From e18305c54a950e5505a028b8437da24b7274f3b6 Mon Sep 17 00:00:00 2001 From: markilue <745518019@qq.com> Date: Tue, 8 Nov 2022 22:02:46 +0800 Subject: [PATCH] =?UTF-8?q?leecode=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../leecode/greedy/CanCompleteCircuit.java | 75 +++++++++++++++++++ .../condition_monitoring/plot/test_plot.py | 50 ++++++++----- .../self_try/Joint_Monitoring_banda.py | 42 ++++++++++- .../self_try/compare/resnet_18.py | 4 +- 4 files changed, 148 insertions(+), 23 deletions(-) create mode 100644 Leecode/src/main/java/com/markilue/leecode/greedy/CanCompleteCircuit.java diff --git a/Leecode/src/main/java/com/markilue/leecode/greedy/CanCompleteCircuit.java b/Leecode/src/main/java/com/markilue/leecode/greedy/CanCompleteCircuit.java new file mode 100644 index 0000000..30a7431 --- /dev/null +++ b/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; + } + + } +} diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py index 0a5cf31..2f88923 100644 --- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py +++ b/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"): - mse = np.loadtxt(file_name, delimiter=",") - print(mse.shape) +def plot_mse(file_name_mse="../others_idea/mse",data:str=''): + mse = np.loadtxt(file_name_mse, delimiter=",") + 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() - plt.rc('font', family='Times New Roman') # 全局字体样式#画混淆矩阵 - 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'] + for i in range(mse.shape[0]): + plt.figure() + plt.rc('font', family='Times New Roman') # 全局字体样式#画混淆矩阵 + 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] - # plot_FNR2(list) + list=[3.43,1.99,1.92,2.17,1.63,1.81,1.78,1.8,0.6] + 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) diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/Joint_Monitoring_banda.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/Joint_Monitoring_banda.py index 893a23c..e4d0966 100644 --- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/Joint_Monitoring_banda.py +++ b/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( - total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :], is_Healthy=True) + # all_data, _, _ = get_training_data_overlapping( + # 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 diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/resnet_18.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/resnet_18.py index 4d73859..a630945 100644 --- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/resnet_18.py +++ b/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