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


#define INNODE 2 // 输入层神经元个数
#define HIDENODE 8 // 隐藏层神经元个数
#define OUTNODE 1 // 输出层神经元个数


/**
 * 步长（学习率）
 */
double StudyRate = 1.6;

/**
 * 允许最大误差
 */
double threshold = 1e-4;

/**
 * 最大迭代次数
 */
int mostTimes = 1e6;

/**
 * 训练集大小
 */
int trainSize = 0;

/**
 * 测试集大小
 */
int testSize = 0;

/**
 * 样本
 */
typedef struct Sample{
    double out[30][OUTNODE]; // 输出
    double in[30][INNODE]; // 输入
}Sample;


/**
 * 神经元结点
 */
typedef struct Node{
    double value; // 当前神经元结点输出的值
    double bias; // 当前神经元结点偏偏置值
    double bias_delta; // 当前神经元结点偏置值的修正值
    double *weight; // 当前神经元结点向下一层结点传播的权值
    double *weight_delta; // 当前神经元结点向下一层结点传播的权值的修正值
}Node;

/**
 *  输入层
 */
Node inputLayer[INNODE];
/**
 * 隐藏层
 */
Node hideLayer[HIDENODE];
/**
 * 输出层
 */
Node outLayer[OUTNODE];



double Max(double a, double b){
    return a > b ? a : b;
}

/**
 * 激活函数sigmoid
 * @param x 输入值
 * @return 输出值
 */
double sigmoid(double x){
	return 1/(1+exp(-x));
	//请补全sigmod函数的计算结果

}


/**
 * 读取训练集
 * @param filename 文件名
 * @return 训练集
 */
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));
	/*在内存中分配足够的空间来存储一个Sample结构，并将指向该内存块的指针存储在result变量中*/

   FILE * file = fopen(filename, "r");//打开文件
   if(file != NULL){
        int count = 0;
        while (fscanf(file, "%lf %lf", &result->in[count][0], &result->in[count][1]) != EOF){
            ++count;
        }
	// 初始化一个整数变量count，用于跟踪读取的数据行数；

    /*利用while循环从测试集文件中逐行读取两个浮点数，直到读取到文件末尾。
    每次成功读取一行数据后，递增count；*/
        testSize = count;
    //将最终的count的值存储在名为testSize的全局变量中，以便后续使用

    printf("%s The file has been successfully read!\n", filename);
    fclose(file);
    return result;
	//返回result
    
   }else{
        fclose(file);
        printf("%s Encountered an error while opening the file!\n\a", filename);
        return NULL;
    }
}

/**
 * 打印样本
 * @param data 要打印的样本
 * @param size 样本大小
 */
void printData(Sample * data, int size){
    if(data==NULL)
    {
    	printf("Sample is empty!");
    	return;
	}
	else
	{
		int count;
		for(count=0;count<size;count++)
		{
			printf("%lf %lf %lf\n",data->in[count][0], data->in[count][1], data->out[count][0]);
		}
		return; 
	}
	/*请补全代码
    
    
    */


}

/**
 * 初始化函数
 */
void init(){
    // 设置时间戳为生成随机序列的种子
    srand(time(NULL));
    
    // 输入层的初始化
    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;
    }

    // 输出层权值初始化
    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;
        }
    }


    // 初始化隐藏层结点
    for (int i = 0; i < HIDENODE; ++i) {
		/*为隐藏层节点 i 分配了一个 double 类型的数组，用于存储该节点向下一层节点传播的权重。
        使用malloc 函数在堆内存中分配足够的空间，以存储 OUTNODE 个 double 类型的权重值。
        */
        hideLayer[i].weight = (double *)malloc(sizeof(double)*OUTNODE);
        /*为隐藏层节点 i 分配了一个用于存储权重修正值的数组。这个数组将在神经网络的训练过程中用于存储权重的更新值。
        使用malloc 函数在堆内存中分配足够的空间，以存储 OUTNODE 个 double 类型的权重值。
        */
        hideLayer[i].weight_delta = (double *)malloc(sizeof(double)*OUTNODE);
        /*为隐藏层节点 i 初始化了一个随机的偏置值。
        这个值通常是一个在 -1.0 到 1.0 之间的随机数，用于调整该节点的激活函数的阈值。
        */
        hideLayer[i].bias = (2.0 * rand() / RAND_MAX) - 1.0;
        /*初始化了隐藏层节点 i 的偏置值修正值，初始值为0.0。*/
        hideLayer[i].bias_delta = 0.0;
    }

    // 初始化隐藏层权值
    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;
    }
}

/**
 * 重置修正值
 */
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() {
    // 初始化
    init();
    // 获取训练集
    Sample * trainSample = getTrainData("TrainData.txt");
//    printData(trainSample, trainSize);


    for (int trainTime = 0; trainTime < mostTimes; ++trainTime) {
        // 重置梯度信息
        resetDelta();

        // 当前训练最大误差
        double error_max = 0.0;

        // 开始训练（累计bp）
        for (int currentTrainSample_Pos = 0; currentTrainSample_Pos < trainSize; ++currentTrainSample_Pos) {

            // 输入自变量
            for (int inputLayer_Pos = 0; inputLayer_Pos < INNODE; ++inputLayer_Pos) {
                inputLayer[inputLayer_Pos].value = trainSample->in[currentTrainSample_Pos][inputLayer_Pos];
            }

            /** ----- 开始正向传播 ----- */
            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];
					/*计算每一个隐藏层节点的value和权值的乘积，相加得到sum

                    */
                }
                
                sum -= outLayer[outLayer_Pos].bias;
                outLayer[outLayer_Pos].value = sigmoid(sum);
				/*更新sum，使sum减去偏置值;

                */
                /*利用sigmod函数对得到的sum进行激活，把激活后的结果赋值给对应的输出层节点value(outLayer[outLayer_Pos].value)。
                
                */
            }


            /** ----- 计算误差 ----- */
            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]);
                // 损失函数
                error += temp * temp / 2.0;
            }
            
            error_max = Max(error_max, error);
            
            
            /** ----- 反向传播 ----- */
            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;
                }
            }

        }


        // 判断误差是否达到允许误差范围
        if(error_max < threshold){
            printf("\a Training completed!Total training count:%d,  maximum error is:%f\n", trainTime + 1, error_max);
            break;
        }

        // 误差无法接受，开始修正

        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;
        }
    }

    // 训练完成，读取测试集
    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;
}