layer.h

#pragma once

#include "Threshold_Func.h"
#include <Eigen/Dense>
#include <EigenRand/EigenRand>
#include <utility>

namespace network {

class Layer {
public:
  using MatrixXd = Eigen::MatrixXd;
  using VectorXd = Eigen::VectorXd;
  Layer(Threshold_Id id, int rows, int columns);

  MatrixXd apply(const MatrixXd &x) const; // vector of values
  MatrixXd derive(const MatrixXd &vec)
      const; // vec is a matrix of y_i = (Ax + b)_i - result of apply

  MatrixXd gradA(const VectorXd &x, const VectorXd &u,
                 const VectorXd &vec) const; // u is a gradient vector

  MatrixXd gradb(const VectorXd &u, const VectorXd &vec) const;

  VectorXd gradx(const VectorXd &u, const VectorXd &vec) const;

  void apply_gradA(const MatrixXd &grad, double step);

  void apply_gradb(const VectorXd &grad, double step);

private:
  static MatrixXd getNormal(int rows, int columns);
  static inline Eigen::Rand::Vmt19937_64 urng = 1;
  Threshold_Func threshold_func_;
  MatrixXd A_;
  MatrixXd b_;
};
} // namespace network