BPnetwork
#include <stdio.h>
#include <math.h>
#include <stdlib.h>
#include <time.h>
#define INNODE 2 
#define HIDENODE 10
#define OUTNODE 1 
double StudyRate = 1.6;
double threshold = 1e-4;
int mostTimes = 1e6;
int trainSize = 0;
int testSize = 0;
typedef struct Sample{
    double out[30][OUTNODE]; 
    double in[30][INNODE]; 
}Sample;
typedef struct Node{
    double value; 
    double bias; 
    double bias_delta; 
    double *weight; 
    double *weight_delta; 
}Node;
Node inputLayer[INNODE];
Node hideLayer[HIDENODE];
Node outLayer[OUTNODE];
double Max(double a, double b){
    return a > b ? a : b;
}
double sigmoid(double x){
       double y,z;
       y=exp(x);
       z=y/(1+y);
       return z;
       
}
Sample * getTrainData(const char * filename){
    Sample * result = (Sample*)malloc(sizeof (Sample));
    FILE * file = fopen(filename, "r");
    if(file != NULL){
        int count = 0;
        while (fscanf(file, "%lf %lf %lf", &result->in[count][0], &result->in[count][1], &result->out[count][0]) != EOF){
            ++count;
        }
        trainSize = count;
        printf("%s The file has been successfully read!\n", filename);
        fclose(file);
        return result;
    } else{
        fclose(file);
        printf("%s Encountered an error while opening the file!\n\a", filename);
        return NULL;
    }
}
Sample * getTestData(const char * filename){
   Sample * result=(Sample*)malloc(sizeof(Sample)); 
   FILE * file = fopen(filename, "r");
   if(file!= NULL){
     int count=0;
     while (fscanf(file, "%lf %lf", &result->in[count][0], &result->in[count][1])!=EOF){
            ++count;
        }
      testSize=count;
    printf("%s The file has been successfully read!\n", filename);
    fclose(file);
    return result;
   }else{
        fclose(file);
        printf("%s Encountered an error while opening the file!\n\a", filename);
        return NULL;
    }
}
void printData(Sample * data, int size){
	int i;
     if(data==NULL){
     	printf("Sample is empty!");
     	return;
	 }
	 else{
	 	for(i=0;i<testSize;i++)
		 {
	 			printf("%f %f ",data->in[i][0],data->in[i][1]);
			 printf("%f",data->out[i][0]);
			 printf("\n");
		 }
	 }
}
void init(){
   srand(time(NULL));
    for (int i = 0; i < INNODE; ++i) {
        inputLayer[i].weight = (double *)malloc(sizeof (double ) * HIDENODE);
        inputLayer[i].weight_delta = (double *) malloc(sizeof (double ) * HIDENODE);
        inputLayer[i].bias = 0.0;
        inputLayer[i].bias_delta = 0.0;
    }
    for (int i = 0; i < INNODE; ++i) {
        for (int j = 0; j < HIDENODE; ++j) {
            inputLayer[i].weight[j] = rand() % 10000 / (double )10000 * 2 - 1.0;
            inputLayer[i].weight_delta[j] = 0.0;
        }
    }
    for (int i = 0; i < HIDENODE; ++i) {
        hideLayer[i].weight = (double *)malloc(sizeof(double)*OUTNODE);
        hideLayer[i].weight_delta =(double*)malloc(sizeof(double)*OUTNODE);
        hideLayer[i].bias =  rand() % 10000 / (double )10000 * 2 - 1.0;
        hideLayer[i].bias_delta = 0.0;
    }
    for (int i = 0; i < HIDENODE; ++i) {
        for (int j = 0; j < OUTNODE; ++j) {
            hideLayer[i].weight[j] = rand() % 10000 / (double )10000 * 2 - 1.0;
            hideLayer[i].weight_delta[j] = 0.0;
        }
    }

    for (int i = 0; i < OUTNODE; ++i) {
        outLayer[i].bias = rand() % 10000 / (double )10000 * 2 - 1.0;
        outLayer[i].bias_delta = 0.0;
    }
    
}
void resetDelta(){
    for (int i = 0; i < INNODE; ++i) {
        for (int j = 0; j < HIDENODE; ++j) {
            inputLayer[i].weight_delta[j] = 0.0;
        }
    }

    for (int i = 0; i < HIDENODE; ++i) {
        hideLayer[i].bias_delta = 0.0;
        for (int j = 0; j < OUTNODE; ++j) {
            hideLayer[i].weight_delta[j] = 0.0;
        }
    }

    for (int i = 0; i < OUTNODE; ++i) {
        outLayer[i].bias_delta = 0.0;
    }
}
int main() {
    init();
    Sample * trainSample = getTrainData("TrainData.txt");
    for (int trainTime = 0; trainTime < mostTimes; ++trainTime) {
        resetDelta();
        double error_max = 0.0;
        for (int currentTrainSample_Pos = 0; currentTrainSample_Pos < trainSize; ++currentTrainSample_Pos) {
            for (int inputLayer_Pos = 0; inputLayer_Pos < INNODE; ++inputLayer_Pos) {
                inputLayer[inputLayer_Pos].value = trainSample->in[currentTrainSample_Pos][inputLayer_Pos];
            }
            for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
                double sum = 0.0;
                for (int inputLayer_Pos = 0; inputLayer_Pos < INNODE; ++inputLayer_Pos) {
                    sum += inputLayer[inputLayer_Pos].value * inputLayer[inputLayer_Pos].weight[hideLayer_Pos];
                }

                sum -= hideLayer[hideLayer_Pos].bias;
                hideLayer[hideLayer_Pos].value = sigmoid(sum);
            } 
            for (int outLayer_Pos = 0;  outLayer_Pos < OUTNODE ; ++outLayer_Pos) {
                double sum = 0.0;
                for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
                    sum+=hideLayer[hideLayer_Pos].value * hideLayer[hideLayer_Pos].weight[outLayer_Pos];
                    
                }
                sum-=outLayer[outLayer_Pos].bias;
                outLayer[outLayer_Pos].value=sigmoid(sum);
            }
            double error = 0.0;
            for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
                double temp = fabs(outLayer[outLayer_Pos].value - trainSample->out[currentTrainSample_Pos][outLayer_Pos]);
                error += temp * temp / 2.0;
            }
            
            error_max = Max(error_max, error);
            for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
                double bias_delta = -(trainSample->out[currentTrainSample_Pos][outLayer_Pos] - outLayer[outLayer_Pos].value)
                        * outLayer[outLayer_Pos].value * (1.0 - outLayer[outLayer_Pos].value);
                outLayer[outLayer_Pos].bias_delta += bias_delta;
            }

            for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
                for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
                    double weight_delta = (trainSample->out[currentTrainSample_Pos][outLayer_Pos] - outLayer[outLayer_Pos].value)
                                          * outLayer[outLayer_Pos].value * (1.0 - outLayer[outLayer_Pos].value)
                                          * hideLayer[hideLayer_Pos].value;
                    hideLayer[hideLayer_Pos].weight_delta[outLayer_Pos] += weight_delta;
                }
            }
            for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
                double sum = 0.0;
                for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
                    sum += -(trainSample->out[currentTrainSample_Pos][outLayer_Pos] - outLayer[outLayer_Pos].value)
                           * outLayer[outLayer_Pos].value * (1.0 - outLayer[outLayer_Pos].value)
                           * hideLayer[hideLayer_Pos].weight[outLayer_Pos];
                }
                hideLayer[hideLayer_Pos].bias_delta += sum * hideLayer[hideLayer_Pos].value * (1.0 - hideLayer[hideLayer_Pos].value);
            }


            for (int inputLayer_Pos = 0; inputLayer_Pos < INNODE; ++inputLayer_Pos) {
                for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
                    double sum  = 0.0;
                    for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
                        sum += (trainSample->out[currentTrainSample_Pos][outLayer_Pos] - outLayer[outLayer_Pos].value)
                               * outLayer[outLayer_Pos].value * (1.0 - outLayer[outLayer_Pos].value)
                               * hideLayer[hideLayer_Pos].weight[outLayer_Pos];
                    }
                    inputLayer[inputLayer_Pos].weight_delta[hideLayer_Pos] += sum * hideLayer[hideLayer_Pos].value * (1.0 - hideLayer[hideLayer_Pos].value)
                                                                              * inputLayer[inputLayer_Pos].value;
                }
            }

        }
        if(error_max < threshold){
            printf("\a Training completed!Total training count:%d,  maximum error is:%f\n", trainTime + 1, error_max);
            break;
        }
        for (int inputLayer_Pos = 0; inputLayer_Pos < INNODE; ++inputLayer_Pos) {
            for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
                inputLayer[inputLayer_Pos].weight[hideLayer_Pos] += StudyRate
                                                                    * inputLayer[inputLayer_Pos].weight_delta[hideLayer_Pos] /
                                                                    (double) trainSize;
            }
        }

        for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
            hideLayer[hideLayer_Pos].bias += StudyRate
                    * hideLayer[hideLayer_Pos].bias_delta / (double )trainSize;
            for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
                hideLayer[hideLayer_Pos].weight[outLayer_Pos] += StudyRate
                        * hideLayer[hideLayer_Pos].weight_delta[outLayer_Pos] / (double )trainSize;
            }
        }

        for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
            outLayer[outLayer_Pos].bias += StudyRate
                    * outLayer[outLayer_Pos].bias_delta / (double )trainSize;
        }
    }
    Sample * testSample = getTestData("TestData.txt");
    printf("The predicted results are as follows:\n");
    for (int currentTestSample_Pos = 0; currentTestSample_Pos < testSize; ++currentTestSample_Pos) {
        for (int inputLayer_Pos = 0; inputLayer_Pos < INNODE; ++inputLayer_Pos) {
            inputLayer[inputLayer_Pos].value = testSample->in[currentTestSample_Pos][inputLayer_Pos];
        }

        for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
            double sum = 0.0;
            for (int inputLayer_Pos = 0; inputLayer_Pos < INNODE; ++inputLayer_Pos) {
                sum += inputLayer[inputLayer_Pos].value * inputLayer[inputLayer_Pos].weight[hideLayer_Pos];
        }
            sum -= hideLayer[hideLayer_Pos].bias;
            hideLayer[hideLayer_Pos].value = sigmoid(sum);
        }

        for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
            double sum = 0.0;
            for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
                sum += hideLayer[hideLayer_Pos].value * hideLayer[hideLayer_Pos].weight[outLayer_Pos];
            }
            sum -= outLayer[outLayer_Pos].bias;
            outLayer[outLayer_Pos].value = sigmoid(sum);
        }

        for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
            testSample->out[currentTestSample_Pos][outLayer_Pos] = outLayer[outLayer_Pos].value;
        }
    }
    printData(testSample, testSize);
    return 0;
}
//练习部分的源程序并为录入代码库，需要自行稍加更改就行