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)