U 3d`@sddZddlZddlmZddlmZmZmZddl m Z ddl m Z m Z Gdd d eeee d ZdS) z)Principal Component Analysis Base ClassesN)linalg) BaseEstimatorTransformerMixinClassNamePrefixFeaturesOutMixin)check_is_fitted)ABCMetaabstractmethodc@sJeZdZdZddZddZedddZd d Zd d Z e d dZ dS)_BasePCAzwBase class for PCA methods. Warning: This class should not be used directly. Use derived classes instead. cCsx|j}|j}|jr.|t|ddtjf}t||jd}t|j ||}|j ddt |d|j7<|S)asCompute data covariance with the generative model. ``cov = components_.T * S**2 * components_ + sigma2 * eye(n_features)`` where S**2 contains the explained variances, and sigma2 contains the noise variances. Returns ------- cov : array of shape=(n_features, n_features) Estimated covariance of data. N) components_explained_variance_whitennpsqrtnewaxismaximumnoise_variance_dotTflatlen)selfr exp_var exp_var_diffZcovr?/tmp/pip-unpacked-wheel-zrfo1fqw/sklearn/decomposition/_base.pyget_covariances "z_BasePCA.get_covariancecCs|jjd}|jdkr&t||jStj|jdddrFt| S|j}|j }|j rt|t |ddtj f}t||jd}t||j|j}|jddt|dd|7<t|jtt||}||jd }|jddt|dd|j7<|S)a8Compute data precision matrix with the generative model. Equals the inverse of the covariance but computed with the matrix inversion lemma for efficiency. Returns ------- precision : array, shape=(n_features, n_features) Estimated precision of data. r rr )ZatolNg?r)r shapeZ n_components_rZeyeriscloserinvrrrrrrrrrr)rZ n_featuresr rrZ precisionrrr get_precision1s  $&z_BasePCA.get_precisionNcCsdS)aPlaceholder for fit. Subclasses should implement this method! Fit the model with X. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data, where `n_samples` is the number of samples and `n_features` is the number of features. Returns ------- self : object Returns the instance itself. Nr)rXyrrrfitRsz _BasePCA.fitcCs^t||j|tjtjgdd}|jdk r4||j}t||jj}|j rZ|t |j }|S)azApply dimensionality reduction to X. X is projected on the first principal components previously extracted from a training set. Parameters ---------- X : array-like of shape (n_samples, n_features) New data, where `n_samples` is the number of samples and `n_features` is the number of features. Returns ------- X_new : array-like of shape (n_samples, n_components) Projection of X in the first principal components, where `n_samples` is the number of samples and `n_components` is the number of the components. F)ZdtyperesetN) rZ_validate_datarZfloat64Zfloat32mean_rr rrrr)rr#Z X_transformedrrr transformds  z_BasePCA.transformcCsL|jr4t|t|jddtjf|j|jSt||j|jSdS)aTransform data back to its original space. In other words, return an input `X_original` whose transform would be X. Parameters ---------- X : array-like of shape (n_samples, n_components) New data, where `n_samples` is the number of samples and `n_components` is the number of components. Returns ------- X_original array-like of shape (n_samples, n_features) Original data, where `n_samples` is the number of samples and `n_features` is the number of features. Notes ----- If whitening is enabled, inverse_transform will compute the exact inverse operation, which includes reversing whitening. N)rrrrrrr r')rr#rrrinverse_transformsz_BasePCA.inverse_transformcCs |jjdS)z&Number of transformed output features.r)r r)rrrr_n_features_outsz_BasePCA._n_features_out)N) __name__ __module__ __qualname____doc__rr"r r%r(r)propertyr*rrrrr s! !r ) metaclass)r.ZnumpyrZscipyrbaserrrZutils.validationrabcrr r rrrrs