def pca_and_weights(meshes, retain_eig_cum_val=0.997, verbose=False):
model = PCAModel(meshes, verbose=verbose)
n_comps_retained = (model.eigenvalues_cumulative_ratio() <
retain_eig_cum_val).sum()
if verbose:
print('\nRetaining {:.2%} of eigenvalues keeps {} components'.format(
retain_eig_cum_val, n_comps_retained))
model.trim_components(retain_eig_cum_val)
if verbose:
meshes = print_progress(meshes, prefix='Calculating weights')
weights = (np.vstack([model.project(m)
for m in meshes]) / np.sqrt(model.eigenvalues))
return model, weights
def test_pca_project():
pca_samples = [PointCloud(np.random.randn(10, 2)) for _ in range(10)]
pca_model = PCAModel(pca_samples)
projected = pca_model.project(pca_samples[0])
assert projected.shape[0] == 9
def test_pca_project():
pca_samples = [PointCloud(np.random.randn(10, 2)) for _ in range(10)]
pca_model = PCAModel(pca_samples)
projected = pca_model.project(pca_samples[0])
assert projected.shape[0] == 9
