Perceptron.h

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#pragma once
 
#include "../Matrix.h"
#include "../matrix_utils.h"
#include "Classifier.h"
#include "SGD.h"
 
class Perceptron : public ANNClassifier
{
public:
  explicit Perceptron(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++) {
      int _errors   = 0;
      auto iterable = zip(X, y);
      for(const auto& elem : iterable) {
        auto xi     = elem.first;
        auto target = elem.second;
 
        auto output  = predict(xi);
        auto delta_w = (target - output);
        update_weights(delta_w, (delta_w * xi).Transpose() * eta);
        _errors += costFunction(delta_w);
      }
      costs(iter, 0) = _errors;
    }
  }
 
  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 (double)(X(0, 0) != 0.0); }
};