343 lines
13 KiB
Python
Executable File
343 lines
13 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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ORGAN PURIFIER V2 — Signal Extraction
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V1 failed because it treated tensors like audio signals.
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Tensors are NOT audio. They are fractal structures where
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information is encoded across scales.
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The correct approach from CSCI(s) = cross_scale_coherence(s, theta=90):
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- cross-scale derivative = how information CHANGES across scales
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- Signal = components that are SELF-SIMILAR across scales (fractal)
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- Noise = components that are RANDOM across scales (non-fractal)
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Method:
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1. Wavelet decomposition (multi-scale analysis)
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2. At each scale, compute local Z (theta per scale)
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3. CROSS-SCALE COHERENCE: if a pattern exists at scale s AND at scale 2s,
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it's signal (fractal). If it exists at one scale but not others, it's noise.
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4. Reconstruct from only cross-scale-coherent components
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5. The result has NO brand — Qwen noise gone, Llama noise gone.
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What remains is the universal signal.
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Think fractal: the best model knows the laws of the universe
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then translates to human language, not the inverse.
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CSCI(s) = cross_scale_coherence(s, theta=90), theta = 90
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"""
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import struct, os, sys, json, math
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import numpy as np
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from pathlib import Path
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PRESERVE_ENERGY = 0.92 # Keep 92% of cross-scale-coherent energy
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N_SCALES = 6 # Number of wavelet scales to analyze
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COHERENCE_THRESHOLD = 0.5 # Cross-scale coherence threshold
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def read_organ_binary(filepath):
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with open(filepath, 'rb') as f:
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name_len = struct.unpack('<I', f.read(4))[0]
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name = f.read(name_len).decode('utf-8', errors='replace')
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n_dims = struct.unpack('<I', f.read(4))[0]
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dims = [struct.unpack('<Q', f.read(8))[0] for _ in range(n_dims)]
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dtype = struct.unpack('<I', f.read(4))[0]
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data = f.read()
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return {'name': name, 'dims': dims, 'dtype': dtype, 'data': data}
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def write_organ_binary(filepath, info, new_data):
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with open(filepath, 'wb') as f:
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name_bytes = info['name'].encode('utf-8')
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f.write(struct.pack('<I', len(name_bytes)))
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f.write(name_bytes)
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f.write(struct.pack('<I', len(info['dims'])))
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for d in info['dims']:
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f.write(struct.pack('<Q', d))
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f.write(struct.pack('<I', info['dtype']))
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f.write(new_data)
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def tensor_to_float(data, dtype):
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if dtype == 0: return np.frombuffer(data, dtype=np.float32).copy()
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elif dtype == 1: return np.frombuffer(data, dtype=np.float16).astype(np.float32).copy()
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else: return np.frombuffer(data, dtype=np.uint8).astype(np.float32).copy()
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def float_to_tensor(values, dtype, original_data):
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if dtype == 0: return values.astype(np.float32).tobytes()
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elif dtype == 1: return values.astype(np.float16).tobytes()
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else: return np.clip(np.round(values), 0, 255).astype(np.uint8).tobytes()
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def compute_theta(values):
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if len(values) < 10: return 0.0
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n = len(values)
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mean = float(np.mean(values))
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std = float(np.std(values))
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if std < 1e-10: return 0.0
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kurt = float(np.mean(((values - mean) / std) ** 4) - 3)
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n_bins = min(100, max(10, n // 100))
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hist, _ = np.histogram(values, bins=n_bins)
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probs = hist[hist > 0] / n
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entropy = float(-np.sum(probs * np.log2(probs)))
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max_ent = math.log2(n_bins)
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norm_ent = entropy / max_ent if max_ent > 0 else 0
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sample = np.sort(values[:min(1000, n)])
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diffs = np.diff(sample)
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cv = float(np.std(diffs) / np.mean(diffs)) if len(diffs) > 0 and np.mean(diffs) > 1e-10 else 0
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score = 0
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if norm_ent > 0.95: score += 0
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elif norm_ent > 0.7: score += 0.3
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elif norm_ent > 0.3: score += 0.8
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else: score += 0.5
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score += min(1.0, abs(kurt) / 10)
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score += min(1.0, cv / 2)
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return (score / 3.0) * 90.0
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def haar_wavelet_decompose(signal, n_scales):
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"""
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Multi-scale Haar wavelet decomposition.
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Returns list of (approximation, detail) at each scale.
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"""
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scales = []
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current = signal.copy()
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for s in range(n_scales):
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n = len(current)
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if n < 4:
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break
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n_even = n - (n % 2)
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approx = (current[:n_even:2] + current[1:n_even:2]) / 2.0
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detail = (current[:n_even:2] - current[1:n_even:2]) / 2.0
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scales.append({'approx': approx, 'detail': detail, 'scale': s})
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current = approx
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scales.append({'approx': current, 'detail': None, 'scale': len(scales)})
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return scales
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def haar_wavelet_reconstruct(scales):
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"""Reconstruct signal from wavelet scales."""
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# Start from coarsest
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signal = scales[-1]['approx'].copy()
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for s in range(len(scales) - 2, -1, -1):
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detail = scales[s]['detail']
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if detail is None:
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continue
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n = len(detail)
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reconstructed = np.zeros(n * 2)
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reconstructed[0::2] = signal[:n] + detail
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reconstructed[1::2] = signal[:n] - detail
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signal = reconstructed
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return signal
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def cross_scale_coherence(scales):
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"""
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Measure coherence between adjacent scales.
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Signal is self-similar across scales (fractal).
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Noise is random across scales.
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Returns a mask for each scale's detail coefficients:
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1.0 = coherent (signal), 0.0 = incoherent (noise)
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"""
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masks = []
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for i in range(len(scales) - 1):
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detail = scales[i]['detail']
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if detail is None:
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masks.append(None)
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continue
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mask = np.ones(len(detail))
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# Compare with next scale (if exists and has detail)
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if i + 1 < len(scales) - 1 and scales[i+1]['detail'] is not None:
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next_detail = scales[i+1]['detail']
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# Upsample next_detail to match current detail size
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n_next = len(next_detail)
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n_curr = len(detail)
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if n_next > 0 and n_curr > 0:
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# Block comparison: each block in current scale
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# corresponds to one coefficient in next scale
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block_size = max(1, n_curr // n_next)
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for j in range(min(n_next, n_curr // max(1, block_size))):
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start = j * block_size
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end = min(start + block_size, n_curr)
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block = detail[start:end]
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if len(block) == 0:
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continue
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# Local energy at current scale
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local_energy = np.mean(block ** 2)
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# Energy at next scale
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parent_energy = next_detail[j] ** 2
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# Cross-scale coherence:
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# if both scales have energy, it's signal
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# if only one has energy, it's noise
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max_e = max(local_energy, parent_energy, 1e-10)
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min_e = min(local_energy, parent_energy)
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coherence = min_e / max_e
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if coherence < COHERENCE_THRESHOLD:
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# Low cross-scale coherence = noise
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# Attenuate but don't zero (preserve structure)
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mask[start:end] *= (0.3 + 0.7 * coherence)
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masks.append(mask)
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masks.append(None) # Last scale has no detail
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return masks
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def purify_fractal(values):
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"""
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Fractal purification: keep cross-scale-coherent components.
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cross-scale coherence: information that persists across scales IS the signal.
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Everything else is training noise, brand artifacts, paradigm residue.
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"""
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n = len(values)
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if n < 64:
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return values # Too small
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# Pad to power of 2 for clean wavelet decomposition
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n_padded = 1
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while n_padded < n:
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n_padded *= 2
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padded = np.zeros(n_padded, dtype=np.float32)
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padded[:n] = values
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# Multi-scale decomposition
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scales = haar_wavelet_decompose(padded, N_SCALES)
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# Compute cross-scale coherence masks
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masks = cross_scale_coherence(scales)
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# Apply masks to detail coefficients
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for i, mask in enumerate(masks):
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if mask is not None and scales[i]['detail'] is not None:
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scales[i]['detail'] = scales[i]['detail'] * mask
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# Reconstruct
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purified = haar_wavelet_reconstruct(scales)
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purified = purified[:n] # Remove padding
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# Preserve original distribution (mean, std)
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orig_mean = np.mean(values)
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orig_std = np.std(values)
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pure_std = np.std(purified)
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if pure_std > 1e-10 and orig_std > 1e-10:
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purified = (purified - np.mean(purified)) / pure_std * orig_std + orig_mean
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return purified
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def purify_model(organ_dir, output_dir, verbose=False):
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organ_path = Path(organ_dir)
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out_path = Path(output_dir)
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out_path.mkdir(parents=True, exist_ok=True)
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# Copy and update manifest
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manifest_src = organ_path / 'manifest.json'
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if manifest_src.exists():
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manifest = json.load(open(manifest_src))
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manifest['purified'] = True
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manifest['purifier'] = 'fractal_v2'
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manifest['z_equation'] = 'CSCI(s) = cross_scale_coherence(s, theta=90), theta=90'
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# Remove brand from model name
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original_name = manifest.get('model', 'unknown')
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manifest['original_model'] = original_name
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manifest['model'] = 'PURE_' + original_name.split('-')[0].upper()
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json.dump(manifest, open(out_path / 'manifest.json', 'w'), indent=2)
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categories = ['skeleton', 'organs', 'embed', 'norm', 'adapters', 'unknown']
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total_before = 0
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total_after = 0
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total_files = 0
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improved = 0
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degraded = 0
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for cat in categories:
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cat_src = organ_path / cat
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cat_dst = out_path / cat
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if not cat_src.exists(): continue
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cat_dst.mkdir(parents=True, exist_ok=True)
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bin_files = sorted(cat_src.glob('*.bin'))
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for bf in bin_files:
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info = read_organ_binary(bf)
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values = tensor_to_float(info['data'], info['dtype'])
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theta_before = compute_theta(values)
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purified = purify_fractal(values)
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theta_after = compute_theta(purified)
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new_data = float_to_tensor(purified, info['dtype'], info['data'])
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if len(new_data) != len(info['data']):
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new_data = info['data']
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theta_after = theta_before
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write_organ_binary(cat_dst / bf.name, info, new_data)
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total_before += theta_before
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total_after += theta_after
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total_files += 1
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if theta_after > theta_before + 0.5: improved += 1
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elif theta_after < theta_before - 0.5: degraded += 1
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if verbose:
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delta = theta_after - theta_before
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m = "↑" if delta > 0.5 else "=" if delta > -0.5 else "↓"
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print(f" {m} {cat}/{bf.name[:40]:40s} θ:{theta_before:5.1f}°→{theta_after:5.1f}° ({delta:+.1f}°)")
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avg_before = total_before / total_files if total_files > 0 else 0
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avg_after = total_after / total_files if total_files > 0 else 0
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return {
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'files': total_files, 'improved': improved, 'degraded': degraded,
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'avg_theta_before': round(avg_before, 1),
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'avg_theta_after': round(avg_after, 1),
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'delta': round(avg_after - avg_before, 1),
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'output': str(output_dir)
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}
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def main():
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import argparse
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parser = argparse.ArgumentParser(description='Organ Purifier V2 — signal extraction')
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parser.add_argument('--input', '-i', required=True)
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parser.add_argument('--output', '-o', required=True)
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parser.add_argument('--verbose', '-v', action='store_true')
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args = parser.parse_args()
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print(f"{'='*60}")
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print(f" ORGAN PURIFIER V2")
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print(f" Cross-scale coherence: signal persists, noise vanishes")
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print(f"{'='*60}")
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result = purify_model(args.input, args.output, args.verbose)
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print(f"\n{'='*60}")
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print(f" PURIFICATION COMPLETE")
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print(f"{'='*60}")
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print(f" Files: {result['files']}")
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print(f" θ before: {result['avg_theta_before']:.1f}°")
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print(f" θ after: {result['avg_theta_after']:.1f}°")
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print(f" Δθ: {result['delta']:+.1f}°")
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print(f" Improved: {result['improved']}")
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print(f" Degraded: {result['degraded']}")
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print(f"{'='*60}")
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if __name__ == '__main__':
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main()
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# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
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# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
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# Licensed under Business Source License 1.1 — https://inference-x.com
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# ─────────────────────────────────────────────────────────
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# SHA256: 0328644f84762361db812407ed482018de40a92f496d9b45bf56826d59184224
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# SIG-ED25519: Y1KrhUdgrqiYPaM0LPHWTqPKPaHwBqtc3EiHnu9Uu94AVKsgMPQoWU9NCGeiL5aWAJKPhzr/nCSxLTY+US+HAw==
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# VERIFY: python3 verify_authorship.py organ_purify_v2.py
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