mirror of
https://github.com/allaunthefox/Research-Stack.git
synced 2026-07-31 03:05:21 +00:00
611 lines
22 KiB
Python
611 lines
22 KiB
Python
"""
|
||
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()
|