9bbe253810
- CMakeLists.txt with libtorch, GoogleTest, GoogleBenchmark, OpenMP, pybind11 - Header files: config, controller, population, fitness, evolution, spectral, oscillation, telemetry, optimizer - Source implementations: controller (full micro-MLP forward pass, mutation, crossover), fitness (Welford's algorithm), oscillation (DFT), spectral (SVD rank), optimizer (sign-SGD stub) - Tests: controller, population, fitness, optimizer (Google Test) - Benchmarks: evolve throughput, optimizer step (Google Benchmark) - Examples: simple optimization, PyTorch/libtorch integration - Python extension: pybind11 bindings with setup.py - README with architecture diagram and build instructions
91 lines
2.1 KiB
C++
91 lines
2.1 KiB
C++
#pragma once
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/**
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* @file fitness.hpp
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* @brief Fitness evaluation — loss signal processing and multi-objective evaluation.
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*
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* Port of: packages/fces/core/fitness_engine.py + fitness.py
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*/
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#include <cmath>
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#include <vector>
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namespace fces {
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/**
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* Running statistics tracker (Welford's algorithm).
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* Thread-safe, O(1) memory, numerically stable.
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*/
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class RunningStats {
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public:
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void update(float value);
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float z_score(float value) const;
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float get_mean() const { return mean_; }
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float get_std() const;
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int get_count() const { return count_; }
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void reset();
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private:
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int count_ = 0;
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float mean_ = 0.0f;
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float m2_ = 0.0f;
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};
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/**
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* FitnessEngine — processes raw loss values into controller fitness signals.
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*/
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class FitnessEngine {
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public:
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explicit FitnessEngine(float grokking_coefficient = 0.1f);
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/**
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* Calculate loss velocity signal.
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*
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* @param current_loss Current step loss
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* @param ema_loss Exponential moving average loss
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* @param mode "relative" or "absolute"
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* @return Velocity signal (negative = improving)
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*/
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float calculate_loss_signal(float current_loss, float ema_loss, const std::string& mode = "relative") const;
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/**
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* Compute Kibble-Zurek Mechanism damping factor.
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* Prevents topological defects during phase transitions.
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*/
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float compute_kzm_damping(float spectral_alpha) const;
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private:
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float grokking_coefficient_;
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};
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/**
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* Fitness metrics for multi-objective evaluation.
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*/
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struct FitnessMetrics {
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float loss_improvement = 0.0f;
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float sparsity_score = 0.0f;
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float stability_score = 0.0f;
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float novelty_score = 0.0f;
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/// Weighted combination
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float total(float alpha = 0.7f, float beta = 0.3f) const {
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return alpha * loss_improvement + beta * sparsity_score;
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}
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};
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/**
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* FuzzyFitnessEvaluator — multi-objective fitness evaluation with fuzzy weighting.
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*/
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class FuzzyFitnessEvaluator {
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public:
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FitnessMetrics evaluate(
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float loss_before,
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float loss_after,
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float sparsity = 0.0f,
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float val_loss = -1.0f
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) const;
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};
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} // namespace fces
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