def loadNeuData(opts): print('loading data...') x_day_dir = opts.input #train_x, train_y, val_x, val_y, test_x, test_y = readSigmf.getData(opts, x_day_dir) dataOpts = load_slice_IQ.loadDataOpts(opts.input, opts.location, opts.D2, num_slice=100000) train_x, train_y, test_x, test_y, NUM_CLASS = load_slice_IQ.loadData(dataOpts, opts.channel_first) if opts.normalize: train_x = load_slice_IQ.normalizeData(train_x) test_x = load_slice_IQ.normalizeData(test_x) return train_x, train_y, test_x, test_y, NUM_CLASS
def loadData2Dict(opts): print('loading data...') # train_x, train_y, val_x, val_y, test_x, test_y = readSigmf.getData(opts, x_day_dir) # D2 means that make it into 2 dimension data dataOpts = load_slice_IQ.loadDataOpts(opts.input, opts.location, opts.D2, num_slice=10000) train_x, train_y, test_x, test_y, NUM_CLASS = load_slice_IQ.loadData(dataOpts, opts.channel_first) if opts.normalize: train_x = load_slice_IQ.normalizeData(train_x) test_x = load_slice_IQ.normalizeData(test_x) data_dict = {} data_dict['x_train'] = train_x data_dict['y_train'] = train_y data_dict['x_test'] = test_x data_dict['y_test'] = test_y print('x_train shape: {}\ty_train shape: {}\tx_test shape: {}\ty_test shape: {}'.format(train_x.shape, train_y.shape, test_x.shape, test_y.shape)) return data_dict