def test_eigenvalues(self): # do it with PCA metric my_cov_matrix = PCAMetric.create_covariance_matrix(testPCAMetric.coordsets) biggest_eigenvalue = PCAMetric.calculate_biggest_eigenvalue(my_cov_matrix) # Do it with prody pca = prody.PCA('pcametric_pca') pca.buildCovariance(testPCAMetric.ensemble) pca.calcModes(n_modes=1) self.assertAlmostEqual(pca.getEigvals()[0], biggest_eigenvalue,10)
def test_covariance_matrix_vs_prody(self): # do it with PCA metric my_cov_matrix = PCAMetric.create_covariance_matrix(testPCAMetric.coordsets) # Do it with prody pca = prody.PCA('pcametric_pca') pca.buildCovariance(testPCAMetric.ensemble) prody_cov_matrix = pca._cov # Compare numpy.testing.assert_almost_equal(my_cov_matrix, prody_cov_matrix,10)
def test_eigenvalues(self): # do it with PCA metric my_cov_matrix = PCAMetric.create_covariance_matrix( testPCAMetric.coordsets) biggest_eigenvalue = PCAMetric.calculate_biggest_eigenvalue( my_cov_matrix) # Do it with prody pca = prody.PCA('pcametric_pca') pca.buildCovariance(testPCAMetric.ensemble) pca.calcModes(n_modes=1) self.assertAlmostEqual(pca.getEigvals()[0], biggest_eigenvalue, 10)
def test_covariance_matrix_vs_prody(self): # do it with PCA metric my_cov_matrix = PCAMetric.create_covariance_matrix( testPCAMetric.coordsets) # Do it with prody pca = prody.PCA('pcametric_pca') pca.buildCovariance(testPCAMetric.ensemble) prody_cov_matrix = pca._cov # Compare numpy.testing.assert_almost_equal(my_cov_matrix, prody_cov_matrix, 10)