From 4b279d5f1d3449ef83d5b1f45727b4598b09e54c Mon Sep 17 00:00:00 2001 From: markilue <745518019@qq.com> Date: Wed, 26 Oct 2022 11:26:43 +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 --- .../com/markilue/leecode/greedy/CanJump.java | 110 +++++++++ .../leecode/greedy/FindContentChildren.java | 4 +- .../markilue/leecode/greedy/MaxProfit.java | 112 +++++++++ .../condition_monitoring/plot/test_plot.py | 233 ++++++++++++++++-- .../self_try/compare/RNet-C.py | 17 +- .../self_try/compare/resnet_18.py | 2 +- 6 files changed, 446 insertions(+), 32 deletions(-) create mode 100644 Leecode/src/main/java/com/markilue/leecode/greedy/CanJump.java create mode 100644 Leecode/src/main/java/com/markilue/leecode/greedy/MaxProfit.java diff --git a/Leecode/src/main/java/com/markilue/leecode/greedy/CanJump.java b/Leecode/src/main/java/com/markilue/leecode/greedy/CanJump.java new file mode 100644 index 0000000..9c0f6cf --- /dev/null +++ b/Leecode/src/main/java/com/markilue/leecode/greedy/CanJump.java @@ -0,0 +1,110 @@ +package com.markilue.leecode.greedy; + +import org.junit.Test; + +/** + * @BelongsProject: Leecode + * @BelongsPackage: com.markilue.leecode.greedy + * @Author: markilue + * @CreateTime: 2022-10-26 10:48 + * @Description: + * TODO 力扣55题 跳跃游戏： + * 给定一个非负整数数组 nums ，你最初位于数组的 第一个下标 。 + * 数组中的每个元素代表你在该位置可以跳跃的最大长度。 + * 判断你是否能够到达最后一个下标 + * @Version: 1.0 + */ +public class CanJump { + + @Test + public void test(){ + int[] nums = {2, 3, 1, 1, 4}; + System.out.println(canJump1(nums)); + } + + @Test + public void test1(){ + int[] nums = {3,2,1,0,4}; + System.out.println(canJump1(nums)); + } + + + /** + * 自己的思路：似乎可以使用递归去做： + * 要判断最后一个下标能否到，就是判断上一个能到这个下标的位置能不能到 + * 速度击败94.15%，内存击败39.95% + * @param nums + * @return + */ + public boolean canJump(int[] nums) { + return jump(nums,nums.length-1); + } + + + public boolean jump(int[] nums,int j) { + + //怎么样也到不了了 + if(j<0){ + return false; + } + if(j==0){ + return true; + } + //寻找上一个能到j位置的点 + int i=j-1; + for (int k=1; i >= 0; i--,k++) { + if(nums[i]= nums.length-1)return true; + } + + return false; + + } + + + /** + * 评论区动态规划法：与自己的方法类似，但是这个最小步长比较巧妙 + * @param nums + * @return + */ + public boolean canJump2(int[] nums) { + int length = nums.length; + if (length == 1) return true; + int minStep = 1; //定义一个数，为达到最后最后一个结点最小需要的步数 + for (int i = length-2; i >0; i--) { //从倒数第二个往第二个开始遍历 + if (nums[i]= minStep; //此时minStep为达到"最后一个元素"(并不是nums[length-1])的最小步数，只要判断第一个元素的值是否大于或等于最小步数就可以了; + } +} diff --git a/Leecode/src/main/java/com/markilue/leecode/greedy/FindContentChildren.java b/Leecode/src/main/java/com/markilue/leecode/greedy/FindContentChildren.java index b73cdb1..7ce8c92 100644 --- a/Leecode/src/main/java/com/markilue/leecode/greedy/FindContentChildren.java +++ b/Leecode/src/main/java/com/markilue/leecode/greedy/FindContentChildren.java @@ -13,8 +13,8 @@ import java.util.Comparator; * @Description: * TODO 力扣455题 分发饼干: * 假设你是一位很棒的家长，想要给你的孩子们一些小饼干。但是，每个孩子最多只能给一块饼干。 - * 对每个孩子 i，都有一个胃口值 g[i]，这是能让孩子们满足胃口的饼干的最小尺寸； - * 并且每块饼干 j，都有一个尺寸 s[j] 。如果 s[j] >= g[i]，我们可以将这个饼干 j 分配给孩子 i ，这个孩子会得到满足。 + * 对每个孩子 i，都有一个胃口值g[i]，这是能让孩子们满足胃口的饼干的最小尺寸； + * 并且每块饼干 j，都有一个尺寸 s[j]。如果 s[j]>= g[i]，我们可以将这个饼干 j 分配给孩子 i ，这个孩子会得到满足。 * 你的目标是尽可能满足越多数量的孩子，并输出这个最大数值。 * * @Version: 1.0 diff --git a/Leecode/src/main/java/com/markilue/leecode/greedy/MaxProfit.java b/Leecode/src/main/java/com/markilue/leecode/greedy/MaxProfit.java new file mode 100644 index 0000000..da602e8 --- /dev/null +++ b/Leecode/src/main/java/com/markilue/leecode/greedy/MaxProfit.java @@ -0,0 +1,112 @@ +package com.markilue.leecode.greedy; + +import org.junit.Test; + +/** + * @BelongsProject: Leecode + * @BelongsPackage: com.markilue.leecode.greedy + * @Author: markilue + * @CreateTime: 2022-10-26 09:49 + * @Description: TODO 力扣122题 买卖股票的最佳时机II: + * 给你一个整数数组 prices ，其中prices[i] 表示某支股票第 i 天的价格。 + * 在每一天，你可以决定是否购买和/或出售股票。你在任何时候最多只能持有 一股 股票。你也可以先购买，然后在 同一天 出售。 + * 返回 你能获得的 最大 利润。 + * @Version: 1.0 + */ +public class MaxProfit { + @Test + public void test(){ + int[] prices = {7, 1, 5, 3, 6, 4}; + System.out.println(maxProfit2(prices)); + } + + @Test + public void test1(){ + int[] prices = {1,2,3,4,5}; + System.out.println(maxProfit(prices)); + } + + @Test + public void test2(){ + int[] prices = {7,6,4,3,15}; + System.out.println(maxProfit(prices)); + } + + + /** + * 思路：由于股票可以当天买当天卖，所以逻辑变成了： + * 只要下一个天比今天小，那么今天卖出去；反之则不卖 + * 速度击败81.82%，内存击败98.42% + * @param prices + * @return + */ + public int maxProfit(int[] prices) { + + //记录买入净额 + int buy = prices[0]; + int total = 0; + + for (int i = 1; i < prices.length; i++) { + if (prices[i] - prices[i - 1] > 0) { + if(i==prices.length-1){ + //比他大还是最后一组，必须卖 + total += prices[i] - buy; + } + //不卖 + continue; + } else { + //卖 + total += prices[i-1] - buy; + buy=prices[i]; + } + } + + + return total; + + + } + + /** + * 代码随想录贪心：速度击败81.82%，内存击败35.76% + * @param prices + * @return + */ + public int maxProfit1(int[] prices) { + + //记录买入净额 + int total = 0; + + for (int i = 1; i < prices.length; i++) { + //省去了判断最后一次的操作，但是要进行多次加法 + total+=Math.max(prices[i]-prices[i-1],0); + } + + return total; + + } + + + /** + * 官方动态规划法： + * 定义状态 dp[i][0] 表示第 i 天交易完后手里没有股票的最大利润，p[i][1] 表示第 i 天交易完后手里持有一支股票的最大利润（i 从 0 开始）。 + * dp[i][0] 的转移方程： + * 1）如果这一天交易完后手里没有股票，那么可能的转移状态为前一天已经没有股票，即 dp[i−1][0]， + * 2）或者前一天结束的时候手里持有一支股票，即 dp[i−1][1]，这时候我们要将其卖出，并获得 prices[i] 的收益。 + * dp[i][0]类似 + * @param prices + * @return + */ + public int maxProfit2(int[] prices) { + int n = prices.length; + int dp0 = 0, dp1 = -prices[0]; + for (int i = 1; i < n; ++i) { + int newDp0 = Math.max(dp0, dp1 + prices[i]); + int newDp1 = Math.max(dp1, dp0 - prices[i]); + dp0 = newDp0; + dp1 = newDp1; + } + return dp0; + } + +} 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 cfca8d7..44ca589 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 @@ -1,6 +1,7 @@ # -*- coding: utf-8 -*- # coding: utf-8 +import matplotlib import matplotlib.pyplot as plt import numpy as np import random @@ -23,6 +24,8 @@ max_file_name = "E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_m source_path = "G:\data\SCADA数据\jb4q_8_delete_total_zero.csv" +list = [64.16, 65.26, 65.11, 66.6, 67.16, 66.28, 73.86, 75.24, 73.98, 76.7, 98.86, 99.45, 99.97] + def plot_result(result_data): parameters = { @@ -127,21 +130,21 @@ def plot_MSE(total_MSE, total_max): pass -def plot_Corr(data, label): +def plot_Corr(data, size: int = 1): parameters = { 'figure.dpi': 600, - 'figure.figsize': (2.8, 2), + 'figure.figsize': (2.8 * size, 2 * size), 'savefig.dpi': 600, 'xtick.direction': 'inout', 'ytick.direction': 'inout', - 'xtick.labelsize': 3, - 'ytick.labelsize': 3, - 'legend.fontsize': 5, + 'xtick.labelsize': 3 * size, + 'ytick.labelsize': 3 * size, + 'legend.fontsize': 5 * size, } plt.rcParams.update(parameters) plt.figure() plt.rc('font', family='Times New Roman') # 全局字体样式#画混淆矩阵 - font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 4} # 设置坐标标签的字体大小，字体 + font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 4 * size} # 设置坐标标签的字体大小，字体 print("计算皮尔逊相关系数") pd_data = pd.DataFrame(data) @@ -149,19 +152,20 @@ def plot_Corr(data, label): print(person) # 画热点图heatmap cmap = sns.heatmap(person, annot=True, annot_kws={ - 'fontsize': 2.6 + 'fontsize': 2.6 * size }) - classes = ['Gs', 'Gio', 'Gip', 'Gp', 'Gwt', 'En', 'Gft', 'Grt', 'Gwt', 'Et', 'Rs', 'Ap', 'Ws', 'Dw', 'Ges', 'Gt', 'Vx','Vy'] + classes = ['Gs', 'Gio', 'Gip', 'Gp', 'Gwt', 'En', 'Gft', 'Grt', 'Gwt', 'Et', 'Rs', 'Ap', 'Ws', 'Dw', 'Ges', 'Gt', + 'Vx', 'Vy'] indices = range(len(person)) - plt.title("Heatmap of correlation coefficient matrix", size=6, fontdict=font1) + plt.title("Heatmap of correlation coefficient matrix", size=6 * size, fontdict=font1) # pad调整label与坐标轴之间的距离 - plt.tick_params(bottom=False, top=False, left=False, right=False, direction='inout', length=2, width=0.5,pad=1) + plt.tick_params(bottom=False, top=False, left=False, right=False, direction='inout', length=2, width=0.5, pad=1) plt.xticks([index + 0.5 for index in indices], classes, rotation=0) # 设置横坐标方向，rotation=45为45度倾斜 plt.yticks([index + 0.5 for index in indices], classes, rotation=0) # 调整色带的标签： cbar = cmap.collections[0].colorbar - cbar.ax.tick_params(labelsize=4, labelcolor="black", length=2, width=0.5,pad=1) + cbar.ax.tick_params(labelsize=4 * size, labelcolor="black", length=2, width=0.5, pad=1) cbar.ax.set_ylabel(ylabel="color scale", color="black", loc="center", fontdict=font1) # plt.axis('off') # 去坐标轴 @@ -171,19 +175,194 @@ def plot_Corr(data, label): pass +def plot_bar(y_data): + parameters = { + 'figure.dpi': 600, + 'figure.figsize': (10, 6), + 'savefig.dpi': 600, + 'xtick.direction': 'in', + 'ytick.direction': 'in', + 'xtick.labelsize': 15, + 'ytick.labelsize': 15, + 'legend.fontsize': 12, + } + plt.rcParams.update(parameters) + plt.figure() + plt.rc('font', family='Times New Roman') # 全局字体样式#画混淆矩阵 + font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 5} # 设置坐标标签的字体大小，字体 + + x_width = [ i for i in range(0, len(y_data))] + # x2_width = [i + 0.3 for i in x_width] + + plt.bar(x_width[0], y_data[0], lw=1, color=['#FAF4E1'], width=0.5, label="CNN", edgecolor='black') + + plt.bar(x_width[1] , y_data[1], lw=1, color=['#F5E3C4'], width=0.5, label="GRU", edgecolor='black') + plt.bar(x_width[2] , y_data[2], lw=1, color=['#EBC99D'], width=0.5, label="CNN-GRU", edgecolor='black') + plt.bar(x_width[3] , y_data[3], lw=1, color=['#FFC79C'], width=0.5, label="DCConv", edgecolor='black') + plt.bar(x_width[4] , y_data[4], lw=1, color=['#BEE9C7'], width=0.5, label="RepDCConv", edgecolor='black') + plt.bar(x_width[5] , y_data[5], lw=1, color=['#B8E9D0'], width=0.5,label="RNet-MSE", edgecolor='black') + plt.bar(x_width[6] , y_data[6], lw=1, color=['#B9E9E2'], width=0.5, label="RNet", edgecolor='black') + plt.bar(x_width[7] , y_data[7], lw=1, color=['#D6E6F2'], width=0.5, label="RNet-SE", edgecolor='black') + plt.bar(x_width[8] , y_data[8], lw=1, color=['#B4D1E9'], width=0.5, label="RNet-L", edgecolor='black') + plt.bar(x_width[9] , y_data[9], lw=1, color=['#AEB5EE'], width=0.5, label="RNet-D", edgecolor='black') + plt.bar(x_width[10] , y_data[10], lw=1, color=['#D2D3FC'], width=0.5, label="ResNet-18", edgecolor='black') + plt.bar(x_width[11] , y_data[11], lw=1, color=['#D5A9FF'], width=0.5, label="ResNet-C", edgecolor='black') + plt.bar(x_width[12] , y_data[12], lw=1, color=['#E000F5'], width=0.5, label="JMNet", edgecolor='black') + + # plt.tick_params(bottom=False, top=False, left=True, right=False, direction='in', pad=1) + plt.xticks([]) + plt.ylabel('False Positive Rate(%)',fontsize=18) + # plt.xlabel('Time', fontsize=5) + # plt.tight_layout() + + num1, num2, num3, num4 = 0.1, 1, 3, 0 + plt.legend(bbox_to_anchor=(num1, num2), loc=num3, borderaxespad=num4, ncol=5, frameon=False, markerscale=0.5) + plt.ylim([-0.01, 5]) + plt.show() + +def acc(y_data=list): + parameters = { + 'figure.dpi': 600, + 'figure.figsize': (10, 6), + 'savefig.dpi': 600, + 'xtick.direction': 'in', + 'ytick.direction': 'in', + 'xtick.labelsize': 15, + 'ytick.labelsize': 15, + 'legend.fontsize': 12, + } + plt.rcParams.update(parameters) + plt.figure() + plt.rc('font', family='Times New Roman') # 全局字体样式#画混淆矩阵 + font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 5} # 设置坐标标签的字体大小，字体 + + x_width = [ i/2 for i in range(0, len(y_data))] + # x2_width = [i + 0.3 for i in x_width] + + plt.bar(x_width[0] , y_data[0], lw=1, color=['#FAF4E1'], width=0.25, label="CNN", edgecolor='black') + plt.bar(x_width[1] , y_data[1], lw=1, color=['#F5E3C4'], width=0.25, label="GRU", edgecolor='black') + plt.bar(x_width[2] , y_data[2], lw=1, color=['#EBC99D'], width=0.25, label="CNN-GRU", edgecolor='black') + plt.bar(x_width[3] , y_data[3], lw=1, color=['#FFC79C'], width=0.25, label="DCConv", edgecolor='black') + plt.bar(x_width[4] , y_data[4], lw=1, color=['#BEE9C7'], width=0.25, label="RepDCConv", edgecolor='black') + plt.bar(x_width[5] , y_data[5], lw=1, color=['#B8E9D0'], width=0.25,label="RNet-MSE", edgecolor='black') + plt.bar(x_width[6] , y_data[6], lw=1, color=['#B9E9E2'], width=0.25, label="RNet", edgecolor='black') + plt.bar(x_width[7] , y_data[7], lw=1, color=['#D6E6F2'], width=0.25, label="RNet-SE", edgecolor='black') + plt.bar(x_width[8] , y_data[8], lw=1, color=['#B4D1E9'], width=0.25, label="RNet-L", edgecolor='black') + plt.bar(x_width[9] , y_data[9], lw=1, color=['#AEB5EE'], width=0.25, label="RNet-D", edgecolor='black') + plt.bar(x_width[10] , y_data[10], lw=1, color=['#D2D3FC'], width=0.25, label="ResNet-18", edgecolor='black') + plt.bar(x_width[11] , y_data[11], lw=1, color=['#D5A9FF'], width=0.25, label="ResNet-C", edgecolor='black') + plt.bar(x_width[12] , y_data[12], lw=1, color=['#E000F5'], width=0.25, label="JMNet", edgecolor='black') + + # plt.tick_params(bottom=False, top=False, left=True, right=False, direction='in', pad=1) + plt.xticks([]) + plt.ylabel('Accuracy(%)',fontsize=18) + # plt.xlabel('Time', fontsize=5) + # plt.tight_layout() + + num1, num2, num3, num4 = 0.08, 1, 3, 0 + plt.legend(bbox_to_anchor=(num1, num2), loc=num3, borderaxespad=num4, ncol=5, frameon=False, markerscale=0.5) + plt.ylim([60, 105]) + plt.show() + +def plot_FNR1(y_data): + parameters = { + 'figure.dpi': 600, + 'figure.figsize': (10, 6), + 'savefig.dpi': 600, + 'xtick.direction': 'in', + 'ytick.direction': 'in', + 'xtick.labelsize': 15, + 'ytick.labelsize': 15, + 'legend.fontsize': 12, + } + plt.rcParams.update(parameters) + plt.figure() + plt.rc('font', family='Times New Roman') # 全局字体样式#画混淆矩阵 + font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 5} # 设置坐标标签的字体大小，字体 + + x_width = range(0, len(y_data)) + # x2_width = [i + 0.3 for i in x_width] + + plt.bar(x_width[0], y_data[0], lw=1, color=['#FAF4E1'], width=0.5*5/6, label="CNN", edgecolor='black') + plt.bar(x_width[1] , y_data[1], lw=1, color=['#F5E3C4'], width=0.5*5/6, label="GRU", edgecolor='black') + plt.bar(x_width[2] , y_data[2], lw=1, color=['#EBC99D'], width=0.5*5/6, label="CNN-GRU", edgecolor='black') + plt.bar(x_width[3] , y_data[3], lw=1, color=['#FFC79C'], width=0.5*5/6, label="DCConv", edgecolor='black') + plt.bar(x_width[4] , y_data[4], lw=1, color=['#BEE9C7'], width=0.5*5/6, label="RepDCConv", edgecolor='black') + plt.bar(x_width[5] , y_data[5], lw=1, color=['#B8E9D0'], width=0.5*5/6,label="RNet-MSE", edgecolor='black') + plt.bar(x_width[6] , y_data[6], lw=1, color=['#B9E9E2'], width=0.5*5/6, label="RNet", edgecolor='black') + plt.bar(x_width[7] , y_data[7], lw=1, color=['#D6E6F2'], width=0.5*5/6, label="RNet-SE", edgecolor='black') + plt.bar(x_width[8] , y_data[8], lw=1, color=['#B4D1E9'], width=0.5*5/6, label="RNet-L", edgecolor='black') + plt.bar(x_width[9] , y_data[9], lw=1, color=['#AEB5EE'], width=0.5*5/6, label="RNet-D", edgecolor='black') + + # plt.tick_params(bottom=False, top=False, left=True, right=False, direction='in', pad=1) + plt.xticks([]) + plt.ylabel('False Negative Rate(%)',fontsize=18) + # plt.xlabel('Time', fontsize=5) + # plt.tight_layout() + + num1, num2, num3, num4 = 0.16, 1, 3, 0 + plt.legend(bbox_to_anchor=(num1, num2), loc=num3, borderaxespad=num4, ncol=5, frameon=False, markerscale=0.5) + plt.ylim([60, 100]) + plt.show() + + +def plot_FNR2(y_data): + parameters = { + 'figure.dpi': 600, + 'figure.figsize': (10, 6), + 'savefig.dpi': 600, + 'xtick.direction': 'in', + 'ytick.direction': 'in', + 'xtick.labelsize': 15, + 'ytick.labelsize': 15, + 'legend.fontsize': 12, + } + plt.rcParams.update(parameters) + plt.figure() + plt.rc('font', family='Times New Roman') # 全局字体样式#画混淆矩阵 + font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 5} # 设置坐标标签的字体大小，字体 + + x_width = range(0, len(y_data)) + # x2_width = [i + 0.3 for i in x_width] + + plt.bar(x_width[0] , y_data[0], lw=1, color=['#FAF4E1'], width=0.5*2/3, label="ResNet-18", edgecolor='black') + plt.bar(x_width[1] , y_data[1], lw=1, color=['#F5E3C4'], width=0.5*2/3, label="RNet-3", edgecolor='black') + plt.bar(x_width[2] , y_data[2], lw=1, color=['#EBC99D'], width=0.5*2/3, label="RNet-4", edgecolor='black') + plt.bar(x_width[3] , y_data[3], lw=1, color=['#FFC79C'], width=0.5*2/3, label="RNet-5", edgecolor='black') + plt.bar(x_width[4] , y_data[4], lw=1, color=['#D6E6F2'], width=0.5*2/3, label="RNet-34", edgecolor='black') + plt.bar(x_width[5] , y_data[5], lw=1, color=['#B4D1E9'], width=0.5*2/3, label="RNet-35", edgecolor='black') + plt.bar(x_width[6] , y_data[6], lw=1, color=['#AEB5EE'], width=0.5*2/3, label="RNet-45", edgecolor='black') + # plt.bar(x_width[7] + 2.0, y_data[10], lw=0.5, color=['#8085e9'], width=1, label="ResNet-18", edgecolor='black') + plt.bar(x_width[7] , y_data[7], lw=1, color=['#D5A9FF'], width=0.5*2/3, label="ResNet-C", edgecolor='black') + plt.bar(x_width[8] , y_data[8], lw=1, color=['#E000F5'], width=0.5*2/3, label="JMNet", edgecolor='black') + + # plt.tick_params(bottom=False, top=False, left=True, right=False, direction='in', pad=1) + plt.xticks([]) + plt.ylabel('False Negative Rate(%)',fontsize=18) + # plt.xlabel('Time', fontsize=5) + # plt.tight_layout() + + num1, num2, num3, num4 = 0.16, 1, 3, 0 + plt.legend(bbox_to_anchor=(num1, num2), loc=num3, borderaxespad=num4, ncol=5, frameon=False, markerscale=0.5) + plt.ylim([0, 5]) + plt.show() + + def test_result(file_name: str = result_file_name): # result_data = np.recfromcsv(file_name) result_data = np.loadtxt(file_name, delimiter=",") result_data = np.array(result_data) print(len(result_data)) - theshold=len(result_data)-1 + theshold = len(result_data) - 1 print(theshold) - print(theshold*2/3) - #计算误报率和漏报率 - positive_rate=result_data[:int(theshold*2/3)][result_data[:int(theshold*2/3)] < 0.66].__len__()/(theshold*2/3) - negative_rate=result_data[int(theshold*2/3):][result_data[int(theshold*2/3):] > 0.66].__len__()/(theshold*1/3) - print("误报率:",positive_rate) - print("漏报率",negative_rate) + print(theshold * 2 / 3) + # 计算误报率和漏报率 + positive_rate = result_data[:int(theshold * 2 / 3)][result_data[:int(theshold * 2 / 3)] < 0.66].__len__() / ( + theshold * 2 / 3) + negative_rate = result_data[int(theshold * 2 / 3):][result_data[int(theshold * 2 / 3):] > 0.66].__len__() / ( + theshold * 1 / 3) + print("误报率:", positive_rate) + print("漏报率", negative_rate) # 画图 data = np.zeros([208, ]) @@ -208,13 +387,25 @@ def test_corr(file_name=source_path, N=10): print(needed_data) print(needed_data.shape) # plot_original_data(needed_data) - person = plot_Corr(needed_data, label) + person = plot_Corr(needed_data, size=3) person = np.array(person) pass +def test_bar(y_data=list): + plot_bar(y_data) + + if __name__ == '__main__': # test_mse() - test_result(file_name='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_345\RNet_345_timestamp120_feature10_result.csv') + # test_result(file_name='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_C\RNet_C_timestamp120_feature10_result2.csv') # test_corr() - pass + # 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] + # test_bar(list) + + # 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) diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-C.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-C.py index e90d4ff..c49af18 100644 --- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-C.py +++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-C.py @@ -44,13 +44,13 @@ save_name = "./model/weight/{0}/weight".format(model_name, feature_num, batch_size, EPOCH) -save_step_two_name = "./model/two_weight/{0}_weight_epoch6_99899_9996/weight".format(model_name, +save_step_two_name = "./model/two_weight/{0}_weight_epoch5_9991_9995/weight".format(model_name, time_stamp, feature_num, batch_size, EPOCH) -save_mse_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_result.csv".format(model_name, +save_mse_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_result2.csv".format(model_name, time_stamp, feature_num, batch_size, @@ -651,7 +651,8 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data if __name__ == '__main__': - total_data = loadData.execute(N=feature_num, file_name=file_name) + # total_data = loadData.execute(N=feature_num, file_name=file_name) + total_data = np.load("G:\data\SCADA数据\靖边8号处理后的数据\原始10SCADA数据/total_data.npy") 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) @@ -675,11 +676,11 @@ if __name__ == '__main__': ### unhealthy_data.shape: (16594,10) healthy_size, _, _ = train_data_healthy.shape unhealthy_size, _, _ = train_data_unhealthy.shape - train_data, train_label1, train_label2, test_data, test_label1, test_label2 = split_test_data( - healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:, :, :], - healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], - healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy, - unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy) + # train_data, train_label1, train_label2, test_data, test_label1, test_label2 = split_test_data( + # healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:, :, :], + # healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], + # healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy, + # unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy) # train_step_two(step_one_model=step_one_model, step_two_model=step_two_model, # train_data=train_data, # train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1)) 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 f97e229..5f3b55c 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 @@ -21,7 +21,7 @@ K = 18 namuda = 0.01 '''保存名称''' -save_name = "./model/{0}.h5".format(model_name, +save_name = "./model/{0}_9990_9998.h5".format(model_name, time_stamp, feature_num, batch_size,