tests.cpp

#include "linear_regression.h"
#include "primitives.h"
#include "rapidcsv.h"

using namespace std;
// Vector1D<double> y = Vector1D<double>(3);

// y[0] = 1;
// y[1] = 2;
// y[2] = 3;

// X[0][0] = 1;
// X[0][1] = 2;
// X[0][2] = 3;
// X[1][0] = 4;
// X[1][1] = 5;
// X[1][2] = 6;
// X[2][0] = 7;
// X[2][1] = 8;
// X[2][2] = 9;

// Vector1D<double> result = Vector1D<double>(3);
// linear_regression<double> lr = linear_regression<double>(3, 3);
// //test dot product , matrix and vector
// result = lr.predict(X);
// std::cout << result << std::endl;
// std::cout << X << std::endl;
// y = y+y; // vector addition
// std::cout << (y) << std::endl;

// rapidcsv::Document doc("../Salary_Data.csv");
// Vector1D<double> X = doc.GetColumn<double>("YearsExperience");
// std::cout<<Xn<<std::endl;
int main() {
  rapidcsv::Document Xdoc("../Xarr.csv");
  rapidcsv::Document Ydoc("../Yarr.csv");

  Matrix<double> Xn = Matrix<double>(Xdoc.GetRowCount(), Xdoc.GetColumnCount());
  Vector1D<double> y = Vector1D<double>(Ydoc.GetColumn<double>(0));
  for (auto i = 0; i < Xdoc.GetRowCount(); i++) {
    Xn[i] = Xdoc.GetRow<double>(i);
  }

  linear_regression<double> lr =
      linear_regression<double>(Xdoc.GetColumnCount());
  lr.fit(Xn, y, 10000);
  cout << "The weights are" << endl;
  cout << lr.getWeights() << endl;
  cout << "The bias" << endl;
  cout << lr.getBias() << endl;

  return 0;
}