U 9%ed @sdZddlZddlmZddlmZmZddlZddl m Z ddl m Z ddl mZmZmZdd lmZdd lmZmZdd lmZmZmZmZdd lmZmZd dlmZm Z m!Z!dZ"d*ddZ#d+ddZ$edgej%geed ddddgeedddddgdedhgdgdgdgddddddddddddZ&dddddddd d!d"Z'Gd#d$d$eeeZ(d,d&d'Z)Gd(d)d)eeZ*dS)-z'Orthogonal matching pursuit algorithms N)sqrt)IntegralReal)linalg)get_lapack_funcs)MultiOutputMixinRegressorMixin _fit_context)check_cv)as_float_array check_array)HiddenInterval StrOptionsvalidate_params)Paralleldelayed) LinearModel_deprecate_normalize_pre_fitzOrthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met.TFcCs |r|d}n t|}t|jj}td|f\}}td|f\} t |j |} |} t d} d} t |j d}|dk r|j dn|}tj ||f|jd}|rt|}ttt |j | }|| ks| |d|krtjttdd q| dkrt |ddd| fj |dd|f|| d| f<tj|d| d| f|| d| fddd d d ||| d| fd}t|dd|fd|}||krtjttdd qt||| | f<nt|dd|f|d <||j | |j |\|j | <|j |<| || | | | <| |<|||| || <||<| d7} | |d| d| f| d| d d d\} }|r| |d| | df<|t |ddd| f| } |dk r|| d|krҐqq| |krqq|r | |d| |ddd| f| fS| |d| | fSdS)aOrthogonal Matching Pursuit step using the Cholesky decomposition. Parameters ---------- X : ndarray of shape (n_samples, n_features) Input dictionary. Columns are assumed to have unit norm. y : ndarray of shape (n_samples,) Input targets. n_nonzero_coefs : int Targeted number of non-zero elements. tol : float, default=None Targeted squared error, if not None overrides n_nonzero_coefs. copy_X : bool, default=True Whether the design matrix X must be copied by the algorithm. A false value is only helpful if X is already Fortran-ordered, otherwise a copy is made anyway. return_path : bool, default=False Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation. Returns ------- gamma : ndarray of shape (n_nonzero_coefs,) Non-zero elements of the solution. idx : ndarray of shape (n_nonzero_coefs,) Indices of the positions of the elements in gamma within the solution vector. coef : ndarray of shape (n_features, n_nonzero_coefs) The first k values of column k correspond to the coefficient value for the active features at that step. The lower left triangle contains garbage. Only returned if ``return_path=True``. n_active : int Number of active features at convergence. Fnrm2swappotrsrrNdtyper stacklevelTFZtranslower overwrite_bZ check_finiterrr#r$)copynpasfortranarrayfinforepsrget_blas_funcsrdotTemptyarangeshape empty_likeargmaxabswarningswarn prematureRuntimeWarningsolve_triangularZnormr)Xyn_nonzero_coefstolcopy_X return_path min_floatrrralphaZresidualgamman_activeindices max_featuresLcoefslamvLkk_rLX/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/sklearn/linear_model/_omp.py _cholesky_ompsp+     6 &   $rNcCs:|r|dnt|}|s$|jjs,|}t|jj}t d|f\} } t d|f\} t t |} |} |}d}t d}d}|dk rt |n|}tj ||f|jd}d|d<|rt|}tt| }||ks| |d |krtjttd d q|dkr||d|f||d|f<tj|d|d|f||d|fdd d dd| ||d|fd }|||f|}||krtjttd d qt||||f<nt|||f|d<| ||||\||<||<| |j||j|\|j|<|j|<| || || |<| |<||||||<||<|d 7}| |d|d|f|d|d dd\}}|r||d||d f<t|ddd|f|}||} |dk r||7}t||d|}||8}t||krqq||krqq|r$|| d||ddd|f|fS|| d||fSdS)aOrthogonal Matching Pursuit step on a precomputed Gram matrix. This function uses the Cholesky decomposition method. Parameters ---------- Gram : ndarray of shape (n_features, n_features) Gram matrix of the input data matrix. Xy : ndarray of shape (n_features,) Input targets. n_nonzero_coefs : int Targeted number of non-zero elements. tol_0 : float, default=None Squared norm of y, required if tol is not None. tol : float, default=None Targeted squared error, if not None overrides n_nonzero_coefs. copy_Gram : bool, default=True Whether the gram matrix must be copied by the algorithm. A false value is only helpful if it is already Fortran-ordered, otherwise a copy is made anyway. copy_Xy : bool, default=True Whether the covariance vector Xy must be copied by the algorithm. If False, it may be overwritten. return_path : bool, default=False Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation. Returns ------- gamma : ndarray of shape (n_nonzero_coefs,) Non-zero elements of the solution. idx : ndarray of shape (n_nonzero_coefs,) Indices of the positions of the elements in gamma within the solution vector. coefs : ndarray of shape (n_features, n_nonzero_coefs) The first k values of column k correspond to the coefficient value for the active features at that step. The lower left triangle contains garbage. Only returned if ``return_path=True``. n_active : int Number of active features at convergence. rrrrNrg?r%rr rTFr"r&)r'r(r)flags writeabler*rr+rr,rr0lenr/r2r3r4r5r6r7r8r9rr.r-inner)GramXyr<Ztol_0r= copy_Gramcopy_Xyr?r@rrrrDrAZtol_currdeltarBrCrErFrGrHrIrJrKbetarLrLrM _gram_omps=     &   $rZz array-likeleftclosedbooleanauto)r:r;r<r= precomputer>r? return_n_iterZprefer_skip_nested_validation)r<r=r`r>r?rac CsNt|d|d}d}|jdkr(|dd}t|}|jddkrBd}|dkrj|dkrjttd|jdd}|dkr||jdkrtd |d kr|jd |jdk}|rt|j |}t |}t|j |} |dk rtj |d d d } nd} t || ||| |d|dS|r2t |jd|jd|jdf} nt |jd|jdf} g} t|jdD]} t||dd| f||||d}|r|\}}}}| dddddt|f} t|j D]0\}}|d|d| |d|d| |f<qn|\}}}|| || f<| |q^|jddkr,| d } |r@t| | fSt| SdS)a1 Orthogonal Matching Pursuit (OMP). Solves n_targets Orthogonal Matching Pursuit problems. An instance of the problem has the form: When parametrized by the number of non-zero coefficients using `n_nonzero_coefs`: argmin ||y - X\gamma||^2 subject to ||\gamma||_0 <= n_{nonzero coefs} When parametrized by error using the parameter `tol`: argmin ||\gamma||_0 subject to ||y - X\gamma||^2 <= tol Read more in the :ref:`User Guide `. Parameters ---------- X : array-like of shape (n_samples, n_features) Input data. Columns are assumed to have unit norm. y : ndarray of shape (n_samples,) or (n_samples, n_targets) Input targets. n_nonzero_coefs : int, default=None Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features. tol : float, default=None Maximum squared norm of the residual. If not None, overrides n_nonzero_coefs. precompute : 'auto' or bool, default=False Whether to perform precomputations. Improves performance when n_targets or n_samples is very large. copy_X : bool, default=True Whether the design matrix X must be copied by the algorithm. A false value is only helpful if X is already Fortran-ordered, otherwise a copy is made anyway. return_path : bool, default=False Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation. return_n_iter : bool, default=False Whether or not to return the number of iterations. Returns ------- coef : ndarray of shape (n_features,) or (n_features, n_targets) Coefficients of the OMP solution. If `return_path=True`, this contains the whole coefficient path. In this case its shape is (n_features, n_features) or (n_features, n_targets, n_features) and iterating over the last axis generates coefficients in increasing order of active features. n_iters : array-like or int Number of active features across every target. Returned only if `return_n_iter` is set to True. See Also -------- OrthogonalMatchingPursuit : Orthogonal Matching Pursuit model. orthogonal_mp_gram : Solve OMP problems using Gram matrix and the product X.T * y. lars_path : Compute Least Angle Regression or Lasso path using LARS algorithm. sklearn.decomposition.sparse_encode : Sparse coding. Notes ----- Orthogonal matching pursuit was introduced in S. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415. (https://www.di.ens.fr/~mallat/papiers/MallatPursuit93.pdf) This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad, M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit Technical Report - CS Technion, April 2008. https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf rorderr'FrTN皙?>The number of atoms cannot be more than the number of featuresr_rrZaxis)r<r= norms_squaredrVrWr?)r>r?)r ndimZreshaper1maxint ValueErrorr(r-r.r)sumorthogonal_mp_gramzerosrangerNrR enumerateappendsqueeze)r:r;r<r=r`r>r?raGrUricoefn_iterskoutrKidxrGn_iterrCxrLrLrM orthogonal_mpste    $ ,  r})r<r=rirVrWr?rac CsXt|d|d}t|}|jdkr4|jddkr4d}|jdkr^|ddtjf}|dk r^|g}|sj|jjsr|}|dkr|dkrt dt |}|dk r|dkrt d|dk r|dkrt d |dkr|dkrt d |dkr|t |krt d |r"tj t ||jdt |f|j d } ntj t ||jdf|j d } g} t|jdD]} t||dd| f||dk r||| nd||d |d} |r| \} }}}| dddddt |f} t|jD]0\}}|d|d| |d|d| |f<qn| \}}}|| || f<| |qR|jddkr6| d} |rJt| | fSt| SdS)a Gram Orthogonal Matching Pursuit (OMP). Solves n_targets Orthogonal Matching Pursuit problems using only the Gram matrix X.T * X and the product X.T * y. Read more in the :ref:`User Guide `. Parameters ---------- Gram : ndarray of shape (n_features, n_features) Gram matrix of the input data: X.T * X. Xy : ndarray of shape (n_features,) or (n_features, n_targets) Input targets multiplied by X: X.T * y. n_nonzero_coefs : int, default=None Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features. tol : float, default=None Maximum squared norm of the residual. If not `None`, overrides `n_nonzero_coefs`. norms_squared : array-like of shape (n_targets,), default=None Squared L2 norms of the lines of y. Required if tol is not None. copy_Gram : bool, default=True Whether the gram matrix must be copied by the algorithm. A false value is only helpful if it is already Fortran-ordered, otherwise a copy is made anyway. copy_Xy : bool, default=True Whether the covariance vector Xy must be copied by the algorithm. If False, it may be overwritten. return_path : bool, default=False Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation. return_n_iter : bool, default=False Whether or not to return the number of iterations. Returns ------- coef : ndarray of shape (n_features,) or (n_features, n_targets) Coefficients of the OMP solution. If `return_path=True`, this contains the whole coefficient path. In this case its shape is (n_features, n_features) or (n_features, n_targets, n_features) and iterating over the last axis yields coefficients in increasing order of active features. n_iters : array-like or int Number of active features across every target. Returned only if `return_n_iter` is set to True. See Also -------- OrthogonalMatchingPursuit : Orthogonal Matching Pursuit model (OMP). orthogonal_mp : Solves n_targets Orthogonal Matching Pursuit problems. lars_path : Compute Least Angle Regression or Lasso path using LARS algorithm. sklearn.decomposition.sparse_encode : Generic sparse coding. Each column of the result is the solution to a Lasso problem. Notes ----- Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415. (https://www.di.ens.fr/~mallat/papiers/MallatPursuit93.pdf) This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad, M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit Technical Report - CS Technion, April 2008. https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf rrcrTNrfzSGram OMP needs the precomputed norms in order to evaluate the error sum of squares.rzEpsilon cannot be negativez$The number of atoms must be positivergrF)rVrWr?)r r(Zasarrayrjr1newaxisrPrQr'rlrRrmrprrqrZrrr.rsrt)rTrUr<r=rirVrWr?rarvrwrxryrKrzrGr{rCr|rLrLrMroshX   &  ,  roc@seZdZUdZeedddddgeedddddgdgdeedhged hdgd Z e e d <ddd dd d d dZ e d dddZdS)OrthogonalMatchingPursuitaOrthogonal Matching Pursuit model (OMP). Read more in the :ref:`User Guide `. Parameters ---------- n_nonzero_coefs : int, default=None Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features. tol : float, default=None Maximum squared norm of the residual. If not None, overrides n_nonzero_coefs. fit_intercept : bool, default=True Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered). normalize : bool, default=False This parameter is ignored when ``fit_intercept`` is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use :class:`~sklearn.preprocessing.StandardScaler` before calling ``fit`` on an estimator with ``normalize=False``. .. versionchanged:: 1.2 default changed from True to False in 1.2. .. deprecated:: 1.2 ``normalize`` was deprecated in version 1.2 and will be removed in 1.4. precompute : 'auto' or bool, default='auto' Whether to use a precomputed Gram and Xy matrix to speed up calculations. Improves performance when :term:`n_targets` or :term:`n_samples` is very large. Note that if you already have such matrices, you can pass them directly to the fit method. Attributes ---------- coef_ : ndarray of shape (n_features,) or (n_targets, n_features) Parameter vector (w in the formula). intercept_ : float or ndarray of shape (n_targets,) Independent term in decision function. n_iter_ : int or array-like Number of active features across every target. n_nonzero_coefs_ : int The number of non-zero coefficients in the solution. If `n_nonzero_coefs` is None and `tol` is None this value is either set to 10% of `n_features` or 1, whichever is greater. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 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 -------- orthogonal_mp : Solves n_targets Orthogonal Matching Pursuit problems. orthogonal_mp_gram : Solves n_targets Orthogonal Matching Pursuit problems using only the Gram matrix X.T * X and the product X.T * y. lars_path : Compute Least Angle Regression or Lasso path using LARS algorithm. Lars : Least Angle Regression model a.k.a. LAR. LassoLars : Lasso model fit with Least Angle Regression a.k.a. Lars. sklearn.decomposition.sparse_encode : Generic sparse coding. Each column of the result is the solution to a Lasso problem. OrthogonalMatchingPursuitCV : Cross-validated Orthogonal Matching Pursuit model (OMP). Notes ----- Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415. (https://www.di.ens.fr/~mallat/papiers/MallatPursuit93.pdf) This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad, M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit Technical Report - CS Technion, April 2008. https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf Examples -------- >>> from sklearn.linear_model import OrthogonalMatchingPursuit >>> from sklearn.datasets import make_regression >>> X, y = make_regression(noise=4, random_state=0) >>> reg = OrthogonalMatchingPursuit().fit(X, y) >>> reg.score(X, y) 0.9991... >>> reg.predict(X[:1,]) array([-78.3854...]) rNr[r\rr^ deprecatedr_r<r= fit_intercept normalizer`_parameter_constraintsTcCs"||_||_||_||_||_dSNr)selfr<r=rrr`rLrLrM__init__s z"OrthogonalMatchingPursuit.__init__rbc Cs.t|j|jjd}|j||ddd\}}|jd}t||d|j||jdd\}}}}}}} |j dkrv|ddt j f}|j dkr|j dkrttd|d|_n|j |_|dkrt|||j|j dddd \} |_nB|j dk rt j|d d d nd} t|| |j|j | dddd \} |_| j|_|||||S)aFit the model using X, y as training data. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) or (n_samples, n_targets) Target values. Will be cast to X's dtype if necessary. Returns ------- self : object Returns an instance of self. Zestimator_nameT)Z multi_output y_numericrNr'rfF)r<r=r`r>rarrrh)rUr<r=rirVrWra)rr __class____name___validate_datar1rr`rrjr(r~r<r=rkrln_nonzero_coefs_r}n_iter_rnror.coef_Z_set_intercept) rr:r; _normalizeZ n_featuresZX_offsetZy_offsetZX_scalerTrUrZnorms_sqrLrLrMfitsX    zOrthogonalMatchingPursuit.fit)r __module__ __qualname____doc__rrrrrrdict__annotations__rr rrLrLrLrMr[s g   rdc Cs(|r$|}|}|}|}|rx|jdd}||8}||8}|jdd} t|dd}|| 8}t|dd}|| 8}|rttj|ddd} t| } |dd| f| | <t|||ddddd} | jd kr| ddtj f} |r| | | | ddtj f<t | j |j |S) aCompute the residues on left-out data for a full LARS path. Parameters ---------- X_train : ndarray of shape (n_samples, n_features) The data to fit the LARS on. y_train : ndarray of shape (n_samples) The target variable to fit LARS on. X_test : ndarray of shape (n_samples, n_features) The data to compute the residues on. y_test : ndarray of shape (n_samples) The target variable to compute the residues on. copy : bool, default=True Whether X_train, X_test, y_train and y_test should be copied. If False, they may be overwritten. fit_intercept : bool, default=True Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered). normalize : bool, default=False This parameter is ignored when ``fit_intercept`` is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use :class:`~sklearn.preprocessing.StandardScaler` before calling ``fit`` on an estimator with ``normalize=False``. .. versionchanged:: 1.2 default changed from True to False in 1.2. .. deprecated:: 1.2 ``normalize`` was deprecated in version 1.2 and will be removed in 1.4. max_iter : int, default=100 Maximum numbers of iterations to perform, therefore maximum features to include. 100 by default. Returns ------- residues : ndarray of shape (n_samples, max_features) Residues of the prediction on the test data. rrhFrrNT)r<r=r`r>r?r) r'meanr r(rrnZ flatnonzeror}rjr~r-r.) ZX_trainZy_trainZX_testZy_testr'rrmax_iterZX_meanZy_meanZnormsZnonzerosrGrLrLrM_omp_path_residuess@;      "rc @seZdZUdZdgdgdeedhgeedddddgdgedgd gd Ze e d <d d ddddd d ddZ e d dddZ dS)OrthogonalMatchingPursuitCVaCross-validated Orthogonal Matching Pursuit model (OMP). See glossary entry for :term:`cross-validation estimator`. Read more in the :ref:`User Guide `. Parameters ---------- copy : bool, default=True Whether the design matrix X must be copied by the algorithm. A false value is only helpful if X is already Fortran-ordered, otherwise a copy is made anyway. fit_intercept : bool, default=True Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered). normalize : bool, default=False This parameter is ignored when ``fit_intercept`` is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use :class:`~sklearn.preprocessing.StandardScaler` before calling ``fit`` on an estimator with ``normalize=False``. .. versionchanged:: 1.2 default changed from True to False in 1.2. .. deprecated:: 1.2 ``normalize`` was deprecated in version 1.2 and will be removed in 1.4. max_iter : int, default=None Maximum numbers of iterations to perform, therefore maximum features to include. 10% of ``n_features`` but at least 5 if available. cv : int, cross-validation generator or iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross-validation, - integer, to specify the number of folds. - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs, :class:`~sklearn.model_selection.KFold` is used. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. .. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold. n_jobs : int, default=None Number of CPUs to use during the cross validation. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. verbose : bool or int, default=False Sets the verbosity amount. Attributes ---------- intercept_ : float or ndarray of shape (n_targets,) Independent term in decision function. coef_ : ndarray of shape (n_features,) or (n_targets, n_features) Parameter vector (w in the problem formulation). n_nonzero_coefs_ : int Estimated number of non-zero coefficients giving the best mean squared error over the cross-validation folds. n_iter_ : int or array-like Number of active features across every target for the model refit with the best hyperparameters got by cross-validating across all folds. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 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 -------- orthogonal_mp : Solves n_targets Orthogonal Matching Pursuit problems. orthogonal_mp_gram : Solves n_targets Orthogonal Matching Pursuit problems using only the Gram matrix X.T * X and the product X.T * y. lars_path : Compute Least Angle Regression or Lasso path using LARS algorithm. Lars : Least Angle Regression model a.k.a. LAR. LassoLars : Lasso model fit with Least Angle Regression a.k.a. Lars. OrthogonalMatchingPursuit : Orthogonal Matching Pursuit model (OMP). LarsCV : Cross-validated Least Angle Regression model. LassoLarsCV : Cross-validated Lasso model fit with Least Angle Regression. sklearn.decomposition.sparse_encode : Generic sparse coding. Each column of the result is the solution to a Lasso problem. Notes ----- In `fit`, once the optimal number of non-zero coefficients is found through cross-validation, the model is fit again using the entire training set. Examples -------- >>> from sklearn.linear_model import OrthogonalMatchingPursuitCV >>> from sklearn.datasets import make_regression >>> X, y = make_regression(n_features=100, n_informative=10, ... noise=4, random_state=0) >>> reg = OrthogonalMatchingPursuitCV(cv=5).fit(X, y) >>> reg.score(X, y) 0.9991... >>> reg.n_nonzero_coefs_ 10 >>> reg.predict(X[:1,]) array([-78.3854...]) r^rrNr[r\Z cv_objectverboser'rrrcvn_jobsrrTFcCs.||_||_||_||_||_||_||_dSrr)rr'rrrrrrrLrLrMrs z$OrthogonalMatchingPursuitCV.__init__rbc sDtjjjdjddd\tdddtjdd}jsnt t t dj d d j d njt jjd fd d |D}t dd |Dtfdd|D}t|jddd }|_t|jd}t tjdtd|W5QRX|j_|j_|j_S)a~Fit the model using X, y as training data. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) Target values. Will be cast to X's dtype if necessary. Returns ------- self : object Returns an instance of self. rTr)rZensure_min_featuresF)r'Zforce_all_finite) classifierrfr)rrc 3s@|]8\}}tt||||jjVqdSr)rrr'r).0traintest)r:rrrr;rLrM 2s z2OrthogonalMatchingPursuitCV.fit..css|]}|jdVqdS)rN)r1rfoldrLrLrMr@scs$g|]}|ddjddqS)Nrrrh)rr)min_early_stoprLrM Bsz3OrthogonalMatchingPursuitCV.fit..rrh)r<rrignore)category) rrrrrr r rrminrkrlr1rrrsplitr(arrayZargminrrrrr5catch_warningsfilterwarnings FutureWarningrrZ intercept_r)rr:r;rZcv_pathsZ mse_foldsZbest_n_nonzero_coefsZomprL)r:rrrrr;rMrs@&  zOrthogonalMatchingPursuitCV.fit)rrrrrrrrrrrrr rrLrLrLrMr|s& |  r)NTF)NNTTF)TTFr)+rr5mathrnumbersrrnumpyr(ZscipyrZscipy.linalg.lapackrbaserr r Zmodel_selectionr utilsr r Zutils._param_validationrrrrZutils.parallelrr_baserrrr7rNrZZndarrayr}rorrrrLrLrLrMsv     x     F a