OlsGDTrainer.h

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#ifndef OLSGDTRAINER_H
#define OLSGDTRAINER_H
#pragma once
 
#include "txeo/Matrix.h"
#include "txeo/Tensor.h"
#include "txeo/TensorShape.h"
#include "txeo/Trainer.h"
 
#include <concepts>
#include <cstddef>
#include <stdexcept>
 
namespace txeo {
enum class LossFunc;
 
template <typename T>
  requires(std::floating_point<T>)
class OlsGDTrainer : public txeo::Trainer<T> {
  public:
    OlsGDTrainer(const OlsGDTrainer &) = delete;
    OlsGDTrainer(OlsGDTrainer &&) = delete;
    OlsGDTrainer &operator=(const OlsGDTrainer &) = delete;
    OlsGDTrainer &operator=(OlsGDTrainer &&) = delete;
    ~OlsGDTrainer() = default;
 
    OlsGDTrainer(txeo::DataTable<T> &&data) : txeo::Trainer<T>(std::move(data)) {};
 
    OlsGDTrainer(const txeo::DataTable<T> &data) : txeo::Trainer<T>(data) {};
 
    txeo::Tensor<T> predict(const txeo::Tensor<T> &input) const override;
 
    [[nodiscard]] T learning_rate() const;
 
    void set_learning_rate(T learning_rate);
 
    void enable_variable_lr() { _variable_lr = true; }
 
    void disable_variable_lr() { _variable_lr = false; }
 
    const txeo::Matrix<T> &weight_bias() const;
 
    T tolerance() const { return _tolerance; }
 
    void set_tolerance(const T &tolerance);
 
    [[nodiscard]] bool is_converged() const { return _is_converged; }
 
    T min_loss() const;
 
  private:
    T _learning_rate{0.01};
    T _tolerance{0.001};
    T _min_loss{0};
    txeo::Matrix<T> _weight_bias{};
    bool _variable_lr{false};
    bool _is_converged{false};
 
    OlsGDTrainer() = default;
    void train(size_t epochs, txeo::LossFunc metric) override;
};
 
class OlsGDTrainerError : public std::runtime_error {
  public:
    using std::runtime_error::runtime_error;
};
 
} // namespace txeo
 
#endif