B  Ž0d~ã@s^dZddlZddlmZddlmZmZddlm Z ddl m Z m Z Gdd „d eee d Z dS) z)Principal Component Analysis Base ClasseséN)Úlinalgé)Ú BaseEstimatorÚTransformerMixin)Úcheck_is_fitted)ÚABCMetaÚabstractmethodc@s>eZdZdZdd„Zdd„Zed dd„ƒZd d „Zd d „Z dS)Ú_BasePCAzwBase class for PCA methods. Warning: This class should not be used directly. Use derived classes instead. cCsx|j}|j}|jr.|t |dd…tjf¡}t ||jd¡}t |j ||¡}|j ddt |ƒd…|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. Ngé) Ú components_Úexplained_variance_ÚwhitenÚnpÚsqrtÚnewaxisÚmaximumÚnoise_variance_ÚdotÚTÚflatÚlen)Úselfr Úexp_varÚ exp_var_diffZcov©rúM/var/www/html/venv/lib/python3.7/site-packages/sklearn/decomposition/_base.pyÚget_covariances "z_BasePCA.get_covariancecCs |jjd}|jdkr&t |¡|jS|j|kr>t | ¡¡S|j}|j }|j rl|t  |dd…tj f¡}t  ||jd¡}t ||j¡|j}|jddt|ƒd…d|7<t |jt t |¡|¡¡}||jd }|jddt|ƒd…d|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 rNggð?r)r ÚshapeZ n_components_rÚeyerrÚinvrr r rrrrrrr)rZ n_featuresr rrÚ precisionrrrÚ get_precision/s   $&z_BasePCA.get_precisionNcCsdS)a¢Placeholder for fit. Subclasses should implement this method! Fit the model with X. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data, where `n_samples` is the number of samples and `n_features` is the number of features. Returns ------- self : object Returns the instance itself. Nr)rÚXÚyrrrÚfitOsz _BasePCA.fitcCs^t|ƒ|j|tjtjgdd}|jdk r4||j}t ||jj¡}|j rZ|t  |j ¡}|S)azApply dimensionality reduction to X. X is projected on the first principal components previously extracted from a training set. Parameters ---------- X : array-like of shape (n_samples, n_features) New data, where `n_samples` is the number of samples and `n_features` is the number of features. Returns ------- X_new : array-like of shape (n_samples, n_components) Projection of X in the first principal components, where `n_samples` is the number of samples and `n_components` is the number of the components. F)ZdtypeÚresetN) rZ_validate_datarZfloat64Zfloat32Úmean_rr rr rr )rr"Z X_transformedrrrÚ transformas  z_BasePCA.transformcCsL|jr4t |t |jdd…tjf¡|j¡|jSt ||j¡|jSdS)açTransform data back to its original space. In other words, return an input `X_original` whose transform would be X. Parameters ---------- X : array-like of shape (n_samples, n_components) New data, where `n_samples` is the number of samples and `n_components` is the number of components. Returns ------- X_original array-like of shape (n_samples, n_features) Original data, where `n_samples` is the number of samples and `n_features` is the number of features. Notes ----- If whitening is enabled, inverse_transform will compute the exact inverse operation, which includes reversing whitening. N)r rrrr rr r&)rr"rrrÚinverse_transform}s  z_BasePCA.inverse_transform)N) Ú__name__Ú __module__Ú __qualname__Ú__doc__rr!rr$r'r(rrrrr s  r )Ú metaclass)r,ÚnumpyrZscipyrÚbaserrZutils.validationrÚabcrrr rrrrÚs