AdalineSGD.h

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#pragma once
 
#include "../Matrix.h"
#include "Classifier.h"
#include "SGD.h"
 
class AdalineSGD : public ANNClassifier
{
public:
  bool shuffle;
  int randomState;
  SGD sgd;
 
public:
  explicit AdalineSGD(double _eta = 0.01, int iter = 10, bool _shuffle = false, int _randomState = 0)
    : ANNClassifier(_eta, iter)
    , shuffle(_shuffle)
    , randomState(_randomState) {
    if(randomState != 0) Random::SetSeed(randomState);
    sgd = SGD(_eta, iter, _shuffle);
  }
 
  void fit(const Matrix<double>& X, const Matrix<double>& y) override {
    initialize_weights(X.columns());
    sgd.fit(X, y, weights);
  }
 
  void partial_fit(const Matrix<double>& X, const Matrix<double>& y) {
    if(!w_initialized) { initialize_weights(X.columns()); }
    sgd.partial_fit(X, y, weights);
  }
 
  Matrix<double> netInput(const Matrix<double>& X) override { return sgd.netInput(X, weights); }
 
  double costFunction([[maybe_unused]] const Matrix<double>& X) override { return 0; }
 
  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 } });
  }
};