o Rh@s|dZddlmZmZddlZddlmZddlm Z m Z m Z ddl m Z mZmZddlmZmZGd d d e e e ed ZdS) z)Principal Component Analysis Base Classes)ABCMetaabstractmethodN)linalg) BaseEstimatorClassNamePrefixFeaturesOutMixinTransformerMixin)_fill_or_add_to_diagonaldevice get_namespace)check_is_fitted validate_datac@sTeZdZdZddZddZedddZd d Zdd d Z ddZ e ddZ dS)_BasePCAzwBase class for PCA methods. Warning: This class should not be used directly. Use derived classes instead. c Cst|j\}}|j}|j}|jr|||ddtjf}||j}|||jk||j dt ||j d}|j ||}t ||j||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)r dtype)r components_explained_variance_whitensqrtnpnewaxisnoise_variance_whereasarrayr rTr )selfxp_rexp_var exp_var_diffcovr!]/home/air/sanwanet/backup_V2/venv/lib/python3.10/site-packages/sklearn/decomposition/_base.pyget_covariances  z_BasePCA.get_covariancec Cst|j\}}|jjd}|jdkr|||jS|r!|jj}ntj}|jdkr/||S|j}|j }|j rF|| |ddt j f}||j}|||jk||jdt|d}||j|j}t|d|||j|||}||jd }t|d|j||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. rrN)r g?r)r rshape n_components_eyerrinvr#rrrrrrrr rr ) rris_array_api_compliant n_features linalg_invrrr precisionr!r!r" get_precision5s2      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!)rXyr!r!r"fitbsz _BasePCA.fitcCsFt||j|j\}}t|t|||j|jgddd}|j||ddS)aApply dimensionality reduction to X. X is projected on the first principal components previously extracted from a training set. Parameters ---------- X : {array-like, sparse matrix} 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. )csrcscF)r accept_sparsereset)r x_is_centered)r rrr r float64float32 _transform)rr.rrr!r!r" transformts z_BasePCA.transformFcCsb||jj}|s|||jd|jj8}|jr/||j}||jj }||||k<||}|S)N)r$) rrreshapemean_rrrfinforeps)rr.rr5 X_transformedscale min_scaler!r!r"r8s   z_BasePCA._transformcCsPt|\}}|jr ||jddtjf|j}|||jS||j|jS)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)r rrrrrrr<)rr.rrscaled_componentsr!r!r"inverse_transforms z_BasePCA.inverse_transformcCs |jjdS)z&Number of transformed output features.r)rr%)rr!r!r"_n_features_outs z_BasePCA._n_features_out)N)F) __name__ __module__ __qualname____doc__r#r-rr0r9r8rCpropertyrDr!r!r!r"rs-   r) metaclass)rHabcrrnumpyrscipyrbaserrrutils._array_apir r r utils.validationr r rr!r!r!r"s