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
146 lines
4.6 KiB
C++
146 lines
4.6 KiB
C++
#pragma once
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/**
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* @file controller.hpp
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* @brief FuzzyController and Genome — the decision-making units of FCES.
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*
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* Each controller contains a Genome (neural network weights) that maps
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* layer statistics to update decisions (multiplier, sign_gate, wd_mult).
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*
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* Port of: packages/fces/core/controller.py
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*/
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#include <array>
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#include <cstdint>
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#include <memory>
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#include <random>
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#include <string>
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#include <vector>
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#include <torch/torch.h>
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namespace fces {
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// Controller input dimension (layer stats features)
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constexpr int GENOME_INPUT_DIM = 9;
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// Controller hidden dimension
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constexpr int GENOME_HIDDEN_DIM = 16;
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// Controller output dimension: [multiplier, sign_gate, wd_mult]
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constexpr int GENOME_OUTPUT_DIM = 3;
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// Total genome size: input->hidden weights + hidden biases + hidden->output weights + output biases
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constexpr int GENOME_SIZE =
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(GENOME_INPUT_DIM * GENOME_HIDDEN_DIM) + // input -> hidden weights
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GENOME_HIDDEN_DIM + // hidden biases
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(GENOME_HIDDEN_DIM * GENOME_OUTPUT_DIM) + // hidden -> output weights
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GENOME_OUTPUT_DIM; // output biases
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/**
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* Genome — the "DNA" of a fuzzy controller.
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* A flat array of floats encoding a micro-MLP.
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*/
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struct Genome {
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std::array<float, GENOME_SIZE> weights{};
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std::array<float, GENOME_SIZE> gene_success{};
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float sigma_gene = 0.1f;
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float plasticity = 1.0f;
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/// Initialize with random weights from a normal distribution
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void randomize(std::mt19937& rng);
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/// Deep copy
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Genome clone() const;
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};
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/**
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* FuzzyController — a single agent in the evolutionary population.
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*
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* Lifecycle:
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* 1. Created via random initialization or crossover/mutation
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* 2. Activated for `selection_interval` steps
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* 3. Evaluated based on loss improvement during its tenure
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* 4. Evolved (crossover/mutation) or culled based on fitness
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*/
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class FuzzyController {
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public:
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/// Unique identifier
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uint64_t id;
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/// The neural genome
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Genome genome;
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/// Fitness scores
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float fitness = 0.0f;
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float lifetime_fitness = 0.0f;
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float ema_fitness = 0.0f;
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int evaluation_count = 0;
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int age = 0;
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/// Origin tracking
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std::string origin = "random";
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/// Trust region violation counter
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int trust_violations = 0;
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/// Rolling fitness history (for Phase 23 strategies)
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std::vector<float> fitness_history;
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// ---------------------------------------------------------------
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// Construction
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// ---------------------------------------------------------------
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FuzzyController();
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explicit FuzzyController(Genome genome);
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// ---------------------------------------------------------------
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// Core Operations
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// ---------------------------------------------------------------
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/**
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* Forward pass through the micro-MLP to produce update decisions.
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*
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* @param layer_stats Vector of per-layer feature maps
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* @param loss_trend Current loss velocity
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* @param step_pct Training progress [0, 1]
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* @param rollback_rate Rolling average rollback frequency
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* @param grad_stability Gradient coefficient of variation
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* @param spectral_alpha Log spectral rank
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* @param stagnation_intensity Stagnation counter / 500
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* @param kzm_damping Kibble-Zurek damping factor
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* @param projected_drift Projected loss drift
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* @return Tensor of shape [num_groups, 3] — (mult, sign_gate, wd_mult)
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*/
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torch::Tensor decide_update(
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const std::vector<std::vector<float>>& layer_stats,
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float loss_trend,
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float step_pct,
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float rollback_rate,
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float grad_stability,
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float spectral_alpha,
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float stagnation_intensity,
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float kzm_damping,
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float projected_drift
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);
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// ---------------------------------------------------------------
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// Evolutionary Operators
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// ---------------------------------------------------------------
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/// Create a mutated child
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FuzzyController mutate(float current_loss, float sigma_scale = 1.0f) const;
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/// Crossover with another controller
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FuzzyController crossover(const FuzzyController& partner, bool use_alignment = true) const;
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/// Create an orthogonal counter-strategy (Phoenix Rebirth)
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FuzzyController create_orthogonal_child(float intensity = 1.0f) const;
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/// Banach-Tarski fission: split into two complementary children
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std::pair<FuzzyController, FuzzyController> banach_tarski_fission(float intensity = 1.0f) const;
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private:
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static std::atomic<uint64_t> next_id_;
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static thread_local std::mt19937 rng_;
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};
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} // namespace fces
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