def test_sum_prediction():
""" Test with a model that predicts sum over four samples """
rng = RandomState(3904890384)
n_samples_in_buffer = 1000
dataset = rng.rand(n_samples_in_buffer*2,5).astype(np.float32)
markers = np.ones((n_samples_in_buffer*2,1)).astype(np.float32)
set_and_markers = np.concatenate((dataset, markers), axis=1)
factor_new=0.001
n_stride = 10
pred_freq = 11
standardized = exponential_running_standardize(dataset,
factor_new=factor_new, init_block_size=n_stride)
model = InputLayer([1,1,4,1])
model = GlobalPoolLayer(model,pool_function=T.sum)
expected = [np.sum(standardized[stop-4:stop], axis=0) for stop in xrange(11, dataset.shape[0], 11)]
expected = np.array(expected)
processor = StandardizeProcessor(factor_new=factor_new,
n_samples_in_buffer=n_samples_in_buffer)
online_model = OnlineModel(model)
online_pred = OnlineCoordinator(processor, online_model, pred_freq=pred_freq,
trainer=NoTrainer())
online_pred.initialize(n_chans=dataset.shape[1])
all_preds = []
for i_start_sample in xrange(0,dataset.shape[0]-n_stride+1,n_stride):
online_pred.receive_samples(set_and_markers[i_start_sample:i_start_sample+n_stride])
if online_pred.has_new_prediction():
pred, _ = online_pred.pop_last_prediction_and_sample_ind()
all_preds.append(pred)
assert np.allclose(np.array(all_preds).squeeze(), expected, rtol=1e-3)
def test_online_predictor():
""" Test whether predictions are done at correct timepoints.
Model actually just returns input """
rng = RandomState(3904890384)
n_samples_in_buffer = 1000
dataset = rng.rand(n_samples_in_buffer*2,5).astype(np.float32)
markers = np.ones((n_samples_in_buffer*2,1)).astype(np.float32)
set_and_markers = np.concatenate((dataset, markers), axis=1)
factor_new=0.001
n_stride = 10
pred_freq = 11
standardized = exponential_running_standardize(dataset,
factor_new=factor_new, init_block_size=n_stride)
model = InputLayer([1,1,1,1])
processor = StandardizeProcessor(factor_new=factor_new,
n_samples_in_buffer=n_samples_in_buffer)
online_model = OnlineModel(model)
online_pred = OnlineCoordinator(processor, online_model, pred_freq=pred_freq,
trainer=NoTrainer())
online_pred.initialize(n_chans=dataset.shape[1])
all_preds = []
for i_start_sample in xrange(0,dataset.shape[0]-n_stride+1,n_stride):
online_pred.receive_samples(set_and_markers[i_start_sample:i_start_sample+n_stride])
if online_pred.has_new_prediction():
pred, _ = online_pred.pop_last_prediction_and_sample_ind()
all_preds.append(pred)
assert np.array_equal(np.array(all_preds).squeeze(), standardized[10::pred_freq])
