# ================================================================================= # AI-COMPRESSOR ENGINE v9.X - "Reality Distortion Core" # The Ultimate Hyperdimensional AI-Driven Compression & Decision Nexus # Turns raw system spacetime into pure, weaponized intelligence # ================================================================================= import numpy as np from numpy.linalg import qr, norm, svd from scipy.stats import entropy from scipy.spatial.distance import pdist, squareform from typing import Tuple, Dict, Any, Optional import hashlib import time import os from threading import RLock import psutil import GPUtil class AICompressorEngine: """ AI-COMPRESSOR ENGINE - Full Singularity-Class Compression & Control System Compresses 48+ dimensional system spacetime into a 12D latent mastery manifold Real-time adaptive, deterministic, and terrifyingly powerful """ def __init__(self, ambient_dim: int = 48, latent_dim: int = 12, chaos_seed: bytes = b"AI_COMPRESSOR_777_REALITY_DISTORTION", temporal_memory: float = 0.98, aggression_factor: float = 22.0): self.ambient_dim = ambient_dim self.latent_dim = latent_dim self.temporal_memory = temporal_memory self.aggression = aggression_factor self.lock = RLock() # --------------------- Deterministic Cosmic Seed --------------------- seed = int(hashlib.sha512(chaos_seed).hexdigest(), 16) % (2**64) self.rng = np.random.default_rng(seed) # --------------------- Adaptive Orthonormal Manifold --------------------- self.manifold = self._build_deterministic_manifold() # --------------------- Evolving Decision Hyperplane --------------------- self.W = self.rng.normal(0, 0.12, size=latent_dim) self.b = 0.0 self.momentum_W = np.zeros_like(self.W) # --------------------- Temporal State Memory --------------------- self.prev_z = np.zeros(latent_dim) self.age = 0 # ============================================================================= # Core Manifold Construction - Perfectly Deterministic & Numerically Perfect # ============================================================================= def _build_deterministic_manifold(self) -> np.ndarray: M = self.rng.standard_normal((self.ambient_dim, self.latent_dim)) # Force perfect column normalization M /= norm(M, axis=0) + 1e-15 # Rank-revealing QR with pivot + diagonal sign consistency Q, R, P = qr(M, mode='reduced', pivoting=True) signs = np.sign(np.diag(R)) Q = Q @ np.diag(signs) # Final SVD polish for maximum numerical orthogonality U, s, Vt = svd(Q, full_matrices=False) Q = U @ Vt # Enforce perfect orthonormality (up to machine precision) assert np.allclose(Q.T @ Q, np.eye(self.latent_dim), atol=1e-14) assert np.allclose(Q @ Q.T @ Q, Q, atol=1e-14) return Q # ============================================================================= # Ultra-High-Fidelity Compression (48D → 12D Latent Mastery) # ============================================================================= def compress(self, v: np.ndarray) -> np.ndarray: """Lossless-style projection into the mastered latent domain""" with self.lock: if v.shape != (self.ambient_dim,): v = np.asarray(v).flatten() if v.shape[0] == self.ambient_dim: v = v[:self.ambient_dim] else: # Auto-pad or truncate with cosmic awareness padded = np.zeros(self.ambient_dim) padded[:min(len(v), self.ambient_dim)] = v[:self.ambient_dim] v = padded z_raw = v @ self.manifold # Temporal smoothing with exponential memory (preserves dynamics) if self.age > 0: z = self.temporal_memory * self.prev_z + (1 - self.temporal_memory) * z_raw else: z = z_raw self.prev_z = z self.age += 1 return z.astype(np.float64) # ============================================================================= # Reality-Warping Decision Function - AI-Powered Boost Control # ============================================================================= def decide(self, z: np.ndarray, cpu_percent: float = None, mem_percent: float = None, gpu_load: float = None, temperature: float = None) -> Tuple[int, str, Dict[str, Any]]: """ Returns (boost_level: 20-200, status, full_telemetry) 200 = "I AM BECOME DEATH, DESTROYER OF FRAME TIMES" """ with self.lock: # Auto-harvest real system metrics if not provided if cpu_percent is None: cpu_percent = psutil.cpu_percent(interval=0.01) if mem_percent is None: mem_percent = psutil.virtual_memory().percent if gpu_load is None: try: gpus = GPUtil.getGPUs() gpu_load = gpus[0].load * 100 if gpus else 0.0 except: gpu_load = 0.0 if temperature is None: try: temps = psutil.sensors_temperatures() temperature = temps.get('coretemp', [dict(current=0)])[0].current except: temperature = 60.0 # Hyperdimensional stress tensor external_stress = np.array([ cpu_percent / 100.0, mem_percent / 100.0, gpu_load / 100.0, max(0, (temperature - 60) / 40.0), # Thermal aggression entropy([cpu_percent, mem_percent, gpu_load, temperature]) # Chaos metric ]) stress_score = np.clip(external_stress.mean() * 2.0 - 0.5, -1.0, 1.0) # Core AI decision scalar raw_score = float(np.dot(z, self.W) + self.b) ai_signal = np.tanh(raw_score * 3.0) # Extreme non-linearity # Final boost calculation - this is where the magic becomes weaponized base = (1.0 - cpu_percent / 100.0) * 120.0 aggression = ai_signal * self.aggression * (1.0 + stress_score) boost = base + aggression + raw_score * 33.0 # Ultra-hard clamp with override for transcendence boost_level = int(np.clip(boost, 20, 200)) # Status hierarchy of power if boost_level >= 180: status = "GOD MODE UNLEASHED" elif boost_level >= 160: status = "REALITY DISTORTION ACTIVE" elif boost_level >= 140: status = "SINGULARITY THROTTLE" elif boost_level >= 120: status = "Hyperflux Overdrive" elif boost_level >= 100: status = "Peak Performance" elif boost_level >= 80: status = "High Performance" else: status = "Nominal" telemetry = { "boost_raw": round(boost, 3), "ai_signal": round(ai_signal, 4), "stress_score": round(stress_score, 4), "latent_entropy": round(entropy(np.abs(z) + 1e-12), 4), "manifold_condition": float(norm(self.manifold.T @ self.manifold - np.eye(self.latent_dim))), "age": self.age, "system": {"cpu": cpu_percent, "mem": mem_percent, "gpu": gpu_load, "temp": temperature} } # Gentle online adaptation of decision plane (Oja-style Hebbian) self.momentum_W = 0.94 * self.momentum_W + 0.06 * np.outer(z, z) self.W += 0.0003 * (z * raw_score - self.W * 0.01) self.W /= norm(self.W) + 1e-12 return boost_level, status, telemetry # ============================================================================= # One-Call To Rule Them All # ============================================================================= def compress_and_decide(self, raw_vector: np.ndarray) -> Tuple[int, str, Dict[str, Any]]: """Single public API call - compress reality, then dominate it""" z = self.compress(raw_vector) boost, status, telemetry = self.decide(z) return boost, status, telemetry # ================================================================================= # Instantiate the Beast # ================================================================================= AI_COMPRESSOR = AICompressorEngine( ambient_dim=48, latent_dim=12, chaos_seed=b"AI_COMPRESSOR_777_REALITY_DISTORTION", temporal_memory=0.98, aggression_factor=28.0 ) # Example usage: # boost, status, info = AI_COMPRESSOR.compress_and_decide(your_48d_vector) # print(f"[AI-COMPRESSOR] Boost: {boost} | {status}")