B ¥ ˆ\Üã@szdZddlZddlmZddlmZmZddlm Z ddl m Z ddl m Z dd lmZmZGd d „d e  eee¡ƒZdS) z)Principal Component Analysis Base ClasseséN)Úlinalgé)Ú BaseEstimatorÚTransformerMixin)Ú check_array)Úcheck_is_fitted)Úsix)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)arCompute 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, shape=(n_features, n_features) Estimated covariance of data. Ngé) Ú components_Úexplained_variance_ÚwhitenÚnpÚsqrtÚnewaxisÚmaximumÚnoise_variance_ÚdotÚTÚflatÚlen)Úselfr Úexp_varÚ exp_var_diffZcov©rú9lib/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_rZeyerrÚinvrrrrrrrrrr)rZ n_featuresr rrZ precisionrrrÚ get_precision0s"   $&z_BasePCA.get_precisionNcCsdS)aœPlaceholder for fit. Subclasses should implement this method! Fit the model with X. Parameters ---------- X : array-like, shape (n_samples, n_features) Training data, where n_samples is the number of samples and n_features is the number of features. Returns ------- self : object Returns the instance itself. Nr)ÚXÚyrrrÚfitQsz _BasePCA.fitcCsXt|ddgtdt|ƒ}|jdk r.||j}t ||jj¡}|jrT|t  |j ¡}|S)anApply dimensionality reduction to X. X is projected on the first principal components previously extracted from a training set. Parameters ---------- X : array-like, shape (n_samples, n_features) New data, where n_samples is the number of samples and n_features is the number of features. Returns ------- X_new : array-like, shape (n_samples, n_components) Examples -------- >>> import numpy as np >>> from sklearn.decomposition import IncrementalPCA >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> ipca = IncrementalPCA(n_components=2, batch_size=3) >>> ipca.fit(X) IncrementalPCA(batch_size=3, copy=True, n_components=2, whiten=False) >>> ipca.transform(X) # doctest: +SKIP Úmean_r )Z all_or_anyN) rÚallrr%rrr rrrr)rr"Z X_transformedrrrÚ transformcs  z_BasePCA.transformcCsL|jr4t |t |jdd…tjf¡|j¡|jSt ||j¡|jSdS)a_Transform data back to its original space. In other words, return an input X_original whose transform would be X. Parameters ---------- X : array-like, shape (n_samples, n_components) New data, where n_samples is the number of samples and n_components is the number of components. Returns ------- X_original array-like, shape (n_samples, n_features) Notes ----- If whitening is enabled, inverse_transform will compute the exact inverse operation, which includes reversing whitening. N)rrrrrrr r%)rr"rrrÚinverse_transformˆsz_BasePCA.inverse_transform)N) Ú__name__Ú __module__Ú __qualname__Ú__doc__rr!r r$r'r(rrrrr s! %r )r,ZnumpyrZscipyrÚbaserrZutilsrZutils.validationrZ externalsrÚabcr r Zwith_metaclassr rrrrÚs