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