def test_diagonal(self): """The whitened data should have all 1s on the covariance matrix.""" a = calculate_whitening_matrix(self.cnt) dat2 = np.dot(self.cnt.data, a) vals = np.diag(np.cov(dat2.T)) np.testing.assert_array_almost_equal(vals, [1. for i in range(len(vals))])
def test_zeros(self): """The whinened data should have all 0s on the non-diagonals of the covariance matrix.""" a = calculate_whitening_matrix(self.cnt) dat2 = np.dot(self.cnt.data, a) cov = np.cov(dat2.T) # substract the diagonals cov -= np.diag(np.diag(cov)) self.assertAlmostEqual(np.sum(cov), 0)
def test_zeros(self): """The whitened data should have all 0s on the non-diagonals of the covariance matrix.""" a = calculate_whitening_matrix(self.cnt) dat2 = apply_spatial_filter(self.cnt, a) cov = np.cov(dat2.data.T) # substract the diagonals cov -= np.diag(np.diag(cov)) self.assertAlmostEqual(np.sum(cov), 0)
def test_calculate_whitening_matrix_copy(self): """calculate_whitening_matrix must not modify arguments.""" cpy = self.cnt.copy() calculate_whitening_matrix(self.cnt) self.assertEqual(self.cnt, cpy)
def test_shape(self): """The whitening filter should have the shape: CHANSxCHANS.""" a = calculate_whitening_matrix(self.cnt) self.assertEqual(a.shape, (CHANS, CHANS))
def test_diagonal(self): """The whitened data should have all 1s on the covariance matrix.""" a = calculate_whitening_matrix(self.cnt) dat2 = apply_spatial_filter(self.cnt, a) vals = np.diag(np.cov(dat2.data.T)) np.testing.assert_array_almost_equal(vals, [1. for i in range(len(vals))])