AdalineGD.h

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#pragma once
 
#include "../Matrix.h"
#include "Classifier.h"
#include "SGD.h"
 
class AdalineGD : public ANNClassifier
{
public:
  explicit AdalineGD(double _eta = 0.01, int iter = 10)
    : ANNClassifier(_eta, iter) { }
 
  void fit(const Matrix<double>& X, const Matrix<double>& y) override {
    initialize_weights(X.columns());
    costs = Matrix<double>(0, n_iter, 1);
    for(int iter = 0; iter < n_iter; iter++) {
      auto output = netInput(X);
      auto errors = y - output;
 
      auto delta_w = (X.Transpose() * errors) * eta;
 
      update_weights(Matrix<double>(eta * errors.sumElements(), weights.rows(), weights.columns()), delta_w);
      auto cost      = costFunction(errors);
      costs(iter, 0) = cost;
    }
  }
 
  Matrix<double> netInput(const Matrix<double>& X) override { return SGD::netInput(X, weights); }
 
  Matrix<double> activation(const Matrix<double>& X) override { return netInput(X); }
 
  Matrix<double> predict(const Matrix<double>& X) override {
    std::function<bool(double)> condition = [](double x) { return bool(x >= 0.0); };
    return where(condition, activation(X), { { 1 } }, { { -1 } });
  }
 
  double costFunction(const Matrix<double>& X) override { return HadamardMulti<double>(X, X).sumElements() / 2.0; }
};