2d v2;
RowVector2d w;
VERIFY_IS_APPROX_EVALUATOR(v2, v);
VERIFY_IS_APPROX_EVALUATOR(v2, v_const);
// Testing Transpose
VERIFY_IS_APPROX_EVALUATOR(w, v.transpose()); // Transpose as rvalue
VERIFY_IS_APPROX_EVALUATOR(w, v_const.transpose());
copy_using_evaluator(w.transpose(), v); // Transpose as lvalue
VERIFY_IS_APPROX(w,v.transpose().eval());
copy_using_evaluator(w.transpose(), v_const);
VERIFY_IS_APPROX(w,v_const.transpose().eval());
// Testing Array evaluator
{
ArrayXXf a(2,3);
ArrayXXf b(3,2);
a << 1,2,3, 4,5,6;
const ArrayXXf a_const(a);
VERIFY_IS_APPROX_EVALUATOR(b, a.transpose());
VERIFY_IS_APPROX_EVALUATOR(b, a_const.transpose());
// Testing CwiseNullaryOp evaluator
copy_using_evaluator(w, RowVector2d::Random());
VERIFY((w.array() >= -1).all() && (w.array() <= 1).all()); // not easy to test ...
VERIFY_IS_APPROX_EVALUATOR(w, RowVector2d::Zero());
VERIFY_IS_APPROX_EVALUATOR(w, RowVector2d::Constant(3));
// mix CwiseNullaryOp and transpose
VERIFY_IS_APPROX_EVALUATOR(w, Vector2d::Zero().transpose());
}
{
// test product expressions
int s = internal::random<int>(1,100);
MatrixXf a(s,s), b(s,s), c(s,s), d(s,s);
a.setRandom();
b.setRandom();
c.setRandom();
d.setRandom();
VERIFY_IS_APPROX_EVALUATOR(d, (a + b));
VERIFY_IS_APPROX_EVALUATOR(d, (a + b).transpose());
VERIFY_IS_APPROX_EVALUATOR2(d, prod(a,b), a*b);
VERIFY_IS_APPROX_EVALUATOR2(d.noalias(), prod(a,b), a*b);
VERIFY_IS_APPROX_EVALUATOR2(d, prod(a,b) + c, a*b + c);
VERIFY_IS_APPROX_EVALUATOR2(d, s * prod(a,b), s * a*b);
VERIFY_IS_APPROX_EVALUATOR2(d, prod(a,b).transpose(), (a*b).transpose());
VERIFY_IS_APPROX_EVALUATOR2(d, prod(a,b) + prod(b,c), a*b + b*c);
// check that prod works even with aliasing present
c = a*a;
copy_using_evaluator(a, prod(a,a));
VERIFY_IS_APPROX(a,c);
// check compound assignment of products
d = c;
add_assign_using_evaluator(c.noalias(), prod(a,b));
d.noalias() += a*b;
VERIFY_IS_APPROX(c, d);
d = c;
subtract_assign_using_evaluator(c.noalias(), prod(a,b));
d.noalias() -= a*b;
VERIFY_IS_APPROX(c, d);
}
{
// test product with all possible sizes
int s = internal::random<int>(1,100);
Matrix<float, 1, 1> m11, res11; m11.setRandom(1,1);
Matrix<float, 1, 4> m14, res14; m14.setRandom(1,4);
Matrix<float, 1,Dynamic> m1X, res1X; m1X.setRandom(1,s);
Matrix<float, 4, 1> m41, res41; m41.setRandom(4,1);
Matrix<float, 4, 4> m44, res44; m44.setRandom(4,4);
Matrix<float, 4,Dynamic> m4X, res4X; m4X.setRandom(4,s);
Matrix<float,Dynamic, 1> mX1, resX1; mX1.setRandom(s,1);
Matrix<float,Dynamic, 4> mX4, resX4; mX4.setRandom(s,4);
Matrix<float,Dynamic,Dynamic> mXX, resXX; mXX.setRandom(s,s);
VERIFY_IS_APPROX_EVALUATOR2(res11, prod(m11,m11), m11*m11);
VERIFY_IS_APPROX_EVALUATOR2(res11, prod(m14,m41), m14*m41);
VERIFY_IS_APPROX_EVALUATOR2(res11, prod(m1X,mX1), m1X*mX1);
VERIFY_IS_APPROX_EVALUATOR2(res14, prod(m11,m14), m11*m14);
VERIFY_IS_APPROX_EVALUATOR2(res14, prod(m14,m44), m14*m44);
VERIFY_IS_APPROX_EVALUATOR2(res14, prod(m1X,mX4), m1X*mX4);
VERIFY_IS_APPROX_EVALUATOR2(res1X, prod(m11,m1X), m11*m1X);
VERIFY_IS_APPROX_EVALUATOR2(res1X, prod(m14,m4X), m14*m4X);
VERIFY_IS_APPROX_EVALUATOR2(res1X, prod(m1X,mXX), m1X*mXX);
VERIFY_IS_APPROX_EVALUATOR2(res41, prod(m41,m11), m41*m11);
VERIFY_IS_APPROX_EVALUATOR2(res41, prod(m44,m41), m44*m41);
VERIFY_IS_APPROX_EVALUATOR2(res41, prod(m4X,mX1), m4X*mX1);
VERIFY_IS_APPROX_EVALUATOR2(res44, prod(m41,m14), m41*m14);
VERIFY_IS_APPROX_EVALUATOR2(res44, prod(m44,m44), m44*m44);
VERIFY_IS_APPROX_EVALUATOR2(res44, prod(m4X,mX4), m4X*mX4);
VERIFY_IS_APPROX_EVALUATOR2(res4X, prod(m41,m1X), m41*m1X);
VERIFY_IS_APPROX_EVALUATOR2(res4X, prod(m44,m4X), m44*m4X);
VERIFY_IS_APPROX_EVALUATOR2(res4X, prod(m4X,mXX), m4X*mXX);
VERIFY_IS_APPROX_EVALUATOR2(resX1, prod(mX1,m11), mX1*m11);
VERIFY_IS_APPROX_EVALUATOR2(resX1, prod(mX4,m41), mX4*m41);
VERIFY_IS_APPROX_EVALUATOR2(resX1, prod(mXX,mX1), mXX*mX1);
VERIFY_IS_APPROX_EVALUATOR2(resX4, prod(mX1,m14), mX1*m14);
VERIFY_IS_APPROX_EVALUATOR2(resX4, prod(mX4,m44), mX4*m44);
VERIFY_IS_APPROX_EVALUATOR2(resX4, prod(mXX,mX4), mXX*mX4);
VERIFY_IS_APPROX_EVALUATOR2(resXX, prod(mX1,m1X), mX1*m1X);
VERIFY_IS_APPROX_EVALUATOR2(resXX, prod(mX4,m4X), mX4*m4X);
VERIFY_IS_APPROX_EVALUATOR2(resXX, prod(mXX,mXX), mXX*mXX);
}
{
ArrayXXf a(2,3);
ArrayXXf b(3,2);
a << 1,2,3, 4,5,6;
const ArrayXXf a_const(a);
// this does not work because Random is eval-before-nested:
// copy_using_evaluator(w, Vector2d::Random().transpose());
// test CwiseUnaryOp
VERIFY_IS_APPROX_EVALUATOR(v2, 3 * v);
VERIFY_IS_APPROX_EVALUATOR(w, (3 * v).transpose());
VERIFY_IS_APPROX_EVALUATOR(b, (a + 3).transpose());
VERIFY_IS_APPROX_EVALUATOR(b, (2 * a_const + 3).transpose());
// test CwiseBinaryOp
VERIFY_IS_APPROX_EVALUATOR(v2, v + Vector2d::Ones());
VERIFY_IS_APPROX_EVALUATOR(w, (v + Vector2d::Ones()).transpose().cwiseProduct(RowVector2d::Constant(3)));
// dynamic matrices and arrays
MatrixXd mat1(6,6), mat2(6,6);
VERIFY_IS_APPROX_EVALUATOR(mat1, MatrixXd::Identity(6,6));
VERIFY_IS_APPROX_EVALUATOR(mat2, mat1);
copy_using_evaluator(mat2.transpose(), mat1);
VERIFY_IS_APPROX(mat2.transpose(), mat1);
ArrayXXd arr1(6,6), arr2(6,6);
VERIFY_IS_APPROX_EVALUATOR(arr1, ArrayXXd::Constant(6,6, 3.0));
VERIFY_IS_APPROX_EVALUATOR(arr2, arr1);
// test automatic resizing
mat2.resize(3,3);
VERIFY_IS_APPROX_EVALUATOR(mat2, mat1);
arr2.resize(9,9);
VERIFY_IS_APPROX_EVALUATOR(arr2, arr1);
// test direct traversal
Matrix3f m3;
Array33f a3;
VERIFY_IS_APPROX_EVALUATOR(m3, Matrix3f::Identity()); // matrix, nullary
// TODO: find a way to test direct traversal with array
VERIFY_IS_APPROX_EVALUATOR(m3.transpose(), Matrix3f::Identity().transpose()); // transpose
VERIFY_IS_APPROX_EVALUATOR(m3, 2 * Matrix3f::Identity()); // unary
VERIFY_IS_APPROX_EVALUATOR(m3, Matrix3f::Identity() + Matrix3f::Zero()); // binary
VERIFY_IS_APPROX_EVALUATOR(m3.block(0,0,2,2), Matrix3f::Identity().block(1,1,2,2)); // block
// test linear traversal
VERIFY_IS_APPROX_EVALUATOR(m3, Matrix3f::Zero()); // matrix, nullary
VERIFY_IS_APPROX_EVALUATOR(a3, Array33f::Zero()); // array
VERIFY_IS_APPROX_EVALUATOR(m3.transpose(), Matrix3f::Zero().transpose()); // transpose
VERIFY_IS_APPROX_EVALUATOR(m3, 2 * Matrix3f::Zero()); // unary
VERIFY_IS_APPROX_EVALUATOR(m3, Matrix3f::Zero() + m3); // binary
// test inner vectorization
Matrix4f m4, m4src = Matrix4f::Random();
Array44f a4, a4src = Matrix4f::Random();
VERIFY_IS_APPROX_EVALUATOR(m4, m4src); // matrix
VERIFY_IS_APPROX_EVALUATOR(a4, a4src); // array
VERIFY_IS_APPROX_EVALUATOR(m4.transpose(), m4src.transpose()); // transpose
// TODO: find out why Matrix4f::Zero() does not allow inner vectorization
VERIFY_IS_APPROX_EVALUATOR(m4, 2 * m4src); // unary
VERIFY_IS_APPROX_EVALUATOR(m4, m4src + m4src); // binary
// test linear vectorization
MatrixXf mX(6,6), mXsrc = MatrixXf::Random(6,6);
ArrayXXf aX(6,6), aXsrc = ArrayXXf::Random(6,6);
VERIFY_IS_APPROX_EVALUATOR(mX, mXsrc); // matrix
VERIFY_IS_APPROX_EVALUATOR(aX, aXsrc); // array
VERIFY_IS_APPROX_EVALUATOR(mX.transpose(), mXsrc.transpose()); // transpose
VERIFY_IS_APPROX_EVALUATOR(mX, MatrixXf::Zero(6,6)); // nullary
VERIFY_IS_APPROX_EVALUATOR(mX, 2 * mXsrc); // unary
VERIFY_IS_APPROX_EVALUATOR(mX, mXsrc + mXsrc); // binary
// test blocks and slice vectorization
VERIFY_IS_APPROX_EVALUATOR(m4, (mXsrc.block<4,4>(1,0)));
VERIFY_IS_APPROX_EVALUATOR(aX, ArrayXXf::Constant(10, 10, 3.0).block(2, 3, 6, 6));
Matrix4f m4ref = m4;
copy_using_evaluator(m4.block(1, 1, 2, 3), m3.bottomRows(2));
m4ref.block(1, 1, 2, 3) = m3.bottomRows(2);
VERIFY_IS_APPROX(m4, m4ref);
mX.setIdentity(20,20);
MatrixXf mXref = MatrixXf::Identity(20,20);
mXsrc = MatrixXf::Random(9,12);
copy_using_evaluator(mX.block(4, 4, 9, 12), mXsrc);
mXref.block(4, 4, 9, 12) = mXsrc;
VERIFY_IS_APPROX(mX, mXref);
// test Map
const float raw[3] = {1,2,3};
float buffer[3] = {0,0,0};
Vector3f v3;
Array3f a3f;
VERIFY_IS_APPROX_EVALUATOR(v3, Map<const Vector3f>(raw));
VERIFY_IS_APPROX_EVALUATOR(a3f, Map<const Array3f>(raw));
Vector3f::Map(buffer) = 2*v3;
VERIFY(buffer[0] == 2);
VERIFY(buffer[1] == 4);
VERIFY(buffer[2] == 6);
// test CwiseUnaryView
mat1.setRandom();
mat2.setIdentity();
MatrixXcd matXcd(6,6), matXcd_ref(6,6);
copy_using_evaluator(matXcd.real(), mat1);
copy_using_evaluator(matXcd.imag(), mat2);
matXcd_ref.real() = mat1;
matXcd_ref.imag() = mat2;
VERIFY_IS_APPROX(matXcd, matXcd_ref);
// test Select
VERIFY_IS_APPROX_EVALUATOR(aX, (aXsrc > 0).select(aXsrc, -aXsrc));
// test Replicate
mXsrc = MatrixXf::Random(6, 6);
VectorXf vX = VectorXf::Random(6);
mX.resize(6, 6);
VERIFY_IS_APPROX_EVALUATOR(mX, mXsrc.colwise() + vX);
matXcd.resize(12, 12);
VERIFY_IS_APPROX_EVALUATOR(matXcd, matXcd_ref.replicate(2,2));
VERIFY_IS_APPROX_EVALUATOR(matXcd, (matXcd_ref.replicate<2,2>()));
// test partial reductions
VectorXd vec1(6);
VERIFY_IS_APPROX_EVALUATOR(vec1, mat1.rowwise().sum());
VERIFY_IS_APPROX_EVALUATOR(vec1, mat1.colwise().sum().transpose());
// test MatrixWrapper and ArrayWrapper
mat1.setRandom(6,6);
arr1.setRandom(6,6);
VERIFY_IS_APPROX_EVALUATOR(mat2, arr1.matrix());
VERIFY_IS_APPROX_EVALUATOR(arr2, mat1.array());
VERIFY_IS_APPROX_EVALUATOR(mat2, (arr1 + 2).matrix());
VERIFY_IS_APPROX_EVALUATOR(arr2, mat1.array() + 2);
mat2.array() = arr1 * arr1;
VERIFY_IS_APPROX(mat2, (arr1 * arr1).matrix());
arr2.matrix() = MatrixXd::Identity(6,6);
VERIFY_IS_APPROX(arr2, MatrixXd::Identity(6,6).array());
// test Reverse
VERIFY_IS_APPROX_EVALUATOR(arr2, arr1.reverse());
VERIFY_IS_APPROX_EVALUATOR(arr2, arr1.colwise().reverse());
VERIFY_IS_APPROX_EVALUATOR(arr2, arr1.rowwise().reverse());
arr2.reverse() = arr1;
VERIFY_IS_APPROX(arr2, arr1.reverse());
mat2.array() = mat1.array().reverse();
VERIFY_IS_APPROX(mat2.array(), mat1.array().reverse());
// test Diagonal
VERIFY_IS_APPROX_EVALUATOR(vec1, mat1.diagonal());
vec1.resize(5);
VERIFY_IS_APPROX_EVALUATOR(vec1, mat1.diagonal(1));
VERIFY_IS_APPROX_EVALUATOR(vec1, mat1.diagonal<-1>());
vec1.setRandom();
mat2 = mat1;
copy_using_evaluator(mat1.diagonal(1), vec1);
mat2.diagonal(1) = vec1;
VERIFY_IS_APPROX(mat1, mat2);
copy_using_evaluator(mat1.diagonal<-1>(), mat1.diagonal(1));
mat2.diagonal<-1>() = mat2.diagonal(1);
VERIFY_IS_APPROX(mat1, mat2);
}
{
// test swapping
MatrixXd mat1, mat2, mat1ref, mat2ref;
mat1ref = mat1 = MatrixXd::Random(6, 6);
mat2ref = mat2 = 2 * mat1 + MatrixXd::Identity(6, 6);
swap_using_evaluator(mat1, mat2);
mat1ref.swap(mat2ref);
VERIFY_IS_APPROX(mat1, mat1ref);
VERIFY_IS_APPROX(mat2, mat2ref);
swap_using_evaluator(mat1.block(0, 0, 3, 3), mat2.block(3, 3, 3, 3));
mat1ref.block(0, 0, 3, 3).swap(mat2ref.block(3, 3, 3, 3));
VERIFY_IS_APPROX(mat1, mat1ref);
VERIFY_IS_APPROX(mat2, mat2ref);
swap_using_evaluator(mat1.row(2), mat2.col(3).transpose());
mat1.row(2).swap(mat2.col(3).transpose());
VERIFY_IS_APPROX(mat1, mat1ref);
VERIFY_IS_APPROX(mat2, mat2ref);
}
{
// test compound assignment
const Matrix4d mat_const = Matrix4d::Random();
Matrix4d mat, mat_ref;
mat = mat_ref = Matrix4d::Identity();
add_assign_using_evaluator(mat, mat_const);
mat_ref += mat_const;
VERIFY_IS_APPROX(mat, mat_ref);
subtract_assign_using_evaluator(mat.row(1), 2*mat.row(2));
mat_ref.row(1) -= 2*mat_ref.row(2);
VERIFY_IS_APPROX(mat, mat_ref);
const ArrayXXf arr_const = ArrayXXf::Random(5,3);
ArrayXXf arr, arr_ref;
arr = arr_ref = ArrayXXf::Constant(5, 3, 0.5);
multiply_assign_using_evaluator(arr, arr_const);
arr_ref *= arr_const;
VERIFY_IS_APPROX(arr, arr_ref);
divide_assign_using_evaluator(arr.row(1), arr.row(2) + 1);
arr_ref.row(1) /= (arr_ref.row(2) + 1);
VERIFY_IS_APPROX(arr, arr_ref);
}
{
// test triangular shapes
MatrixXd A = MatrixXd::Random(6,6), B(6,6), C(6,6), D(6,6);
A.setRandom();B.setRandom();
VERIFY_IS_APPROX_EVALUATOR2(B, A.triangularView<Upper>(), MatrixXd(A.triangularView<Upper>()));
A.setRandom();B.setRandom();
VERIFY_IS_APPROX_EVALUATOR2(B, A.triangularView<UnitLower>(), MatrixXd(A.triangularView<UnitLower>()));
A.setRandom();B.setRandom();
VERIFY_IS_APPROX_EVALUATOR2(B, A.triangularView<UnitUpper>(), MatrixXd(A.triangularView<UnitUpper>()));
A.setRandom();B.setRandom();
C = B; C.triangularView<Upper>() = A;
copy_using_evaluator(B.triangularView<Upper>(), A);
VERIFY(B.isApprox(C) && "copy_using_evaluator(B.triangularView<Upper>(), A)");
A.setRandom();B.setRandom();
C = B; C.triangularView<Lower>() = A.triangularView<Lower>();
copy_using_evaluator(B.triangularView<Lower>(), A.triangularView<Lower>());
VERIFY(B.isApprox(C) && "copy_using_evaluator(B.triangularView<Lower>(), A.triangularView<Lower>())");
A.setRandom();B.setRandom();
C = B; C.triangularView<Lower>() = A.triangularView<Upper>().transpose();
copy_using_evaluator(B.triangularView<Lower>(), A.triangularView<Upper>().transpose());
VERIFY(B.isApprox(C) && "copy_using_evaluator(B.triangularView<Lower>(), A.triangularView<Lower>().transpose())");
A.setRandom();B.setRandom(); C = B; D = A;
C.triangularView<Upper>().swap(D.triangularView<Upper>());
swap_using_evaluator(B.triangularView<Upper>(), A.triangularView<Upper>());
VERIFY(B.isApprox(C) && "swap_using_evaluator(B.triangularView<Upper>(), A.triangularView<Upper>())");
VERIFY_IS_APPROX_EVALUATOR2(B, prod(A.triangularView<Upper>(),A), MatrixXd(A.triangularView<Upper>()*A));
VERIFY_IS_APPROX_EVALUATOR2(B, prod(A.selfadjointView<Upper>(),A), MatrixXd(A.selfadjointView<Upper>()*A));
}
{
// test diagonal shapes
VectorXd d = VectorXd::Random(6);
MatrixXd A = MatrixXd::Random(6,6), B(6,6);
A.setRandom();B.setRandom();
VERIFY_IS_APPROX_EVALUATOR2(B, lazyprod(d.asDiagonal(),A), MatrixXd(d.asDiagonal()*A));
VERIFY_IS_APPROX_EVALUATOR2(B, lazyprod(A,d.asDiagonal()), MatrixXd(A*d.asDiagonal()));
}
{
// test CoeffReadCost
Matrix4d a, b;
VERIFY_IS_EQUAL( get_cost(a), 1 );
VERIFY_IS_EQUAL( get_cost(a+b), 3);
VERIFY_IS_EQUAL( get_cost(2*a+b), 4);
VERIFY_IS_EQUAL( get_cost(a*b), 1);
VERIFY_IS_EQUAL( get_cost(a.lazyProduct(b)), 15);
VERIFY_IS_EQUAL( get_cost(a*(a*b)), 1);
VERIFY_IS_EQUAL( get_cost(a.lazyProduct(a*b)), 15);
VERIFY_IS_EQUAL( get_cost(a*(a+b)), 1);
VERIFY_IS_EQUAL( get_cost(a.lazyProduct(a+b)), 15);
}
}
| C++ |
2D | JaeHyunLee94/mpm2d | external/eigen-3.3.9/test/lu.cpp | .cpp | 9,867 | 284 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
#include <Eigen/LU>
using namespace std;
template<typename MatrixType>
typename MatrixType::RealScalar matrix_l1_norm(const MatrixType& m) {
return m.cwiseAbs().colwise().sum().maxCoeff();
}
template<typename MatrixType> void lu_non_invertible()
{
typedef typename MatrixType::RealScalar RealScalar;
/* this test covers the following files:
LU.h
*/
Index rows, cols, cols2;
if(MatrixType::RowsAtCompileTime==Dynamic)
{
rows = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE);
}
else
{
rows = MatrixType::RowsAtCompileTime;
}
if(MatrixType::ColsAtCompileTime==Dynamic)
{
cols = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE);
cols2 = internal::random<int>(2,EIGEN_TEST_MAX_SIZE);
}
else
{
cols2 = cols = MatrixType::ColsAtCompileTime;
}
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime
};
typedef typename internal::kernel_retval_base<FullPivLU<MatrixType> >::ReturnType KernelMatrixType;
typedef typename internal::image_retval_base<FullPivLU<MatrixType> >::ReturnType ImageMatrixType;
typedef Matrix<typename MatrixType::Scalar, ColsAtCompileTime, ColsAtCompileTime>
CMatrixType;
typedef Matrix<typename MatrixType::Scalar, RowsAtCompileTime, RowsAtCompileTime>
RMatrixType;
Index rank = internal::random<Index>(1, (std::min)(rows, cols)-1);
// The image of the zero matrix should consist of a single (zero) column vector
VERIFY((MatrixType::Zero(rows,cols).fullPivLu().image(MatrixType::Zero(rows,cols)).cols() == 1));
// The kernel of the zero matrix is the entire space, and thus is an invertible matrix of dimensions cols.
KernelMatrixType kernel = MatrixType::Zero(rows,cols).fullPivLu().kernel();
VERIFY((kernel.fullPivLu().isInvertible()));
MatrixType m1(rows, cols), m3(rows, cols2);
CMatrixType m2(cols, cols2);
createRandomPIMatrixOfRank(rank, rows, cols, m1);
FullPivLU<MatrixType> lu;
// The special value 0.01 below works well in tests. Keep in mind that we're only computing the rank
// of singular values are either 0 or 1.
// So it's not clear at all that the epsilon should play any role there.
lu.setThreshold(RealScalar(0.01));
lu.compute(m1);
MatrixType u(rows,cols);
u = lu.matrixLU().template triangularView<Upper>();
RMatrixType l = RMatrixType::Identity(rows,rows);
l.block(0,0,rows,(std::min)(rows,cols)).template triangularView<StrictlyLower>()
= lu.matrixLU().block(0,0,rows,(std::min)(rows,cols));
VERIFY_IS_APPROX(lu.permutationP() * m1 * lu.permutationQ(), l*u);
KernelMatrixType m1kernel = lu.kernel();
ImageMatrixType m1image = lu.image(m1);
VERIFY_IS_APPROX(m1, lu.reconstructedMatrix());
VERIFY(rank == lu.rank());
VERIFY(cols - lu.rank() == lu.dimensionOfKernel());
VERIFY(!lu.isInjective());
VERIFY(!lu.isInvertible());
VERIFY(!lu.isSurjective());
VERIFY_IS_MUCH_SMALLER_THAN((m1 * m1kernel), m1);
VERIFY(m1image.fullPivLu().rank() == rank);
VERIFY_IS_APPROX(m1 * m1.adjoint() * m1image, m1image);
m2 = CMatrixType::Random(cols,cols2);
m3 = m1*m2;
m2 = CMatrixType::Random(cols,cols2);
// test that the code, which does resize(), may be applied to an xpr
m2.block(0,0,m2.rows(),m2.cols()) = lu.solve(m3);
VERIFY_IS_APPROX(m3, m1*m2);
// test solve with transposed
m3 = MatrixType::Random(rows,cols2);
m2 = m1.transpose()*m3;
m3 = MatrixType::Random(rows,cols2);
lu.template _solve_impl_transposed<false>(m2, m3);
VERIFY_IS_APPROX(m2, m1.transpose()*m3);
m3 = MatrixType::Random(rows,cols2);
m3 = lu.transpose().solve(m2);
VERIFY_IS_APPROX(m2, m1.transpose()*m3);
// test solve with conjugate transposed
m3 = MatrixType::Random(rows,cols2);
m2 = m1.adjoint()*m3;
m3 = MatrixType::Random(rows,cols2);
lu.template _solve_impl_transposed<true>(m2, m3);
VERIFY_IS_APPROX(m2, m1.adjoint()*m3);
m3 = MatrixType::Random(rows,cols2);
m3 = lu.adjoint().solve(m2);
VERIFY_IS_APPROX(m2, m1.adjoint()*m3);
}
template<typename MatrixType> void lu_invertible()
{
/* this test covers the following files:
LU.h
*/
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
Index size = MatrixType::RowsAtCompileTime;
if( size==Dynamic)
size = internal::random<Index>(1,EIGEN_TEST_MAX_SIZE);
MatrixType m1(size, size), m2(size, size), m3(size, size);
FullPivLU<MatrixType> lu;
lu.setThreshold(RealScalar(0.01));
do {
m1 = MatrixType::Random(size,size);
lu.compute(m1);
} while(!lu.isInvertible());
VERIFY_IS_APPROX(m1, lu.reconstructedMatrix());
VERIFY(0 == lu.dimensionOfKernel());
VERIFY(lu.kernel().cols() == 1); // the kernel() should consist of a single (zero) column vector
VERIFY(size == lu.rank());
VERIFY(lu.isInjective());
VERIFY(lu.isSurjective());
VERIFY(lu.isInvertible());
VERIFY(lu.image(m1).fullPivLu().isInvertible());
m3 = MatrixType::Random(size,size);
m2 = lu.solve(m3);
VERIFY_IS_APPROX(m3, m1*m2);
MatrixType m1_inverse = lu.inverse();
VERIFY_IS_APPROX(m2, m1_inverse*m3);
RealScalar rcond = (RealScalar(1) / matrix_l1_norm(m1)) / matrix_l1_norm(m1_inverse);
const RealScalar rcond_est = lu.rcond();
// Verify that the estimated condition number is within a factor of 10 of the
// truth.
VERIFY(rcond_est > rcond / 10 && rcond_est < rcond * 10);
// test solve with transposed
lu.template _solve_impl_transposed<false>(m3, m2);
VERIFY_IS_APPROX(m3, m1.transpose()*m2);
m3 = MatrixType::Random(size,size);
m3 = lu.transpose().solve(m2);
VERIFY_IS_APPROX(m2, m1.transpose()*m3);
// test solve with conjugate transposed
lu.template _solve_impl_transposed<true>(m3, m2);
VERIFY_IS_APPROX(m3, m1.adjoint()*m2);
m3 = MatrixType::Random(size,size);
m3 = lu.adjoint().solve(m2);
VERIFY_IS_APPROX(m2, m1.adjoint()*m3);
// Regression test for Bug 302
MatrixType m4 = MatrixType::Random(size,size);
VERIFY_IS_APPROX(lu.solve(m3*m4), lu.solve(m3)*m4);
}
template<typename MatrixType> void lu_partial_piv()
{
/* this test covers the following files:
PartialPivLU.h
*/
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
Index size = internal::random<Index>(1,4);
MatrixType m1(size, size), m2(size, size), m3(size, size);
m1.setRandom();
PartialPivLU<MatrixType> plu(m1);
VERIFY_IS_APPROX(m1, plu.reconstructedMatrix());
m3 = MatrixType::Random(size,size);
m2 = plu.solve(m3);
VERIFY_IS_APPROX(m3, m1*m2);
MatrixType m1_inverse = plu.inverse();
VERIFY_IS_APPROX(m2, m1_inverse*m3);
RealScalar rcond = (RealScalar(1) / matrix_l1_norm(m1)) / matrix_l1_norm(m1_inverse);
const RealScalar rcond_est = plu.rcond();
// Verify that the estimate is within a factor of 10 of the truth.
VERIFY(rcond_est > rcond / 10 && rcond_est < rcond * 10);
// test solve with transposed
plu.template _solve_impl_transposed<false>(m3, m2);
VERIFY_IS_APPROX(m3, m1.transpose()*m2);
m3 = MatrixType::Random(size,size);
m3 = plu.transpose().solve(m2);
VERIFY_IS_APPROX(m2, m1.transpose()*m3);
// test solve with conjugate transposed
plu.template _solve_impl_transposed<true>(m3, m2);
VERIFY_IS_APPROX(m3, m1.adjoint()*m2);
m3 = MatrixType::Random(size,size);
m3 = plu.adjoint().solve(m2);
VERIFY_IS_APPROX(m2, m1.adjoint()*m3);
}
template<typename MatrixType> void lu_verify_assert()
{
MatrixType tmp;
FullPivLU<MatrixType> lu;
VERIFY_RAISES_ASSERT(lu.matrixLU())
VERIFY_RAISES_ASSERT(lu.permutationP())
VERIFY_RAISES_ASSERT(lu.permutationQ())
VERIFY_RAISES_ASSERT(lu.kernel())
VERIFY_RAISES_ASSERT(lu.image(tmp))
VERIFY_RAISES_ASSERT(lu.solve(tmp))
VERIFY_RAISES_ASSERT(lu.determinant())
VERIFY_RAISES_ASSERT(lu.rank())
VERIFY_RAISES_ASSERT(lu.dimensionOfKernel())
VERIFY_RAISES_ASSERT(lu.isInjective())
VERIFY_RAISES_ASSERT(lu.isSurjective())
VERIFY_RAISES_ASSERT(lu.isInvertible())
VERIFY_RAISES_ASSERT(lu.inverse())
PartialPivLU<MatrixType> plu;
VERIFY_RAISES_ASSERT(plu.matrixLU())
VERIFY_RAISES_ASSERT(plu.permutationP())
VERIFY_RAISES_ASSERT(plu.solve(tmp))
VERIFY_RAISES_ASSERT(plu.determinant())
VERIFY_RAISES_ASSERT(plu.inverse())
}
void test_lu()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( lu_non_invertible<Matrix3f>() );
CALL_SUBTEST_1( lu_invertible<Matrix3f>() );
CALL_SUBTEST_1( lu_verify_assert<Matrix3f>() );
CALL_SUBTEST_2( (lu_non_invertible<Matrix<double, 4, 6> >()) );
CALL_SUBTEST_2( (lu_verify_assert<Matrix<double, 4, 6> >()) );
CALL_SUBTEST_3( lu_non_invertible<MatrixXf>() );
CALL_SUBTEST_3( lu_invertible<MatrixXf>() );
CALL_SUBTEST_3( lu_verify_assert<MatrixXf>() );
CALL_SUBTEST_4( lu_non_invertible<MatrixXd>() );
CALL_SUBTEST_4( lu_invertible<MatrixXd>() );
CALL_SUBTEST_4( lu_partial_piv<MatrixXd>() );
CALL_SUBTEST_4( lu_verify_assert<MatrixXd>() );
CALL_SUBTEST_5( lu_non_invertible<MatrixXcf>() );
CALL_SUBTEST_5( lu_invertible<MatrixXcf>() );
CALL_SUBTEST_5( lu_verify_assert<MatrixXcf>() );
CALL_SUBTEST_6( lu_non_invertible<MatrixXcd>() );
CALL_SUBTEST_6( lu_invertible<MatrixXcd>() );
CALL_SUBTEST_6( lu_partial_piv<MatrixXcd>() );
CALL_SUBTEST_6( lu_verify_assert<MatrixXcd>() );
CALL_SUBTEST_7(( lu_non_invertible<Matrix<float,Dynamic,16> >() ));
// Test problem size constructors
CALL_SUBTEST_9( PartialPivLU<MatrixXf>(10) );
CALL_SUBTEST_9( FullPivLU<MatrixXf>(10, 20); );
}
}
| C++ |
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