【机器学习学习过程中的笔记1——Stochastic gradient descent 和 Batch gradient descent 】

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<span style="font-size:18px;">#include "stdio.h"  #include<iostream>using namespace std;#include "stdio.h"  int main(void){float matrix[4][2] = { { 1, 4 }, { 2, 5 }, { 5, 1 }, { 4, 2 } };float result[4] = { 19, 26, 19, 20 };float theta[2] = { 2, 5 };                   float learning_rate = 0.01;float loss = 1000.0;                    for (int i = 0; i<100 && loss>0.0001; ++i){float error_sum[2] = { 0.0, 0.0 };for (int j = 0; j<4; ++j){float h = 0.0;for (int k = 0; k<2; ++k){h += matrix[j][k] * theta[k];}for (int k = 0; k < 2; ++k){error_sum[k] += (result[j] - h)*matrix[j][k];}}for (int k = 0; k<2; ++k){theta[k] += learning_rate*error_sum[k];}printf("*************************************\n");printf("theta now: %f,%f\n", theta[0], theta[1]);loss = 0.0;for (int j = 0; j<4; ++j){float sum = 0.0;for (int k = 0; k<2; ++k){sum += matrix[j][k] * theta[k];}loss += (sum - result[j])*(sum - result[j]);}printf("loss  now: %f\n", loss);}system("pause");// return 0;}</span>

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