def gen_data(class_num, subsample_size, window_size, rnd_number):
accessPath = "%s/patchSize%d" %(access , window_size)
filelist = fetchFile(accessPath , subsample_size)
#print(filelist)
image_num = class_num * subsample_size
mydata = myData.load_data(filelist[0:image_num] , window_size)
#print(mydata['data'][0:10])
scaler = preprocessing.StandardScaler()
x_norm = scaler.fit_transform(mydata['data'])
return train_test_split(x_norm, mydata['target'], train_size=0.5, random_state=rnd_number)
def gen_data(class_num, subsample_size, window_size, rnd_number):
filelist = fetchFile(accessPath , subsample_size)
mydata = myData.load_data(filelist[0:class_num] , window_size)
scaler = preprocessing.StandardScaler()
x_norm = scaler.fit_transform(mydata['data'])
return train_test_split(x_norm, mydata['target'], train_size=0.5, random_state=rnd_number)
