def test_csp_init(nfilter, metric, log, get_covmats, get_labels):
n_classes, n_matrices, n_channels = 2, 6, 3
covmats = get_covmats(n_matrices, n_channels)
labels = get_labels(n_matrices, n_classes)
csp = CSP(nfilter=nfilter, metric=metric, log=log)
csp.fit(covmats, labels)
Xtr = csp.transform(covmats)
if log:
assert Xtr.shape == (n_matrices, n_channels)
else:
assert Xtr.shape == (n_matrices, n_channels, n_channels)
assert csp.filters_.shape == (n_channels, n_channels)
assert csp.patterns_.shape == (n_channels, n_channels)
def test_CSP():
"""Test CSP"""
n_trials = 90
X = generate_cov(n_trials, 3)
labels = np.array([0, 1, 2]).repeat(n_trials // 3)
# Test Init
csp = CSP()
assert_true(csp.nfilter == 4)
assert_true(csp.metric == 'euclid')
assert_true(csp.log)
csp = CSP(3, 'riemann', False)
assert_true(csp.nfilter == 3)
assert_true(csp.metric == 'riemann')
assert_true(not csp.log)
assert_raises(TypeError, CSP, 'foo')
assert_raises(ValueError, CSP, metric='foo')
assert_raises(TypeError, CSP, log='foo')
# Test fit
csp = CSP()
csp.fit(X, labels % 2) # two classes
csp.fit(X, labels) # 3 classes
assert_raises(ValueError, csp.fit, X, labels * 0.) # 1 class
assert_raises(ValueError, csp.fit, X, labels[:1]) # unequal # of samples
assert_raises(TypeError, csp.fit, X, 'foo') # y must be an array
assert_raises(TypeError, csp.fit, 'foo', labels) # X must be an array
assert_raises(ValueError, csp.fit, X[:, 0], labels)
assert_raises(ValueError, csp.fit, X, X)
assert_array_equal(csp.filters_.shape, [X.shape[1], X.shape[1]])
assert_array_equal(csp.patterns_.shape, [X.shape[1], X.shape[1]])
# Test transform
Xt = csp.transform(X)
assert_array_equal(Xt.shape, [len(X), X.shape[1]])
assert_raises(TypeError, csp.transform, 'foo')
assert_raises(ValueError, csp.transform, X[:, 1:, :]) # unequal # of chans
csp.log = False
Xt = csp.transform(X)
def test_CSP():
"""Test CSP"""
n_trials = 90
X = generate_cov(n_trials, 3)
labels = np.array([0, 1, 2]).repeat(n_trials // 3)
# Test Init
csp = CSP()
assert csp.nfilter == 4
assert csp.metric == 'euclid'
assert csp.log
csp = CSP(3, 'riemann', False)
assert csp.nfilter == 3
assert csp.metric == 'riemann'
assert not csp.log
with pytest.raises(TypeError):
CSP('foo')
with pytest.raises(ValueError):
CSP(metric='foo')
with pytest.raises(TypeError):
CSP(log='foo')
# Test fit
csp = CSP()
csp.fit(X, labels % 2) # two classes
csp.fit(X, labels) # 3 classes
with pytest.raises(ValueError):
csp.fit(X, labels * 0.) # 1 class
with pytest.raises(ValueError):
csp.fit(X, labels[:1]) # unequal # of samples
with pytest.raises(TypeError):
csp.fit(X, 'foo') # y must be an array
with pytest.raises(TypeError):
csp.fit('foo', labels) # X must be an array
with pytest.raises(ValueError):
csp.fit(X[:, 0], labels)
with pytest.raises(ValueError):
csp.fit(X, X)
assert_array_equal(csp.filters_.shape, [X.shape[1], X.shape[1]])
assert_array_equal(csp.patterns_.shape, [X.shape[1], X.shape[1]])
# Test transform
Xt = csp.transform(X)
assert_array_equal(Xt.shape, [len(X), X.shape[1]])
with pytest.raises(TypeError):
csp.transform('foo')
with pytest.raises(ValueError):
csp.transform(X[:, 1:, :]) # unequal # of chans
csp.log = False
Xt = csp.transform(X)