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295 lines
11 KiB
295 lines
11 KiB
2 weeks ago
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# -*- coding: utf-8 -*-
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"""
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Created on Sun Feb 28 10:04:26 2016
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PCA source code
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@author: liudiwei
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"""
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import xlsxwriter as xw
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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 scipy.stats import norm
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from scipy.stats.distributions import chi2
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import json
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import sys
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import pymssql
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import requests
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import datetime
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from scipy.stats import norm
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from scipy.stats import f
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from scipy.stats import chi2
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import jenkspy
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import xlrd
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import time
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# import PCA_Test_offline
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import config
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"""
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参数：
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- XMat：传入的是一个numpy的矩阵格式，行表示样本数，列表示特征
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- k：表示取前k个特征值对应的特征向量
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返回值：
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- finalData：参数一指的是返回的低维矩阵，对应于输入参数二
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- reconData：参数二对应的是移动坐标轴后的矩阵
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"""
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def min_pos(X):
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X[X <= 0] = np.max(X)
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m = np.min(X)
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re = np.where(X == m)
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min_i = re[0]
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min_j = re[1]
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if m < 0:
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m = 0
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return m, min_i, min_j
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def Lars(X, Y, D, DIAG, t, limit_line, lamuta):
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n, m = X.shape
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beta = np.zeros((1, m))
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A = []
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N_Co_added = 0
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i = 0
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mse = []
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for k in range(m):
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i += 1
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ero = np.array(Y - beta[-1, :].T)
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# c=np.dot(P,DIAG,P.T,ero)
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c = np.dot(D, DIAG).dot(D.T).dot(ero) # 计算相关性
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C = np.max(np.abs(c))
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mse.append(np.dot(ero.T, D).dot(DIAG).dot(D.T).dot(ero))
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if mse[k] < limit_line:
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break
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elif k == 0:
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addTndex = np.where(abs(c) == C)[-1][0]
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A.append(addTndex) # 活动集
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# 更新正在添加的相应协方差索引的数量
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N_Co_added = N_Co_added + 1
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A_c = list(set(range(0, m)).difference(set(A))) # 非活动集
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s_A = np.diag(np.sign(c[A]))
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num_Zero_Coeff = len(A_c)
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## 计算 X_A， A_A ， u_A ，the inner product vecto
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X_A = np.dot(X[:, A], s_A).reshape(n, -1)
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G_A = np.dot(X_A.T, X_A)
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One_A = np.ones((len(A), 1))
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s = One_A.copy()
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if G_A.shape == ():
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inv_GA = 1 / G_A
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else:
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inv_GA = np.linalg.pinv(G_A)
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# G_a_inv_red_cols = np.sum(inv_GA, 1)
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A_A = 1 / np.sqrt(np.dot(s.T, inv_GA).dot(s))
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w_A = (A_A * inv_GA).dot(s) # w_A: (less then 90%)构成等角的单位向量
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u_A = np.dot(X_A, w_A) # .reshape(n)
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a = X.T.dot(u_A) # inner product vector
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gamma_Test = np.zeros((num_Zero_Coeff, 2))
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# gamma=[]
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if N_Co_added == m - 1:
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gamma = C / A_A
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else:
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for j in range(num_Zero_Coeff):
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j_p = A_c[j]
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first_term = (C - c[j_p]) / (A_A - a[j_p])
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second_term = (C + c[j_p]) / (A_A + a[j_p])
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gamma_Test[j, :] = np.array([first_term, second_term]).reshape(1, -1)
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gamma, min_i, min_j = min_pos(gamma_Test)
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# gamma.append(m_s)
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addTndex = A_c[np.min(min_i)]
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beta_temp = np.zeros((m, 1))
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beta_temp[A] = beta[k, A].reshape(-1, 1) + np.dot(s_A, gamma * w_A)
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beta = np.vstack((beta, beta_temp.transpose())) # 更新的系数即故障f
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return beta, mse
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# import sklearn
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# q=sklearn.linear_model.Lars
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class MSSQL:
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def __init__(self,host,user,pwd,database):
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self.host = host
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self.user = user
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self.pwd = pwd
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self.db = database
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def __GetConnect(self):
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"""
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得到连接信息
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返回: conn.cursor()
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"""
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if not self.db:
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raise(NameError,"没有设置数据库信息")
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self.conn = pymssql.connect(host=self.host,user=self.user,password=self.pwd,database=self.db,port=config._PORT, charset="utf8")
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cur = self.conn.cursor()
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if not cur:
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raise(NameError,"连接数据库失败")
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else:
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return cur
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def ExecQuery(self,sql):
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"""
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执行查询语句
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返回的是一个包含tuple的list，list的元素是记录行，tuple的元素是每行记录的字段
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"""
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cur = self.__GetConnect()
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cur.execute(sql)
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resList = cur.fetchall()
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#查询完毕后必须关闭连接
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self.conn.close()
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return resList
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def ExecNonQuery(self,sql):
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"""
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执行非查询语句
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调用示例：
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cur = self.__GetConnect()
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cur.execute(sql)
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self.conn.commit()
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self.conn.close()
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"""
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cur = self.__GetConnect()
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cur.execute(sql)
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self.conn.commit()
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self.conn.close()
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def get_model_by_ID(model_id, version="v-test"):
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ms = MSSQL(host=config._SQL_IP, user="sa", pwd="powerSIS#123", database="alert")
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resList = ms.ExecQuery(f"SELECT Model_info FROM [alert].[dbo].[Model_CFG] where model_id={model_id}")
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return json.loads(resList[0][0])
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def get_model_by_id_and_version(model_id, version):
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ms = MSSQL(host=config._SQL_IP, user="sa", pwd="powerSIS#123", database="alert")
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resList = ms.ExecQuery(f"SELECT Model_info FROM [alert].[dbo].[model_version] where model_id={model_id} and version='{version}'")
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return json.loads(resList[0][0])
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def pca(model, Data_origin):
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Data = (Data_origin - model["Train_X_mean"]) / model["Train_X_std"]
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featValue = np.array(model["featValue"]) # 训练数据的特征值
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k=(model["K"]) # 主元个数
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featVec = np.array(model["featVec"]) # 训练数据的特征向量
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selectVec1 = np.array(model["selectVec"])
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selectVec=featVec[:, 0:k]
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index = np.argsort(-np.array(featValue)) # 按照featValue进行从大到小排序
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featValue_sort = featValue[index] # 排序后的特征值
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############----------*********-SPE-**************----------########################
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numbel_variable = featValue.shape[0]
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C_ = np.eye(numbel_variable) - np.dot(selectVec, selectVec.T)
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SPE_list = []
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for i in range(Data.shape[0]):
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Y = Data[i, :] # 测试数据的每一行
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#########*********************计算SPE******************************
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SPE_line =np.dot(Y, C_).dot(Y.T)###SPE根号
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SPE_list.append(SPE_line)
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paraState = np.zeros([np.array(Data_origin).shape[0], np.array(Data_origin).shape[1]])
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finalData = np.dot(Data, selectVec).dot(selectVec.T)
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reconData = np.add(np.multiply(finalData, model["Train_X_std"]), model["Train_X_mean"]) # 重构值
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errorData =Data_origin - reconData # 偏差值
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# cos检验值
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R = 0
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res={}
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for index in range(0, reconData.shape[1]):
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vector1 = Data_origin[:, index]
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vector2 = np.array(reconData)[:, index]
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R += np.dot(vector1, vector2.T) / (np.sqrt(np.sum(vector1 ** 2)) * np.sqrt(np.sum(vector2 ** 2)))
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R /= reconData.shape[1]
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#items = [('reconData', np.around(reconData, decimals=3).tolist())
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# , ('errorData', np.around(errorData, decimals=3).tolist()), ('R', R.tolist()), ('SPE', np.array(SPE_list).tolist()),
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# ('paraState', paraState.tolist())]
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#res["sampleData"]=np.transpose(Data_origin.tolist())
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res["sampleData"]=np.transpose(np.array(Data_origin)).tolist()
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res["reconData"]=np.around(np.transpose(np.array(reconData)), decimals=3).tolist()
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res["errorData"]=np.around(np.transpose(np.array(errorData)), decimals=3).tolist()
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res["R"]=np.around(R, decimals=3).tolist()
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res["SPE"]=np.around(np.transpose(np.array(SPE_list)), decimals=3).tolist()
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res["paraState"]=np.transpose(np.array(paraState)).tolist()
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#result = json.dumps(dict(items)) # json.dumps(result)
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#return result
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return res
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def get_history_value(points,time,interval):
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#url="http://192.168.1.201:8080/openPlant/getMultiplePointHistorys"
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url=f"http://{config._EXA_IP}:9000/exawebapi/exatime/GetSamplingValueArrayFloat"
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headers = {"Content-Type": "application/json;charset=utf-8"}#,"token":get_token()
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point_array = points.split(",")
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time_span = time.split(";")
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value_array = []
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for item in point_array:
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value_group = []
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for time_piece in time_span:
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st = time_piece.split(",")[0]
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et = time_piece.split(",")[1]
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para = {"ItemName": item, "StartingTime": st, "TerminalTime": et, "SamplingPeriod": interval}
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response = requests.get(url, headers=headers, params=para)
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content = response.text.replace('"[','[').replace(']"',']')
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value = json.loads(content)
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if not isinstance(value, list):
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print("aaa")
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for row in value:
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value_group.append(row[1])
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value_array.append(value_group)
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return np.transpose(np.array(value_array))
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#return valres
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def main(info):
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model_id = info["Model_id"]
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try:
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version = info["version"]
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except KeyError:
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version = "v-test"
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if version == "v-test":
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res = get_model_by_ID(model_id)
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else:
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res = get_model_by_id_and_version(model_id, version)
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Test_Data = info["Test_Data"]
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points = Test_Data["points"]
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time1 = Test_Data["time"]
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interval = Test_Data["interval"]
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model = res["para"]["Model_info"]
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Data = get_history_value(points, time1, interval)
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result = pca(model, Data)
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index = time1.index(",")
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result["time"] = time1[:index:]
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return result
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# 根据数据集data.txt
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if __name__ == "__main__":
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info_str='{"Test_Data":{"time":"2021-01-13 12:52:40,2021-01-14 12:52:40","points":"JL_D2_20SCS02A:MAG10CT311.PNT,JL_D2_20DAS01B:MAG10CE101.PNT,JL_D2_20MCS01A:MAG10AN001ZT.PNT,JL_D2_20SCS02A:MAG10CT312.PNT,JL_D2_20DAS01B:MAG10CE102.PNT,JL_D2_20MCS01A:MAG10AN002ZT.PNT,JL_D2_20SCS02A:MAG10CT313.PNT,JL_D2_20DAS01B:MAG10CE103.PNT,JL_D2_20MCS01A:MAG10AN003ZT.PNT,JL_D2_20SCS02A:MAG10CT314.PNT,JL_D2_20DAS01B:MAG10CE104.PNT,JL_D2_20MCS01A:MAG10AN004ZT.PNT,JL_D2_20SCS02A:MAG10CT315.PNT,JL_D2_20DAS01B:MAG10CE105.PNT,JL_D2_20MCS01A:MAG10AN005ZT.PNT,JL_D2_20SCS02A:MAG10CT316.PNT,JL_D2_20DAS01B:MAG10CE106.PNT,JL_D2_20MCS01A:MAG10AN006ZT.PNT,JL_D2_20SCS02A:MAG10CT317.PNT,JL_D2_20DAS01B:MAG10CE107.PNT,JL_D2_20MCS01A:MAG10AN007ZT.PNT,JL_D2_20DAS01B:MAJ10CT101.PNT,JL_D2_20DAS01B:MAJ10CT102.PNT,JL_D2_20SCS02A:MAG10CT101.PNT,JL_D2_20SCS02A:MAG10CT102.PNT,JL_D2_20DAS01B:MAG03CG101.PNT,JL_D2_20DAS01B:MAG03CS101.PNT","interval":300000},"Model_id":528,"version":"v-test"}'
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info = json.loads(info_str)
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print(main(info))
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# model_id=info["Model_id"]
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# Test_Data = info["Test_Data"]
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# points = Test_Data["points"]
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# time = Test_Data["time"]
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# interval = Test_Data["interval"]
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# Data = get_history_value(points, time, interval)
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# # workbook = xw.Workbook("pca_test.xlsx")
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# # worksheet = workbook.add_worksheet()
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# # for row, item in enumerate(Data.tolist()):
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# # for col, cell in enumerate(item):
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# # worksheet.write(row, col, cell)
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# # workbook.close()
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# model = PCA_Test_offline.get_model_by_ID(model_id)["para"]["Model_info"]
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# result = pca(model,Data)#模型参数，训练数据
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# aaa=json.dumps(result)
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# print (result)
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