Upload trinity_3\core_clean.py with huggingface_hub

trinity_3//core_clean.py

@@ -0,0 +1,483 @@
+"""
+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