trinity_3/core_clean.py

"""
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