leecode更新
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markilue
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d8f403cc14
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package com.markilue.leecode.backtrace;
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import org.junit.Test;
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import java.util.ArrayList;
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import java.util.List;
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/**
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* @BelongsProject: Leecode
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* @BelongsPackage: com.markilue.leecode.backtrace
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* @Author: markilue
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* @CreateTime: 2022-10-12 11:23
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* @Description:
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* TODO 力扣77题 组合：
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* 给定两个整数 n 和 k，返回范围 [1, n] 中所有可能的 k 个数的组合。
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* 你可以按 任何顺序 返回答案。
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* @Version: 1.0
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*/
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public class Combine {
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@Test
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public void test(){
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int n = 4, k = 2;
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List<List<Integer>> combine = combine2(4, 2);
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System.out.println(combine);
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}
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@Test
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public void test1(){
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int n = 4, k = 2;
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List<List<Integer>> combine = combine(1, 1);
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System.out.println(combine);
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}
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@Test
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public void test2(){
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int n = 4, k = 2;
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List<List<Integer>> combine = combine(6, 3);
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System.out.println(combine);
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}
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/**
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* 由于k不确定，因此使用for循环解决显然不现实，这里考虑使用递归法
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* 速度击败45.7%，内存击败41.95%
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* @param n
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* @param k
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* @return
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*/
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public List<List<Integer>> combine(int n, int k) {
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for (int i = 1; i <= n; i++) {
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list(i,n,k);
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cur.remove(cur.size()-1);
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}
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return result;
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}
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List<List<Integer>> result=new ArrayList<>();
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List<Integer> cur=new ArrayList<>();
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/**
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* 传递还需要多少个数k,和可选的范围
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* @param val 当前可以传的值
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* @param k 还需要多少个数
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*/
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public void list(int val,int n,int k){
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cur.add(val);
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if(k==1){
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ArrayList<Integer> list1 = new ArrayList<>();
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list1.addAll(cur);
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result.add(list1);
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return;
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}
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for (int i = val+1; i <= n; i++) {
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list(i,n,k-1);
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cur.remove(cur.size()-1);
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}
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}
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/**
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* 对照模板的写法
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*
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* @param n
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* @param k
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* @return
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*/
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public List<List<Integer>> combine1(int n, int k) {
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result.clear();
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cur.clear();
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backtracking(n,k,1);
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return result;
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}
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public void backtracking(int n,int k,int val){
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if(k==cur.size()){
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ArrayList<Integer> list1 = new ArrayList<>();
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list1.addAll(cur);
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result.add(list1);
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return;
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}
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//这里使用n-(k-cur.size())+1剪枝，即第一层最多可以取到n-(k-cur.size())+1，因为比如 5个数取3个数，第一层最多到3（即3 4 5）
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for (int i = val; i <= n-(k-cur.size())+1; i++) {
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cur.add(i);
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backtracking(n,k,i+1);
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cur.remove(cur.size()-1);
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}
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}
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/**
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* 非递归法(字典序法)：核心是利用两个规则，变幻出二进制的各种组合，从而遍历出所有的可能：
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* 规则一：
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* xx 的最低位为 11，这种情况下，如果末尾由 tt 个连续的 11，我们直接将倒数第 tt 位的 11 和倒数第 t + 1t+1 位的 00 替换，
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* 就可以得到 {next}(x)next(x)。如 0011 -> 0101，0101 -> 0110，1001 -> 1010，1001111 -> 1010111。
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*
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* 规则二：
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* xx 的最低位为 00，这种情况下，末尾有 tt 个连续的 00，而这 tt 个连续的 00 之前有 mm 个连续的 11，
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* 我们可以将倒数第 t + mt+m 位置的 11 和倒数第 t + m + 1t+m+1 位的 00 对换，
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* 然后把倒数第 t + 1t+1 位到倒数第 t + m - 1t+m−1 位的 11 移动到最低位。
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* 如 0110 -> 1001，1010 -> 1100，1011100 -> 1100011。
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*
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* 具体参考leecode笔记上的例子变换
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*
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* @param k
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* @return
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*/
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public List<List<Integer>> combine2(int n, int k) {
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List<Integer> temp = new ArrayList<Integer>();
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List<List<Integer>> ans = new ArrayList<List<Integer>>();
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// 初始化
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// 将 temp 中 [0, k - 1] 每个位置 i 设置为 i + 1，即 [0, k - 1] 存 [1, k]
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// 末尾加一位 n + 1 作为哨兵
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for (int i = 1; i <= k; ++i) {
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temp.add(i);
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}
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temp.add(n + 1);
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int j = 0;
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while (j < k) {
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ans.add(new ArrayList<Integer>(temp.subList(0, k)));
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j = 0;
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// 寻找第一个 temp[j] + 1 != temp[j + 1] 的位置 t
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// 我们需要把 [0, t - 1] 区间内的每个位置重置成 [1, t]
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while (j < k && temp.get(j) + 1 == temp.get(j + 1)) {
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temp.set(j, j + 1);
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++j;
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}
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// j 是第一个 temp[j] + 1 != temp[j + 1] 的位置
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temp.set(j, temp.get(j) + 1);
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}
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return ans;
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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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'''
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@Author : dingjiawen
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@Date : 2022/10/11 18:53
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@Usage :
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@Desc :
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'''
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# -*- coding: utf-8 -*-
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# coding: utf-8
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'''
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@Author : dingjiawen
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@Date : 2022/10/11 18:52
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@Usage : 对比实验，与JointNet相同深度,进行预测
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@Desc :
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'''
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# -*- coding: utf-8 -*-
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# coding: utf-8
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import tensorflow as tf
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import tensorflow.keras
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from model.DepthwiseCon1D.DepthwiseConv1D import DepthwiseConv1D
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from model.Dynamic_channelAttention.Dynamic_channelAttention import DynamicChannelAttention
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from condition_monitoring.data_deal import loadData
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from model.Joint_Monitoring.Joint_Monitoring3 import Joint_Monitoring
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from model.CommonFunction.CommonFunction import *
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from sklearn.model_selection import train_test_split
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from tensorflow.keras.models import load_model, save_model
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from keras.callbacks import EarlyStopping
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'''超参数设置'''
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time_stamp = 120
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feature_num = 10
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batch_size = 16
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learning_rate = 0.001
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EPOCH = 101
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model_name = "DCConv"
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'''EWMA超参数'''
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K = 18
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namuda = 0.01
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'''保存名称'''
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save_name = "./model/{0}_timestamp{1}_feature{2}.h5".format(model_name,
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time_stamp,
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feature_num,
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batch_size,
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EPOCH)
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save_step_two_name = "../hard_model/two_weight/{0}_timestamp{1}_feature{2}_weight_epoch14/weight".format(model_name,
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time_stamp,
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feature_num,
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batch_size,
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EPOCH)
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# save_name = "../model/joint/{0}_timestamp{1}_feature{2}.h5".format(model_name,
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# time_stamp,
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# feature_num,
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# batch_size,
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# EPOCH)
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# save_step_two_name = "../model/joint_two/{0}_timestamp{1}_feature{2}.h5".format(model_name,
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# time_stamp,
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# feature_num,
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# batch_size,
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# EPOCH)
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'''文件名'''
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file_name = "G:\data\SCADA数据\jb4q_8_delete_total_zero.csv"
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'''
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文件说明：jb4q_8_delete_total_zero.csv是删除了只删除了全是0的列的文件
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文件从0:415548行均是正常值(2019/7.30 00:00:00 - 2019/9/18 11:14:00)
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从415549:432153行均是异常值(2019/9/18 11:21:01 - 2021/1/18 00:00:00)
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'''
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'''文件参数'''
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# 最后正常的时间点
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healthy_date = 415548
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# 最后异常的时间点
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unhealthy_date = 432153
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# 异常容忍程度
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unhealthy_patience = 5
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def remove(data, time_stamp=time_stamp):
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rows, cols = data.shape
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print("remove_data.shape:", data.shape)
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num = int(rows / time_stamp)
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return data[:num * time_stamp, :]
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pass
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# 不重叠采样
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def get_training_data(data, time_stamp: int = time_stamp):
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removed_data = remove(data=data)
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rows, cols = removed_data.shape
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print("removed_data.shape:", data.shape)
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print("removed_data:", removed_data)
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train_data = np.reshape(removed_data, [-1, time_stamp, cols])
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print("train_data:", train_data)
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batchs, time_stamp, cols = train_data.shape
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for i in range(1, batchs):
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each_label = np.expand_dims(train_data[i, 0, :], axis=0)
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if i == 1:
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train_label = each_label
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else:
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train_label = np.concatenate([train_label, each_label], axis=0)
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print("train_data.shape:", train_data.shape)
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print("train_label.shape", train_label.shape)
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return train_data[:-1, :], train_label
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# 重叠采样
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def get_training_data_overlapping(data, time_stamp: int = time_stamp, is_Healthy: bool = True):
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rows, cols = data.shape
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train_data = np.empty(shape=[rows - time_stamp - 1, time_stamp, cols])
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train_label = np.empty(shape=[rows - time_stamp - 1, cols])
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for i in range(rows):
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if i + time_stamp >= rows:
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break
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if i + time_stamp < rows - 1:
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train_data[i] = data[i:i + time_stamp]
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train_label[i] = data[i + time_stamp]
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print("重叠采样以后：")
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print("data:", train_data) # (300334,120,10)
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print("label:", train_label) # (300334,10)
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if is_Healthy:
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train_label2 = np.ones(shape=[train_label.shape[0]])
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else:
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train_label2 = np.zeros(shape=[train_label.shape[0]])
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print("label2:", train_label2)
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return train_data, train_label, train_label2
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# 归一化
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def normalization(data):
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rows, cols = data.shape
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print("归一化之前:", data)
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print(data.shape)
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print("======================")
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# 归一化
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max = np.max(data, axis=0)
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max = np.broadcast_to(max, [rows, cols])
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min = np.min(data, axis=0)
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min = np.broadcast_to(min, [rows, cols])
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data = (data - min) / (max - min)
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print("归一化之后:", data)
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print(data.shape)
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return data
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# 正则化
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def Regularization(data):
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rows, cols = data.shape
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print("正则化之前:", data)
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print(data.shape)
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print("======================")
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# 正则化
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mean = np.mean(data, axis=0)
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mean = np.broadcast_to(mean, shape=[rows, cols])
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dst = np.sqrt(np.var(data, axis=0))
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dst = np.broadcast_to(dst, shape=[rows, cols])
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data = (data - mean) / dst
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print("正则化之后:", data)
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print(data.shape)
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return data
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pass
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def EWMA(data, K=K, namuda=namuda):
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# t是啥暂时未知
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t = 0
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mid = np.mean(data, axis=0)
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standard = np.sqrt(np.var(data, axis=0))
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UCL = mid + K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
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LCL = mid - K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
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return mid, UCL, LCL
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pass
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def get_MSE(data, label, new_model):
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predicted_data = new_model.predict(data)
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temp = np.abs(predicted_data - label)
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temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape))
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temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape)
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temp3 = temp1 / temp2
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mse = np.sum((temp1 / temp2) ** 2, axis=1)
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print("z:", mse)
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print(mse.shape)
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# mse=np.mean((predicted_data-label)**2,axis=1)
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print("mse", mse)
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dims, = mse.shape
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mean = np.mean(mse)
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std = np.sqrt(np.var(mse))
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max = mean + 3 * std
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# min = mean-3*std
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max = np.broadcast_to(max, shape=[dims, ])
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# min = np.broadcast_to(min,shape=[dims,])
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mean = np.broadcast_to(mean, shape=[dims, ])
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# plt.plot(max)
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# plt.plot(mse)
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# plt.plot(mean)
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# # plt.plot(min)
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# plt.show()
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#
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#
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return mse, mean, max
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# pass
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def condition_monitoring_model():
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input = tf.keras.Input(shape=[time_stamp, feature_num])
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conv1 = tf.keras.layers.Conv1D(filters=256, kernel_size=1)(input)
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GRU1 = tf.keras.layers.GRU(128, return_sequences=False)(conv1)
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d1 = tf.keras.layers.Dense(300)(GRU1)
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output = tf.keras.layers.Dense(10)(d1)
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model = tf.keras.Model(inputs=input, outputs=output)
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return model
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# trian_data:(300455,120,10)
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# trian_label1:(300455,10)
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# trian_label2:(300455,)
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def shuffle(train_data, train_label1, train_label2, is_split: bool = False, split_size: float = 0.2):
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(train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(train_data,
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train_label1,
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train_label2,
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test_size=split_size,
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shuffle=True,
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random_state=100)
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if is_split:
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return train_data, train_label1, train_label2, test_data, test_label1, test_label2
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train_data = np.concatenate([train_data, test_data], axis=0)
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train_label1 = np.concatenate([train_label1, test_label1], axis=0)
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train_label2 = np.concatenate([train_label2, test_label2], axis=0)
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# print(train_data.shape)
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# print(train_label1.shape)
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# print(train_label2.shape)
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# print(train_data.shape)
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return train_data, train_label1, train_label2
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pass
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def split_test_data(healthy_data, healthy_label1, healthy_label2, unhealthy_data, unhealthy_label1, unhealthy_label2,
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split_size: float = 0.2, shuffle: bool = True):
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data = np.concatenate([healthy_data, unhealthy_data], axis=0)
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label1 = np.concatenate([healthy_label1, unhealthy_label1], axis=0)
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label2 = np.concatenate([healthy_label2, unhealthy_label2], axis=0)
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(train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(data,
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label1,
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label2,
|
||||
test_size=split_size,
|
||||
shuffle=shuffle,
|
||||
random_state=100)
|
||||
|
||||
# print(train_data.shape)
|
||||
# print(train_label1.shape)
|
||||
# print(train_label2.shape)
|
||||
# print(train_data.shape)
|
||||
|
||||
return train_data, train_label1, train_label2, test_data, test_label1, test_label2
|
||||
|
||||
pass
|
||||
|
||||
|
||||
def test(step_one_model, step_two_model, test_data, test_label1, test_label2):
|
||||
history_loss = []
|
||||
history_val_loss = []
|
||||
|
||||
val_loss, val_accuracy = step_two_model.get_val_loss(val_data=test_data, val_label1=test_label1,
|
||||
val_label2=test_label2,
|
||||
is_first_time=False, step_one_model=step_one_model)
|
||||
|
||||
history_val_loss.append(val_loss)
|
||||
print("val_accuracy:", val_accuracy)
|
||||
print("val_loss:", val_loss)
|
||||
|
||||
|
||||
def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False):
|
||||
# 获取模型的所有参数的个数
|
||||
# step_two_model.count_params()
|
||||
total_result = []
|
||||
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, :, :]
|
||||
_, _, _, output4 = step_two_model.call(each_test_data, is_first_time=False)
|
||||
total_result.append(output4)
|
||||
total_result = np.reshape(total_result, [total_result.__len__(), -1])
|
||||
total_result = np.reshape(total_result, [-1, ])
|
||||
if isPlot:
|
||||
plt.scatter(list(range(total_result.shape[0])), total_result, c='black', s=10)
|
||||
# 画出 y=1 这条水平线
|
||||
plt.axhline(0.5, c='red', label='Failure threshold')
|
||||
# 箭头指向上面的水平线
|
||||
# plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
|
||||
# alpha=0.9, overhang=0.5)
|
||||
# plt.text(35000, 0.9, "Truth Fault", fontsize=10, color='black', verticalalignment='top')
|
||||
plt.axvline(test_data.shape[0] * 2 / 3, c='blue', ls='-.')
|
||||
plt.xlabel("time")
|
||||
plt.ylabel("confience")
|
||||
plt.text(total_result.shape[0] * 4 / 5, 0.6, "Fault", fontsize=10, color='black', verticalalignment='top',
|
||||
horizontalalignment='center',
|
||||
bbox={'facecolor': 'grey',
|
||||
'pad': 10})
|
||||
plt.text(total_result.shape[0] * 1 / 3, 0.4, "Norm", fontsize=10, color='black', verticalalignment='top',
|
||||
horizontalalignment='center',
|
||||
bbox={'facecolor': 'grey',
|
||||
'pad': 10})
|
||||
plt.grid()
|
||||
# plt.ylim(0, 1)
|
||||
# plt.xlim(-50, 1300)
|
||||
# plt.legend("", loc='upper left')
|
||||
plt.show()
|
||||
return total_result
|
||||
|
||||
|
||||
def GRU_Model():
|
||||
input = tf.keras.Input(shape=[time_stamp, feature_num])
|
||||
input = tf.cast(input, tf.float32)
|
||||
|
||||
LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal",dilation_rate=2)(input)
|
||||
LSTM = tf.keras.layers.Conv1D(20, 3, padding="causal",dilation_rate=4)(LSTM)
|
||||
LSTM = tf.keras.layers.Conv1D(40, 3, padding="causal",dilation_rate=8)(LSTM)
|
||||
LSTM = tf.keras.layers.Conv1D(40, 3, padding="causal",dilation_rate=16)(LSTM)
|
||||
LSTM = tf.keras.layers.Conv1D(40, 3, padding="causal",dilation_rate=32)(LSTM)
|
||||
LSTM = tf.keras.layers.Conv1D(40, 3, padding="causal",dilation_rate=64)(LSTM)
|
||||
LSTM = tf.keras.layers.Conv1D(40, 3, padding="causal",dilation_rate=128)(LSTM)
|
||||
# LSTM = tf.keras.layers.Conv1D(40, 3, padding="causal",dilation_rate=2)(LSTM)
|
||||
|
||||
|
||||
# bn = tf.keras.layers.BatchNormalization()(LSTM)
|
||||
|
||||
d1 = tf.keras.layers.Dense(20)(LSTM)
|
||||
# bn = tf.keras.layers.BatchNormalization()(d1)
|
||||
|
||||
output = tf.keras.layers.Dense(10, name='output')(d1)
|
||||
model = tf.keras.Model(inputs=input, outputs=output)
|
||||
return model
|
||||
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(
|
||||
total_data[healthy_date - time_stamp + unhealthy_patience:unhealthy_date, :],
|
||||
is_Healthy=False)
|
||||
#### TODO 第一步训练
|
||||
# 单次测试
|
||||
model = GRU_Model()
|
||||
|
||||
model.compile(optimizer=tf.optimizers.Adam(0.01), loss=tf.losses.mse)
|
||||
model.summary()
|
||||
early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=3, mode='min', verbose=1)
|
||||
|
||||
checkpoint = tf.keras.callbacks.ModelCheckpoint(
|
||||
filepath=save_name,
|
||||
monitor='val_loss',
|
||||
verbose=1,
|
||||
save_best_only=True,
|
||||
mode='min',
|
||||
period=1)
|
||||
|
||||
history = model.fit(train_data_healthy[:30000, :, :], train_label1_healthy[:30000, :], epochs=20,
|
||||
batch_size=32, validation_split=0.2, shuffle=False, verbose=1,
|
||||
callbacks=[checkpoint, early_stop])
|
||||
model.save(save_name)
|
||||
|
||||
## TODO testing
|
||||
test_data, test_label = get_training_data(total_data[:300455, :])
|
||||
newModel = tf.keras.models.load_model(save_name)
|
||||
mse, mean, max = get_MSE(test_data, test_label, new_model=newModel)
|
||||
|
||||
test_data, test_label = get_training_data(total_data[20000:, :])
|
||||
predicted_data = newModel.predict(test_data)
|
||||
rows, cols = predicted_data.shape
|
||||
|
||||
temp = np.abs(predicted_data - test_label)
|
||||
temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape))
|
||||
temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape)
|
||||
temp3 = temp1 / temp2
|
||||
mse = np.sum((temp1 / temp2) ** 2, axis=1)
|
||||
|
||||
plt.plot(mse)
|
||||
plt.plot(mean)
|
||||
plt.plot(max)
|
||||
plt.show()
|
||||
|
||||
data = pd.DataFrame(mse).ewm(span=3).mean()
|
||||
print(data)
|
||||
data = np.array(data)
|
||||
|
||||
index, _ = data.shape
|
||||
|
||||
for i in range(2396):
|
||||
if data[i, 0] > 5:
|
||||
data[i, 0] = data[i - 1, :]
|
||||
print(data)
|
||||
mean = data[2000:2396, :].mean()
|
||||
std = data[2000:2396, :].std()
|
||||
mean = np.broadcast_to(mean, shape=[500, ])
|
||||
std = np.broadcast_to(std, shape=[500, ])
|
||||
plt.plot(data[2000:2396, :])
|
||||
plt.plot(mean)
|
||||
plt.plot(mean + 3 * std)
|
||||
plt.plot(mean - 3 * std)
|
||||
plt.show()
|
||||
|
||||
pass
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
|
||||
# coding: utf-8
|
||||
|
||||
'''
|
||||
@Author : dingjiawen
|
||||
@Date : 2022/10/11 18:52
|
||||
@Usage : 对比实验，与JointNet相同深度,进行预测
|
||||
@Desc :
|
||||
'''
|
||||
|
|
@ -0,0 +1,473 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
|
||||
# coding: utf-8
|
||||
|
||||
'''
|
||||
@Author : dingjiawen
|
||||
@Date : 2022/10/11 18:52
|
||||
@Usage : 对比实验，与JointNet相同深度,进行预测
|
||||
@Desc :
|
||||
'''
|
||||
|
||||
import tensorflow as tf
|
||||
import tensorflow.keras
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
from model.DepthwiseCon1D.DepthwiseConv1D import DepthwiseConv1D
|
||||
from model.Dynamic_channelAttention.Dynamic_channelAttention import DynamicChannelAttention
|
||||
from condition_monitoring.data_deal import loadData
|
||||
from model.Joint_Monitoring.Joint_Monitoring3 import Joint_Monitoring
|
||||
|
||||
from model.CommonFunction.CommonFunction import *
|
||||
from sklearn.model_selection import train_test_split
|
||||
from tensorflow.keras.models import load_model, save_model
|
||||
from keras.callbacks import EarlyStopping
|
||||
|
||||
'''超参数设置'''
|
||||
time_stamp = 120
|
||||
feature_num = 10
|
||||
batch_size = 16
|
||||
learning_rate = 0.001
|
||||
EPOCH = 101
|
||||
model_name = "GRU"
|
||||
'''EWMA超参数'''
|
||||
K = 18
|
||||
namuda = 0.01
|
||||
'''保存名称'''
|
||||
|
||||
save_name = "./model/{0}_timestamp{1}_feature{2}.h5".format(model_name,
|
||||
time_stamp,
|
||||
feature_num,
|
||||
batch_size,
|
||||
EPOCH)
|
||||
save_step_two_name = "../hard_model/two_weight/{0}_timestamp{1}_feature{2}_weight_epoch14/weight".format(model_name,
|
||||
time_stamp,
|
||||
feature_num,
|
||||
batch_size,
|
||||
EPOCH)
|
||||
|
||||
# save_name = "../model/joint/{0}_timestamp{1}_feature{2}.h5".format(model_name,
|
||||
# time_stamp,
|
||||
# feature_num,
|
||||
# batch_size,
|
||||
# EPOCH)
|
||||
# save_step_two_name = "../model/joint_two/{0}_timestamp{1}_feature{2}.h5".format(model_name,
|
||||
# time_stamp,
|
||||
# feature_num,
|
||||
# batch_size,
|
||||
# EPOCH)
|
||||
'''文件名'''
|
||||
file_name = "G:\data\SCADA数据\jb4q_8_delete_total_zero.csv"
|
||||
|
||||
'''
|
||||
文件说明：jb4q_8_delete_total_zero.csv是删除了只删除了全是0的列的文件
|
||||
文件从0:415548行均是正常值(2019/7.30 00:00:00 - 2019/9/18 11:14:00)
|
||||
从415549:432153行均是异常值(2019/9/18 11:21:01 - 2021/1/18 00:00:00)
|
||||
'''
|
||||
'''文件参数'''
|
||||
# 最后正常的时间点
|
||||
healthy_date = 415548
|
||||
# 最后异常的时间点
|
||||
unhealthy_date = 432153
|
||||
# 异常容忍程度
|
||||
unhealthy_patience = 5
|
||||
|
||||
|
||||
def remove(data, time_stamp=time_stamp):
|
||||
rows, cols = data.shape
|
||||
print("remove_data.shape:", data.shape)
|
||||
num = int(rows / time_stamp)
|
||||
|
||||
return data[:num * time_stamp, :]
|
||||
pass
|
||||
|
||||
|
||||
# 不重叠采样
|
||||
def get_training_data(data, time_stamp: int = time_stamp):
|
||||
removed_data = remove(data=data)
|
||||
rows, cols = removed_data.shape
|
||||
print("removed_data.shape:", data.shape)
|
||||
print("removed_data:", removed_data)
|
||||
train_data = np.reshape(removed_data, [-1, time_stamp, cols])
|
||||
print("train_data:", train_data)
|
||||
batchs, time_stamp, cols = train_data.shape
|
||||
|
||||
for i in range(1, batchs):
|
||||
each_label = np.expand_dims(train_data[i, 0, :], axis=0)
|
||||
if i == 1:
|
||||
train_label = each_label
|
||||
else:
|
||||
train_label = np.concatenate([train_label, each_label], axis=0)
|
||||
|
||||
print("train_data.shape:", train_data.shape)
|
||||
print("train_label.shape", train_label.shape)
|
||||
return train_data[:-1, :], train_label
|
||||
|
||||
|
||||
# 重叠采样
|
||||
def get_training_data_overlapping(data, time_stamp: int = time_stamp, is_Healthy: bool = True):
|
||||
rows, cols = data.shape
|
||||
train_data = np.empty(shape=[rows - time_stamp - 1, time_stamp, cols])
|
||||
train_label = np.empty(shape=[rows - time_stamp - 1, cols])
|
||||
for i in range(rows):
|
||||
if i + time_stamp >= rows:
|
||||
break
|
||||
if i + time_stamp < rows - 1:
|
||||
train_data[i] = data[i:i + time_stamp]
|
||||
train_label[i] = data[i + time_stamp]
|
||||
|
||||
print("重叠采样以后：")
|
||||
print("data:", train_data) # (300334,120,10)
|
||||
print("label:", train_label) # (300334,10)
|
||||
|
||||
if is_Healthy:
|
||||
train_label2 = np.ones(shape=[train_label.shape[0]])
|
||||
else:
|
||||
train_label2 = np.zeros(shape=[train_label.shape[0]])
|
||||
|
||||
print("label2:", train_label2)
|
||||
|
||||
return train_data, train_label, train_label2
|
||||
|
||||
|
||||
# 归一化
|
||||
def normalization(data):
|
||||
rows, cols = data.shape
|
||||
print("归一化之前:", data)
|
||||
print(data.shape)
|
||||
print("======================")
|
||||
|
||||
# 归一化
|
||||
max = np.max(data, axis=0)
|
||||
max = np.broadcast_to(max, [rows, cols])
|
||||
min = np.min(data, axis=0)
|
||||
min = np.broadcast_to(min, [rows, cols])
|
||||
|
||||
data = (data - min) / (max - min)
|
||||
print("归一化之后:", data)
|
||||
print(data.shape)
|
||||
|
||||
return data
|
||||
|
||||
|
||||
# 正则化
|
||||
def Regularization(data):
|
||||
rows, cols = data.shape
|
||||
print("正则化之前:", data)
|
||||
print(data.shape)
|
||||
print("======================")
|
||||
|
||||
# 正则化
|
||||
mean = np.mean(data, axis=0)
|
||||
mean = np.broadcast_to(mean, shape=[rows, cols])
|
||||
dst = np.sqrt(np.var(data, axis=0))
|
||||
dst = np.broadcast_to(dst, shape=[rows, cols])
|
||||
data = (data - mean) / dst
|
||||
print("正则化之后:", data)
|
||||
print(data.shape)
|
||||
|
||||
return data
|
||||
pass
|
||||
|
||||
|
||||
def EWMA(data, K=K, namuda=namuda):
|
||||
# t是啥暂时未知
|
||||
t = 0
|
||||
mid = np.mean(data, axis=0)
|
||||
standard = np.sqrt(np.var(data, axis=0))
|
||||
UCL = mid + K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
|
||||
LCL = mid - K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
|
||||
return mid, UCL, LCL
|
||||
pass
|
||||
|
||||
|
||||
def get_MSE(data, label, new_model, isStandard: bool = True, isPlot: bool = True):
|
||||
predicted_data = new_model.predict(data)
|
||||
|
||||
temp = np.abs(predicted_data - label)
|
||||
temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape))
|
||||
temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape)
|
||||
temp3 = temp1 / temp2
|
||||
mse = np.sum((temp1 / temp2) ** 2, axis=1)
|
||||
print("z:", mse)
|
||||
print(mse.shape)
|
||||
|
||||
# mse=np.mean((predicted_data-label)**2,axis=1)
|
||||
print("mse", mse)
|
||||
if isStandard:
|
||||
dims, = mse.shape
|
||||
mean = np.mean(mse)
|
||||
std = np.sqrt(np.var(mse))
|
||||
max = mean + 3 * std
|
||||
print("max:", max)
|
||||
# min = mean-3*std
|
||||
max = np.broadcast_to(max, shape=[dims, ])
|
||||
# min = np.broadcast_to(min,shape=[dims,])
|
||||
mean = np.broadcast_to(mean, shape=[dims, ])
|
||||
if isPlot:
|
||||
plt.plot(max)
|
||||
plt.plot(mse)
|
||||
plt.plot(mean)
|
||||
# plt.plot(min)
|
||||
plt.show()
|
||||
else:
|
||||
if isPlot:
|
||||
plt.plot(mse)
|
||||
# plt.plot(min)
|
||||
plt.show()
|
||||
return mse
|
||||
|
||||
return mse, mean, max
|
||||
# pass
|
||||
|
||||
|
||||
def condition_monitoring_model():
|
||||
input = tf.keras.Input(shape=[time_stamp, feature_num])
|
||||
conv1 = tf.keras.layers.Conv1D(filters=256, kernel_size=1)(input)
|
||||
GRU1 = tf.keras.layers.GRU(128, return_sequences=False)(conv1)
|
||||
d1 = tf.keras.layers.Dense(300)(GRU1)
|
||||
output = tf.keras.layers.Dense(10)(d1)
|
||||
|
||||
model = tf.keras.Model(inputs=input, outputs=output)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
# trian_data:(300455,120,10)
|
||||
# trian_label1:(300455,10)
|
||||
# trian_label2:(300455,)
|
||||
def shuffle(train_data, train_label1, train_label2, is_split: bool = False, split_size: float = 0.2):
|
||||
(train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(train_data,
|
||||
train_label1,
|
||||
train_label2,
|
||||
test_size=split_size,
|
||||
shuffle=True,
|
||||
random_state=100)
|
||||
if is_split:
|
||||
return train_data, train_label1, train_label2, test_data, test_label1, test_label2
|
||||
train_data = np.concatenate([train_data, test_data], axis=0)
|
||||
train_label1 = np.concatenate([train_label1, test_label1], axis=0)
|
||||
train_label2 = np.concatenate([train_label2, test_label2], axis=0)
|
||||
# print(train_data.shape)
|
||||
# print(train_label1.shape)
|
||||
# print(train_label2.shape)
|
||||
# print(train_data.shape)
|
||||
|
||||
return train_data, train_label1, train_label2
|
||||
pass
|
||||
|
||||
|
||||
def split_test_data(healthy_data, healthy_label1, healthy_label2, unhealthy_data, unhealthy_label1, unhealthy_label2,
|
||||
split_size: float = 0.2, shuffle: bool = True):
|
||||
data = np.concatenate([healthy_data, unhealthy_data], axis=0)
|
||||
label1 = np.concatenate([healthy_label1, unhealthy_label1], axis=0)
|
||||
label2 = np.concatenate([healthy_label2, unhealthy_label2], axis=0)
|
||||
(train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(data,
|
||||
label1,
|
||||
label2,
|
||||
test_size=split_size,
|
||||
shuffle=shuffle,
|
||||
random_state=100)
|
||||
|
||||
# print(train_data.shape)
|
||||
# print(train_label1.shape)
|
||||
# print(train_label2.shape)
|
||||
# print(train_data.shape)
|
||||
|
||||
return train_data, train_label1, train_label2, test_data, test_label1, test_label2
|
||||
|
||||
pass
|
||||
|
||||
|
||||
def test(step_one_model, step_two_model, test_data, test_label1, test_label2):
|
||||
history_loss = []
|
||||
history_val_loss = []
|
||||
|
||||
val_loss, val_accuracy = step_two_model.get_val_loss(val_data=test_data, val_label1=test_label1,
|
||||
val_label2=test_label2,
|
||||
is_first_time=False, step_one_model=step_one_model)
|
||||
|
||||
history_val_loss.append(val_loss)
|
||||
print("val_accuracy:", val_accuracy)
|
||||
print("val_loss:", val_loss)
|
||||
|
||||
|
||||
def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False):
|
||||
# 获取模型的所有参数的个数
|
||||
# step_two_model.count_params()
|
||||
total_result = []
|
||||
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, :, :]
|
||||
_, _, _, output4 = step_two_model.call(each_test_data, is_first_time=False)
|
||||
total_result.append(output4)
|
||||
total_result = np.reshape(total_result, [total_result.__len__(), -1])
|
||||
total_result = np.reshape(total_result, [-1, ])
|
||||
if isPlot:
|
||||
plt.scatter(list(range(total_result.shape[0])), total_result, c='black', s=10)
|
||||
# 画出 y=1 这条水平线
|
||||
plt.axhline(0.5, c='red', label='Failure threshold')
|
||||
# 箭头指向上面的水平线
|
||||
# plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
|
||||
# alpha=0.9, overhang=0.5)
|
||||
# plt.text(35000, 0.9, "Truth Fault", fontsize=10, color='black', verticalalignment='top')
|
||||
plt.axvline(test_data.shape[0] * 2 / 3, c='blue', ls='-.')
|
||||
plt.xlabel("time")
|
||||
plt.ylabel("confience")
|
||||
plt.text(total_result.shape[0] * 4 / 5, 0.6, "Fault", fontsize=10, color='black', verticalalignment='top',
|
||||
horizontalalignment='center',
|
||||
bbox={'facecolor': 'grey',
|
||||
'pad': 10})
|
||||
plt.text(total_result.shape[0] * 1 / 3, 0.4, "Norm", fontsize=10, color='black', verticalalignment='top',
|
||||
horizontalalignment='center',
|
||||
bbox={'facecolor': 'grey',
|
||||
'pad': 10})
|
||||
plt.grid()
|
||||
# plt.ylim(0, 1)
|
||||
# plt.xlim(-50, 1300)
|
||||
# plt.legend("", loc='upper left')
|
||||
plt.show()
|
||||
return total_result
|
||||
|
||||
|
||||
def GRU_Model():
|
||||
input = tf.keras.Input(shape=[time_stamp, feature_num])
|
||||
input = tf.cast(input, tf.float32)
|
||||
|
||||
LSTM = tf.keras.layers.GRU(units=10, return_sequences=True)(input)
|
||||
LSTM = tf.keras.layers.GRU(units=20, return_sequences=True)(LSTM)
|
||||
LSTM = tf.keras.layers.GRU(units=40, return_sequences=True)(LSTM)
|
||||
LSTM = tf.keras.layers.GRU(units=40, return_sequences=True)(LSTM)
|
||||
LSTM = tf.keras.layers.GRU(units=40, return_sequences=True)(LSTM)
|
||||
LSTM = tf.keras.layers.GRU(units=40, return_sequences=True)(LSTM)
|
||||
LSTM = tf.keras.layers.GRU(units=40, return_sequences=True)(LSTM)
|
||||
LSTM = tf.keras.layers.GRU(units=40, return_sequences=False)(LSTM)
|
||||
# bn = tf.keras.layers.BatchNormalization()(LSTM)
|
||||
|
||||
d1 = tf.keras.layers.Dense(20)(LSTM)
|
||||
# bn = tf.keras.layers.BatchNormalization()(d1)
|
||||
|
||||
output = tf.keras.layers.Dense(10, name='output')(d1)
|
||||
model = tf.keras.Model(inputs=input, outputs=output)
|
||||
return model
|
||||
pass
|
||||
|
||||
|
||||
# healthy_data是健康数据,用于确定阈值，all_data是完整的数据,用于模型出结果
|
||||
def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data, unhealthy_label, isPlot: bool = False,
|
||||
isSave: bool = False):
|
||||
# TODO 计算MSE确定阈值
|
||||
|
||||
mse, mean, max = get_MSE(healthy_data, healthy_label, model)
|
||||
|
||||
# 误报率的计算
|
||||
total, = mse.shape
|
||||
faultNum = 0
|
||||
faultList = []
|
||||
for i in range(total):
|
||||
if (mse[i] > max[i]):
|
||||
faultNum += 1
|
||||
faultList.append(mse[i])
|
||||
|
||||
fault_rate = faultNum / total
|
||||
print("误报率:", fault_rate)
|
||||
|
||||
# 漏报率计算
|
||||
missNum = 0
|
||||
missList = []
|
||||
mse1 = get_MSE(unhealthy_data, unhealthy_label, model,isStandard=False)
|
||||
all,= mse1.shape
|
||||
for i in range(all):
|
||||
if (mse1[i] < max[0]):
|
||||
missNum += 1
|
||||
missList.append(mse1[i])
|
||||
|
||||
miss_rate = missNum / all
|
||||
print("漏报率:", miss_rate)
|
||||
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(
|
||||
total_data[healthy_date - time_stamp + unhealthy_patience:unhealthy_date, :],
|
||||
is_Healthy=False)
|
||||
#### TODO 第一步训练
|
||||
# 单次测试
|
||||
model = GRU_Model()
|
||||
|
||||
model.compile(optimizer=tf.optimizers.Adam(0.01), loss=tf.losses.mse)
|
||||
model.summary()
|
||||
early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=5, mode='min', verbose=1)
|
||||
|
||||
checkpoint = tf.keras.callbacks.ModelCheckpoint(
|
||||
filepath=save_name,
|
||||
monitor='val_loss',
|
||||
verbose=1,
|
||||
save_best_only=True,
|
||||
mode='min',
|
||||
period=1)
|
||||
|
||||
history = model.fit(train_data_healthy[:30000,:,:], train_label1_healthy[:30000,:], epochs=10,
|
||||
batch_size=32,validation_split=0.2,shuffle=False, verbose=1,callbacks=[checkpoint,early_stop])
|
||||
# model.save(save_name)
|
||||
|
||||
## TODO testing
|
||||
# test_data, test_label = get_training_data(total_data[:healthy_date, :])
|
||||
# newModel = tf.keras.models.load_model(save_name)
|
||||
# mse, mean, max = get_MSE(test_data, test_label, new_model=newModel)
|
||||
|
||||
healthy_size, _, _ = train_data_healthy.shape
|
||||
unhealthy_size, _, _ = train_data_unhealthy.shape
|
||||
all_data, _, _ = get_training_data_overlapping(
|
||||
total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :], is_Healthy=True)
|
||||
|
||||
newModel = tf.keras.models.load_model(save_name)
|
||||
getResult(newModel, healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
|
||||
healthy_label=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
|
||||
unhealthy_data=train_data_unhealthy, unhealthy_label=train_label1_unhealthy)
|
||||
# mse, mean, max = get_MSE(train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
|
||||
# train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], new_model=newModel)
|
||||
|
||||
|
||||
|
||||
# test_data, test_label = get_training_data(total_data[20000:, :])
|
||||
# predicted_data = newModel.predict(test_data)
|
||||
# rows, cols = predicted_data.shape
|
||||
#
|
||||
# temp = np.abs(predicted_data - test_label)
|
||||
# temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape))
|
||||
# temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape)
|
||||
# temp3 = temp1 / temp2
|
||||
# mse = np.sum((temp1 / temp2) ** 2, axis=1)
|
||||
#
|
||||
# plt.plot(mse)
|
||||
# plt.plot(mean)
|
||||
# plt.plot(max)
|
||||
# plt.show()
|
||||
#
|
||||
# data = pd.DataFrame(mse).ewm(span=3).mean()
|
||||
# print(data)
|
||||
# data = np.array(data)
|
||||
#
|
||||
# index, _ = data.shape
|
||||
#
|
||||
# for i in range(2396):
|
||||
# if data[i, 0] > 5:
|
||||
# data[i, 0] = data[i - 1, :]
|
||||
# print(data)
|
||||
# mean = data[2000:2396, :].mean()
|
||||
# std = data[2000:2396, :].std()
|
||||
# mean = np.broadcast_to(mean, shape=[500, ])
|
||||
# std = np.broadcast_to(std, shape=[500, ])
|
||||
# plt.plot(data[2000:2396, :])
|
||||
# plt.plot(mean)
|
||||
# plt.plot(mean + 3 * std)
|
||||
# plt.plot(mean - 3 * std)
|
||||
# plt.show()
|
||||
|
||||
pass
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
|
||||
# coding: utf-8
|
||||
|
||||
'''
|
||||
@Author : dingjiawen
|
||||
@Date : 2022/10/11 18:55
|
||||
@Usage :
|
||||
@Desc :
|
||||
'''
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
|
||||
# coding: utf-8
|
||||
|
||||
'''
|
||||
@Author : dingjiawen
|
||||
@Date : 2022/10/11 18:54
|
||||
@Usage :
|
||||
@Desc :
|
||||
'''
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
|
||||
# coding: utf-8
|
||||
|
||||
'''
|
||||
@Author : dingjiawen
|
||||
@Date : 2022/10/11 18:53
|
||||
@Usage :
|
||||
@Desc :
|
||||
'''
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
|
||||
# coding: utf-8
|
||||
|
||||
'''
|
||||
@Author : dingjiawen
|
||||
@Date : 2022/10/11 18:53
|
||||
@Usage :
|
||||
@Desc :
|
||||
'''
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
|
||||
# coding: utf-8
|
||||
|
||||
'''
|
||||
@Author : dingjiawen
|
||||
@Date : 2022/10/11 19:00
|
||||
@Usage :
|
||||
@Desc :
|
||||
'''
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
|
||||
# coding: utf-8
|
||||
|
||||
'''
|
||||
@Author : dingjiawen
|
||||
@Date : 2022/10/11 18:55
|
||||
@Usage :
|
||||
@Desc :
|
||||
'''
|
||||
|
|
@ -0,0 +1,579 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
|
||||
# coding: utf-8
|
||||
|
||||
'''
|
||||
@Author : dingjiawen
|
||||
@Date : 2022/10/11 18:53
|
||||
@Usage : 对比实验，与JointNet相同深度,不加DCAU,进行预测
|
||||
@Desc :
|
||||
'''
|
||||
|
||||
import tensorflow as tf
|
||||
import tensorflow.keras
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
from model.DepthwiseCon1D.DepthwiseConv1D import DepthwiseConv1D
|
||||
from model.Dynamic_channelAttention.Dynamic_channelAttention import DynamicChannelAttention
|
||||
from condition_monitoring.data_deal import loadData
|
||||
from model.Joint_Monitoring.Joint_Monitoring3 import Joint_Monitoring
|
||||
|
||||
from model.CommonFunction.CommonFunction import *
|
||||
from sklearn.model_selection import train_test_split
|
||||
from tensorflow.keras.models import load_model, save_model
|
||||
|
||||
|
||||
|
||||
|
||||
'''超参数设置'''
|
||||
time_stamp = 120
|
||||
feature_num = 10
|
||||
batch_size = 16
|
||||
learning_rate = 0.001
|
||||
EPOCH = 101
|
||||
model_name = "RNet"
|
||||
'''EWMA超参数'''
|
||||
K = 18
|
||||
namuda = 0.01
|
||||
'''保存名称'''
|
||||
|
||||
save_name = "../hard_model/weight/{0}_timestamp{1}_feature{2}_weight_epoch8/weight".format(model_name,
|
||||
time_stamp,
|
||||
feature_num,
|
||||
batch_size,
|
||||
EPOCH)
|
||||
save_step_two_name = "../hard_model/two_weight/{0}_timestamp{1}_feature{2}_weight_epoch14/weight".format(model_name,
|
||||
time_stamp,
|
||||
feature_num,
|
||||
batch_size,
|
||||
EPOCH)
|
||||
|
||||
# save_name = "../model/joint/{0}_timestamp{1}_feature{2}.h5".format(model_name,
|
||||
# time_stamp,
|
||||
# feature_num,
|
||||
# batch_size,
|
||||
# EPOCH)
|
||||
# save_step_two_name = "../model/joint_two/{0}_timestamp{1}_feature{2}.h5".format(model_name,
|
||||
# time_stamp,
|
||||
# feature_num,
|
||||
# batch_size,
|
||||
# EPOCH)
|
||||
'''文件名'''
|
||||
file_name = "G:\data\SCADA数据\jb4q_8_delete_total_zero.csv"
|
||||
|
||||
'''
|
||||
文件说明：jb4q_8_delete_total_zero.csv是删除了只删除了全是0的列的文件
|
||||
文件从0:415548行均是正常值(2019/7.30 00:00:00 - 2019/9/18 11:14:00)
|
||||
从415549:432153行均是异常值(2019/9/18 11:21:01 - 2021/1/18 00:00:00)
|
||||
'''
|
||||
'''文件参数'''
|
||||
# 最后正常的时间点
|
||||
healthy_date = 415548
|
||||
# 最后异常的时间点
|
||||
unhealthy_date = 432153
|
||||
# 异常容忍程度
|
||||
unhealthy_patience = 5
|
||||
|
||||
|
||||
def remove(data, time_stamp=time_stamp):
|
||||
rows, cols = data.shape
|
||||
print("remove_data.shape:", data.shape)
|
||||
num = int(rows / time_stamp)
|
||||
|
||||
return data[:num * time_stamp, :]
|
||||
pass
|
||||
|
||||
|
||||
# 不重叠采样
|
||||
def get_training_data(data, time_stamp: int = time_stamp):
|
||||
removed_data = remove(data=data)
|
||||
rows, cols = removed_data.shape
|
||||
print("removed_data.shape:", data.shape)
|
||||
print("removed_data:", removed_data)
|
||||
train_data = np.reshape(removed_data, [-1, time_stamp, cols])
|
||||
print("train_data:", train_data)
|
||||
batchs, time_stamp, cols = train_data.shape
|
||||
|
||||
for i in range(1, batchs):
|
||||
each_label = np.expand_dims(train_data[i, 0, :], axis=0)
|
||||
if i == 1:
|
||||
train_label = each_label
|
||||
else:
|
||||
train_label = np.concatenate([train_label, each_label], axis=0)
|
||||
|
||||
print("train_data.shape:", train_data.shape)
|
||||
print("train_label.shape", train_label.shape)
|
||||
return train_data[:-1, :], train_label
|
||||
|
||||
|
||||
# 重叠采样
|
||||
def get_training_data_overlapping(data, time_stamp: int = time_stamp, is_Healthy: bool = True):
|
||||
rows, cols = data.shape
|
||||
train_data = np.empty(shape=[rows - time_stamp - 1, time_stamp, cols])
|
||||
train_label = np.empty(shape=[rows - time_stamp - 1, cols])
|
||||
for i in range(rows):
|
||||
if i + time_stamp >= rows:
|
||||
break
|
||||
if i + time_stamp < rows - 1:
|
||||
train_data[i] = data[i:i + time_stamp]
|
||||
train_label[i] = data[i + time_stamp]
|
||||
|
||||
print("重叠采样以后：")
|
||||
print("data:", train_data) # (300334,120,10)
|
||||
print("label:", train_label) # (300334,10)
|
||||
|
||||
if is_Healthy:
|
||||
train_label2 = np.ones(shape=[train_label.shape[0]])
|
||||
else:
|
||||
train_label2 = np.zeros(shape=[train_label.shape[0]])
|
||||
|
||||
print("label2:", train_label2)
|
||||
|
||||
return train_data, train_label, train_label2
|
||||
|
||||
|
||||
# RepConv重参数化卷积
|
||||
def RepConv(input_tensor, k=3):
|
||||
_, _, output_dim = input_tensor.shape
|
||||
conv1 = tf.keras.layers.Conv1D(filters=output_dim, kernel_size=k, strides=1, padding='SAME')(input_tensor)
|
||||
b1 = tf.keras.layers.BatchNormalization()(conv1)
|
||||
|
||||
conv2 = tf.keras.layers.Conv1D(filters=output_dim, kernel_size=1, strides=1, padding='SAME')(input_tensor)
|
||||
b2 = tf.keras.layers.BatchNormalization()(conv2)
|
||||
|
||||
b3 = tf.keras.layers.BatchNormalization()(input_tensor)
|
||||
|
||||
out = tf.keras.layers.Add()([b1, b2, b3])
|
||||
out = tf.nn.relu(out)
|
||||
return out
|
||||
|
||||
|
||||
# RepBlock模块
|
||||
def RepBlock(input_tensor, num: int = 3):
|
||||
for i in range(num):
|
||||
input_tensor = RepConv(input_tensor)
|
||||
return input_tensor
|
||||
|
||||
|
||||
# GAP 全局平均池化
|
||||
def Global_avg_channelAttention(input_tensor):
|
||||
_, length, channel = input_tensor.shape
|
||||
DWC1 = DepthwiseConv1D(kernel_size=1, padding='SAME')(input_tensor)
|
||||
GAP = tf.keras.layers.GlobalAvgPool1D()(DWC1)
|
||||
c1 = tf.keras.layers.Conv1D(filters=channel, kernel_size=1, padding='SAME')(GAP)
|
||||
s1 = tf.nn.sigmoid(c1)
|
||||
output = tf.multiply(input_tensor, s1)
|
||||
return output
|
||||
|
||||
|
||||
# GDP 全局动态池化
|
||||
def Global_Dynamic_channelAttention(input_tensor):
|
||||
_, length, channel = input_tensor.shape
|
||||
DWC1 = DepthwiseConv1D(kernel_size=1, padding='SAME')(input_tensor)
|
||||
|
||||
# GAP
|
||||
GAP = tf.keras.layers.GlobalAvgPool1D()(DWC1)
|
||||
c1 = tf.keras.layers.Conv1D(filters=channel, kernel_size=1, padding='SAME')(GAP)
|
||||
s1 = tf.nn.sigmoid(c1)
|
||||
|
||||
# GMP
|
||||
GMP = tf.keras.layers.GlobalMaxPool1D()(DWC1)
|
||||
c2 = tf.keras.layers.Conv1D(filters=channel, kernel_size=1, padding='SAME')(GMP)
|
||||
s3 = tf.nn.sigmoid(c2)
|
||||
|
||||
output = tf.multiply(input_tensor, s1)
|
||||
return output
|
||||
|
||||
|
||||
# 归一化
|
||||
def normalization(data):
|
||||
rows, cols = data.shape
|
||||
print("归一化之前:", data)
|
||||
print(data.shape)
|
||||
print("======================")
|
||||
|
||||
# 归一化
|
||||
max = np.max(data, axis=0)
|
||||
max = np.broadcast_to(max, [rows, cols])
|
||||
min = np.min(data, axis=0)
|
||||
min = np.broadcast_to(min, [rows, cols])
|
||||
|
||||
data = (data - min) / (max - min)
|
||||
print("归一化之后:", data)
|
||||
print(data.shape)
|
||||
|
||||
return data
|
||||
|
||||
|
||||
# 正则化
|
||||
def Regularization(data):
|
||||
rows, cols = data.shape
|
||||
print("正则化之前:", data)
|
||||
print(data.shape)
|
||||
print("======================")
|
||||
|
||||
# 正则化
|
||||
mean = np.mean(data, axis=0)
|
||||
mean = np.broadcast_to(mean, shape=[rows, cols])
|
||||
dst = np.sqrt(np.var(data, axis=0))
|
||||
dst = np.broadcast_to(dst, shape=[rows, cols])
|
||||
data = (data - mean) / dst
|
||||
print("正则化之后:", data)
|
||||
print(data.shape)
|
||||
|
||||
return data
|
||||
pass
|
||||
|
||||
|
||||
def EWMA(data, K=K, namuda=namuda):
|
||||
# t是啥暂时未知
|
||||
t = 0
|
||||
mid = np.mean(data, axis=0)
|
||||
standard = np.sqrt(np.var(data, axis=0))
|
||||
UCL = mid + K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
|
||||
LCL = mid - K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
|
||||
return mid, UCL, LCL
|
||||
pass
|
||||
|
||||
|
||||
def get_MSE(data, label, new_model):
|
||||
predicted_data = new_model.predict(data)
|
||||
|
||||
temp = np.abs(predicted_data - label)
|
||||
temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape))
|
||||
temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape)
|
||||
temp3 = temp1 / temp2
|
||||
mse = np.sum((temp1 / temp2) ** 2, axis=1)
|
||||
print("z:", mse)
|
||||
print(mse.shape)
|
||||
|
||||
# mse=np.mean((predicted_data-label)**2,axis=1)
|
||||
print("mse", mse)
|
||||
|
||||
dims, = mse.shape
|
||||
|
||||
mean = np.mean(mse)
|
||||
std = np.sqrt(np.var(mse))
|
||||
max = mean + 3 * std
|
||||
# min = mean-3*std
|
||||
max = np.broadcast_to(max, shape=[dims, ])
|
||||
# min = np.broadcast_to(min,shape=[dims,])
|
||||
mean = np.broadcast_to(mean, shape=[dims, ])
|
||||
|
||||
# plt.plot(max)
|
||||
# plt.plot(mse)
|
||||
# plt.plot(mean)
|
||||
# # plt.plot(min)
|
||||
# plt.show()
|
||||
#
|
||||
#
|
||||
return mse, mean, max
|
||||
# pass
|
||||
|
||||
|
||||
def condition_monitoring_model():
|
||||
input = tf.keras.Input(shape=[time_stamp, feature_num])
|
||||
conv1 = tf.keras.layers.Conv1D(filters=256, kernel_size=1)(input)
|
||||
GRU1 = tf.keras.layers.GRU(128, return_sequences=False)(conv1)
|
||||
d1 = tf.keras.layers.Dense(300)(GRU1)
|
||||
output = tf.keras.layers.Dense(10)(d1)
|
||||
|
||||
model = tf.keras.Model(inputs=input, outputs=output)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
# trian_data:(300455,120,10)
|
||||
# trian_label1:(300455,10)
|
||||
# trian_label2:(300455,)
|
||||
def shuffle(train_data, train_label1, train_label2, is_split: bool = False, split_size: float = 0.2):
|
||||
(train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(train_data,
|
||||
train_label1,
|
||||
train_label2,
|
||||
test_size=split_size,
|
||||
shuffle=True,
|
||||
random_state=100)
|
||||
if is_split:
|
||||
return train_data, train_label1, train_label2, test_data, test_label1, test_label2
|
||||
train_data = np.concatenate([train_data, test_data], axis=0)
|
||||
train_label1 = np.concatenate([train_label1, test_label1], axis=0)
|
||||
train_label2 = np.concatenate([train_label2, test_label2], axis=0)
|
||||
# print(train_data.shape)
|
||||
# print(train_label1.shape)
|
||||
# print(train_label2.shape)
|
||||
# print(train_data.shape)
|
||||
|
||||
return train_data, train_label1, train_label2
|
||||
pass
|
||||
|
||||
|
||||
def split_test_data(healthy_data, healthy_label1, healthy_label2, unhealthy_data, unhealthy_label1, unhealthy_label2,
|
||||
split_size: float = 0.2, shuffle: bool = True):
|
||||
data = np.concatenate([healthy_data, unhealthy_data], axis=0)
|
||||
label1 = np.concatenate([healthy_label1, unhealthy_label1], axis=0)
|
||||
label2 = np.concatenate([healthy_label2, unhealthy_label2], axis=0)
|
||||
(train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(data,
|
||||
label1,
|
||||
label2,
|
||||
test_size=split_size,
|
||||
shuffle=shuffle,
|
||||
random_state=100)
|
||||
|
||||
# print(train_data.shape)
|
||||
# print(train_label1.shape)
|
||||
# print(train_label2.shape)
|
||||
# print(train_data.shape)
|
||||
|
||||
return train_data, train_label1, train_label2, test_data, test_label1, test_label2
|
||||
|
||||
pass
|
||||
|
||||
|
||||
# trian_data:(300455,120,10)
|
||||
# trian_label1:(300455,10)
|
||||
# trian_label2:(300455,)
|
||||
def train_step_one(train_data, train_label1, train_label2):
|
||||
model = Joint_Monitoring()
|
||||
# # # # TODO 需要运行编译一次,才能打印model.summary()
|
||||
# model.build(input_shape=(batch_size, filter_num, dims))
|
||||
# model.summary()
|
||||
history_loss = []
|
||||
history_val_loss = []
|
||||
learning_rate = 1e-3
|
||||
for epoch in range(EPOCH):
|
||||
|
||||
print()
|
||||
print("EPOCH:", epoch, "/", EPOCH, ":")
|
||||
train_data, train_label1, train_label2 = shuffle(train_data, train_label1, train_label2)
|
||||
if epoch == 0:
|
||||
train_data, train_label1, train_label2, val_data, val_label1, val_label2 = shuffle(train_data, train_label1,
|
||||
train_label2,
|
||||
is_split=True)
|
||||
# print()
|
||||
# print("EPOCH:", epoch, "/", EPOCH, ":")
|
||||
# 用于让train知道，这是这个epoch中的第几次训练
|
||||
z = 0
|
||||
# 用于batch_size次再训练
|
||||
k = 1
|
||||
for data_1, label_1, label_2 in zip(train_data, train_label1, train_label2):
|
||||
size, _, _ = train_data.shape
|
||||
data_1 = tf.expand_dims(data_1, axis=0)
|
||||
label_1 = tf.expand_dims(label_1, axis=0)
|
||||
label_2 = tf.expand_dims(label_2, axis=0)
|
||||
if batch_size != 1:
|
||||
if k % batch_size == 1:
|
||||
data = data_1
|
||||
label1 = label_1
|
||||
label2 = label_2
|
||||
else:
|
||||
data = tf.concat([data, data_1], axis=0)
|
||||
label1 = tf.concat([label1, label_1], axis=0)
|
||||
label2 = tf.concat([label2, label_2], axis=0)
|
||||
else:
|
||||
data = data_1
|
||||
label1 = label_1
|
||||
label2 = label_2
|
||||
|
||||
if k % batch_size == 0:
|
||||
# label = tf.expand_dims(label, axis=-1)
|
||||
loss_value, accuracy_value = model.train(input_tensor=data, label1=label1, label2=label2,
|
||||
learning_rate=learning_rate,
|
||||
is_first_time=True)
|
||||
print(z * batch_size, "/", size, ":===============>", "loss:", loss_value.numpy())
|
||||
k = 0
|
||||
z = z + 1
|
||||
k = k + 1
|
||||
val_loss, val_accuracy = model.get_val_loss(val_data=val_data, val_label1=val_label1, val_label2=val_label2,
|
||||
is_first_time=True)
|
||||
SaveBestModel(model=model, save_name=save_name, history_loss=history_val_loss, loss_value=val_loss.numpy())
|
||||
# SaveBestH5Model(model=model, save_name=save_name, history_loss=history_val_loss, loss_value=val_loss.numpy())
|
||||
history_val_loss.append(val_loss)
|
||||
history_loss.append(loss_value.numpy())
|
||||
print('Training loss is :', loss_value.numpy())
|
||||
print('Validating loss is :', val_loss.numpy())
|
||||
if IsStopTraining(history_loss=history_val_loss, patience=7):
|
||||
break
|
||||
if Is_Reduce_learning_rate(history_loss=history_val_loss, patience=3):
|
||||
if learning_rate >= 1e-4:
|
||||
learning_rate = learning_rate * 0.1
|
||||
pass
|
||||
|
||||
|
||||
def train_step_two(step_one_model, step_two_model, train_data, train_label1, train_label2):
|
||||
# step_two_model = Joint_Monitoring()
|
||||
# step_two_model.build(input_shape=(batch_size, time_stamp, feature_num))
|
||||
# step_two_model.summary()
|
||||
history_loss = []
|
||||
history_val_loss = []
|
||||
history_accuracy = []
|
||||
learning_rate = 1e-3
|
||||
for epoch in range(EPOCH):
|
||||
print()
|
||||
print("EPOCH:", epoch, "/", EPOCH, ":")
|
||||
train_data, train_label1, train_label2 = shuffle(train_data, train_label1, train_label2)
|
||||
if epoch == 0:
|
||||
train_data, train_label1, train_label2, val_data, val_label1, val_label2 = shuffle(train_data, train_label1,
|
||||
train_label2,
|
||||
is_split=True)
|
||||
# print()
|
||||
# print("EPOCH:", epoch, "/", EPOCH, ":")
|
||||
# 用于让train知道，这是这个epoch中的第几次训练
|
||||
z = 0
|
||||
# 用于batch_size次再训练
|
||||
k = 1
|
||||
accuracy_num = 0
|
||||
for data_1, label_1, label_2 in zip(train_data, train_label1, train_label2):
|
||||
size, _, _ = train_data.shape
|
||||
data_1 = tf.expand_dims(data_1, axis=0)
|
||||
label_1 = tf.expand_dims(label_1, axis=0)
|
||||
label_2 = tf.expand_dims(label_2, axis=0)
|
||||
if batch_size != 1:
|
||||
if k % batch_size == 1:
|
||||
data = data_1
|
||||
label1 = label_1
|
||||
label2 = label_2
|
||||
else:
|
||||
data = tf.concat([data, data_1], axis=0)
|
||||
label1 = tf.concat([label1, label_1], axis=0)
|
||||
label2 = tf.concat([label2, label_2], axis=0)
|
||||
else:
|
||||
data = data_1
|
||||
label1 = label_1
|
||||
label2 = label_2
|
||||
|
||||
if k % batch_size == 0:
|
||||
# label = tf.expand_dims(label, axis=-1)
|
||||
output1, output2, output3, _ = step_one_model.call(inputs=data, is_first_time=True)
|
||||
loss_value, accuracy_value = step_two_model.train(input_tensor=data, label1=label1, label2=label2,
|
||||
learning_rate=learning_rate,
|
||||
is_first_time=False, pred_3=output1, pred_4=output2,
|
||||
pred_5=output3)
|
||||
accuracy_num += accuracy_value
|
||||
print(z * batch_size, "/", size, ":===============>", "loss:", loss_value.numpy(), "| accuracy:",
|
||||
accuracy_num / ((z + 1) * batch_size))
|
||||
k = 0
|
||||
z = z + 1
|
||||
k = k + 1
|
||||
|
||||
val_loss, val_accuracy = step_two_model.get_val_loss(val_data=val_data, val_label1=val_label1,
|
||||
val_label2=val_label2,
|
||||
is_first_time=False, step_one_model=step_one_model)
|
||||
SaveBestModelByAccuracy(model=step_two_model, save_name=save_step_two_name, history_accuracy=history_accuracy,
|
||||
accuracy_value=val_accuracy)
|
||||
history_val_loss.append(val_loss)
|
||||
history_loss.append(loss_value.numpy())
|
||||
history_accuracy.append(val_accuracy)
|
||||
print('Training loss is : {0} | Training accuracy is : {1}'.format(loss_value.numpy(),
|
||||
accuracy_num / ((z + 1) * batch_size)))
|
||||
print('Validating loss is : {0} | Validating accuracy is : {1}'.format(val_loss.numpy(), val_accuracy))
|
||||
if IsStopTraining(history_loss=history_val_loss, patience=7):
|
||||
break
|
||||
if Is_Reduce_learning_rate(history_loss=history_val_loss, patience=3):
|
||||
if learning_rate >= 1e-4:
|
||||
learning_rate = learning_rate * 0.1
|
||||
pass
|
||||
|
||||
|
||||
def test(step_one_model, step_two_model, test_data, test_label1, test_label2):
|
||||
history_loss = []
|
||||
history_val_loss = []
|
||||
|
||||
val_loss, val_accuracy = step_two_model.get_val_loss(val_data=test_data, val_label1=test_label1,
|
||||
val_label2=test_label2,
|
||||
is_first_time=False, step_one_model=step_one_model)
|
||||
|
||||
history_val_loss.append(val_loss)
|
||||
print("val_accuracy:", val_accuracy)
|
||||
print("val_loss:", val_loss)
|
||||
|
||||
|
||||
def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False):
|
||||
# 获取模型的所有参数的个数
|
||||
# step_two_model.count_params()
|
||||
total_result = []
|
||||
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, :, :]
|
||||
_, _, _, output4 = step_two_model.call(each_test_data, is_first_time=False)
|
||||
total_result.append(output4)
|
||||
total_result = np.reshape(total_result, [total_result.__len__(), -1])
|
||||
total_result = np.reshape(total_result, [-1, ])
|
||||
if isPlot:
|
||||
plt.scatter(list(range(total_result.shape[0])), total_result, c='black', s=10)
|
||||
# 画出 y=1 这条水平线
|
||||
plt.axhline(0.5, c='red', label='Failure threshold')
|
||||
# 箭头指向上面的水平线
|
||||
# plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
|
||||
# alpha=0.9, overhang=0.5)
|
||||
# plt.text(35000, 0.9, "Truth Fault", fontsize=10, color='black', verticalalignment='top')
|
||||
plt.axvline(test_data.shape[0] * 2 / 3, c='blue', ls='-.')
|
||||
plt.xlabel("time")
|
||||
plt.ylabel("confience")
|
||||
plt.text(total_result.shape[0] * 4 / 5, 0.6, "Fault", fontsize=10, color='black', verticalalignment='top',
|
||||
horizontalalignment='center',
|
||||
bbox={'facecolor': 'grey',
|
||||
'pad': 10})
|
||||
plt.text(total_result.shape[0] * 1 / 3, 0.4, "Norm", fontsize=10, color='black', verticalalignment='top',
|
||||
horizontalalignment='center',
|
||||
bbox={'facecolor': 'grey',
|
||||
'pad': 10})
|
||||
plt.grid()
|
||||
# plt.ylim(0, 1)
|
||||
# plt.xlim(-50, 1300)
|
||||
# plt.legend("", loc='upper left')
|
||||
plt.show()
|
||||
return total_result
|
||||
|
||||
|
||||
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(
|
||||
total_data[healthy_date - time_stamp + unhealthy_patience:unhealthy_date, :],
|
||||
is_Healthy=False)
|
||||
#### TODO 第一步训练
|
||||
# 单次测试
|
||||
# train_step_one(train_data=train_data_healthy[:32, :, :], train_label1=train_label1_healthy[:32, :],train_label2=train_label2_healthy[:32, ])
|
||||
# train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy)
|
||||
|
||||
# 导入第一步已经训练好的模型,一个继续训练，一个只输出结果
|
||||
# step_one_model = Joint_Monitoring()
|
||||
# step_one_model.load_weights(save_name)
|
||||
#
|
||||
# step_two_model = Joint_Monitoring()
|
||||
# step_two_model.load_weights(save_name)
|
||||
|
||||
#### TODO 第二步训练
|
||||
### healthy_data.shape: (300333,120,10)
|
||||
### 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_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))
|
||||
|
||||
### TODO 测试测试集
|
||||
step_one_model = Joint_Monitoring()
|
||||
step_one_model.load_weights(save_name)
|
||||
step_two_model = Joint_Monitoring()
|
||||
step_two_model.load_weights(save_step_two_name)
|
||||
# test(step_one_model=step_one_model, step_two_model=step_two_model, test_data=test_data, test_label1=test_label1,
|
||||
# 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 = 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)
|
||||
|
||||
pass
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
|
||||
# coding: utf-8
|
||||
|
||||
'''
|
||||
@Author : dingjiawen
|
||||
@Date : 2022/10/11 18:51
|
||||
@Usage :
|
||||
@Desc :
|
||||
'''
|
||||
Loading…
Reference in New Issue