def test_vectorizer(): """Test Vectorizer.""" data = np.random.rand(150, 18, 6) vect = Vectorizer() result = vect.fit_transform(data) assert_equal(result.ndim, 2) # check inverse_trasnform orig_data = vect.inverse_transform(result) assert_equal(orig_data.ndim, 3) assert_array_equal(orig_data, data) assert_array_equal(vect.inverse_transform(result[1:]), data[1:]) # check with different shape assert_equal(vect.fit_transform(np.random.rand(150, 18, 6, 3)).shape, (150, 324)) assert_equal(vect.fit_transform(data[1:]).shape, (149, 108)) # check if raised errors are working correctly vect.fit(np.random.rand(105, 12, 3)) assert_raises(ValueError, vect.transform, np.random.rand(105, 12, 3, 1)) assert_raises(ValueError, vect.inverse_transform, np.random.rand(102, 12, 12))
def test_vectorizer(): """Test Vectorizer.""" data = np.random.rand(150, 18, 6) vect = Vectorizer() result = vect.fit_transform(data) assert_equal(result.ndim, 2) # check inverse_trasnform orig_data = vect.inverse_transform(result) assert_equal(orig_data.ndim, 3) assert_array_equal(orig_data, data) assert_array_equal(vect.inverse_transform(result[1:]), data[1:]) # check with different shape assert_equal(vect.fit_transform(np.random.rand(150, 18, 6, 3)).shape, (150, 324)) assert_equal(vect.fit_transform(data[1:]).shape, (149, 108)) # check if raised errors are working correctly vect.fit(np.random.rand(105, 12, 3)) assert_raises(ValueError, vect.transform, np.random.rand(105, 12, 3, 1)) assert_raises(ValueError, vect.inverse_transform, np.random.rand(102, 12, 12))
labels = np.concatenate(labels) # get the data # sacale the data to (0,1) data = [] for tf in glob(os.path.join(working_dir, '*-tfr.h5')): tfcs = mne.time_frequency.read_tfrs(tf)[0] data_ = tfcs.data # define a (0,1) scaler scaler = MinMaxScaler(feature_range=(0, 1)) # define a vectorizer so we can transform the data from 3D to 2D vectorizer = Vectorizer() data_vec = vectorizer.fit_transform(data_) data_scaled = scaler.fit_transform(data_vec) # after we scale the data to (0,1), we transform the data from 2D back to 3D data_scaled = vectorizer.inverse_transform(data_scaled) del tfcs del data_, data_vec data.append(data_scaled) del data_scaled data = np.concatenate(data, axis=0) # shuffle the order of the feature matrix and the labels for _ in range(10): data, labels = utils.shuffle(data, labels) # customized the temporal decoding process # define 10-fold cross validation cv = StratifiedShuffleSplit(n_splits=10, random_state=12345) coefs = []