From 35e7a9a832943d25e41edd65b420103ae756815f Mon Sep 17 00:00:00 2001 From: psc7qx9jf Date: Fri, 17 Nov 2023 19:20:44 +0800 Subject: [PATCH] ADD file via upload --- main.c | 420 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 420 insertions(+) create mode 100644 main.c diff --git a/main.c b/main.c new file mode 100644 index 0000000..b22dd97 --- /dev/null +++ b/main.c @@ -0,0 +1,420 @@ +#include +#include +#include +#include + + +#define INNODE 2 // 输入层神经元个数 +#define HIDENODE 10// 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){ + double e=2.71828; + double jieguo=1.0/(1.0+(1.0/pow(e,x))) ; + return jieguo; //请补全sigmod函数的计算结果 + /*double jieguo; + double k,g; + k=tanh(x); + g=tanh(x/2); + jieguo=-(2*g+k*k)/(1+k); + return jieguo;*/ +} + + +/** + * 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; + } +} + +/** + * 读取测试集 + * @param filename 文件名 + * @return 测试集 + */ +Sample * getTestData(const char * filename){ + /*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*/ +Sample*result=(Sample*)malloc(sizeof(Sample)); + FILE * file = fopen(filename, "r");//打开文件 + if(file != NULL){ +int count=0; // 初始化一个整数变量count，用于跟踪读取的数据行数； +while(fscanf(file,"%lf %lf",&result->in[count][0],&result->in[count][1],&result->out[count][1])!=EOF){ + ++count; +} +testSize=count; + + /*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；*/ + + //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!\n"); + return; + } + for(int i=0;iin[i][0],data->in[i][1],data->out[i][0]); + } /*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 = (int*)malloc(OUTNODE*sizeof(double)); + /*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 =(int*)malloc(OUTNODE*sizeof(double)); + /*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 = (double)rand()/((double)RAND_MAX/2)-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) { + /*计算每一个隐藏层节点的value和权值的乘积，相加得到sum + + */ + sum+=hideLayer[hideLayer_Pos].value*hideLayer[hideLayer_Pos].weight[outLayer_Pos]; + } + /*更新sum，使sum减去偏置值; + + */ + sum-=outLayer[outLayer_Pos].bias; + /*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)。*/ + outLayer[outLayer_Pos].value=sigmoid(sum); + } + + + /** ----- 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; +}