class_docstr: Orthogonal Matching Pursuit model (OMP).
  
  Read more in the :ref:`User Guide <omp>`.
  
  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...])
class_docstr_tok: ["Orthogonal","Matching","Pursuit","model","(","OMP",")",".","Read","more","in","the",":","ref",":","`","User","Guide","<","omp",">","`",".","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","...","]",")"]
code: def __init__(
      self,
      *,
      n_nonzero_coefs=None,
      tol=None,
      fit_intercept=True,
      normalize="deprecated",
      precompute="auto",
  ):
      self.n_nonzero_coefs = n_nonzero_coefs
      self.tol = tol
      self.fit_intercept = fit_intercept
      self.normalize = normalize
      self.precompute = precompute
  
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 1 to create a new OrthogonalMatchingPursuit object from inherited base classes: MultiOutputMixin, RegressorMixin, LinearModel with example: obj = OrthogonalMatchingPursuit()