diff --git a/bp (2).c b/bp (2).c
new file mode 100644
index 0000000..88f1abd
--- /dev/null
+++ b/bp (2).c	
@@ -0,0 +1,411 @@
+#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 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){
+    //请补全sigmod函数的计算结果
+return 1.0/(1.0+exp(-x));
+}
+
+
+/**
+ * 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){
+    // 初始化一个整数变量count，用于跟踪读取的数据行数；
+     int count = 0;
+    /*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；*/
+ while (fscanf(file, "%lf %lf", &result->in[count][0], &result->in[count][1]) != EOF){
+            ++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
+testSize=count;
+    printf("%s The file has been successfully read!\n", filename);
+    fclose(file);
+    //返回result
+    return 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){
+    /*请补全代码
+    
+    
+    */
+if(data==NULL){
+	printf("Sample is empty!");
+	return;
+}
+for(int i=0;i<size;++i){
+	printf("%d,x1=%f,x2=%f,y=%f",i+1,data->in[i][0],data->in[i][1],data->out[i][0]);
+}
+
+}
+
+/**
+ * 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 = rand() % 10000 / (double )10000 * 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;
+
+                */
+                /*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)。
+                
+                */sum-=outLayer[outLayer_Pos].bias;
+                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;
+}