def main(argv=None):
# mnist = input_data.read_data_sets("/tmp/data", one_hot=True)
train_data = matfile_reader.dataset_reader(
r'G:\04 实验数据\02 实验台实验数据\实验数据_20180119\bearing_dataset_2000.mat')
# train_data = pd.read_excel("traindataset.xlsx")
print("train dateset read over")
# test_data = pd.read_excel("E:\\故障诊断实验数据\\实验数据_20171201\\原始数据_数据集\\test_dataset.xlsx")
train_samples, train_labels, data_tag = data_preprocess(train_data)
train(train_samples, train_labels, data_tag)
def main(argv=None):
# test_data = pd.read_excel("testdataset.xlsx")
test_data = matfile_reader.dataset_reader(r'G:\04 实验数据\02 实验台实验数据\实验数据_20180119\bearing_dataset_2000.mat', train=False)
# test_data=test_data[0:10000,:]
print("train dateset read over")
# test_data = pd.read_excel("E:\\故障诊断实验数据\\实验数据_20171201\\原始数据_数据集\\test_dataset.xlsx")
test_samples, test_labels, data_tag = AlexNet_1d_diagnosis_train.data_preprocess(test_data)
evaluate(test_samples, test_labels, data_tag)
import scipy.io as scio
import numpy as np
from AlexNet_1d_diagnosis import matfile_reader
#parameters
noise_factor=0.5
#read data
data_path='E:/bearing_dataset_1024.mat'
train_dataset=matfile_reader.dataset_reader(data_path)
test_dataset=matfile_reader.dataset_reader(data_path,train=False)
train_data_sample=train_dataset[:,0:1024]
train_data_label=train_dataset[:,-1]
test_data_sample=test_dataset[:,0:1024]
test_data_label=test_dataset[:,-1]
#add noise to test datasamples and train datasamples
train_noise=train_data_sample+noise_factor*np.random.randn(*train_data_sample.shape)
test_noise=test_data_sample+noise_factor*np.random.randn(*test_data_sample.shape)
datasample_train_noise=np.column_stack((train_noise,train_data_label))
datasample_test_noise=np.column_stack((test_noise,test_data_label))
#save processed data
scio.savemat('E:/bearing_dataset_1024_with_noise.mat',{'traindata':datasample_train_noise,'testdata':datasample_test_noise})
import scipy.io as scio
import pandas as pd
from AlexNet_1d_diagnosis import matfile_reader
import numpy as np
train_data = matfile_reader.dataset_reader("E:\\bearing_dataset_2000.mat")
test_data = matfile_reader.dataset_reader('E:\\bearing_dataset_2000.mat',
train=False)
np.savetxt('train_data.csv', train_data, delimiter=',')
np.savetxt('test_data.csv', test_data, delimiter=',')