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)