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# `sklearn.linear_model`.OrthogonalMatchingPursuit

class sklearn.linear_model.OrthogonalMatchingPursuit(*, n_nonzero_coefs=None, tol=None, fit_intercept=True, normalize='deprecated', precompute='auto')

  Orthogonal Matching Pursuit model (OMP).

  Read more in the 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 `StandardScaler` before calling `fit` on an estimator with `normalize=False`.

      Changed in version 1.2: default changed from True to False in 1.2.

      Deprecated since version 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 n_targets or 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 fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 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...])

  Methods

  `fit`(X, y) | Fit the model using X, y as training data.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `predict`(X) | Predict using the linear model.
  `score`(X, y[, sample_weight]) | Return the coefficient of determination of the prediction.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.

  fit(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,) or (n_samples, n_targets)

        Target values. Will be cast to X’s dtype if necessary.

    Returns:
      self object

        Returns an instance of self.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  predict(X)

    Predict using the linear model.

    Parameters:
      X array-like or sparse matrix, shape (n_samples, n_features)

        Samples.

    Returns:
      C array, shape (n_samples,)

        Returns predicted values.

  score(X, y, sample_weight=None)

    Return the coefficient of determination of the prediction.

    The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares `((y_true - y_pred)** 2).sum()` and \(v\) is the total sum of squares `((y_true - y_true.mean()) ** 2).sum()`. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of `y`, disregarding the input features, would get a \(R^2\) score of 0.0.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape `(n_samples, n_samples_fitted)`, where `n_samples_fitted` is the number of samples used in the fitting for the estimator.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        True values for `X`.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights.

    Returns:
      score float

        \(R^2\) of `self.predict(X)` w.r.t. `y`.

    Notes

    The \(R^2\) score used when calling `score` on a regressor uses `multioutput='uniform_average'` from version 0.23 to keep consistent with default value of `r2_score`. This influences the `score` method of all the multioutput regressors (except for `MultiOutputRegressor`).

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `<component>__<parameter>` so that it’s possible to update each component of a nested object.

    Parameters:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → OrthogonalMatchingPursuit

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `score` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `score`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

## Examples using `sklearn.linear_model.OrthogonalMatchingPursuit`

Orthogonal Matching Pursuit

Orthogonal Matching Pursuit