a j@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_diffZcovr AE:\soccer\ml_env\Lib\site-packages\sklearn/decomposition/_base.pyget_covariances  z_BasePCA.get_covariancec Cst|j\}}|jjd}|jdkr4|||jS|rB|jj}ntj}|jdkr^||S|j}|j }|j r|| |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 rshapeZ n_components_eyerrinvr"rrrrrrrr rr ) rrZis_array_api_compliantZ n_featuresZ 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. )ZcsrZcscF)rZ accept_sparsereset)r x_is_centered)r rrr r Zfloat64Zfloat32 _transform)rr)rrr r r! transformts z_BasePCA.transformFcCsb||jj}|s*|||jd|jj8}|jr^||j}||jj }||||k<||}|S)N)r#) rrZreshapemean_rrrZfinforeps)rr)rr-Z X_transformedscaleZ min_scaler r r!r.s   z_BasePCA._transformcCsTt|\}}|jr@||jddtjf|j}|||jS||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)r rrrrrrr1)rr)rrZscaled_componentsr r r!inverse_transforms  z_BasePCA.inverse_transformcCs |jjdS)z&Number of transformed output features.r)rr$)rr r r!_n_features_outsz_BasePCA._n_features_out)N)F) __name__ __module__ __qualname____doc__r"r(rr+r/r.r4propertyr5r r r r!rs-   r) metaclass)r9abcrrnumpyrZscipyrbaserrrZutils._array_apir r r Zutils.validationr r rr r r r!s