def test_can_get_params():
s = InvertedEncoding()
param_out = s.get_params()
logger.info('Returned Parameters: ' +
str(param_out.get('n_channels')) +
', ' + str(param_out.get('range_start')) +
', ' + str(param_out.get('range_stop')))
def test_can_set_params():
s = InvertedEncoding()
s.set_params(n_channels=10,
stimulus_mode='circular',
range_start=-90,
range_stop=270,
channel_exp=4,
verbose=False)
def test_can_predict_from_data():
Invt_model = InvertedEncoding()
Invt_model.fit(X, y)
m_reconstruct = []
for j in np.arange(dim):
preds = Invt_model.predict(X2[n_*j:n_*(j+1), :])
tmp = circmean(np.deg2rad(preds))
m_reconstruct.append(np.rad2deg(tmp))
logger.info('Reconstructed angles: ' + str(m_reconstruct))
def test_stimulus_mask_shift_positive():
Invt_model = InvertedEncoding(6, 5, range_start=10,
range_stop=190,
stimulus_resolution=60)
chans, _ = Invt_model._define_channels()
Invt_model.set_params(channels_=chans)
with pytest.warns(RuntimeWarning):
C = Invt_model._define_trial_activations(np.array([70]))
tmp_C = np.repeat([0, 1, 0], 60) @ chans.transpose()
assert np.all((C - tmp_C) < 1e-7)
def test_instantiate_improper_range():
with pytest.raises(ValueError):
s = InvertedEncoding(6, 5, 'halfcircular', range_start=20,
range_stop=0)
assert s, "Invalid InvertedEncoding instance"
def test_can_instantiate():
s = InvertedEncoding()
assert s, "Invalid InvertedEncoding instance"
def test_can_fit_circular_space():
s = InvertedEncoding(6, 5, 'circular', range_stop=360)
s.fit(X, y)
def test_data_amount():
x = np.random.rand(5, 1000)
s = InvertedEncoding()
with pytest.raises(ValueError):
s.fit(x, np.random.rand(5))
assert s, "Invalid data"
def test_stimulus_mode():
with pytest.raises(ValueError):
s = InvertedEncoding(6, 5, 'random')
assert s, "Invalid InvertedEncoding instance"
def test_can_score():
Invt_model = InvertedEncoding()
Invt_model.fit(X, y)
score = Invt_model.score(X2, y)
logger.info('Scores: ' + str(score))
def test_cannot_predict_from_data():
Invt_model = InvertedEncoding()
Invt_model.fit(X, y)
with pytest.raises(ValueError):
_ = Invt_model.predict(X2[0:n_, :].transpose())
def test_mismatched_observations():
with pytest.raises(ValueError):
Invt_model = InvertedEncoding()
Invt_model.fit(X, y[:-50])
def test_extra_data_dimensions():
with pytest.raises(ValueError):
n, dim1, dim2 = 300, 3, 3
X = np.random.rand(n//3, dim1, dim2)
Invt_model = InvertedEncoding()
Invt_model.fit(X, y)
def test_ill_conditioned_train_data():
Invt_model = InvertedEncoding()
with pytest.raises(ValueError):
X = np.array([[0, 0, 0], [1, 1, 1]])
Invt_model.fit(X, np.array([0, 0, 0]))
def test_cannot_fit_data():
with pytest.raises(ValueError):
Invt_model = InvertedEncoding()
Invt_model.fit(X.transpose(), y)
def test_stimulus_resolution():
s = InvertedEncoding(6, 5, stimulus_resolution=360)
assert s.stim_res == 360
def test_cannot_instantiate_channels():
with pytest.raises(ValueError):
s = InvertedEncoding(n_channels=0)
assert s, "Invalid InvertedEncoding instance"
def test_cannot_score():
with pytest.raises(ValueError):
Invt_model = InvertedEncoding()
Invt_model.fit(X, y)
score = Invt_model.score(X2.transpose(), y)
logger.info('Scores: ' + str(score))
def test_range_stimulus_mode_halfcirc():
with pytest.raises(ValueError):
s = InvertedEncoding(6, 5, 'halfcircular', -10, 350)
assert s, "Invalid InvertedEncoding instance"
def test_stimulus_resolution_odd():
Invt_model = InvertedEncoding(stimulus_resolution=59)
with pytest.raises(NotImplementedError):
Invt_model.fit(X, y)
def test_data_dimensions():
x = np.random.rand(5, 10, 2)
s = InvertedEncoding()
with pytest.raises(ValueError):
s.fit(x, np.random.rand(5))
def test_can_fit_data():
Invt_model = InvertedEncoding()
Invt_model.fit(X, y)