74c52fadc7
Phase 6 — State persistence: - Full state serialization: version, weights (3D), examples (features+labels), mlpConfig, noiseLevel, slewMs, output/input ranges - Validation on load: version check, graceful missing field handling - .nisps preset save/load via right-click menu (osdialog file dialogs) - Param values included in preset files Phase 7 — Derived outputs: - Mean, STD, delta computed on audio thread (trivial cost) - Novelty/confidence: nearest_example_distance() computed on background thread after each training/perturbation job, cached for audio thread - Defaults with 0 examples: novelty=10V, confidence=0V nisps-core IML additions: - get_weights() / set_weights() for MLP weight serialization - get_example_features/labels() / load_examples() for dataset serialization - nearest_example_distance() for novelty/confidence metric - get_example_count() / get_max_examples() for UI display
111 lines
3.4 KiB
C++
111 lines
3.4 KiB
C++
#ifndef NISPS_IML_HPP
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#define NISPS_IML_HPP
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#include "mlp.hpp"
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#include "dataset.hpp"
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#include <vector>
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#include <cstddef>
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#include <functional>
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namespace nisps {
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template<typename Float = float>
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class IML {
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public:
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enum class Mode { Inference, Training };
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using LogFn = void(*)(const char*);
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IML(size_t n_inputs, size_t n_outputs,
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std::vector<size_t> hidden_layers = {10, 10, 14},
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size_t max_iterations = 1000,
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Float learning_rate = 1.0f,
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Float convergence_threshold = 0.00001f);
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// Input
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void set_input(size_t index, Float value);
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void set_inputs(const Float* values, size_t count);
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// Output (valid after process())
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const Float* get_outputs() const;
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size_t num_inputs() const { return n_inputs_; }
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size_t num_outputs() const { return n_outputs_; }
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// Set outputs directly (for programmatic training without hardware)
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void set_output(size_t index, Float value);
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void set_outputs(const Float* values, size_t count);
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// Runtime
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void process();
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// Training workflow
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void set_mode(Mode mode);
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Mode get_mode() const { return mode_; }
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void save_example();
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void add_example(const Float* inputs, size_t n_in, const Float* outputs, size_t n_out);
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void clear_dataset();
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void randomise_weights();
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// Spread-aware weight randomization
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// spread: 0 = uniform [-1,1], 1 = Xavier-scaled per layer
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void randomise_weights(Float spread);
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// Spread-aware weight perturbation (for RL exploration)
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// speed: noise magnitude, spread: 0 = flat noise, 1 = Xavier-scaled + weight decay
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void move_weights(Float speed, Float spread);
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// ── Serialization accessors ───────────────────────────────────────
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// Weight access (delegates to MLP)
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typename MLP<Float>::mlp_weights get_weights() const;
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void set_weights(typename MLP<Float>::mlp_weights& weights);
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// Dataset access
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size_t get_example_count() const;
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size_t get_max_examples() const;
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// Returns copies of the dataset vectors
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std::vector<std::vector<Float>> get_example_features() const;
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std::vector<std::vector<Float>> get_example_labels() const;
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// Bulk-load examples (clears existing, adds all)
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void load_examples(const std::vector<std::vector<Float>>& features,
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const std::vector<std::vector<Float>>& labels);
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// Nearest-neighbor distance for novelty/confidence computation
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// Returns the minimum Euclidean distance from `input` to any training example
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Float nearest_example_distance(const Float* input, size_t n_in) const;
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// Optional logging
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void set_logger(LogFn fn) { log_fn_ = fn; }
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private:
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void log(const char* msg) const {
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if (log_fn_) log_fn_(msg);
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}
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void train();
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size_t n_inputs_;
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size_t n_outputs_;
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size_t max_iterations_;
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Float learning_rate_;
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Float convergence_threshold_;
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Mode mode_ = Mode::Inference;
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bool input_updated_ = false;
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bool perform_inference_ = true;
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std::vector<Float> input_state_;
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std::vector<Float> output_state_;
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std::unique_ptr<Dataset> dataset_;
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std::unique_ptr<MLP<Float>> mlp_;
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typename MLP<Float>::mlp_weights stored_weights_;
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bool weights_randomised_ = false;
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LogFn log_fn_ = nullptr;
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};
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} // namespace nisps
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#include "iml_impl.hpp"
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#endif // NISPS_IML_HPP
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