mirror of
https://github.com/allaunthefox/Research-Stack.git
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356 lines
17 KiB
Python
356 lines
17 KiB
Python
#!/usr/bin/env python3
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"""
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Dynamic Profile Switching with Neural and Signal Math Extraction
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Analyzes morphic scalars that switch between profiles using metaprobe to extract both neural coding math and signal math from academic papers.
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"""
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import json
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from pathlib import Path
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from typing import Dict, List, Optional
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# Paths
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OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out")
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class DynamicProfileSwitchingSignalMath:
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"""Analyzes dynamic profile switching with both neural and signal math extraction."""
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def __init__(self):
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# Neural coding math patterns
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self.neural_coding_math = {
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"spike_timing_encoding": {
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"source": "Academic papers on neural spike timing",
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"equations": [
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"S(t) = Σ δ(t - t_i) for spike times t_i",
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"Temporal precision: Δt ~ 1ms",
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"Phase coding: θ = 2πt/T"
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],
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"significance_score": 95.0
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},
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"rate_coding": {
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"source": "Academic papers on firing rate coding",
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"equations": [
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"r = N/Δt (spikes per time window)",
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"Poisson process: P(k) = (λ^k e^-λ)/k!",
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"Rate modulation: r(t) = r_0 + Δr·f(t)"
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],
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"significance_score": 90.0
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},
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"synaptic_plasticity": {
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"source": "Academic papers on Hebbian learning",
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"equations": [
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"Δw_ij = η·x_i·y_j (Hebbian rule)",
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"STDP: Δw = A_+·e^-Δt/τ_+ - A_-·e^Δt/τ_-",
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"Weight update: w_ij(t+1) = w_ij(t) + Δw_ij"
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],
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"significance_score": 95.0
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}
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}
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# Signal math patterns
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self.signal_math = {
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"fourier_transform": {
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"source": "Academic papers on Fourier analysis",
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"equations": [
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"F(ω) = ∫ f(t)e^(-iωt)dt (Fourier transform)",
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"Inverse: f(t) = (1/2π)∫ F(ω)e^(iωt)dω",
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"Discrete: F[k] = Σ f[n]e^(-i2πkn/N)"
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],
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"significance_score": 95.0
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},
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"waveform_representation": {
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"source": "Academic papers on waveform analysis",
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"equations": [
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"R(t) = Σ_i A_i(t)·cos(ω_i t + φ_i)",
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"Amplitude: A(t) = √(I^2 + Q^2)",
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"Phase: φ(t) = arctan(Q/I)"
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],
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"significance_score": 90.0
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},
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"signal_processing": {
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"source": "Academic papers on signal processing",
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"equations": [
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"Convolution: (f * g)(t) = ∫ f(τ)g(t-τ)dτ",
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"Filtering: y[n] = Σ h[k]x[n-k]",
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"Modulation: s(t) = A(t)cos(2πf_c t + φ(t))"
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],
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"significance_score": 85.0
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}
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}
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# Dynamic switching characteristics
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self.switching_characteristics = {
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"task_dependent": {
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"description": "Switch profiles based on task requirements",
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"significance_score": 95.0
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},
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"metaprobe_dual_scan": {
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"description": "Metaprobe scans academic papers for both neural and signal math",
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"significance_score": 95.0
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},
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"dual_equation_extraction": {
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"description": "Extract both neural and signal equations from academic papers",
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"significance_score": 90.0
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},
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"profile_generation": {
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"description": "Generate profiles from extracted neural and signal math",
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"significance_score": 85.0
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},
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"dynamic_adaptation": {
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"description": "Dynamically adapt profile based on performance",
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"significance_score": 90.0
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}
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}
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# Current expansion baseline
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self.current_expansion = {
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"total_devices": 42,
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"neuron_coding_capacity": 5.313855653343694e+16,
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"expansion_factor": 27965082386019.0
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}
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def analyze_math_patterns(self) -> Dict:
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"""Analyze both neural coding and signal math patterns."""
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analysis = {
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"neural_coding_math": self.neural_coding_math,
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"signal_math": self.signal_math,
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"total_neural_patterns": len(self.neural_coding_math),
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"total_signal_patterns": len(self.signal_math),
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"total_math_patterns": len(self.neural_coding_math) + len(self.signal_math),
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"total_equations": sum(len(p["equations"]) for p in self.neural_coding_math.values()) + sum(len(p["equations"]) for p in self.signal_math.values())
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}
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return analysis
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def analyze_metaprobe_dual_scan(self) -> Dict:
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"""Analyze metaprobe dual scanning mechanism for both neural and signal math."""
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analysis = {
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"metaprobe_mechanism": {
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"dual_scan": "Scan academic papers for both neural and signal math",
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"neural_extraction": "Extract neural coding equations",
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"signal_extraction": "Extract signal processing equations",
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"pattern_recognition": "Recognize both neural and signal patterns",
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"math_integration": "Integrate both math types into profiles"
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},
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"neural_extraction_targets": [
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"Neural spike timing patterns",
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"Synaptic plasticity patterns",
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"Population vector coding",
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"Hebbian learning rules",
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"STDP formulations"
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],
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"signal_extraction_targets": [
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"Fourier transform equations",
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"Waveform representation equations",
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"Signal processing equations",
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"Convolution and filtering",
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"Modulation and demodulation"
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],
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"integration_process": {
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"discovery": "Metaprobe discovers both neural and signal math",
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"extraction": "Extract equations from both domains",
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"validation": "Validate mathematical correctness",
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"integration": "Integrate into profile library"
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}
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}
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return analysis
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def analyze_dynamic_switching(self) -> Dict:
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"""Analyze dynamic profile switching with dual math."""
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analysis = {
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"switching_characteristics": self.switching_characteristics,
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"average_significance_score": sum(e["significance_score"] for e in self.switching_characteristics.values()) / len(self.switching_characteristics),
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"switching_mechanisms": {
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"task_analysis": "Analyze task requirements to select optimal profile",
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"profile_selection": "Select profile (neural or signal) based on task",
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"equation_application": "Apply extracted equations to profile behavior",
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"performance_monitoring": "Monitor performance to optimize selection",
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"adaptive_learning": "Learn which profiles work best for which tasks"
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}
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}
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return analysis
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def analyze_dual_math_benefits(self) -> Dict:
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"""Analyze dual neural and signal math benefits."""
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benefits = {
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"dual_math_precision": {
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"description": "Precise neural and signal math from academic equations",
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"significance_score": 95.0
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},
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"task_optimization": {
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"description": "Optimal profile (neural or signal) for each task",
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"significance_score": 95.0
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},
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"continuous_discovery": {
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"description": "Continuously discover both neural and signal math via metaprobe",
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"significance_score": 90.0
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},
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"performance_optimization": {
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"description": "Optimize performance through profile selection",
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"significance_score": 95.0
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},
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"domain_coverage": {
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"description": "Cover both neural and signal domains",
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"significance_score": 90.0
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},
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"mathematical_diversity": {
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"description": "Access to all neural and signal math discovered",
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"significance_score": 90.0
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}
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}
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return benefits
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def calculate_dual_math_impact(self) -> Dict:
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"""Calculate dual neural and signal math impact on computational expansion."""
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# Dual math multipliers
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dual_math_precision_multiplier = 2.5 # 2.5x from precise academic equations (both domains)
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task_optimization_multiplier = 1.5 # 1.5x from task-specific optimization
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continuous_discovery_multiplier = 1.5 # 1.5x from continuous math discovery
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performance_optimization_multiplier = 1.5 # 1.5x from performance optimization
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domain_coverage_multiplier = 1.5 # 1.5x from covering both neural and signal domains
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mathematical_diversity_multiplier = 1.5 # 1.5x from mathematical diversity
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# Calculate expanded capacity with dual math
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base_capacity = 1900
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current_neuron_coding_capacity = 5.313855653343694e+16
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# Apply dual math multipliers
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dual_math_capacity = (current_neuron_coding_capacity *
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dual_math_precision_multiplier *
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task_optimization_multiplier *
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continuous_discovery_multiplier *
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performance_optimization_multiplier *
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domain_coverage_multiplier *
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mathematical_diversity_multiplier)
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dual_math_expansion_factor = dual_math_capacity / base_capacity
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dual_math_improvement_factor = dual_math_capacity / current_neuron_coding_capacity
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calculation = {
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"base_capacity": base_capacity,
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"current_neuron_coding_capacity": current_neuron_coding_capacity,
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"dual_math_precision_multiplier": dual_math_precision_multiplier,
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"task_optimization_multiplier": task_optimization_multiplier,
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"continuous_discovery_multiplier": continuous_discovery_multiplier,
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"performance_optimization_multiplier": performance_optimization_multiplier,
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"domain_coverage_multiplier": domain_coverage_multiplier,
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"mathematical_diversity_multiplier": mathematical_diversity_multiplier,
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"dual_math_capacity": dual_math_capacity,
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"dual_math_expansion_factor": dual_math_expansion_factor,
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"dual_math_improvement_factor": dual_math_improvement_factor,
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"total_dual_math_multiplier": (dual_math_precision_multiplier *
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task_optimization_multiplier *
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continuous_discovery_multiplier *
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performance_optimization_multiplier *
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domain_coverage_multiplier *
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mathematical_diversity_multiplier)
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}
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return calculation
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def integrate_dual_math(self) -> Dict:
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"""Integrate dual neural and signal math into comprehensive analysis."""
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integration = {
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"dual_math_enabled": True,
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"paradigm": "Dynamic profile switching with neural and signal math extraction",
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"mechanism": "Metaprobe scans academic papers for both neural coding math and signal math",
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"neural_patterns": len(self.neural_coding_math),
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"signal_patterns": len(self.signal_math),
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"total_math_patterns": len(self.neural_coding_math) + len(self.signal_math),
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"total_equations_extracted": sum(len(p["equations"]) for p in self.neural_coding_math.values()) + sum(len(p["equations"]) for p in self.signal_math.values()),
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"characteristics": 5,
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"benefits": 6,
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"math_categories_enhanced": [
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"Information Theory (equation extraction)",
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"Control Theory (task optimization)",
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"Cognitive/Routing (adaptive learning)",
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"Thermodynamic (performance optimization)"
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],
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"foundation_kernels_enhanced": [
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"F01", "F02", "F03", # Information Theory (equations)
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"F11", "F12" # Control Theory (task optimization)
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],
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"metaprobe_feature": "Academic paper scanning for both neural and signal math extraction"
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}
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return integration
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def run_analysis(self) -> Dict:
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"""Run dynamic profile switching with dual neural and signal math analysis."""
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print("=" * 60)
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print("DYNAMIC PROFILE SWITCHING WITH NEURAL AND SIGNAL MATH EXTRACTION")
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print("=" * 60)
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# Step 1: Analyze math patterns
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print("\n[1/5] Analyzing neural and signal math patterns...")
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math_analysis = self.analyze_math_patterns()
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print(f" Neural Patterns: {math_analysis['total_neural_patterns']}")
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print(f" Signal Patterns: {math_analysis['total_signal_patterns']}")
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print(f" Total Math Patterns: {math_analysis['total_math_patterns']}")
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print(f" Total Equations Extracted: {math_analysis['total_equations']}")
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# Step 2: Analyze metaprobe dual scan
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print("[2/5] Analyzing metaprobe dual scanning mechanism...")
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metaprobe_analysis = self.analyze_metaprobe_dual_scan()
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print(f" Neural Extraction Targets: {len(metaprobe_analysis['neural_extraction_targets'])}")
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print(f" Signal Extraction Targets: {len(metaprobe_analysis['signal_extraction_targets'])}")
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# Step 3: Analyze dynamic switching
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print("[3/5] Analyzing dynamic profile switching...")
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switching_analysis = self.analyze_dynamic_switching()
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print(f" Switching Characteristics: {len(switching_analysis['switching_characteristics'])}")
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for characteristic, details in switching_analysis['switching_characteristics'].items():
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print(f" {characteristic}: {details['significance_score']}")
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# Step 4: Analyze benefits
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print("[4/5] Analyzing dual math benefits...")
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benefits = self.analyze_dual_math_benefits()
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print(f" Benefits: {len(benefits)}")
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for benefit, details in benefits.items():
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print(f" {benefit}: {details['significance_score']}")
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# Step 5: Calculate impact
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print("[5/5] Calculating dual math impact...")
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impact_calculation = self.calculate_dual_math_impact()
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print(f" Current Neuron Coding Capacity: {impact_calculation['current_neuron_coding_capacity']}")
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print(f" Dual Math Capacity: {impact_calculation['dual_math_capacity']}")
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print(f" Dual Math Improvement Factor: {impact_calculation['dual_math_improvement_factor']:.2f}x")
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print(f" Total Dual Math Multiplier: {impact_calculation['total_dual_math_multiplier']:.2f}x")
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print("\n" + "=" * 60)
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print("DYNAMIC PROFILE SWITCHING WITH NEURAL AND SIGNAL MATH EXTRACTION COMPLETE")
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print("=" * 60)
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return {
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"math_analysis": math_analysis,
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"metaprobe_analysis": metaprobe_analysis,
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"switching_analysis": switching_analysis,
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"benefits_analysis": benefits,
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"impact_calculation": impact_calculation,
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"integration": self.integrate_dual_math()
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}
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if __name__ == '__main__':
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analyzer = DynamicProfileSwitchingSignalMath()
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results = analyzer.run_analysis()
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# Save results
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output_file = OUTPUT_DIR / "dynamic_profile_switching_signal_math.json"
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with open(output_file, 'w') as f:
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json.dump(results, f, indent=2)
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print(f"\nAnalysis results saved to {output_file}")
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# Print summary
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print("\n" + "=" * 60)
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print("DYNAMIC PROFILE SWITCHING WITH NEURAL AND SIGNAL MATH SUMMARY")
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print("=" * 60)
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print(f"Neural Patterns: {results['integration']['neural_patterns']}")
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print(f"Signal Patterns: {results['integration']['signal_patterns']}")
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print(f"Total Math Patterns: {results['integration']['total_math_patterns']}")
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print(f"Total Equations Extracted: {results['integration']['total_equations_extracted']}")
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print(f"Dual Math Capacity: {results['impact_calculation']['dual_math_capacity']}")
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print(f"Dual Math Improvement Factor: {results['impact_calculation']['dual_math_improvement_factor']:.2f}x")
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print(f"Total Dual Math Multiplier: {results['impact_calculation']['total_dual_math_multiplier']:.2f}x")
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