a h @sdZddlZddlmZddlmZmZddlZddl m Z ddl m Z ddl mZmZmZdd lmZdd lmZmZmZdd lmZmZmZdd lmZmZmZmZm Z dd l!m"Z"m#Z#ddl$m%Z%ddl&m'Z'm(Z(dZ)d.ddZ*d/ddZ+edgej,geedddddgeedddddgdedhgdgdgdgddddddddddd d!Z-edgdgeeddd"ddgeedddddgddgdgdgdgdgd# dddddddddd$d%d&Z.Gd'd(d(eee'Z/d0d*d+Z0Gd,d-d-ee'Z1dS)1z&Orthogonal matching pursuit algorithmsN)sqrt)IntegralReal)linalg)get_lapack_funcs)MultiOutputMixinRegressorMixin _fit_context)check_cv)Bunchas_float_array check_array)Interval StrOptionsvalidate_params)MetadataRouter MethodMapping_raise_for_params_routing_enabledprocess_routing)Paralleldelayed) validate_data) LinearModel_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}|dur|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| } |dur|| 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))copynpasfortranarrayfinfor$epsrget_blas_funcsrdotTemptyarangeshape empty_likeargmaxabswarningswarn prematureRuntimeWarningsolve_triangularZnormr)Xyn_nonzero_coefstolcopy_X return_path min_floatrr r"alphaZresidualgamman_activeindices max_featuresLcoefslamvLkk_rQU/var/www/html/assistant/venv/lib/python3.9/site-packages/sklearn/linear_model/_omp.py _cholesky_omp$sj+     6 &  $rScCs:|r|dnt|}|s$|jjs,|}t|jj}t d|f\} } t d|f\} t t |} |} |}d}t d}d}|durt |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|}||} |dur||7}t||d|}||8}t||krqq||krqq|r$|| d||ddd|f|fS|| d||fSdS)aOrthogonal Matching Pursuit step on a precomputed Gram matrix. This function uses the Cholesky decomposition method. Parameters ---------- Gram : ndarray of shape (n_features, n_features) Gram matrix of the input data matrix. Xy : ndarray of shape (n_features,) Input targets. n_nonzero_coefs : int Targeted number of non-zero elements. tol_0 : float, default=None Squared norm of y, required if tol is not None. tol : float, default=None Targeted squared error, if not None overrides n_nonzero_coefs. copy_Gram : bool, default=True Whether the gram matrix must be copied by the algorithm. A false value is only helpful if it is already Fortran-ordered, otherwise a copy is made anyway. copy_Xy : bool, default=True Whether the covariance vector Xy must be copied by the algorithm. If False, it may be overwritten. return_path : bool, default=False Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation. Returns ------- gamma : ndarray of shape (n_nonzero_coefs,) Non-zero elements of the solution. idx : ndarray of shape (n_nonzero_coefs,) Indices of the positions of the elements in gamma within the solution vector. coefs : ndarray of shape (n_features, n_nonzero_coefs) The first k values of column k correspond to the coefficient value for the active features at that step. The lower left triangle contains garbage. Only returned if ``return_path=True``. n_active : int Number of active features at convergence. rrr!rNr#g?r*rr%rTFr'r+)r,r-r.flags writeabler/r$r0rr1rr5lenr4r7r8r9r:r;r<r=r>rr3r2inner)GramXyrAZtol_0rB copy_Gramcopy_XyrDrErr r"rIrFZtol_currdeltarGrHrJrKrLrMrNrOrPbetarQrQrR _gram_ompsz=     &  $r_z array-likeleftclosedbooleanauto)r?r@rArB precomputerCrD return_n_iterZprefer_skip_nested_validation)rArBrerCrDrfc CsNt|d|d}d}|jdkr(|dd}t|}|jddkrBd}|durj|durjttd|jdd}|dur||jdkrtd |d kr|jd |jdk}|rt|j |}t |}t|j |} |durtj |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)a^ 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 Examples -------- >>> from sklearn.datasets import make_regression >>> from sklearn.linear_model import orthogonal_mp >>> X, y = make_regression(noise=4, random_state=0) >>> coef = orthogonal_mp(X, y) >>> coef.shape (100,) >>> X[:1,] @ coef array([-78.68...]) rorderr,FrTN皙?>The number of atoms cannot be more than the number of featuresrdrrZaxis)rArB norms_squaredr[r\rD)rCrD)rndimZreshaper6maxint ValueErrorr-r2r3r.sumorthogonal_mp_gramzerosrangerSrW enumerateappendsqueeze)r?r@rArBrerCrDrfGrZrncoefn_iterskoutrPidxrLn_iterrHxrQrQrR orthogonal_mp"sjp    $ ,  rZneither) rYrZrArBrnr[r\rDrf)rArBrnr[r\rDrfc CsXt|d|d}t|}|jdkr4|jddkr4d}|jdkr^|ddtjf}|dur^|g}|sj|jjsr|}|dur|durt dt |}|dur|durt d|dur|dkrt d |dur|dkrt d |dur|t |krt d |r"tj t ||jdt |f|j d } ntj t ||jdf|j d } g} t|jdD]} t||dd| f||dur||| nd||d |d} |r| \} }}}| dddddt |f} t|jD]0\}}|d|d| |d|d| |f<qn| \}}}|| || f<| |qR|jddkr6| d} |rJt| | fSt| SdS)an 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 : array-like of shape (n_features, n_features) Gram matrix of the input data: `X.T * X`. Xy : array-like 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 : list 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 Examples -------- >>> from sklearn.datasets import make_regression >>> from sklearn.linear_model import orthogonal_mp_gram >>> X, y = make_regression(noise=4, random_state=0) >>> coef = orthogonal_mp_gram(X.T @ X, X.T @ y) >>> coef.shape (100,) >>> X[:1,] @ coef array([-78.68...]) rrhrTNrkzSGram OMP needs the precomputed norms in order to evaluate the error sum of squares.rzEpsilon cannot be negativez$The number of atoms must be positiverlr#F)r[r\rD)rr-Zasarrayror6newaxisrUrVr,rqrWrrrur$rvr_rwr3rxry)rYrZrArBrnr[r\rDrfr{r|r}r~rPrrLrrHrrQrQrRrtshq   &  ,  rtc@szeZdZUdZeedddddgeedddddgdgedhdgd Ze e d <ddd dd d d Z e d dddZ dS)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. Ignored if `tol` is set. When `None` and `tol` is also `None`, this value is either set to 10% of `n_features` or 1, whichever is greater. 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). 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 or None The number of non-zero coefficients in the solution or `None` when `tol` is set. 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`rarrcrdrArB fit_interceptre_parameter_constraintsTcCs||_||_||_||_dSNr)selfrArBrrerQrQrR__init__sz"OrthogonalMatchingPursuit.__init__rgc Cs,t|||ddd\}}|jd}t||d|j|jdd\}}}}}}}|jdkrb|ddtjf}|jdur|j durt t d|d|_ n|j durd|_ n|j|_ |durt |||j |j dddd\} |_nB|j durtj|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. T)Z multi_output y_numericrNr,rkF)rArBrerCrfrrrm)rZrArBrnr[r\rf)rr6rrerror-rrArBrprqn_nonzero_coefs_rn_iter_rsrtr3coef_Z_set_intercept) rr?r@Z n_featuresZX_offsetZy_offsetZX_scalerYrZrZnorms_sqrQrQrRfitsH     zOrthogonalMatchingPursuit.fit)__name__ __module__ __qualname____doc__rrrrrdict__annotations__rr rrQrQrQrRrs Z   rdc Cs|r$|}|}|}|}|rx|jdd}||8}||8}|jdd}t|dd}||8}t|dd}||8}t|||ddddd} | jdkr| ddtjf} t| j|j|S) a[Compute 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). 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. rrmFrNT)rArBrerCrDr) r,meanr rror-rr2r3) ZX_trainZy_trainZX_testZy_testr,rmax_iterZX_meanZy_meanrLrQrQrR_omp_path_residues3s4,     rc@szeZdZUdZdgdgeedddddgdgedgdgd Zeed <d d dddd d d dZ e d dddZ ddZ 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). 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_ np.int64(10) >>> reg.predict(X[:1,]) array([-78.3854...]) rcrNr`raZ cv_objectverboser,rrcvn_jobsrrTFcCs(||_||_||_||_||_||_dSrr)rr,rrrrrrQrQrRrs z$OrthogonalMatchingPursuitCV.__init__rgc  sLt|dtddd\tdddtjdd}trXtdfi|}nt}tid|_j st t t d j d d j d nj tjjd fd d|jfi|jjD}t dd|Dtfdd|D}t|jddd }|_t|jd} | j_| j_| 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,) Target values. Will be cast to X's dtype if necessary. **fit_params : dict Parameters to pass to the underlying splitter. .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`, which can be set by using ``sklearn.set_config(enable_metadata_routing=True)``. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- self : object Returns an instance of self. rTr)rZensure_min_featuresF)r,Zensure_all_finite) classifier)splitrkr)rrc 3s>|]6\}}tt||||jjVqdSr)rrr,r).0traintest)r?rrr@rQrR 1s z2OrthogonalMatchingPursuitCV.fit..css|]}|jdVqdS)rN)r6rfoldrQrQrRr>cs$g|]}|ddjddqS)Nrrrm)rr)min_early_stoprQrR @rz3OrthogonalMatchingPursuitCV.fit..rrm)rAr)rrr r rrrr splitterrminrprqr6rrrrr-arrayZargminrrrrrrZ intercept_r) rr?r@Z fit_paramsrZ routed_paramsZcv_pathsZ mse_foldsZbest_n_nonzero_coefsZomprQ)r?rrrr@rRrs>  &  zOrthogonalMatchingPursuitCV.fitcCs*t|jjdj|jtjdddd}|S)ajGet metadata routing of this object. Please check :ref:`User Guide ` on how the routing mechanism works. .. versionadded:: 1.4 Returns ------- routing : MetadataRouter A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. )ownerrr)ZcallerZcallee)rZmethod_mapping)r __class__raddrr)rZrouterrQrQrRget_metadata_routingNs z0OrthogonalMatchingPursuitCV.get_metadata_routing) rrrrrrrrrrr rrrQrQrQrRr~s$ n   Gr)NTF)NNTTF)TTr)2rr:mathrnumbersrrnumpyr-ZscipyrZscipy.linalg.lapackrbaserr r Zmodel_selectionr utilsr r rZutils._param_validationrrrZutils.metadata_routingrrrrrZutils.parallelrrZutils.validationrZ_baserrr<rSr_ZndarrayrrtrrrrQrQrQrRs      x    % %4 K