class_docstr: Cross-validated Orthogonal Matching Pursuit model (OMP).
  
  See glossary entry for :term:`cross-validation estimator`.
  
  Read more in the :ref:`User Guide <omp>`.
  
  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 <cross_validation>` 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 <n_jobs>`
      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...])
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docstr: 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.
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code: def fit(self, X, y):
      """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.
      """
      _normalize = _deprecate_normalize(
          self.normalize, estimator_name=self.__class__.__name__
      )
  
      X, y = self._validate_data(
          X, y, y_numeric=True, ensure_min_features=2
      )
      X = as_float_array(X, copy=False, force_all_finite=False)
      cv = check_cv(self.cv, classifier=False)
      max_iter = (
          min(max(int(0.1 * X.shape[1]), 5), X.shape[1])
          if not self.max_iter
          else self.max_iter
      )
      cv_paths = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
          delayed(_omp_path_residues)(
              X[train],
              y[train],
              X[test],
              y[test],
              self.copy,
              self.fit_intercept,
              _normalize,
              max_iter,
          )
          for train, test in cv.split(X)
      )
  
      min_early_stop = min(fold.shape[0] for fold in cv_paths)
      mse_folds = np.array(
          [
              (fold[:min_early_stop] ** 2).mean(axis=1)
              for fold in cv_paths
          ]
      )
      best_n_nonzero_coefs = np.argmin(mse_folds.mean(axis=0)) + 1
      self.n_nonzero_coefs_ = best_n_nonzero_coefs
      omp = OrthogonalMatchingPursuit(
          n_nonzero_coefs=best_n_nonzero_coefs,
          fit_intercept=self.fit_intercept,
          normalize=_normalize,
      )
  
      # avoid duplicating warning for deprecated normalize
      with warnings.catch_warnings():
          warnings.filterwarnings("ignore", category=FutureWarning)
          omp.fit(X, y)
  
      self.coef_ = omp.coef_
      self.intercept_ = omp.intercept_
      self.n_iter_ = omp.n_iter_
      return self
  
imports: import warnings
  from math import sqrt
  from numbers import Integral, Real
  import numpy
  from scipy import linalg
  from scipy.linalg.lapack import get_lapack_funcs
  from ..base import MultiOutputMixin, RegressorMixin, _fit_context
  from ..model_selection import check_cv
  from ..utils import as_float_array, check_array
  from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
  from ..utils.parallel import Parallel, delayed
  from ._base import LinearModel, _deprecate_normalize, _pre_fit
label_desc: Use image node_id 2 for calling the OrthogonalMatchingPursuitCV obj's underlying member method code with example usage: obj.fit(X, y) and returns: self