Contoh regresi linier dan regresi kuadratik pada C++

regresi_kuadratik.cpp

#include <iostream>
#include <iomanip>
#include <cmath>

using namespace std;

// ==================
// CONTOH DATA (X, Y)
// ==================
const int n = 6;
float data[n][2] = {
  { 0, 2.1 },
  { 1, 7.7 },
  { 2, 13.6 },
  { 3, 27.2 },
  { 4, 40.9 },
  { 5, 61.1 }
};

// only support 3x3 for now
void printGaussMatrix (float X[3][3], float Y[3]) {
  for (int i = 0; i < 3; ++i) {
    for (int j = 0; j < 3; ++j) {
      cout.width(10);
      cout << right << X[i][j];
    }
    cout << "  | ";
    cout.width(10);
    cout << right << Y[i] << '\n';
  }
}

int main () {
  cout.precision(2);
  cout.setf(ios::fixed);
  float sumX = 0, sumX2 = 0, sumX3 = 0, sumX4 = 0;
  float sumY = 0, sumXY = 0, sumX2Y = 0;

  for (int i = 0; i < n; ++i) {
    sumX += data[i][0];
    sumX2 += pow(data[i][0], 2);
    sumX3 += pow(data[i][0], 3);
    sumX4 += pow(data[i][0], 4);
    sumY += data[i][1];
    sumXY += data[i][0] * data[i][1];
    sumX2Y += pow(data[i][0], 2) * data[i][1];
  }

  float X[3][3] = {
    n,      sumX,   sumX2,
    sumX,   sumX2,  sumX3,
    sumX2,  sumX3,  sumX4
  };

  float Y[3] = {
    sumY,
    sumXY,
    sumX2Y
  };

  cout << "Iter# " << 0 << ":" << endl;
  printGaussMatrix(X, Y);
  cout << endl;

  // ELIMINASI GAUSS
  const int XROWS = 3;
  const int XCOLS = 3;
  const int YROWS = 3;
  for (int i = 0; i < XCOLS - 1; ++i) {
    for (int j = i + 1; j < XROWS; ++j) {
      float d = X[j][i] / X[i][i];

      for (int k = 0; k < XCOLS; ++k) {
        X[j][k] = X[j][k] - X[i][k] * d;
      }

      Y[j] = Y[j] - Y[i] * d;

      cout << "Iter# "
           << i + 1
           << ","
           << j + 1
           << " pembagi: "
           << d
           << endl;
      printGaussMatrix(X, Y);
      cout << endl;
    }
  }

  // SUBSTITUSI MUNDUR

  float A[3]; // i => 0, 1, 2
  float s;

  for (int i = 2; i >= 0; i--) {
    s = 0;
    if (i < 2) {
      for (int j = i + 1; j <= 2; ++j) {
        s += X[i][j] * A[j];
      }
    }
    A[i] = (Y[i] - s) / X[i][i];
    cout << "A"
         << i
         << " = "
         << A[i]
         << endl;
  }

  float totalErrorY = 0;

  for (int i = 0; i < n; ++i) {
    float prediction = A[0]
                     + A[1] * data[i][0]
                     + A[2] * pow(data[i][0], 2);
    totalErrorY += abs(data[i][1] - prediction);
  }

  float meanAbsErrorY = totalErrorY / n;

  cout << "y = "
       << A[0]
       << " + "
       << A[1]
       << "x"
       << " + "
       << A[2]
       << "x2"
       << ", err ~"
       << meanAbsErrorY
       << endl;

  return 0;
}

regresi_linier.cpp

#include <iostream>
#include <cmath>

using namespace std;

// ========================
// DATA PERTUMBUHAN TANAMAN
// ========================
const int n = 7;
int data[n][2] = {
  { 1, 2 },
  { 3, 5 },
  { 7, 12 },
  { 9, 14 },
  { 11, 15 },
  { 20, 20 },
  { 22, 25 }
};

int main () {
  int sumXY = 0, sumX = 0, sumY = 0, sumX2 = 0;

  for (int i = 0; i < n; ++i) {
    sumXY += data[i][0] * data[i][1];
    sumX += data[i][0];
    sumY += data[i][1];
    sumX2 += pow(data[i][0], 2);
  }

  float avgY = sumY / n;
  float avgX = sumX / n;

  float a1 = (n * sumXY - sumX * sumY)
           / (n * sumX2 - pow(sumX, 2));

  float a0 = avgY - a1 * avgX;

  float totalErrorY = 0;

  for (int i = 0; i < n; ++i) {
    float prediction = a0 + a1 * data[i][0];
    totalErrorY += abs(data[i][1] - prediction);
  }

  float meanAbsErrorY = totalErrorY / n;

  cout << "y = "
       << a0
       << " + "
       << a1
       << "x"
       << ", err ~"
       << meanAbsErrorY
       << endl;

  return 0;
}