h dZddlmZmZddlZddlmZmZddlZ ddl m Z ddl m Z mZmZddl mZdd l mZdd lmZmZdd lmZdd lmZdd lmZddlmZmZddlmZmZddl m!Z!gdZ"eedkrddl m#Z$nddl m$Z$dZ% d*dZ&dZ'd+dZ(dZ)Gdd eeeee e!Z*Gd"d#e*Z+Gd$d%e*Z,Gd&d'e*Z-Gd(d)eee Z.dS),zG The :mod:`sklearn.pls` module implements Partial Least Squares (PLS). )IntegralRealN)ABCMetaabstractmethod)svd) BaseEstimatorRegressorMixinTransformerMixin)MultiOutputMixin)ClassNamePrefixFeaturesOutMixin) check_arraycheck_consistent_length) sp_version) parse_version)svd_flip)check_is_fitted FLOAT_DTYPES)Interval StrOptions)ConvergenceWarning) PLSCanonical PLSRegressionPLSSVDz1.7)pinv)pinv2c t|dd\}}}|jj}ddd}t j|||zt j|jz}t j||k}|ddd|f}||d|z}t j t j t j ||d|S)NF) full_matrices check_finiteg@@g.A)fd) rdtypecharlowernpmaxfinfoepssum transpose conjugatedot)ausvhtfactorcondranks \/var/www/html/Sam_Eipo/venv/lib/python3.11/site-packages/sklearn/cross_decomposition/_pls.py _pinv2_oldr6&s 1E>>>HAq"  AS ! !F 6!99vay 28A;;? 2D 6!d(  D !!!UdU( A5D5MA < RVAr%4%y%9%9:: ; ;;Aư>Fcntj|jj t fd|jD}n"#t $r}t d|d}~wwxYwd}|dkrt|t|} } t|D]r} |dkrtj | |} n0tj |j|tj ||z } | tj tj | | zz} tj || } |dkrtj | | }n5tj |j| tj | j| z }|r-|tj tj ||zz}tj ||tj ||zz }| |z }tj |||ks|j ddkrn| }t| dz}||krtj dt| ||fS)a?Return the first left and right singular vectors of X'Y. Provides an alternative to the svd(X'Y) and uses the power method instead. With norm_y_weights to True and in mode A, this corresponds to the algorithm section 11.3 of the Wegelin's review, except this starts at the "update saliences" part. c3pK|]0}tjtj|k,|V1dSN)r%anyabs).0colr(s r5 z;_get_first_singular_vectors_power_method..Es?GGsRVBF3KK#4E-F-FGsGGGGGGr7Y residual is constantNdBz$Maximum number of iterations reached)r%r'r"r(nextT StopIterationr6ranger,sqrtshapewarningswarnr)XYmodemax_itertolnorm_y_weightsy_scoree x_weights_oldX_pinvY_pinvi x_weightsx_score y_weightsx_weights_diffn_iterr(s @r5(_get_first_singular_vectors_power_methodr`8s- (17   C=GGGGacGGGGG ===4551<=M s{{$A 1  8__"" 3;;vw//IIqsG,,rvgw/G/GGIRWRVIy99::S@@ &I&& 3;;vw//IIqsG,,rvgi/I/III  E  9!=!=>>D DI&I&&"&I*F*F*LM"]2 6.. 1 1C 7 7171:?? E! UF  <>PQQQ i ''s A A! AA!ctj|j|}t|d\}}}|dddf|dddffS)zbReturn the first left and right singular vectors of X'Y. Here the whole SVD is computed. FrNr)r%r,rHr)rOrPCU_Vts r5_get_first_singular_vectors_svdrgssP qsAA1E***HAq" QQQT7Bq!!!tH r7Tc|d}||z}|d}||z}|rK|dd}d||dk<||z}|dd}d||dk<||z}n>tj|jd}tj|jd}||||||fS)z{Center X, Y and scale if the scale parameter==True Returns ------- X, Y, x_mean, y_mean, x_std, y_std raxisrF)rjddofg?)meanstdr%onesrL)rOrPscalex_meany_meanx_stdy_stds r5_center_scale_xyru}sVVV^^FKA VVV^^FKA $11%%!esl U 11%%!esl U  ## ## a --r7ctjtj|}tj||}||z}||z}dS)z7Same as svd_flip but works on 1d arrays, and is inplaceN)r%argmaxr?sign)r.vbiggest_abs_val_idxrxs r5 _svd_flip_1dr{sG)BF1II.. 71() * *DIAIAAAr7c BeZdZUdZeedddgdgeddhged d hged d hgeedddgeed ddgdgdZe e d<e dddd d dddddZ dZ ddZddZd dZddZedZdZdS)!_PLSaPartial Least Squares (PLS) This class implements the generic PLS algorithm. Main ref: Wegelin, a survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case https://stat.uw.edu/sites/default/files/files/reports/2000/tr371.pdf rFNleftclosedboolean regression canonicalr8rErnipalsr n_componentsrpdeflation_moderQ algorithmrRrScopy_parameter_constraintsrTr9r:)rprrQrrRrSrcv||_||_||_||_||_||_||_||_dSr=)rrrQrprrRrSr) selfrrprrQrrRrSrs r5__init__z _PLS.__init__sB),  "   r7c . |t||||tj|jd}t |dtj|jd}|jdkr|dd}|j d}|j d}|j d}|j }|j d kr|nt|||}||krtd |d |d |j d k|_|j}t|||j\} } |_|_|_|_tj||f|_tj||f|_tj||f|_tj||f|_tj||f|_tj||f|_g|_tj| jj} tA|D]w} |j!dkrtj"tj#| d| zkd} d| dd| f< tI| | |j%|j&|j'|\}}}nD#tP$r7}tS|dkrtUj+d| Yd}~nd}~wwxYw|j,|n|j!dkrt[| | \}}t]||tj/| |}|rd}ntj/||}tj/| ||z }tj/|| tj/||z }| tj0||z} |j d krCtj/|| tj/||z }| tj0||z} |j d krCtj/|| tj/||z }| tj0||z} ||jdd| f<||jdd| f<||jdd| f<||jdd| f<||jdd| f<||jdd| f<ytj/|jtctj/|jj2|jd|_3tj/|jtctj/|jj2|jd|_4tj/|j3|jj2|_5|j5|jzj2|_5|j|_6|j3j d|_7|S)Fit model to data. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of predictors. Y : array-like of shape (n_samples,) or (n_samples, n_targets) Target vectors, where `n_samples` is the number of samples and `n_targets` is the number of response variables. Returns ------- self : object Fitted model. rr"rensure_min_samplesrPF input_namer"r ensure_2drFrr`n_components` upper bound is . Got instead. Reduce `n_components`.rr rirlN)rQrRrSrTrCz$Y residual is constant at iteration r)r)8_validate_paramsr_validate_datar%float64rrndimreshaperLrrmin ValueError_norm_y_weightsrurp_x_mean_y_mean_x_std_y_stdzeros x_weights_ y_weights_ _x_scores _y_scores x_loadings_ y_loadings_n_iter_r'r"r(rJrallr?r`rQrRrSrIstrrMrNappendrgr{r,outerrrH x_rotations_ y_rotations__coef_ intercept__n_features_out)rrOrPnpqrrank_upper_boundrTXkYkY_epskYk_maskr[r]rrVx_scoresy_ssy_scores x_loadings y_loadingss r5fitz_PLS.fits$ 1%%%    RZdiA     #RZdi5    6Q;; "a  A GAJ GAJ GAJ( !% 3| C C11QPQST * * *F1AFF#FFF  $2kA-HX q$*H H DB dlDK(A|#455(A|#4551l"3441l"3448Q $5668Q $566 ""&|$$= 0= 0A~))&b5j!8qAAA!$111g: A!Y!% H'5  !!%1vv!999M"L"L"LMMMEEEEE   ##G,,,,5(('Fr2'N'N$ 9 I . . .vb),,H 4vi33vb),,t3H"--x0J0JJJ "(8Z00 0B"k11VHb11BF8X4N4NN bhx444"l22VHb11BF8X4N4NN bhx444$-DOAAAqD !$-DOAAAqD !#+DN111a4 #+DN111a4 %/D QQQT "%/D QQQT " "F O "&)+T_==E R R R  F O "&)+T_==E R R R   fT.0@0BCC {T[03 ,#06q9 s(J K+KKct||||td}||jz}||jz}t j||j}|lt|dd|t}|j dkr| dd}||j z}||j z}t j||j }||fS|S)a.Apply the dimension reduction. Parameters ---------- X : array-like of shape (n_samples, n_features) Samples to transform. Y : array-like of shape (n_samples, n_targets), default=None Target vectors. copy : bool, default=True Whether to copy `X` and `Y`, or perform in-place normalization. Returns ------- x_scores, y_scores : array-like or tuple of array-like Return `x_scores` if `Y` is not given, `(x_scores, y_scores)` otherwise. Frr"resetNrP)rrrr"rFr)rrrrrr%r,rrrrrrr)rrOrPrrrs r5 transformz_PLS.transformis&    L  N N T\ T[6!T.// =cU\Av{{IIb!$$  A  Ava!233HX% %r7cXt|t|dt}tj||jj}||jz}||jz }|Nt|dt}tj||j j}||j z}||j z }||fS|S)aeTransform data back to its original space. 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 pls components. Y : array-like of shape (n_samples, n_components) New target, where `n_samples` is the number of samples and `n_components` is the number of pls components. Returns ------- X_reconstructed : ndarray of shape (n_samples, n_features) Return the reconstructed `X` data. Y_reconstructed : ndarray of shape (n_samples, n_targets) Return the reconstructed `X` target. Only returned when `Y` is given. Notes ----- This transformation will only be exact if `n_components=n_features`. rO)rr"NrP) rrrr%matmulrrHrrrrr)rrOrPX_reconstructedY_reconstructeds r5inverse_transformz_PLS.inverse_transforms2  c > > >)At'7'9::4;&4<' =A#\BBBA i4+;+=>>O t{ *O t| +O"O3 3r7ct||||td}||jz}||jz}||jjz}||jzS)aUPredict targets of given samples. Parameters ---------- X : array-like of shape (n_samples, n_features) Samples. copy : bool, default=True Whether to copy `X` and `Y`, or perform in-place normalization. Returns ------- y_pred : ndarray of shape (n_samples,) or (n_samples, n_targets) Returns predicted values. Notes ----- This call requires the estimation of a matrix of shape `(n_features, n_targets)`, which may be an issue in high dimensional space. Fr)rrrrrrrHr)rrOrYpreds r5predictz _PLS.predictsa,    L  N N T\ T[DKM!t&&r7cV|||||S)aLearn and apply the dimension reduction on the train data. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of predictors. y : array-like of shape (n_samples, n_targets), default=None Target vectors, where `n_samples` is the number of samples and `n_targets` is the number of response variables. Returns ------- self : ndarray of shape (n_samples, n_components) Return `x_scores` if `Y` is not given, `(x_scores, y_scores)` otherwise. rrrrOys r5 fit_transformz_PLS.fit_transform&$xx1~~''1---r7ct|dr2t|ddr!tjdtd|_|jjS)z%The coefficients of the linear model.r _coef_warningTzThe attribute `coef_` will be transposed in version 1.3 to be consistent with other linear models in scikit-learn. Currently, `coef_` has a shape of (n_features, n_targets) and in the future it will have a shape of (n_targets, n_features).F)hasattrgetattrrMrN FutureWarningrrrHrs r5coef_z _PLS.coef_s\ 4 " " 'wt_d'K'K ' M@    "'D {}r7c dddS)NTF) poor_score requires_yrs r5 _more_tagsz_PLS._more_tagss"%888r7r)NTr=T)__name__ __module__ __qualname____doc__rrrrrdict__annotations__rrrrrrrpropertyrrrr7r5r}r}s"(AtFCCCD%:|[&ABBCS#J''( j%!2334Xh4???@q$v6667  $ $D   #   ^*TTTl%%%%N****X''''>....(X&99999r7r}) metaclassceZdZUdZiejZeed<dD]Ze e d dddddfd Z fd Z xZ S) ra PLS regression. PLSRegression is also known as PLS2 or PLS1, depending on the number of targets. Read more in the :ref:`User Guide `. .. versionadded:: 0.8 Parameters ---------- n_components : int, default=2 Number of components to keep. Should be in `[1, min(n_samples, n_features, n_targets)]`. scale : bool, default=True Whether to scale `X` and `Y`. max_iter : int, default=500 The maximum number of iterations of the power method when `algorithm='nipals'`. Ignored otherwise. tol : float, default=1e-06 The tolerance used as convergence criteria in the power method: the algorithm stops whenever the squared norm of `u_i - u_{i-1}` is less than `tol`, where `u` corresponds to the left singular vector. copy : bool, default=True Whether to copy `X` and `Y` in :term:`fit` before applying centering, and potentially scaling. If `False`, these operations will be done inplace, modifying both arrays. Attributes ---------- x_weights_ : ndarray of shape (n_features, n_components) The left singular vectors of the cross-covariance matrices of each iteration. y_weights_ : ndarray of shape (n_targets, n_components) The right singular vectors of the cross-covariance matrices of each iteration. x_loadings_ : ndarray of shape (n_features, n_components) The loadings of `X`. y_loadings_ : ndarray of shape (n_targets, n_components) The loadings of `Y`. x_scores_ : ndarray of shape (n_samples, n_components) The transformed training samples. y_scores_ : ndarray of shape (n_samples, n_components) The transformed training targets. x_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `X`. y_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `Y`. coef_ : ndarray of shape (n_features, n_targets) The coefficients of the linear model such that `Y` is approximated as `Y = X @ coef_ + intercept_`. intercept_ : ndarray of shape (n_targets,) The intercepts of the linear model such that `Y` is approximated as `Y = X @ coef_ + intercept_`. .. versionadded:: 1.1 n_iter_ : list of shape (n_components,) Number of iterations of the power method, for each component. n_features_in_ : int Number of features seen during :term:`fit`. feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- PLSCanonical : Partial Least Squares transformer and regressor. Examples -------- >>> from sklearn.cross_decomposition import PLSRegression >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> pls2 = PLSRegression(n_components=2) >>> pls2.fit(X, Y) PLSRegression() >>> Y_pred = pls2.predict(X) rrrQrrTr9r:rprRrSrc Zt||ddd|||dS)Nrr8rrsuperrrrrprRrSr __class__s r5rzPLSRegression.__init__tsH %'  r7c|t|||j|_|j|_|S)r)rrr x_scores_r y_scores_)rrOrPrs r5rzPLSRegression.fits4$  Aq r7r) rrrrr}rrrparampoprr __classcell__rs@r5rrs``D$Cd&A#BDBBB8**""5))))  '+cu4        r7rceZdZUdZiejZeed<dD]Ze e d dddddd fd Z xZ S) ra Partial Least Squares transformer and regressor. Read more in the :ref:`User Guide `. .. versionadded:: 0.8 Parameters ---------- n_components : int, default=2 Number of components to keep. Should be in `[1, min(n_samples, n_features, n_targets)]`. scale : bool, default=True Whether to scale `X` and `Y`. algorithm : {'nipals', 'svd'}, default='nipals' The algorithm used to estimate the first singular vectors of the cross-covariance matrix. 'nipals' uses the power method while 'svd' will compute the whole SVD. max_iter : int, default=500 The maximum number of iterations of the power method when `algorithm='nipals'`. Ignored otherwise. tol : float, default=1e-06 The tolerance used as convergence criteria in the power method: the algorithm stops whenever the squared norm of `u_i - u_{i-1}` is less than `tol`, where `u` corresponds to the left singular vector. copy : bool, default=True Whether to copy `X` and `Y` in fit before applying centering, and potentially scaling. If False, these operations will be done inplace, modifying both arrays. Attributes ---------- x_weights_ : ndarray of shape (n_features, n_components) The left singular vectors of the cross-covariance matrices of each iteration. y_weights_ : ndarray of shape (n_targets, n_components) The right singular vectors of the cross-covariance matrices of each iteration. x_loadings_ : ndarray of shape (n_features, n_components) The loadings of `X`. y_loadings_ : ndarray of shape (n_targets, n_components) The loadings of `Y`. x_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `X`. y_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `Y`. coef_ : ndarray of shape (n_features, n_targets) The coefficients of the linear model such that `Y` is approximated as `Y = X @ coef_ + intercept_`. intercept_ : ndarray of shape (n_targets,) The intercepts of the linear model such that `Y` is approximated as `Y = X @ coef_ + intercept_`. .. versionadded:: 1.1 n_iter_ : list of shape (n_components,) Number of iterations of the power method, for each component. Empty if `algorithm='svd'`. n_features_in_ : int Number of features seen during :term:`fit`. feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- CCA : Canonical Correlation Analysis. PLSSVD : Partial Least Square SVD. Examples -------- >>> from sklearn.cross_decomposition import PLSCanonical >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> plsca = PLSCanonical(n_components=2) >>> plsca.fit(X, Y) PLSCanonical() >>> X_c, Y_c = plsca.transform(X, Y) r)rrQrTrr9r:)rprrRrSrc Zt||dd||||dS)Nrr8rr)rrrprrRrSrrs r5rzPLSCanonical.__init__sH %&  r7r rrrrr}rrrrrrrrs@r5rrs]]~$Cd&A#BDBBB+**""5))))              r7rceZdZUdZiejZeed<dD]Ze e d dddddfd Z xZ S) CCAap Canonical Correlation Analysis, also known as "Mode B" PLS. Read more in the :ref:`User Guide `. Parameters ---------- n_components : int, default=2 Number of components to keep. Should be in `[1, min(n_samples, n_features, n_targets)]`. scale : bool, default=True Whether to scale `X` and `Y`. max_iter : int, default=500 The maximum number of iterations of the power method. tol : float, default=1e-06 The tolerance used as convergence criteria in the power method: the algorithm stops whenever the squared norm of `u_i - u_{i-1}` is less than `tol`, where `u` corresponds to the left singular vector. copy : bool, default=True Whether to copy `X` and `Y` in fit before applying centering, and potentially scaling. If False, these operations will be done inplace, modifying both arrays. Attributes ---------- x_weights_ : ndarray of shape (n_features, n_components) The left singular vectors of the cross-covariance matrices of each iteration. y_weights_ : ndarray of shape (n_targets, n_components) The right singular vectors of the cross-covariance matrices of each iteration. x_loadings_ : ndarray of shape (n_features, n_components) The loadings of `X`. y_loadings_ : ndarray of shape (n_targets, n_components) The loadings of `Y`. x_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `X`. y_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `Y`. coef_ : ndarray of shape (n_features, n_targets) The coefficients of the linear model such that `Y` is approximated as `Y = X @ coef_ + intercept_`. intercept_ : ndarray of shape (n_targets,) The intercepts of the linear model such that `Y` is approximated as `Y = X @ coef_ + intercept_`. .. versionadded:: 1.1 n_iter_ : list of shape (n_components,) Number of iterations of the power method, for each component. n_features_in_ : int Number of features seen during :term:`fit`. feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- PLSCanonical : Partial Least Squares transformer and regressor. PLSSVD : Partial Least Square SVD. Examples -------- >>> from sklearn.cross_decomposition import CCA >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [3.,5.,4.]] >>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> cca = CCA(n_components=1) >>> cca.fit(X, Y) CCA(n_components=1) >>> X_c, Y_c = cca.transform(X, Y) rrrTr9r:rc Zt||ddd|||dS)NrrErrrrs r5rz CCA.__init__ysH %&  r7rrrs@r5rrsUUn$Cd&A#BDBBB8**""5))))  '+cu4            r7rcpeZdZUdZeedddgdgdgdZeed<dd d d d Z d Z ddZ ddZ dS)raPartial Least Square SVD. This transformer simply performs a SVD on the cross-covariance matrix `X'Y`. It is able to project both the training data `X` and the targets `Y`. The training data `X` is projected on the left singular vectors, while the targets are projected on the right singular vectors. Read more in the :ref:`User Guide `. .. versionadded:: 0.8 Parameters ---------- n_components : int, default=2 The number of components to keep. Should be in `[1, min(n_samples, n_features, n_targets)]`. scale : bool, default=True Whether to scale `X` and `Y`. copy : bool, default=True Whether to copy `X` and `Y` in fit before applying centering, and potentially scaling. If `False`, these operations will be done inplace, modifying both arrays. Attributes ---------- x_weights_ : ndarray of shape (n_features, n_components) The left singular vectors of the SVD of the cross-covariance matrix. Used to project `X` in :meth:`transform`. y_weights_ : ndarray of (n_targets, n_components) The right singular vectors of the SVD of the cross-covariance matrix. Used to project `X` in :meth:`transform`. n_features_in_ : int Number of features seen during :term:`fit`. feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- PLSCanonical : Partial Least Squares transformer and regressor. CCA : Canonical Correlation Analysis. Examples -------- >>> import numpy as np >>> from sklearn.cross_decomposition import PLSSVD >>> X = np.array([[0., 0., 1.], ... [1., 0., 0.], ... [2., 2., 2.], ... [2., 5., 4.]]) >>> Y = np.array([[0.1, -0.2], ... [0.9, 1.1], ... [6.2, 5.9], ... [11.9, 12.3]]) >>> pls = PLSSVD(n_components=2).fit(X, Y) >>> X_c, Y_c = pls.transform(X, Y) >>> X_c.shape, Y_c.shape ((4, 2), (4, 2)) rFNr~rrrrprrrT)rprc0||_||_||_dSr=r)rrrprs r5rzPLSSVD.__init__s(  r7c>|t||||tj|jd}t |dtj|jd}|jdkr|dd}|j }t|j d|j d|j d}||krtd |d |d t|||j\}}|_|_|_|_tj|j|}t+|d \}}}|d d d |f}|d |}t-||\}}|j} ||_| |_|jj d|_|S)aJFit model to data. Parameters ---------- X : array-like of shape (n_samples, n_features) Training samples. Y : array-like of shape (n_samples,) or (n_samples, n_targets) Targets. Returns ------- self : object Fitted estimator. rrrPFrrFrrrrrrbN)rrrr%rrrrrrrrLrrurprrrrr,rHrrrrr) rrOrPrrrcrdr/rfVs r5rz PLSSVD.fits 1%%%    RZdiA     #RZdi5    6Q;; "a  A ( qwqz171:qwqzBB * * *F1AFF#FFF  FV q$*F F B1dlDL$+t{ F13NNq...1b aaa,    B2 D#4Q7 r7ct|||tjd}||jz |jz }tj||j}|nt|ddtj}|j dkr| dd}||j z |j z }tj||j }||fS|S)a  Apply the dimensionality reduction. Parameters ---------- X : array-like of shape (n_samples, n_features) Samples to be transformed. Y : array-like of shape (n_samples,) or (n_samples, n_targets), default=None Targets. Returns ------- x_scores : array-like or tuple of array-like The transformed data `X_transformed` if `Y is not None`, `(X_transformed, Y_transformed)` otherwise. F)r"rNrP)rrr"rFr)rrr%rrrr,rrrrrrr)rrOrPXrrYrrs r5rzPLSSVD.transforms&    5  A A$,$+ -6"do.. =A#bjQQQAv{{IIb!$$dl"dk1Bvb$/22HX% %r7cV|||||S)aLearn and apply the dimensionality reduction. Parameters ---------- X : array-like of shape (n_samples, n_features) Training samples. y : array-like of shape (n_samples,) or (n_samples, n_targets), default=None Targets. Returns ------- out : array-like or tuple of array-like The transformed data `X_transformed` if `Y is not None`, `(X_transformed, Y_transformed)` otherwise. rrs r5rzPLSSVD.fit_transform/rr7rr=) rrrrrrrrrrrrrrr7r5rrsAAH"(AtFCCCD $$D 4 666p@......r7r)r8r9r:Fr)/rnumbersrrrMabcrrnumpyr% scipy.linalgrbaser r r r r utilsrr utils.fixesrr utils.extmathrutils.validationrrutils._param_validationrr exceptionsr__all__rrr6r`rgrur{r}rrrrrr7r5rs#"""""""''''''''BBBBBBBBBB######22222288888888$$$$$$''''''$$$$$$<<<<<<<<::::::::++++++ 5 5 5u%%%%+******""""""<<<&=B8(8(8(8(v....4c9c9c9c9c9# c9c9c9c9L QQQQQDQQQh     4   Dh h h h h $h h h Vy.y.y.y.y. ,.> y.y.y.y.y.r7