leecode更新

Leecode/src/main/java/com/markilue/leecode/greedy/CanCompleteCircuit.java

@@ -0,0 +1,75 @@
+package com.markilue.leecode.greedy;
+
+import org.junit.Test;
+
+/**
+ * @BelongsProject: Leecode
+ * @BelongsPackage: com.markilue.leecode.greedy
+ * @Author: markilue
+ * @CreateTime: 2022-11-08  20:16
+ * @Description: TODO  力扣134题  加油站：
+ * 在一条环路上有 n 个加油站，其中第 i 个加油站有汽油 gas[i] 升。
+ * 你有一辆油箱容量无限的的汽车，从第 i 个加油站开往第 i+1 个加油站需要消耗汽油 cost[i] 升。你从其中的一个加油站出发，开始时油箱为空。
+ * 给定两个整数数组 gas 和 cost ，如果你可以绕环路行驶一周，则返回出发时加油站的编号，否则返回 -1 。如果存在解，则 保证 它是 唯一 的。
+ * @Version: 1.0
+ */
+public class CanCompleteCircuit {
+
+    @Test
+    public void test() {
+        int[] gas = {1, 2, 3, 4, 5}, cost = {3, 4, 5, 1, 2};
+        System.out.println(canCompleteCircuit(gas, cost));
+
+    }
+
+    @Test
+    public void test1() {
+        int[] gas = {2,3,4}, cost = {3,4,3};
+        System.out.println(canCompleteCircuit(gas, cost));
+
+    }
+
+
+    /**
+     * 本人思路：可以先通过gas数组里的值判断是否大于cost值；
+     * 如果大于需要继续判断累加值，如果开始累加小于，则前面全部弃用从头开始
+     * 速度击败78.2%，内存击败77.8%
+     * @param gas
+     * @param cost
+     * @return
+     */
+    public int canCompleteCircuit(int[] gas, int[] cost) {
+
+        int length = gas.length;
+
+        int total = 0;
+        int sum =0 ;
+        int flag =0;
+        boolean f=false;
+
+        for (int i = 0; i < length; i++) {
+
+            total+=gas[i]-cost[i];
+            if (sum == 0 && gas[i] < cost[i]) {
+                continue;
+            }
+            sum+=gas[i]-cost[i];
+
+            if(!f&&sum>0){
+                flag=i;
+                f=true;
+            }
+            if(sum<=0){
+                sum=0;
+                f=false;
+            }
+        }
+
+        if(total>=0){
+            return flag;
+        }else {
+            return -1;
+        }
+
+    }
+}

TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py

@@ -548,9 +548,13 @@ def plot_hot_one(data):
     pass
 
 
-def plot_mse(file_name="../others_idea/mse"):
+def plot_mse(file_name_mse="../others_idea/mse",data:str=''):
-    mse = np.loadtxt(file_name, delimiter=",")
+    mse = np.loadtxt(file_name_mse, delimiter=",")
-    print(mse.shape)
+    raw_data=np.loadtxt(data,delimiter=",")
+    raw_data=raw_data[:,:mse.shape[1]]
+    print("mse:",mse.shape)
+    print("raw_data:",raw_data.shape)
+    res=raw_data-mse
     mse.shape[0]*2/3
     # mse = mse[2000:2300]
     # mse = mse[1800:2150]
@@ -566,20 +570,25 @@ def plot_mse(file_name="../others_idea/mse"):
         'legend.fontsize': 5,
     }
     plt.rcParams.update(parameters)
-    plt.figure()
+    for i in range(mse.shape[0]):
-    plt.rc('font', family='Times New Roman')  # 全局字体样式#画混淆矩阵
+        plt.figure()
-    indices = [mse.shape[0] * i / 4 for i in range(5)]
+        plt.rc('font', family='Times New Roman')  # 全局字体样式#画混淆矩阵
-    classes = ['13/09/17', '14/09/17', '15/09/17', '16/09/17', '17/09/17']
+        indices = [mse.shape[0] * i / 4 for i in range(5)]
+        classes = ['13/09/17', '14/09/17', '15/09/17', '16/09/17', '17/09/17']
+
+        # plt.xticks([index + 0.5 for index in indices], classes, rotation=25)  # 设置横坐标方向，rotation=45为45度倾斜
+        plt.ylabel('MSE', fontsize=8)
+        plt.xlabel('Time', fontsize=8)
+        plt.tight_layout()
+        plt.axvline(res.shape[1] * 2 / 3, c='purple', ls='-.', lw=0.5, label="real fault")
+        plt.plot(res[i, :], lw=0.5)
 
-    # plt.xticks([index + 0.5 for index in indices], classes, rotation=25)  # 设置横坐标方向，rotation=45为45度倾斜
-    plt.ylabel('MSE', fontsize=8)
-    plt.xlabel('Time', fontsize=8)
-    plt.tight_layout()
 
-    plt.plot(mse, lw=0.5)
 
     plt.show()
 
+
+
 def plot_3d():
     # 线
     fig = plt.figure()
@@ -728,9 +737,9 @@ def test_model_visualization(model_name=file_name):
 
 
 if __name__ == '__main__':
-    test_mse()
+    # test_mse(fi)
     # test_result(
-    #     file_name='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\ResNet\ResNet_banda_result1.csv')
+    #     file_name='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\ResNet\ResNet_banda_result3.csv')
     # test_corr()
     # acc()
     # list = [3.77, 2.64, 2.35, 2.05, 1.76, 1.09, 0.757, 0.82, 1.1, 0.58, 0, 0.03, 0.02]
@@ -739,8 +748,8 @@ if __name__ == '__main__':
     # list=[99.99,98.95,99.95,96.1,95,99.65,76.25,72.64,75.87,68.74]
     # plot_FNR1(list)
     # #
-    # list=[3.43,1.99,1.92,2.17,1.63,1.81,1.78,1.8,0.6]
+    list=[3.43,1.99,1.92,2.17,1.63,1.81,1.78,1.8,0.6]
-    # plot_FNR2(list)
+    plot_FNR2(list)
 
     # 查看网络某一层的权重
     # test_model_visualization(model_name = "E:\跑模型\论文写作/SE.txt")
@@ -750,7 +759,14 @@ if __name__ == '__main__':
     # test_model_weight_l(file_name)
 
     # 单独预测图
-    # plot_mse('E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_D/banda\RNet_D_banda_mse_predict1.csv')
+    plot_mse('E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\JM_banda/banda_joint_result_predict3.csv',data='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\JM_banda/raw_data.csv')
 
     #画3d图
     # plot_3d()
+
+
+    #原始数据图
+    # file_names='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\JM_banda/raw_data.csv'
+    # data= np.loadtxt(file_names,delimiter=',')
+
+    # print(data.shape)

TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/Joint_Monitoring_banda.py

@@ -57,7 +57,7 @@ save_step_two_name = "../hard_model/two_weight/{0}_epoch24_9875_9867/weight".for
 #                                                                                 batch_size,
 #                                                                                 EPOCH)
 
-save_mse_name=r"./compare/mse/JM_banda/{0}_mse.csv".format(model_name)
+save_mse_name=r"./compare/mse/JM_banda/{0}_result.csv".format(model_name)
 '''文件名'''
 file_name = "G:\data\SCADA数据\SCADA_已处理_粤水电达坂城2020.1月-5月\风机15.csv"
 
@@ -544,9 +544,40 @@ def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False
     return total_result
 
 
+def show_trend(step_two_model: Joint_Monitoring, test_data,isSave:bool=True):
+    # 获取模型的所有参数的个数
+    # step_two_model.count_params()
+
+
+    size, length, dims = test_data.shape
+    for epoch in range(0, size - batch_size + 1, batch_size):
+        each_test_data = test_data[epoch:epoch + batch_size, :, :]
+        output1, output2, output3, _ = step_two_model.call(each_test_data, is_first_time=True)
+        if epoch==0:
+            result1=output1
+            result2=output2
+            result3=output3
+        else:
+            result1=np.concatenate([result1,output1],axis=0)
+            result2=np.concatenate([result2,output2],axis=0)
+            result3=np.concatenate([result3,output3],axis=0)
+
+    # 转置
+    result1=np.transpose(result1,(1,0))
+    result2=np.transpose(result2,(1,0))
+    result3=np.transpose(result3,(1,0))
+    # 误报率，漏报率，准确性的计算
+    if isSave:
+        for data,j in zip([result1,result2,result3],range(3)):
+            save_mse_name1 = save_mse_name[:-4] + "_predict" + str(j + 1) + ".csv"
+            np.savetxt(save_mse_name1, data, delimiter=',')
+
+    pass
+
 if __name__ == '__main__':
     total_data = loadData.execute(N=feature_num, file_name=file_name)
     total_data = normalization(data=total_data)
+
     train_data_healthy, train_label1_healthy, train_label2_healthy = get_training_data_overlapping(
         total_data[:healthy_date, :], is_Healthy=True)
     train_data_unhealthy, train_label1_unhealthy, train_label2_unhealthy = get_training_data_overlapping(
@@ -587,11 +618,14 @@ if __name__ == '__main__':
     #      test_label2=np.expand_dims(test_label2, axis=-1))
 
     ###TODO 展示全部的结果
-    all_data, _, _ = get_training_data_overlapping(
+    # all_data, _, _ = get_training_data_overlapping(
-        total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :], is_Healthy=True)
+    #     total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :], is_Healthy=True)
+    save_data=np.transpose(total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :])
+    np.savetxt('./compare/mse/JM_banda/raw_data.csv',save_data ,delimiter=',')
     # all_data = np.concatenate([])
     # 单次测试
     # showResult(step_two_model, test_data=all_data[:32], isPlot=True)
-    showResult(step_two_model, test_data=all_data, isPlot=True)
+    # show_trend(step_two_model,all_data,isSave=True)
+    # showResult(step_two_model, test_data=all_data, isPlot=True)
 
     pass

TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/resnet_18.py

@@ -42,7 +42,7 @@ save_max_name = "./mse/ResNet/{0}_timestamp{1}_feature{2}_max.csv".format(model_
                                                                           feature_num,
                                                                           batch_size,
                                                                           EPOCH)
-save_mse_name1 = "./mse/ResNet/{0}_result1.csv".format(model_name,
+save_mse_name1 = "./mse/ResNet/{0}_result3.csv".format(model_name,
                                                                              time_stamp,
                                                                              feature_num,
                                                                              batch_size,
@@ -395,7 +395,7 @@ if __name__ == '__main__':
     # history = model.fit(train_data, train_label, epochs=20, batch_size=16, validation_data=(test_data, test_label),
     #                     callbacks=[checkpoint, early_stop])
     # model.save("./model/ResNet.h5")
-    model = tf.keras.models.load_model("model/ResNet_banda/ResNet_banda_epoch12_9942.h5")
+    model = tf.keras.models.load_model("model/ResNet_banda/ResNet_banda_epoch10_9884.h5")
 
     # 结果展示