"""Private utilities.""" import numpy as np from sklearn.utils.validation import NotFittedError # Copied from scikit-learn 0.19. def _validate_covars(covars, covariance_type, n_components): """Do basic checks on matrix covariance sizes and values.""" from scipy import linalg if covariance_type == 'spherical': if len(covars) != n_components: raise ValueError("'spherical' covars have length n_components") elif np.any(covars <= 0): raise ValueError("'spherical' covars must be positive") elif covariance_type == 'tied': if covars.shape[0] != covars.shape[1]: raise ValueError("'tied' covars must have shape (n_dim, n_dim)") elif (not np.allclose(covars, covars.T) or np.any(linalg.eigvalsh(covars) <= 0)): raise ValueError("'tied' covars must be symmetric, " "positive-definite") elif covariance_type == 'diag': if len(covars.shape) != 2: raise ValueError("'diag' covars must have shape " "(n_components, n_dim)") elif np.any(covars <= 0): raise ValueError("'diag' covars must be positive") elif covariance_type == 'full': if len(covars.shape) != 3: raise ValueError("'full' covars must have shape " "(n_components, n_dim, n_dim)") elif covars.shape[1] != covars.shape[2]: raise ValueError("'full' covars must have shape " "(n_components, n_dim, n_dim)") for n, cv in enumerate(covars): if (not np.allclose(cv, cv.T) or np.any(linalg.eigvalsh(cv) <= 0)): raise ValueError("component %d of 'full' covars must be " "symmetric, positive-definite" % n) else: raise ValueError("covariance_type must be one of " + "'spherical', 'tied', 'diag', 'full'") # Copied from scikit-learn 0.19. def distribute_covar_matrix_to_match_covariance_type( tied_cv, covariance_type, n_components): """Create all the covariance matrices from a given template.""" if covariance_type == 'spherical': cv = np.tile(tied_cv.mean() * np.ones(tied_cv.shape[1]), (n_components, 1)) elif covariance_type == 'tied': cv = tied_cv elif covariance_type == 'diag': cv = np.tile(np.diag(tied_cv), (n_components, 1)) elif covariance_type == 'full': cv = np.tile(tied_cv, (n_components, 1, 1)) else: raise ValueError("covariance_type must be one of " + "'spherical', 'tied', 'diag', 'full'") return cv # Adapted from scikit-learn 0.21. def check_is_fitted(estimator, attribute): if not hasattr(estimator, attribute): raise NotFittedError( "This %s instance is not fitted yet. Call 'fit' with " "appropriate arguments before using this method." % type(estimator).__name__)