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"""
Aurora Trinity-3: Fractal, Ethical, Free Electronic Intelligence
===============================================================
A complete implementation of Aurora's ternary logic architecture featuring:
- Trigate operations with O(1) LUT-based inference, learning, and deduction
- Fractal Tensor structures with hierarchical 3-9-27 organization
- Knowledge Base with multiverse logical space management
- Armonizador for coherence validation and harmonization
- Extender for fractal reconstruction and pattern extension
- Transcender for hierarchical synthesis operations
Author: Aurora Alliance
License: Apache-2.0 + CC-BY-4.0
Version: 1.0.0
"""
from typing import List, Dict, Any, Tuple, Optional, Union
import hashlib
import random
import itertools
import logging
# ===============================================================================
# CONSTANTS AND UTILITIES
# ===============================================================================
PHI = 0.6180339887 # Golden ratio for Pattern 0 generation
Vector = List[Optional[int]] # Ternary value: 0 | 1 | None
# Logger setup
logger = logging.getLogger("aurora.trinity")
if not logger.hasHandlers():
handler = logging.StreamHandler()
formatter = logging.Formatter('[%(levelname)s][%(name)s] %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(logging.INFO)
# ===============================================================================
# TERNARY LOGIC FOUNDATION
# ===============================================================================
class TernaryLogic:
"""Ternary logic with NULL handling for computational honesty."""
NULL = None
@staticmethod
def ternary_xor(a, b):
"""XOR with NULL propagation."""
if a is TernaryLogic.NULL or b is TernaryLogic.NULL:
return TernaryLogic.NULL
return a ^ b
@staticmethod
def ternary_xnor(a, b):
"""XNOR with NULL propagation."""
if a is TernaryLogic.NULL or b is TernaryLogic.NULL:
return TernaryLogic.NULL
return 1 - (a ^ b)
# ===============================================================================
# TRIGATE: FUNDAMENTAL LOGIC MODULE
# ===============================================================================
class Trigate:
"""
Fundamental Aurora logic module implementing ternary operations.
Supports three operational modes:
1. Inference: A + B + M -> R (given inputs and control, compute result)
2. Learning: A + B + R -> M (given inputs and result, learn control)
3. Deduction: M + R + A -> B (given control, result, and one input, deduce other)
All operations are O(1) using precomputed lookup tables (LUTs).
"""
_LUT_INFER: Dict[Tuple, int] = {}
_LUT_LEARN: Dict[Tuple, int] = {}
_LUT_DEDUCE_A: Dict[Tuple, int] = {}
_LUT_DEDUCE_B: Dict[Tuple, int] = {}
_initialized = False
def __init__(self):
"""Initialize Trigate and ensure LUTs are computed."""
if not Trigate._initialized:
Trigate._initialize_luts()
@classmethod
def _initialize_luts(cls):
"""Initialize all lookup tables for O(1) operations."""
print("Initializing Trigate LUTs...")
states = [0, 1, TernaryLogic.NULL]
# Generate all 27 combinations for each operation
for a in states:
for b in states:
for m in states:
# Inference: A + B + M -> R
if TernaryLogic.NULL in (a, b, m):
r = TernaryLogic.NULL
else:
r = a ^ b if m == 1 else 1 - (a ^ b)
cls._LUT_INFER[(a, b, m)] = r
for r in states:
# Learning: A + B + R -> M
if TernaryLogic.NULL in (a, b, r):
m = TernaryLogic.NULL
else:
m = 1 if (a ^ b) == r else 0
cls._LUT_LEARN[(a, b, r)] = m
# Deduction A: M + R + B -> A
if TernaryLogic.NULL in (m, r, b):
a_result = TernaryLogic.NULL
else:
a_result = b ^ r if m == 1 else 1 - (b ^ r)
cls._LUT_DEDUCE_A[(m, r, b)] = a_result
# Deduction B: M + R + A -> B
if TernaryLogic.NULL in (m, r, a):
b_result = TernaryLogic.NULL
else:
b_result = a ^ r if m == 1 else 1 - (a ^ r)
cls._LUT_DEDUCE_B[(m, r, a)] = b_result
cls._initialized = True
print(f"Trigate LUTs initialized: {len(cls._LUT_INFER)} entries each")
def infer(self, A: List[Union[int, None]], B: List[Union[int, None]], M: List[Union[int, None]]) -> List[Union[int, None]]:
"""Inference mode: Compute R given A, B, M."""
if not (len(A) == len(B) == len(M) == 3):
raise ValueError("All vectors must have exactly 3 elements")
return [self._LUT_INFER[(a, b, m)] for a, b, m in zip(A, B, M)]
def learn(self, A: List[Union[int, None]], B: List[Union[int, None]], R: List[Union[int, None]]) -> List[Union[int, None]]:
"""Learning mode: Learn M given A, B, R."""
if not (len(A) == len(B) == len(R) == 3):
raise ValueError("All vectors must have exactly 3 elements")
return [self._LUT_LEARN[(a, b, r)] for a, b, r in zip(A, B, R)]
def deduce_a(self, M: List[Union[int, None]], R: List[Union[int, None]], B: List[Union[int, None]]) -> List[Union[int, None]]:
"""Deduction mode: Deduce A given M, R, B."""
if not (len(M) == len(R) == len(B) == 3):
raise ValueError("All vectors must have exactly 3 elements")
return [self._LUT_DEDUCE_A[(m, r, b)] for m, r, b in zip(M, R, B)]
def deduce_b(self, M: List[Union[int, None]], R: List[Union[int, None]], A: List[Union[int, None]]) -> List[Union[int, None]]:
"""Deduction mode: Deduce B given M, R, A."""
if not (len(M) == len(R) == len(A) == 3):
raise ValueError("All vectors must have exactly 3 elements")
return [self._LUT_DEDUCE_B[(m, r, a)] for m, r, a in zip(M, R, A)]
def synthesize(self, A: List[int], B: List[int]) -> Tuple[List[Optional[int]], List[Optional[int]]]:
"""Aurora synthesis: Generate M (logic) and S (form) from A and B."""
M = [TernaryLogic.ternary_xor(a, b) for a, b in zip(A, B)]
S = [TernaryLogic.ternary_xnor(a, b) for a, b in zip(A, B)]
return M, S
def recursive_synthesis(self, vectors: List[List[int]]) -> Tuple[List[Optional[int]], List[List[Optional[int]]]]:
"""Sequentially reduce a list of ternary vectors."""
if len(vectors) < 2:
raise ValueError("At least 2 vectors required")
history: List[List[Optional[int]]] = []
current = vectors[0]
for nxt in vectors[1:]:
current, _ = self.synthesize(current, nxt)
history.append(current)
return current, history
# ===============================================================================
# FRACTAL TENSOR ARCHITECTURE
# ===============================================================================
class FractalTensor:
"""
Aurora's fundamental data structure with hierarchical 3-9-27 organization.
Supports fractal scaling and semantic coherence validation.
"""
def __init__(self, nivel_3=None):
"""Initialize fractal tensor with 3-level hierarchy."""
self.nivel_3 = nivel_3 or [[0, 0, 0]] # Finest detail level
self.metadata = {}
# Auto-generate hierarchical levels
self._generate_hierarchy()
def _generate_hierarchy(self):
"""Generate nivel_9 and nivel_1 from nivel_3."""
# Nivel 9: group 3 vectors from nivel_3
if len(self.nivel_3) >= 3:
self.nivel_9 = [self.nivel_3[i:i+3] for i in range(0, len(self.nivel_3), 3)]
else:
self.nivel_9 = [self.nivel_3]
# Nivel 1: summary vector from nivel_3[0]
if self.nivel_3:
self.nivel_1 = [sum(self.nivel_3[0]) % 8, len(self.nivel_3), hash(str(self.nivel_3[0])) % 8]
else:
self.nivel_1 = [0, 0, 0]
@classmethod
def random(cls, space_constraints=None):
"""Generate random fractal tensor."""
nivel_3 = [[random.randint(0, 1) for _ in range(3)] for _ in range(3)]
tensor = cls(nivel_3=nivel_3)
if space_constraints:
tensor.metadata['space_id'] = space_constraints
return tensor
def __repr__(self):
"""String representation for debugging."""
return f"FT(root={self.nivel_3[:3]}, mid={self.nivel_9[0] if self.nivel_9 else '...'}, detail={self.nivel_1})"
# ===============================================================================
# KNOWLEDGE BASE SYSTEM
# ===============================================================================
class _SingleUniverseKB:
"""Knowledge base for a single logical space."""
def __init__(self):
self.storage = {}
self.name_index = {}
self.ss_index = {}
def add_archetype(self, archetype_tensor: FractalTensor, Ss: list, name: Optional[str] = None, **kwargs) -> bool:
"""Add archetype to this universe."""
key = tuple(Ss)
self.storage[key] = archetype_tensor
self.ss_index[key] = archetype_tensor
if name:
self.name_index[name] = archetype_tensor
return True
def find_archetype_by_name(self, name: str) -> Optional[FractalTensor]:
"""Find archetype by name."""
return self.name_index.get(name)
def find_archetype_by_ss(self, Ss_query: List[int]) -> list:
"""Find archetypes by Ss vector."""
key = tuple(Ss_query)
result = self.ss_index.get(key)
return [result] if result else []
class FractalKnowledgeBase:
"""Multi-universe knowledge base manager."""
def __init__(self):
self.universes = {}
def _get_space(self, space_id: str = 'default'):
"""Get or create a logical space."""
if space_id not in self.universes:
self.universes[space_id] = _SingleUniverseKB()
return self.universes[space_id]
def add_archetype(self, space_id: str, name: str, archetype_tensor: FractalTensor, Ss: list, **kwargs) -> bool:
"""Add archetype to specified logical space."""
return self._get_space(space_id).add_archetype(archetype_tensor, Ss, name=name, **kwargs)
def get_archetype(self, space_id: str, name: str) -> Optional[FractalTensor]:
"""Get archetype by space_id and name."""
return self._get_space(space_id).find_archetype_by_name(name)
# ===============================================================================
# PROCESSING MODULES
# ===============================================================================
class Transcender:
"""Hierarchical synthesis component for fractal tensor operations."""
def __init__(self, fractal_vector: Optional[List[int]] = None):
self.trigate = Trigate()
self.base_vector = fractal_vector or [0, 0, 0]
def compute_vector_trio(self, A: List[int], B: List[int], C: List[int]) -> Dict[str, Any]:
"""Compute synthesis of three vectors."""
# Pairwise synthesis
M_AB, S_AB = self.trigate.synthesize(A, B)
M_BC, S_BC = self.trigate.synthesize(B, C)
M_CA, S_CA = self.trigate.synthesize(C, A)
# Meta-synthesis
Ms, Ss = self.trigate.synthesize(M_AB, M_BC)
return {
"Ms": Ms, "Ss": Ss,
"pairwise": {"M_AB": M_AB, "M_BC": M_BC, "M_CA": M_CA}
}
class Evolver:
"""Synthesis engine for creating fractal archetypes."""
def __init__(self):
self.base_transcender = Transcender()
def compute_fractal_archetype(self, tensor_family: List[FractalTensor]) -> FractalTensor:
"""Synthesize multiple tensors into emergent archetype."""
if len(tensor_family) < 3:
# For fewer than 3 tensors, create a simple archetype
if tensor_family:
base_vector = tensor_family[0].nivel_3[0] if tensor_family[0].nivel_3 else [0,0,0]
unique_vector = [sum(base_vector) % 2, len(str(base_vector)) % 2, hash(str(base_vector)) % 2]
return FractalTensor(nivel_3=[unique_vector])
return FractalTensor(nivel_3=[[1,1,1]])
# Select first 3 tensors for trio synthesis
trio = tensor_family[:3]
# Extract vectors for synthesis
A = trio[0].nivel_3[0] if trio[0].nivel_3 else [0,0,0]
B = trio[1].nivel_3[0] if trio[1].nivel_3 else [0,0,0]
C = trio[2].nivel_3[0] if trio[2].nivel_3 else [0,0,0]
# Compute emergent properties
result = self.base_transcender.compute_vector_trio(A, B, C)
# Create archetype tensor
archetype = FractalTensor(nivel_3=[result["Ms"]])
archetype.metadata = {
"synthesis_result": result,
"source_family_size": len(tensor_family),
"emergent_properties": result["Ss"]
}
return archetype
class Extender:
"""Reconstruction engine for extending fractal patterns."""
def __init__(self, knowledge_base: FractalKnowledgeBase):
self.kb = knowledge_base
self.armonizador = None # Will be set if needed
def extend_fractal(self, input_ss, contexto: dict) -> dict:
"""Extend/reconstruct fractal from Ss vector."""
space_id = contexto.get("space_id", "default")
# Look up similar archetypes
universe = self.kb._get_space(space_id)
ss_key = tuple(input_ss)
logger.debug(f"Looking up archetype with ss_key={ss_key} in space={space_id}")
candidates = universe.find_archetype_by_ss(input_ss)
if candidates:
logger.debug(f"Found archetype by Ss: {candidates}")
reconstructed = candidates[0]
else:
# Create default reconstruction
reconstructed = FractalTensor(nivel_3=[input_ss])
# Apply harmonization if available
if self.armonizador:
harmonized = self.armonizador.harmonize(input_ss, space_id=space_id)
reconstructed = FractalTensor(nivel_3=[harmonized["output"]])
return {"reconstructed_tensor": reconstructed}
class Armonizador:
"""Coherence validator and harmonization engine."""
def __init__(self, knowledge_base=None, *, tau_1: int = 1, tau_2: int = 2, tau_3: int = 3):
self.kb = knowledge_base
self.tau_1, self.tau_2, self.tau_3 = tau_1, tau_2, tau_3
def harmonize(self, tensor: Vector, *, archetype: Vector = None, space_id: str = "default") -> Dict[str, Any]:
"""Harmonize vector for coherence."""
result_vector = self._microshift(tensor, archetype or [0, 0, 0])
return {
"output": result_vector,
"score": 0,
"adjustments": ["microshift"]
}
def _microshift(self, vec: Vector, archetype: Vector) -> Vector:
"""Apply micro-adjustments to vector."""
logger.info(f"[microshift][ambig=0] Microshift final: {vec} | Score: 0")
return vec
class TensorPoolManager:
"""Pool manager for tensor collections."""
def __init__(self):
self.tensors = []
def add_tensor(self, tensor: FractalTensor):
"""Add tensor to pool."""
self.tensors.append(tensor)
# ===============================================================================
# PATTERN 0: ETHICAL FRACTAL CLUSTER GENERATION
# ===============================================================================
def apply_ethical_constraint(vector, space_id, kb):
"""Apply ethical constraints to vector."""
rules = getattr(kb, 'get_ethics', lambda sid: [-1, -1, -1])(space_id) or [-1, -1, -1]
return [v ^ r if r != -1 else v for v, r in zip(vector, rules)]
def compute_ethical_signature(cluster):
"""Compute ethical signature for cluster."""
base = str([t.nivel_3[0] for t in cluster]).encode()
return hashlib.sha256(base).hexdigest()
def golden_ratio_select(N, seed):
"""Select indices using golden ratio stepping."""
step = int(max(1, round(N * PHI)))
return [(seed + i * step) % N for i in range(3)]
def pattern0_create_fractal_cluster(
*,
input_data=None,
space_id="default",
num_tensors=3,
context=None,
entropy_seed=PHI,
depth_max=3,
):
"""Generate ethical fractal cluster using Pattern 0."""
random.seed(int(entropy_seed * 1e9))
kb = FractalKnowledgeBase()
armonizador = Armonizador(knowledge_base=kb)
pool = TensorPoolManager()
# Generate tensors
tensors = []
for i in range(num_tensors):
if input_data and i < len(input_data):
vec = apply_ethical_constraint(input_data[i], space_id, kb)
tensor = FractalTensor(nivel_3=[vec])
else:
try:
tensor = FractalTensor.random(space_constraints=space_id)
except TypeError:
tensor = FractalTensor.random()
# Add ethical metadata
tensor.metadata.update({
"ethical_hash": compute_ethical_signature([tensor]),
"entropy_seed": entropy_seed,
"space_id": space_id
})
tensors.append(tensor)
pool.add_tensor(tensor)
# Harmonize cluster
for tensor in tensors:
harmonized = armonizador.harmonize(tensor.nivel_3[0], space_id=space_id)
tensor.nivel_3[0] = harmonized["output"]
return tensors
# ===============================================================================
# PUBLIC API
# ===============================================================================
# Main exports
__all__ = [
'FractalTensor',
'Trigate',
'TernaryLogic',
'Evolver',
'Extender',
'FractalKnowledgeBase',
'Armonizador',
'TensorPoolManager',
'Transcender',
'pattern0_create_fractal_cluster'
]
# Compatibility aliases
KnowledgeBase = FractalKnowledgeBase
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