def test_calculate_cca_copy(self):
"""caluclate_cca must not modify argument."""
cpy_x = self.dat_x.copy()
cpy_y = self.dat_y.copy()
calculate_cca(self.dat_x, self.dat_y)
self.assertEqual(self.dat_x, cpy_x)
self.assertEqual(self.dat_y, cpy_y)
def test_calculate_cca_copy(self):
"""caluclate_cca must not modify argument."""
cpy_x = self.dat_x.copy()
cpy_y = self.dat_y.copy()
calculate_cca(self.dat_x, self.dat_y)
self.assertEqual(self.dat_x, cpy_x)
self.assertEqual(self.dat_y, cpy_y)
def test_calculate_cca_swapaxes(self):
"""caluclate_cca must work with nonstandard timeaxis."""
res1 = calculate_cca(swapaxes(self.dat_x, 0, 1), swapaxes(self.dat_y, 0, 1), timeaxis=1)
res2 = calculate_cca(self.dat_x, self.dat_y)
np.testing.assert_array_equal(res1[0], res2[0])
np.testing.assert_array_equal(res1[1], res2[1])
np.testing.assert_array_equal(res1[2], res2[2])
def test_calculate_cca_swapaxes(self):
"""caluclate_cca must work with nonstandard timeaxis."""
res1 = calculate_cca(swapaxes(self.dat_x, 0, 1), swapaxes(self.dat_y, 0, 1), timeaxis=1)
res2 = calculate_cca(self.dat_x, self.dat_y)
np.testing.assert_array_equal(res1[0], res2[0])
np.testing.assert_array_equal(res1[1], res2[1])
np.testing.assert_array_equal(res1[2], res2[2])
def test_raise_error_with_non_continuous_data(self):
"""Raise error if ``dat_x`` is not continuous Data object."""
dat = Data(randn(2, self.SAMPLES, self.CHANNELS_X),
axes=[[0, 1], self.dat_x.axes[0], self.dat_x.axes[1]],
names=['class', 'time', 'channel'],
units=['#', 'ms', '#'])
with self.assertRaises(AssertionError):
calculate_cca(dat, self.dat_x)
def test_raise_error_with_non_continuous_data(self):
"""Raise error if ``dat_x`` is not continuous Data object."""
dat = Data(randn(2, self.SAMPLES, self.CHANNELS_X),
axes=[[0, 1], self.dat_x.axes[0], self.dat_x.axes[1]],
names=['class', 'time', 'channel'],
units=['#', 'ms', '#'])
with self.assertRaises(AssertionError):
calculate_cca(dat, self.dat_x)
def test_diff_between_canonical_variables(self):
"""Test if the scaled canonical variables are almost same."""
rho, w_x, w_y = calculate_cca(self.dat_x, self.dat_y)
cv_x = apply_spatial_filter(self.dat_x, w_x)
cv_y = apply_spatial_filter(self.dat_y, w_y)
def scale(x):
tmp = x.data - x.data.mean()
return tmp / tmp[np.argmax(np.abs(tmp))]
diff = scale(cv_x) - scale(cv_y)
diff = np.sum(np.abs(diff)) / self.SAMPLES
self.assertTrue(diff < 0.1)
def test_diff_between_canonical_variables(self):
"""Test if the scaled canonical variables are almost same."""
rho, w_x, w_y = calculate_cca(self.dat_x, self.dat_y)
cv_x = np.dot(self.X, w_x)
cv_y = np.dot(self.Y, w_y)
def scale(x):
tmp = x - x.mean()
return tmp / tmp[np.argmax(np.abs(tmp))]
diff = scale(cv_x) - scale(cv_y)
diff = np.sum(np.abs(diff)) / self.SAMPLES
self.assertTrue(diff < 0.1)
def test_raise_error_with_different_length_data(self):
"""Raise error if the length of ``dat_x`` and ``dat_y`` is different."""
dat = append(self.dat_x, self.dat_x)
with self.assertRaises(AssertionError):
calculate_cca(dat, self.dat_y)
def test_rho(self):
"""Test if the canonical correlation coefficient almost equals 1."""
rho, w_x, w_y = calculate_cca(self.dat_x, self.dat_y)
self.assertAlmostEqual(rho, 1.0, delta=0.01)
def test_raise_error_with_different_length_data(self):
"""Raise error if the length of ``dat_x`` and ``dat_y`` is different."""
dat = append(self.dat_x, self.dat_x)
with self.assertRaises(AssertionError):
calculate_cca(dat, self.dat_y)
def test_rho(self):
"""Test if the canonical correlation coefficient almost equals 1."""
rho, w_x, w_y = calculate_cca(self.dat_x, self.dat_y)
self.assertAlmostEqual(rho, 1.0, delta=0.01)