参考文献：
[1]强化学习与深度学习通过C语言模拟

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此篇博客和之前一篇博客 https://blog.csdn.net/captainAAAjohn/article/details/108570209 \url{https://blog.csdn.net/captainAAAjohn/article/details/108570209} https://blog.csdn.net/captainAAAjohn/article/details/108570209后半部分为参考文献中所有代码的复刻，书中的一些bug被改，但是可能仍存在小错误，毕竟只是为了了解其他人的思路，没有其他目的。

C相对于python可能更快一些吧，这也很好理解，毕竟如果你想将中文翻译成英文，最快的方式就是直接将中文翻译成英文，而不是先将中文翻译成其他语言再翻译成英文。

如果报错“undefined reference to …”
这证明你的c文件创立的路径不太对，所以编译器无法找到math.h的定义。
解决方法有两种
第一种就是修改文件的路径使编译器可以找到math的定义
第二种也是比较常见的方式，但是治标不治本。
\qquad 首先打开终端，然后cd到文件的根目录(ps：编译器的终端窗口是默认在文件所在根目录的)
\qquad 在终端中输入:gcc -o 编译后文件名称 c文件名称.c -lm
\qquad 文件根目录中出现 “编译后文件名称” 后在终端中继续输入:./编译后文件名称.

项目后续不断更新。

# include <stdio.h>
# include <stdlib.h>
# include <math.h>

# define INPUTNO 2 /*Cell num of input*/
# define ALPHA 1/*learning rate*/
# define MAXNO 100/*maximum of training data*/
# define BIGNUM 100/*initialize the error*/
# define LIMIT 0.001/*limit of the random_error*/
# define SEED 63553/*random seed*/
# define _CRT_SECURE_NO_WARNINGS

void initwo(double wo[INPUTNO+1]);/*randomly initialize the weighten of input*/
int getdata(double e[][INPUTNO+1]); /*input the learning data*/
double forward(double wo[INPUTNO+1],double e[INPUTNO+1]);/*compute in turn*/
void olearn(double wo[INPUTNO+1],double e[INPUTNO+1], double o);/*output the weighten*/
void printweight(double wo[INPUTNO+1]);/*output the result*/
double s(double u); /*sigmoid function*/
double drand(void);/*generate number from -1 to 1*/



int main() {

    double wo[INPUTNO + 1];/*w of output*/
    double e[MAXNO][INPUTNO + 1];/*learning data*/
    double o;/*output*/
    double err = BIGNUM;/*evaluate the error*/
    int i, j;/*for cycle*/
    int n_of_e;/*size of learning data*/
    int count = 0;

    srand(SEED);/*initialize the random seed*/
    /*initialize and print the weight*/
    initwo(wo);
    printweight(wo);

    /*input the learning data*/
    n_of_e = getdata(e);

    while (err > LIMIT) {
        err = 0.0;
        for (j = 0; j < n_of_e; j++) {
            /*compute in turn*/
            o = forward(wo, e[j]);/*positively forward*/
            olearn(wo,e[j],o);/*neg forward*/
            err+=(o-e[j][INPUTNO])*(o-e[j][INPUTNO]);/*compute the error which need to be minimize*/
        }
        ++count;
    }
    printweight(wo);
    for(i=0;i<n_of_e;i++){
        printf("%d\n",i);
        for(j=0;j<INPUTNO+1;j++){
            printf("%lf",e[i][j]);
            o=forward(wo,e[i]);
            printf("%lf\n",o);
        }
    }
    return 0;
}

/*
The training data is divided according to the number of neuron units, and function will return the
 number of training data in the spilit dataset
 */
double s(double u){
    return 1.0/(1.0+exp(-u));
}
int getdata(double e[][INPUTNO+1]){
    int n_of_e=0;
    int j;
    while((scanf("%lf",&e[n_of_e][j])!=EOF)){
        ++j;
        if(j>INPUTNO){
            j=0;
            n_of_e+=1;
            if(n_of_e>=MAXNO){
                fprintf(stderr,"The data input has been reached the superior limit\n");
                break;
            }
        }
    }
    return n_of_e;
}

/*
 *here might make mistakes, pay attention plz.
 */
void initwo(double wo[INPUTNO+1]){
    int i;
    for(i=0;i<INPUTNO;i++){
        wo[i]=drand();
    }
}

/*forward function*/
double forward(double wo[INPUTNO+1],double e[INPUTNO+1]){
    int i;
    double o;
    o=0;
    for(i=0;i<INPUTNO;i++){
        o+=e[i]*wo[i];/*consider wo as both weights and bias*/
        o-=wo[i];/*consider wo as both weights and bias*/
    }
    /*s represent the sigmoid function*/
    return s(o);
}

void olearn(double wo[INPUTNO+1],double e[INPUTNO+1], double o){
    int i;
    double d;
    d=(e[INPUTNO]-o)*o*(1-o);
    for(i=0;i<INPUTNO;i++){
        wo[i]+=ALPHA*e[i]*d;/*function from deriving the sigmoid function*/
    }
    wo[i]+=ALPHA*(-1.0)*d;
}


void printweight(double wo[INPUTNO+1]){
    int i;

    for(i=0;i<INPUTNO+1;i++){
        printf("%lf\n",wo[i]);
        printf("\n");
    }
}



double drand(void){
    double rndno;
    rndno=rand()/(RAND_MAX/2);
    rndno-=1;
    return rndno;
}