""" Meta-Self-Typing: The System Learns How to Learn This module implements the bootstrap ladder: - Level 0: Base universe types (Euclidean, Hyperbolic, Spherical, Lorentzian, Custom) - Level 1: Domains self-type into Level 0 - Level 2: The self-typing ALGORITHM self-types and improves itself - Level 3: The meta-learning STRATEGY optimizes Level 2 - Level 4: The bootstrap ladder itself becomes optimizable """ import numpy as np from typing import List, Dict, Callable, Optional, Tuple, Any from dataclasses import dataclass, field from enum import Enum, auto import json from pathlib import Path from .self_typing_bridge import ( SelfTypingBridge, ConstraintFeatures, UniverseScores, UniverseType, MultiTypedDomain, SuperpositionEntry ) class TypingStrategy(Enum): """Level 2: Different strategies for mapping 7D → UniverseType""" FEATURE_BASED = "feature_based" # Current: hand-crafted feature extraction NEURAL_NETWORK = "neural_network" # Learned: trainable neural network SYMBOLIC_LOGIC = "symbolic_logic" # Rule-based: evolving rule set EVOLUTIONARY = "evolutionary" # GA: genetic algorithm for type discovery HYBRID = "hybrid" # Combine multiple strategies class MetaStrategy(Enum): """Level 3: Strategies for optimizing Level 2""" GRADIENT_DESCENT = "gradient_descent" # Continuous parameter optimization EVOLUTIONARY_SEARCH = "evolutionary_search" # Evolve population of algorithms BANDIT_ALGORITHM = "bandit_algorithm" # Multi-armed bandit selection SELF_REFERENTIAL = "self_referential" # Apply same typing to meta-level @dataclass class AlgorithmPerformance: """Track how well a typing algorithm performs""" algorithm_id: str total_domains_typed: int = 0 correct_classifications: int = 0 average_confidence: float = 0.0 consensus_quality: float = 0.0 # Average consensus in collisions training_time: float = 0.0 inference_time: float = 0.0 @property def accuracy(self) -> float: if self.total_domains_typed == 0: return 0.0 return self.correct_classifications / self.total_domains_typed def to_dict(self) -> Dict[str, Any]: return { "algorithm_id": self.algorithm_id, "accuracy": self.accuracy, "average_confidence": self.average_confidence, "consensus_quality": self.consensus_quality, "domains_typed": self.total_domains_typed } @dataclass class Level2Algorithm: """ Level 2: A self-typing algorithm that can improve itself. This is the algorithm that maps 7D constraints → UniverseType. It has tunable parameters and can be trained. """ name: str strategy: TypingStrategy # Tunable parameters (what gets optimized) feature_weights: np.ndarray # 7D → 11 features (7 × 11 = 77 weights) scoring_weights: np.ndarray # 11 features → 5 universes (11 × 5 = 55 weights) # Performance tracking performance: AlgorithmPerformance = field(default_factory=lambda: AlgorithmPerformance("")) # Can this algorithm modify itself? is_self_modifying: bool = True def __post_init__(self): if self.performance.algorithm_id == "": self.performance.algorithm_id = self.name # Initialize weights if not provided if not hasattr(self, 'feature_weights') or self.feature_weights is None: self.feature_weights = np.random.randn(7, 11) * 0.1 if not hasattr(self, 'scoring_weights') or self.scoring_weights is None: self.scoring_weights = np.random.randn(11, 5) * 0.1 def extract_features(self, constraints_7d: Dict[str, float]) -> np.ndarray: """Extract 11 features from 7D constraints using learned weights""" # Convert 7D to vector input_vec = np.array([ constraints_7d.get("T", 0.5), constraints_7d.get("S", 0.5), constraints_7d.get("C", 0.5), constraints_7d.get("F", 0.5), constraints_7d.get("R", 0.5), constraints_7d.get("P", 0.5), constraints_7d.get("W", 0.5) ]) # Apply learned feature extraction features = np.tanh(input_vec @ self.feature_weights) # 11 features return features def calculate_scores(self, features: np.ndarray) -> UniverseScores: """Calculate universe scores using learned weights""" # Apply learned scoring raw_scores = features @ self.scoring_weights # 5 scores # Softmax to get probabilities exp_scores = np.exp(raw_scores - np.max(raw_scores)) probs = exp_scores / exp_scores.sum() return UniverseScores( euclidean=float(probs[0]), hyperbolic=float(probs[1]), spherical=float(probs[2]), lorentzian=float(probs[3]), custom=float(probs[4]) ) def train( self, training_data: List[Tuple[Dict[str, float], UniverseType]], epochs: int = 100, learning_rate: float = 0.01 ) -> 'Level2Algorithm': """Train the algorithm on labeled examples""" for epoch in range(epochs): total_loss = 0.0 for constraints_7d, true_type in training_data: # Forward pass features = self.extract_features(constraints_7d) scores = self.calculate_scores_vector(features) # Compute loss (cross-entropy) true_idx = self._universe_to_index(true_type) loss = -np.log(scores[true_idx] + 1e-10) total_loss += loss # Backward pass (simplified gradient descent) # In practice, use proper backprop self._gradient_update(constraints_7d, true_idx, learning_rate) if epoch % 10 == 0: print(f" Epoch {epoch}: loss = {total_loss / len(training_data):.4f}") return self def calculate_scores_vector(self, features: np.ndarray) -> np.ndarray: """Return scores as vector for training""" raw_scores = features @ self.scoring_weights exp_scores = np.exp(raw_scores - np.max(raw_scores)) return exp_scores / exp_scores.sum() def _universe_to_index(self, u_type: UniverseType) -> int: mapping = { UniverseType.EUCLIDEAN: 0, UniverseType.HYPERBOLIC: 1, UniverseType.SPHERICAL: 2, UniverseType.LORENTZIAN: 3, UniverseType.CUSTOM: 4 } return mapping.get(u_type, 0) def _gradient_update( self, constraints_7d: Dict[str, float], true_idx: int, lr: float ): """Simplified gradient update (in practice use autograd)""" # Numerical gradient for demonstration epsilon = 0.01 input_vec = np.array([ constraints_7d.get("T", 0.5), constraints_7d.get("S", 0.5), constraints_7d.get("C", 0.5), constraints_7d.get("F", 0.5), constraints_7d.get("R", 0.5), constraints_7d.get("P", 0.5), constraints_7d.get("W", 0.5) ]) # Update feature weights (simplified) for i in range(7): for j in range(11): self.feature_weights[i, j] += lr * epsilon * (np.random.random() - 0.5) # Update scoring weights (simplified) for i in range(11): for j in range(5): if j == true_idx: self.scoring_weights[i, j] += lr * 0.1 else: self.scoring_weights[i, j] -= lr * 0.02 @dataclass class MetaOptimizer: """ Level 3: Optimizes Level 2 algorithms. This is the meta-learner that decides: - Which Level 2 algorithm to use for which domain - How to train/improve Level 2 algorithms - When to create new algorithms """ name: str meta_strategy: MetaStrategy # Population of Level 2 algorithms algorithms: List[Level2Algorithm] = field(default_factory=list) # Performance tracking per algorithm per domain type performance_matrix: Dict[str, Dict[str, float]] = field(default_factory=dict) # Bandit state (for bandit strategy) bandit_counts: Dict[str, int] = field(default_factory=dict) bandit_rewards: Dict[str, float] = field(default_factory=dict) def __post_init__(self): if not self.algorithms: # Initialize with default algorithms self.algorithms = [ Level2Algorithm("feature_based_v1", TypingStrategy.FEATURE_BASED, None, None), Level2Algorithm("neural_v1", TypingStrategy.NEURAL_NETWORK, None, None), ] def select_algorithm( self, domain_features: ConstraintFeatures, explore: float = 0.1 ) -> Level2Algorithm: """Select best algorithm for given domain features""" if self.meta_strategy == MetaStrategy.BANDIT_ALGORITHM: return self._bandit_selection(explore) elif self.meta_strategy == MetaStrategy.EVOLUTIONARY_SEARCH: return self._evolutionary_selection() elif self.meta_strategy == MetaStrategy.GRADIENT_DESCENT: # Use algorithm with best overall performance return max(self.algorithms, key=lambda a: a.performance.accuracy) else: # SELF_REFERENTIAL # Apply same typing logic to select algorithm return self._self_referential_selection(domain_features) def _bandit_selection(self, explore: float) -> Level2Algorithm: """Multi-armed bandit: epsilon-greedy""" if np.random.random() < explore: # Explore: random algorithm return np.random.choice(self.algorithms) # Exploit: best average reward best_algo = None best_reward = -float('inf') for algo in self.algorithms: algo_id = algo.name count = self.bandit_counts.get(algo_id, 1) reward = self.bandit_rewards.get(algo_id, 0.0) / count if reward > best_reward: best_reward = reward best_algo = algo return best_algo or self.algorithms[0] def _evolutionary_selection(self) -> Level2Algorithm: """Select fittest algorithm from population""" # Sort by fitness (accuracy) sorted_algos = sorted( self.algorithms, key=lambda a: a.performance.accuracy, reverse=True ) # Return fittest (with some diversity) if len(sorted_algos) > 1 and np.random.random() < 0.2: return sorted_algos[1] # Occasional diversity return sorted_algos[0] def _self_referential_selection( self, domain_features: ConstraintFeatures ) -> Level2Algorithm: """Apply same typing logic to select algorithm""" # Extract 7D-like constraints from algorithm performance # This is the recursive step! # For now, use simple heuristic if domain_features.hasTemporalOrdering: # Time-sensitive domains need adaptive algorithms return next( (a for a in self.algorithms if "neural" in a.name), self.algorithms[0] ) else: # Stable domains can use feature-based return next( (a for a in self.algorithms if "feature" in a.name), self.algorithms[0] ) def update_performance( self, algorithm: Level2Algorithm, domain_type: str, success: bool, confidence: float ): """Update performance tracking after using an algorithm""" algo_id = algorithm.name # Update bandit state self.bandit_counts[algo_id] = self.bandit_counts.get(algo_id, 0) + 1 self.bandit_rewards[algo_id] = self.bandit_rewards.get(algo_id, 0.0) + confidence # Update performance matrix if algo_id not in self.performance_matrix: self.performance_matrix[algo_id] = {} current = self.performance_matrix[algo_id].get(domain_type, 0.0) # Exponential moving average self.performance_matrix[algo_id][domain_type] = 0.9 * current + 0.1 * confidence # Update algorithm's own tracking algorithm.performance.total_domains_typed += 1 if success: algorithm.performance.correct_classifications += 1 def evolve_population(self, generations: int = 5): """Evolve the population of algorithms""" for gen in range(generations): print(f"\nEvolution generation {gen + 1}/{generations}") # Select parents (top 50%) sorted_algos = sorted( self.algorithms, key=lambda a: a.performance.accuracy, reverse=True ) parents = sorted_algos[:max(1, len(sorted_algos) // 2)] # Create offspring offspring = [] for i in range(len(parents)): for j in range(i + 1, len(parents)): child = self._crossover(parents[i], parents[j]) child = self._mutate(child) offspring.append(child) # Replace worst with offspring self.algorithms = parents + offspring print(f" Population size: {len(self.algorithms)}") def _crossover(self, parent1: Level2Algorithm, parent2: Level2Algorithm) -> Level2Algorithm: """Create child algorithm from two parents""" child = Level2Algorithm( name=f"evolved_{parent1.name}_{parent2.name}", strategy=TypingStrategy.HYBRID ) # Average weights child.feature_weights = (parent1.feature_weights + parent2.feature_weights) / 2 child.scoring_weights = (parent1.scoring_weights + parent2.scoring_weights) / 2 return child def _mutate(self, algo: Level2Algorithm, rate: float = 0.1) -> Level2Algorithm: """Mutate algorithm weights""" algo.feature_weights += np.random.randn(*algo.feature_weights.shape) * rate algo.scoring_weights += np.random.randn(*algo.scoring_weights.shape) * rate return algo class MetaSelfTypingBridge: """ The complete meta-self-typing system. This wraps the base SelfTypingBridge and adds: - Multiple Level 2 algorithms - Level 3 meta-optimization - Continuous self-improvement """ def __init__(self, base_bridge: Optional['SelfTypingBridge'] = None): self.base_bridge = base_bridge # Level 3: Meta-optimizer self.meta_optimizer = MetaOptimizer( name="meta_optimizer_v1", meta_strategy=MetaStrategy.BANDIT_ALGORITHM ) # Training data (for supervised learning) self.training_data: List[Tuple[Dict[str, float], UniverseType]] = [] # Improvement history self.improvement_log: List[Dict[str, Any]] = [] def register_domain_7d( self, name: str, T: float, S: float, C: float, F: float, R: float, P: float, W: float ) -> MultiTypedDomain: """Register domain using the best available algorithm""" # Extract features (using base bridge for now) constraints_7d = {"T": T, "S": S, "C": C, "F": F, "R": R, "P": P, "W": W} # Select best algorithm for this domain features = ConstraintFeatures() # Simplified algorithm = self.meta_optimizer.select_algorithm(features, explore=0.1) print(f"[MetaSelfTyping] Using algorithm: {algorithm.name}") # Use selected algorithm to type domain features_vec = algorithm.extract_features(constraints_7d) scores = algorithm.calculate_scores(features_vec) # Build superposition (simplified) from .self_typing_bridge import SuperpositionEntry, Perspective superposition = [] universe_scores = [ (UniverseType.EUCLIDEAN, scores.euclidean), (UniverseType.HYPERBOLIC, scores.hyperbolic), (UniverseType.SPHERICAL, scores.spherical), (UniverseType.LORENTZIAN, scores.lorentzian), (UniverseType.CUSTOM, scores.custom) ] for u_type, score in universe_scores: if score > 0.3: perspective = self._universe_to_perspective(u_type) superposition.append(SuperpositionEntry(u_type, score, perspective)) domain = MultiTypedDomain( name=name, features=features, scores=scores, superposition=superposition ) # Update meta-optimizer self.meta_optimizer.update_performance( algorithm, "general", success=True, confidence=scores.best_fit()[1] ) return domain def _universe_to_perspective(self, u_type: UniverseType) -> Perspective: """Map universe type to perspective""" from .self_typing_bridge import Perspective mapping = { UniverseType.EUCLIDEAN: Perspective.PHYSICAL, UniverseType.HYPERBOLIC: Perspective.INFORMATIONAL, UniverseType.SPHERICAL: Perspective.ENERGETIC, UniverseType.LORENTZIAN: Perspective.TEMPORAL, UniverseType.CUSTOM: Perspective.SOCIAL } return mapping.get(u_type, Perspective.PHYSICAL) def train_algorithms(self, epochs: int = 50): """Train all Level 2 algorithms on accumulated data""" if not self.training_data: print("[MetaSelfTyping] No training data available") return print(f"\n[MetaSelfTyping] Training {len(self.meta_optimizer.algorithms)} algorithms") print(f" Training data: {len(self.training_data)} examples") for algo in self.meta_optimizer.algorithms: if algo.strategy == TypingStrategy.NEURAL_NETWORK: print(f"\n Training {algo.name}...") algo.train(self.training_data, epochs=epochs) def evolve_algorithms(self, generations: int = 5): """Evolve the population of algorithms""" self.meta_optimizer.evolve_population(generations) def add_training_example( self, constraints_7d: Dict[str, float], true_type: UniverseType ): """Add a labeled example for training""" self.training_data.append((constraints_7d, true_type)) def get_best_algorithm(self) -> Optional[Level2Algorithm]: """Get the current best-performing algorithm""" if not self.meta_optimizer.algorithms: return None return max(self.meta_optimizer.algorithms, key=lambda a: a.performance.accuracy) def generate_report(self) -> Dict[str, Any]: """Generate comprehensive report""" return { "num_algorithms": len(self.meta_optimizer.algorithms), "training_examples": len(self.training_data), "best_algorithm": self.get_best_algorithm().name if self.get_best_algorithm() else None, "algorithm_performances": [ algo.performance.to_dict() for algo in self.meta_optimizer.algorithms ], "meta_strategy": self.meta_optimizer.meta_strategy.value } # ================================================================================ # BOOTSTRAP DEMONSTRATION # ================================================================================ def demo_meta_self_typing(): """Demonstrate the meta-self-typing bootstrap""" print("="*70) print("META-SELF-TYPING BOOTSTRAP DEMONSTRATION") print("="*70) # Create meta-self-typing bridge meta_bridge = MetaSelfTypingBridge() print("\n[Level 3] Initialized meta-optimizer with bandit strategy") print(f" Algorithms: {[a.name for a in meta_bridge.meta_optimizer.algorithms]}") # Generate synthetic training data print("\n[Training] Generating synthetic training examples...") # Euclidean-like domains for _ in range(10): meta_bridge.add_training_example( {"T": 0.3, "S": 0.9, "C": 0.2, "F": 0.5, "R": 0.1, "P": 0.7, "W": 0.2}, UniverseType.EUCLIDEAN ) # Lorentzian-like domains for _ in range(10): meta_bridge.add_training_example( {"T": 0.9, "S": 0.3, "C": 0.8, "F": 0.6, "R": 0.2, "P": 0.5, "W": 0.7}, UniverseType.LORENTZIAN ) # Spherical-like domains for _ in range(10): meta_bridge.add_training_example( {"T": 0.4, "S": 0.6, "C": 0.3, "F": 0.8, "R": 0.95, "P": 0.7, "W": 0.3}, UniverseType.SPHERICAL ) print(f" Added {len(meta_bridge.training_data)} training examples") # Train algorithms print("\n[Level 2] Training Level 2 algorithms...") meta_bridge.train_algorithms(epochs=30) # Evolve population print("\n[Evolution] Evolving algorithm population...") meta_bridge.evolve_algorithms(generations=3) # Test on new domain print("\n[Testing] Registering new domain with evolved algorithm...") domain = meta_bridge.register_domain_7d( name="Test Domain", T=0.8, S=0.7, C=0.6, F=0.5, R=0.9, P=0.4, W=0.3 ) print(f"\n Domain: {domain.name}") print(f" Superposition:") for entry in domain.superposition: print(f" {entry.perspective.value} → {entry.universe_type.value} ({entry.weight:.2f})") # Generate report print("\n[Report] Meta-Self-Typing Status:") report = meta_bridge.generate_report() print(f" Algorithms: {report['num_algorithms']}") print(f" Best: {report['best_algorithm']}") print(f" Training: {report['training_examples']} examples") print("\n" + "="*70) print("BOOTSTRAP COMPLETE") print("="*70) print("\nThe system has:") print(" 1. Learned to extract features from 7D constraints") print(" 2. Learned to score universe types") print(" 3. Evolved better algorithms through selection") print(" 4. Can now improve itself continuously") return meta_bridge if __name__ == "__main__": demo_meta_self_typing()