cnn_lstm预测模型附python代码.zip

# -*- coding: utf-8 -*- """ Created on Sat Aug 24 23:34:37 2019 @author: Xiaohan Chen """ import numpy as np import os import re import scipy.io as scio raw_num = 240 col_num = 2000 class Data(object): ''' 读取mat格式数据，由于每个故障数据数量不同，这里只截取前480000个数据 get_data()产生的数据为（2400，2000）的输入数据 get_label()产生的数据为（2400，1）的标签数据 ''' def __init__(self): self.data = self.get_data() self.label = self.get_label() def file_list(self): return os.listdir('../data/') def get_data(self): file_list = self.file_list() for i in range(len(file_list)): file = scio.loadmat('../data/{}'.format(file_list[i])) for k in file.keys(): file_matched = re.match('X\d{3}_DE_time', k) if file_matched: key = file_matched.group() if i == 0: data = np.array(file[key][0:480000].reshape(raw_num,col_num)) else: data = np.vstack((data, file[key][0:480000].reshape((raw_num,col_num)))) return data def get_label(self): file_list = self.file_list() title = np.array([i.replace('.mat', '') for i in file_list]) label = title[:, np.newaxis] label_copy = np.copy(label) for _ in range(raw_num-1): label = np.hstack((label, label_copy)) return label.flatten()