C++代码实现梯度下降算法并付出测试用例
C++代码实现梯度下降算法并给出测试用例
此处仅给出代码实现，具体原理及过程请看前面的博文
测试文件输入格式如下：
含义如下：2 10 0.01 10 2104 3 399900 1600 3 329900 2400 3 369000 1416 2 232000 3000 4 539900 1985 4 299900 1534 3 314900 1427 3 198999 1380 3 212000 1494 3 242500 输出如下：53052890086.237 51993904900.868 50956770155.817 49941026552.120 48946224657.273 47971924687.609 47017696295.619 46083118362.109 45167778793.089 44271274321.262 30014.457 8183.543 4763.016
特征值的归一化方法与之前的博文不太一样，采用了另一种归一方式：
具体的实现代码如下：
#include <iostream>
#include <stdio.h>
#include <math.h>
using namespace std;
double predict(double* w,double* data,int feature_num){
double sum=0;
for(int i=0;i<feature_num;i++){
sum+=w[i]*data[i];
}
return sum;
}
double Theta(double **training_set,int featue_num,int training_num,double* w){
double sum=0;
for(int i=0;i<training_num;i++){
sum+=(training_set[i][featue_num]-predict(w,training_set[i],featue_num))*(training_set[i][featue_num]-predict(w,training_set[i],featue_num));
}
return sum/(2*training_num);
}
void gradient_descent(double** training_set,int feature_num,int training_num,double* w,double a,int iterator_time){
while(iterator_time--){
double* del_theta=new double[feature_num];
for(int i=0;i<feature_num;i++){
del_theta[i]=0;
for(int j=0;j<training_num;j++){
del_theta[i]+=(predict(w,training_set[j],feature_num)-training_set[j][feature_num])*training_set[j][i];
}
}
//w[i]的更新必须等所有的del_theta测算出来了才可以！不然更新的会影响没更新的
//上述问题在代码内表示即是下面的for循环不能和上面的合并！
for(int i=0;i<feature_num;i++)
w[i]-=a*del_theta[i]/(double)training_num;
printf("%.3lf\n", Theta(training_set,feature_num,training_num,w));
delete[] del_theta;
}
for(int i=0;i<feature_num-1;i++){
printf("%.3lf ", w[i]);
}
printf("%.3lf\n", w[feature_num-1]);
return;
}
void feature_normalize(double **feature_set,int feature_num,int training_num){
//特征归一化，此处特征归一化方法略有不同于梯度下降那篇的特征归一化方法
//问题：特征归一化Y的值需要归一化么？不需要！
double *average=new double[feature_num];
double *stanrd_divition=new double[feature_num];
for(int i=1;i<feature_num;i++){
double sum=0;
for(int j=0;j<training_num;j++){
sum+=feature_set[j][i];
}
average[i]=sum/training_num;
}
for(int i=1;i<feature_num;i++){
double sum=0;
for(int j=0;j<training_num;j++){
sum+=(feature_set[j][i]-average[i])*(feature_set[j][i]-average[i]);
}
stanrd_divition[i]=sqrt((sum/(training_num-1)));
}
for(int i=1;i<feature_num;i++)
for(int j=0;j<training_num;j++){
feature_set[j][i]=(feature_set[j][i]-average[i])/(double)stanrd_divition[i];
}
delete[] stanrd_divition;
delete[] average;
}
int main(){
int feature_num,training_num,times;
double a;
//自己测试时请修改路径
freopen("in.txt","r",stdin);
while(cin>>feature_num>>training_num>>a>>times){
double **featurn_set=new double*[training_num];
for(int i=0;i<training_num;i++){
featurn_set[i]=new double[feature_num+2];
}
for(int i=0;i<training_num;i++) featurn_set[i][0]=1;
for(int i=0;i<training_num;i++){
for(int j=1;j<=feature_num+1;j++){
cin>>featurn_set[i][j];
}
}
//在特征标准化的时候w0完全一样，就直接赋值为一，不要进行标准化了，不然会出错
feature_normalize(featurn_set,feature_num+1,training_num);
for(int i=0;i<training_num;i++) featurn_set[i][0]=1;
double* w=new double[feature_num+1];
for(int i=0;i<=feature_num;i++)
w[i]=0;
gradient_descent(featurn_set,feature_num+1,training_num,w,a,times);
for(int i=0;i<training_num;i++) delete[] featurn_set[i];
delete[] featurn_set;
delete[] w;
}
return 0;
}
执行结果如下：