0PhdZddlmZmZddlZddlmZddlm Z m Z m Z ddl m Z mZmZddlmZmZGd d e e e e ZdS) z)Principal Component Analysis Base Classes)ABCMetaabstractmethodN)linalg) BaseEstimatorClassNamePrefixFeaturesOutMixinTransformerMixin)_fill_or_add_to_diagonaldevice get_namespace)check_is_fitted validate_datac`eZdZdZdZdZed dZdZd dZ d Z e d Z dS) _BasePCAzwBase class for PCA methods. Warning: This class should not be used directly. Use derived classes instead. c t|j\}}|j}|j}|jr,|||ddt jfz}||jz }|||jk|| dt||j }|j |z|z}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_diffcovs [/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/decomposition/_base.pyget_covariancez_BasePCA.get_covariancesd.//A& * ; H%2: 0F(G(GGK!55 xx d* *  JJs6'??'-J H H  }|+{: d&:B??? c t|j\}}|jjd}|jdkr|||jz S|r |jj}n t j}|jdkr||S|j}|j }|j r,|| |ddtj fz}||jz }|||jk||dt!|}||jz|jz }t%|d|z ||j||z|z}||jdz z}t%|d|jz ||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_invrr!r" precisions r$ get_precisionz_BasePCA.get_precision5s&343C%D%D" "%+A.    " "66*%%(<< < ! $JJJ  3 & &:d113344 4& * ; H%2: 0F(G(GGK!55 xx d* *  JJs6'??J 3 3   +-/$2FF  C,,>CCCMJJy$9$99KG t+Q.//  C$2F,FKKKr&NcdS)aPlaceholder for fit. Subclasses should implement this method! Fit the model with X. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data, where `n_samples` is the number of samples and `n_features` is the number of features. Returns ------- self : object Returns the instance itself. N)rXys r$fitz _BasePCA.fitbsr&ct||j|j\}}t|t |||j|jgdd}|||dS)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 rfloat64float32 _transform)rr4rr s r$ transformz_BasePCA.transformtss$a!143KLLA   :rz*(    qRu===r&Fc||jjz}|s+|||jd|jjzz}|jrG||j}||jj }||||k<||z}|S)N)r() rrreshapemean_rrrfinforeps)rr4rr< X_transformedscale min_scales r$r?z_BasePCA._transformsD,..  R RZZ G<>>>(555@))X)))r&r) metaclass)rRabcrrnumpyrscipyrbaserrr utils._array_apir r r utils.validationr rrr3r&r$r[s// ('''''''SSSSSSSSSSNNNNNNNNNN========z)z)z)z)z)#%5}PWz)z)z)z)z)z)r&