import numpy as np import zlib import bz2 import lzma from typing import Dict, List, Tuple class CCT_Compression_Engine: """ Self-Learning Compression using CCT-ULP Framework Combines: File 1 (CCT), File 2 (SuperByte), File 3 (ODE-CCT), File 5 (16-Element), File 6 (Constants) """ def __init__(self, entropy_threshold: float = 0.27): self.entropy_threshold = entropy_threshold self.entropy_history = [] self.algorithm_history = [] # E01: Stationary Constants (Information Laws) self.stationary_constants = { 'shannon_limit': None, 'kolmogorov_bound': None, 'causality': True # Decompression must reverse compression } # E02: Probability Variables (Algorithm Parameters) self.probability_variables = { 'algorithm': 'auto', # Will be selected 'compression_level': 6, 'block_size': 4096, 'use_transform': False, 'dictionary_size': 32768 } # E07/E08: Available Algorithms (Transform Basis) self.algorithms = { 'zlib': lambda d, l: zlib.compress(d, l), 'bz2': lambda d, l: bz2.compress(d, l), 'lzma': lambda d, l: lzma.compress(d, preset=l), 'zstd': lambda d, l: self._zstd_compress(d, l), 'none': lambda d, l: d # Baseline } # 16-Element Compression Matrix self.elements = self._initialize_compression_elements() def _initialize_compression_elements(self) -> Dict[str, float]: """Initialize 16-Element Compression Matrix""" return { 'E01_Entropy_Bound': 0.0, 'E02_Pattern_Density': 0.0, 'E03_Redundancy_Rate': 0.0, 'E04_Algorithm_Select': 0.0, 'E05_Block_Optimal': 0.0, 'E06_Dictionary_Size': 0.0, 'E07_Transform_Type': 0.0, 'E08_Prediction_Error': 0.0, 'E09_Entropy_Current': 0.0, 'E10_Collapse_Rate': 0.0, 'E11_Memory_Cost': 0.0, 'E12_Compute_Cost': 0.0, 'E13_Recovery_Fidelity': 0.0, 'E14_Context_Window': 0.0, 'E15_Novelty_Score': 0.0, 'E16_Compression_Stability': 0.0 } def calculate_compression_entropy(self, data: bytes, params: Dict) -> float: """ E09: Entropy Metric Measures compressibility as normalized entropy """ # Get compressed size algo = params.get('algorithm', 'zlib') level = params.get('compression_level', 6) if algo == 'auto': algo = 'zlib' # Default for measurement try: compressed = self.algorithms[algo](data, level) compression_ratio = len(compressed) / len(data) except: compression_ratio = 1.0 # Normalize to [0, 1] entropy scale # 0 = perfectly compressed, 1 = incompressible entropy = np.tanh(compression_ratio) self.elements['E09_Entropy_Current'] = entropy return entropy def test_parameter_resistance(self, data: bytes, param_name: str) -> float: """ Truth Identification: Test what resists bending High resistance = Stationary Constant (Truth) """ base_params = {**self.stationary_constants, **self.probability_variables} base_entropy = self.calculate_compression_entropy(data, base_params) # Perturb parameter test_params = base_params.copy() if param_name == 'compression_level': test_params[param_name] = min(9, test_params[param_name] + 2) elif param_name == 'block_size': test_params[param_name] = test_params[param_name] * 2 elif param_name == 'algorithm': test_params[param_name] = 'bz2' # Switch algorithm test_entropy = self.calculate_compression_entropy(data, test_params) # Resistance = entropy sensitivity resistance = abs(test_entropy - base_entropy) / 0.1 return resistance def learn_and_compress(self, data: bytes, max_iterations: int = 10) -> Dict: """ CCT-ULP Self-Learning Compression Loop """ print("="*70) print("CCT SELF-LEARNING COMPRESSION ENGINE") print("="*70) # Calculate initial entropy current_entropy = self.calculate_compression_entropy(data, self.probability_variables) self.entropy_history.append(current_entropy) print(f"Initial Entropy: {current_entropy:.4f} (Target < {self.entropy_threshold})") print(f"Data Size: {len(data)} bytes") print("-"*70) # CCT Learning Loop for iteration in range(max_iterations): print(f"\n--- CCT Cycle {iteration + 1} ---") # Step 1: Truth Identification for param in list(self.probability_variables.keys()): if param == 'algorithm': continue # Skip algorithm selection for resistance test resistance = self.test_parameter_resistance(data, param) if resistance > 0.5: print(f" [!] '{param}' has HIGH resistance → Stationary") # Could promote to stationary_constants else: print(f" [~] '{param}' is bendable → Probability") # Step 2: Check Stability if current_entropy < self.entropy_threshold: print(f"\n[✓] System Stable (Entropy < 0.27)") break # Step 3: Variable Bending (Algorithm Selection) print(f"\n[⚠] Entropy High. Testing algorithms...") best_algo = None best_entropy = 1.0 for algo_name in self.algorithms.keys(): if algo_name == 'none': continue test_params = {**self.probability_variables, 'algorithm': algo_name} entropy = self.calculate_compression_entropy(data, test_params) print(f" {algo_name}: Entropy = {entropy:.4f}") if entropy < best_entropy: best_entropy = entropy best_algo = algo_name # Update to best algorithm if best_algo: self.probability_variables['algorithm'] = best_algo current_entropy = best_entropy self.algorithm_history.append(best_algo) print(f"\n[✓] Selected: {best_algo} (Entropy: {current_entropy:.4f})") self.entropy_history.append(current_entropy) # Step 4: Singularity Check if iteration == max_iterations - 1: print(f"\n[!] Max iterations reached. Partial collapse.") # E16: Conditional Collapse (Final Compression) final_params = {**self.stationary_constants, **self.probability_variables} compressed = self.algorithms[final_params['algorithm']]( data, final_params['compression_level'] ) # Calculate final metrics compression_ratio = len(compressed) / len(data) space_saved = (1 - compression_ratio) * 100 result = { 'status': 'COLLAPSED' if current_entropy < self.entropy_threshold else 'PARTIAL', 'algorithm': final_params['algorithm'], 'original_size': len(data), 'compressed_size': len(compressed), 'compression_ratio': compression_ratio, 'space_saved_percent': space_saved, 'final_entropy': current_entropy, 'iterations': iteration + 1, 'elements': self.elements } print("\n" + "="*70) print("FINAL COMPRESSION METRICS:") print(f" Status: {result['status']}") print(f" Algorithm: {result['algorithm']}") print(f" Original: {result['original_size']} bytes") print(f" Compressed: {result['compressed_size']} bytes") print(f" Ratio: {result['compression_ratio']:.4f}") print(f" Space Saved: {result['space_saved_percent']:.1f}%") print(f" Final Entropy: {result['final_entropy']:.4f}") print("="*70) return result, compressed def _zstd_compress(self, data: bytes, level: int) -> bytes: """Zstandard compression (requires zstd library)""" try: import zstd return zstd.compress(data, level) except ImportError: return zlib.compress(data, level) # Fallback