leecode更新
This commit is contained in:
markilue
parent
82d051a82a
commit
4b279d5f1d
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@ -0,0 +1,110 @@
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package com.markilue.leecode.greedy;
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import org.junit.Test;
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/**
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* @BelongsProject: Leecode
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* @BelongsPackage: com.markilue.leecode.greedy
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* @Author: markilue
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* @CreateTime: 2022-10-26 10:48
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* @Description:
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* TODO 力扣55题 跳跃游戏：
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* 给定一个非负整数数组 nums ，你最初位于数组的 第一个下标 。
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* 数组中的每个元素代表你在该位置可以跳跃的最大长度。
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* 判断你是否能够到达最后一个下标
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* @Version: 1.0
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*/
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public class CanJump {
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@Test
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public void test(){
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int[] nums = {2, 3, 1, 1, 4};
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System.out.println(canJump1(nums));
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}
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@Test
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public void test1(){
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int[] nums = {3,2,1,0,4};
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System.out.println(canJump1(nums));
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}
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/**
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* 自己的思路：似乎可以使用递归去做：
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* 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
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* 速度击败94.15%，内存击败39.95%
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* @param nums
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* @return
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*/
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public boolean canJump(int[] nums) {
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return jump(nums,nums.length-1);
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}
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public boolean jump(int[] nums,int j) {
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//怎么样也到不了了
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if(j<0){
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return false;
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}
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if(j==0){
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return true;
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}
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//寻找上一个能到j位置的点
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int i=j-1;
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for (int k=1; i >= 0; i--,k++) {
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if(nums[i]<k){
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continue;
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}else {
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//找到了上一次能到j位置的点
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break;
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}
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}
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return jump(nums,i);
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}
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/**
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* 代码随想录贪心：利用计算最大覆盖面积去算
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*
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* 速度击败94.15%，内存击败13.37%
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* @param nums
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* @return
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*/
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public boolean canJump1(int[] nums) {
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if(nums.length==1){
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return true;
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}
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int cover=0;
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for (int i = 0; i <= cover; i++) {
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//最大覆盖面积
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cover=Math.max(cover,nums[i]+i);
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if(cover>= nums.length-1)return true;
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}
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return false;
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}
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/**
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* 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
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* @param nums
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* @return
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*/
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public boolean canJump2(int[] nums) {
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int length = nums.length;
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if (length == 1) return true;
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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
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for (int i = length-2; i >0; i--) { //从倒数第二个往第二个开始遍历
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if (nums[i]<minStep){ // 如果当前元素的值小于最小步数,则到达最后一个元素的最小步数+1;
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minStep++;
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}else {
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minStep = 1; //如果当前元素的值大于或等于最小步数，则一定能到达最后一个元素，
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// 此时可以就当前元素认为是最后一个元素，并对于前一个元素来说最小步数为1;
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}
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}
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return nums[0] >= 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;
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}
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}
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@ -13,8 +13,8 @@ import java.util.Comparator;
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* @Description:
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* TODO 力扣455题 分发饼干:
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* 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。
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* 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；
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* 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。
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* 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；
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* 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。
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* 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。
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*
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* @Version: 1.0
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@ -0,0 +1,112 @@
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package com.markilue.leecode.greedy;
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import org.junit.Test;
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/**
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* @BelongsProject: Leecode
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* @BelongsPackage: com.markilue.leecode.greedy
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* @Author: markilue
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* @CreateTime: 2022-10-26 09:49
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* @Description: TODO 力扣122题 买卖股票的最佳时机II:
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* 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。
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* 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。
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* 返回 你能获得的 最大 利润。
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* @Version: 1.0
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*/
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public class MaxProfit {
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@Test
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public void test(){
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int[] prices = {7, 1, 5, 3, 6, 4};
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System.out.println(maxProfit2(prices));
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}
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@Test
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public void test1(){
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int[] prices = {1,2,3,4,5};
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System.out.println(maxProfit(prices));
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}
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@Test
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public void test2(){
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int[] prices = {7,6,4,3,15};
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System.out.println(maxProfit(prices));
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}
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/**
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* 思路：由于股票可以当天买当天卖，所以逻辑变成了：
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* 只要下一个天比今天小，那么今天卖出去；反之则不卖
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* 速度击败81.82%，内存击败98.42%
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* @param prices
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* @return
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*/
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public int maxProfit(int[] prices) {
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//记录买入净额
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int buy = prices[0];
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int total = 0;
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for (int i = 1; i < prices.length; i++) {
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if (prices[i] - prices[i - 1] > 0) {
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if(i==prices.length-1){
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//比他大还是最后一组，必须卖
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total += prices[i] - buy;
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}
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//不卖
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continue;
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} else {
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//卖
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total += prices[i-1] - buy;
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buy=prices[i];
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}
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}
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return total;
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}
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/**
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* 代码随想录贪心：速度击败81.82%，内存击败35.76%
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* @param prices
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* @return
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*/
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public int maxProfit1(int[] prices) {
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//记录买入净额
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int total = 0;
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for (int i = 1; i < prices.length; i++) {
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//省去了判断最后一次的操作，但是要进行多次加法
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total+=Math.max(prices[i]-prices[i-1],0);
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}
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return total;
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}
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/**
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* 官方动态规划法：
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* 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）。
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* dp[i][0] 的转移方程：
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* 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]，
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* 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。
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* dp[i][0]类似
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* @param prices
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* @return
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*/
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public int maxProfit2(int[] prices) {
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int n = prices.length;
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int dp0 = 0, dp1 = -prices[0];
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for (int i = 1; i < n; ++i) {
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int newDp0 = Math.max(dp0, dp1 + prices[i]);
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int newDp1 = Math.max(dp1, dp0 - prices[i]);
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dp0 = newDp0;
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dp1 = newDp1;
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}
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return dp0;
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}
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}
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# -*- coding: utf-8 -*-
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# coding: utf-8
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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import random
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source_path = "G:\data\SCADA数据\jb4q_8_delete_total_zero.csv"
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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]
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def plot_result(result_data):
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parameters = {
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pass
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def plot_Corr(data, label):
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def plot_Corr(data, size: int = 1):
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parameters = {
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'figure.dpi': 600,
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'figure.figsize': (2.8, 2),
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'figure.figsize': (2.8 * size, 2 * size),
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'savefig.dpi': 600,
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'xtick.direction': 'inout',
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'ytick.direction': 'inout',
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'xtick.labelsize': 3,
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'ytick.labelsize': 3,
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'legend.fontsize': 5,
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'xtick.labelsize': 3 * size,
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'ytick.labelsize': 3 * size,
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'legend.fontsize': 5 * size,
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}
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plt.rcParams.update(parameters)
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plt.figure()
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plt.rc('font', family='Times New Roman') # 全局字体样式#画混淆矩阵
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font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 4} # 设置坐标标签的字体大小，字体
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font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 4 * size} # 设置坐标标签的字体大小，字体
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print("计算皮尔逊相关系数")
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pd_data = pd.DataFrame(data)
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print(person)
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# 画热点图heatmap
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cmap = sns.heatmap(person, annot=True, annot_kws={
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'fontsize': 2.6
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'fontsize': 2.6 * size
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})
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classes = ['Gs', 'Gio', 'Gip', 'Gp', 'Gwt', 'En', 'Gft', 'Grt', 'Gwt', 'Et', 'Rs', 'Ap', 'Ws', 'Dw', 'Ges', 'Gt', 'Vx','Vy']
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classes = ['Gs', 'Gio', 'Gip', 'Gp', 'Gwt', 'En', 'Gft', 'Grt', 'Gwt', 'Et', 'Rs', 'Ap', 'Ws', 'Dw', 'Ges', 'Gt',
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'Vx', 'Vy']
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indices = range(len(person))
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plt.title("Heatmap of correlation coefficient matrix", size=6, fontdict=font1)
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plt.title("Heatmap of correlation coefficient matrix", size=6 * size, fontdict=font1)
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# pad调整label与坐标轴之间的距离
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plt.tick_params(bottom=False, top=False, left=False, right=False, direction='inout', length=2, width=0.5, pad=1)
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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
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# 调整色带的标签：
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cbar = cmap.collections[0].colorbar
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cbar.ax.tick_params(labelsize=4, labelcolor="black", length=2, width=0.5,pad=1)
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cbar.ax.tick_params(labelsize=4 * size, labelcolor="black", length=2, width=0.5, pad=1)
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cbar.ax.set_ylabel(ylabel="color scale", color="black", loc="center", fontdict=font1)
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# plt.axis('off') # 去坐标轴
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pass
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def plot_bar(y_data):
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parameters = {
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'figure.dpi': 600,
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'figure.figsize': (10, 6),
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'savefig.dpi': 600,
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'xtick.direction': 'in',
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'ytick.direction': 'in',
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'xtick.labelsize': 15,
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'ytick.labelsize': 15,
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'legend.fontsize': 12,
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}
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plt.rcParams.update(parameters)
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plt.figure()
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plt.rc('font', family='Times New Roman') # 全局字体样式#画混淆矩阵
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font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 5} # 设置坐标标签的字体大小，字体
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x_width = [ i for i in range(0, len(y_data))]
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# x2_width = [i + 0.3 for i in x_width]
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plt.bar(x_width[0], y_data[0], lw=1, color=['#FAF4E1'], width=0.5, label="CNN", edgecolor='black')
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plt.bar(x_width[1] , y_data[1], lw=1, color=['#F5E3C4'], width=0.5, label="GRU", edgecolor='black')
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plt.bar(x_width[2] , y_data[2], lw=1, color=['#EBC99D'], width=0.5, label="CNN-GRU", edgecolor='black')
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plt.bar(x_width[3] , y_data[3], lw=1, color=['#FFC79C'], width=0.5, label="DCConv", edgecolor='black')
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plt.bar(x_width[4] , y_data[4], lw=1, color=['#BEE9C7'], width=0.5, label="RepDCConv", edgecolor='black')
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plt.bar(x_width[5] , y_data[5], lw=1, color=['#B8E9D0'], width=0.5,label="RNet-MSE", edgecolor='black')
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plt.bar(x_width[6] , y_data[6], lw=1, color=['#B9E9E2'], width=0.5, label="RNet", edgecolor='black')
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plt.bar(x_width[7] , y_data[7], lw=1, color=['#D6E6F2'], width=0.5, label="RNet-SE", edgecolor='black')
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plt.bar(x_width[8] , y_data[8], lw=1, color=['#B4D1E9'], width=0.5, label="RNet-L", edgecolor='black')
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plt.bar(x_width[9] , y_data[9], lw=1, color=['#AEB5EE'], width=0.5, label="RNet-D", edgecolor='black')
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plt.bar(x_width[10] , y_data[10], lw=1, color=['#D2D3FC'], width=0.5, label="ResNet-18", edgecolor='black')
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plt.bar(x_width[11] , y_data[11], lw=1, color=['#D5A9FF'], width=0.5, label="ResNet-C", edgecolor='black')
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plt.bar(x_width[12] , y_data[12], lw=1, color=['#E000F5'], width=0.5, label="JMNet", edgecolor='black')
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# plt.tick_params(bottom=False, top=False, left=True, right=False, direction='in', pad=1)
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plt.xticks([])
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plt.ylabel('False Positive Rate(%)',fontsize=18)
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# plt.xlabel('Time', fontsize=5)
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# plt.tight_layout()
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num1, num2, num3, num4 = 0.1, 1, 3, 0
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plt.legend(bbox_to_anchor=(num1, num2), loc=num3, borderaxespad=num4, ncol=5, frameon=False, markerscale=0.5)
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plt.ylim([-0.01, 5])
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plt.show()
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def acc(y_data=list):
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parameters = {
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'figure.dpi': 600,
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'figure.figsize': (10, 6),
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'savefig.dpi': 600,
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'xtick.direction': 'in',
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'ytick.direction': 'in',
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'xtick.labelsize': 15,
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'ytick.labelsize': 15,
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'legend.fontsize': 12,
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}
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plt.rcParams.update(parameters)
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plt.figure()
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plt.rc('font', family='Times New Roman') # 全局字体样式#画混淆矩阵
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font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 5} # 设置坐标标签的字体大小，字体
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x_width = [ i/2 for i in range(0, len(y_data))]
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# x2_width = [i + 0.3 for i in x_width]
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|
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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=",")
|
||||
|
|
@ -180,8 +357,10 @@ def test_result(file_name: str = result_file_name):
|
|||
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)
|
||||
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)
|
||||
|
||||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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))
|
||||
|
|
|
|||
|
|
@ -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,
|
||||
|
|
|
|||
Loading…
Reference in New Issue