C++ LinearRegression代码实现

#pragma once
#ifndef ML_LINEAEEEGRESSION_H
#define ML_LINEARREGRESSION_H
class LinearRegression {
public:
    /*特征*/
    double *x;
    /*预测值*/
    double *y;
    /*样本数量*/
    int m;
    /*系数*/
    double *theta;
    /*创建实例*/
    LinearRegression(double x[], double y[], int m);
    /*训练 */
    void train(double alpha, int iterations);
    /*预测*/
    double predict(double x);
private:
    /*计算损失模型*/
    static double compute_cost(double x[], double y[], double theta[], int m);
    /*计算单个预测值*/
    static double h(double x, double theta[]);
    /*预测*/
    static double *calculate_predictions(double x[], double theta[], int m);
    /*梯度下降*/
    static double *gradient_descent(double x[], double y[], double alpha, int iter, double *j, int m);

};
#endif // !ML_LINEAEEEGRESSION_H

#include "iostream"
#include "linearRegression.h"
#include "Utils.h"
using namespace std;

/*初始化*/
LinearRegression::LinearRegression(double x[], double y[], int m)
{
    this->x = x;
    this->y = y;
    this->m = m;
}

/*
alpha:learn rate
iterations:iterators
*/
void LinearRegression::train(double alpha, int iterations)
{
    double *J = new double[iterations];
    this->theta = gradient_descent(x, y, alpha, iterations, J, m);
    cout << "J=";
    for (int i = 0; i < iterations; ++i)
    {
        cout << J[i] << " " << endl;;
    }
    cout << "\n" << "Theta: " << theta[0] << " " << theta[1] << endl;
}
/*预测*/
double LinearRegression::predict(double x)
{
    cout << "y‘:" << h(x, theta) << endl;
    return h(x, theta);
}

/*计算损失模型*/
double LinearRegression::compute_cost(double x[], double y[], double theta[], int m)
{
    double *predictions = calculate_predictions(x, theta, m);
    double *diff = Utils::array_diff(predictions, y, m);
    double *sq_errors = Utils::array_pow(diff, m, 2);
    return (1.0 / (2 * m))*Utils::array_sum(sq_errors, m);
}
/*计算单个预测值*/
double LinearRegression::h(double x, double theta[])
{
    return theta[0] + theta[1] * x;
}
/*预测*/
double *LinearRegression::calculate_predictions(double x[], double theta[], int m)
{
    double *predictions = new double[m];
    for (int i = 0; i < m; i++)
    {
        predictions[i] = h(x[i], theta);
    }
    return predictions;
}
/*梯度下降*/
double *LinearRegression::gradient_descent(double x[], double y[], double alpha, int iter, double *J, int m)
{
    double *theta = new double[2];
    theta[0] = 1;
    theta[1] = 1;
    for (int i = 0; i < iter; i++)
    {
        double *predictions = calculate_predictions(x, theta, m);
        double *diff = Utils::array_diff(predictions, y, m);
        double *error_x1 = diff;
        double *error_x2 = Utils::array_multiplication(diff, x, m);
        /*这里可以设定J损失函数的阈值,也可以设定梯度变化量的阈值*/
        theta[0] = theta[0] - alpha*(1.0 / m) * Utils::array_sum(error_x1, m);
        theta[1] = theta[1] - alpha*(1.0 / m)*Utils::array_sum(error_x2, m);
        J[i] = compute_cost(x, y, theta, m);    
    }
    return theta;
}

#include "iostream"
#include "linearRegression.h"

using namespace std;

int main()
{
    double x[10] = {1,2,3,4,5};
    double y[10] = {4,6,8,11,12};
    
    LinearRegression test(x,y,10);
    test.train(0.1, 100);
    test.predict(7);
    system("pause");
    return 0;
}