class sklearn.linear_model.OrthogonalMatchingPursuitCV(*, copy=True, fit_intercept=True, normalize=True, max_iter=None, cv=None, n_jobs=None, verbose=False) [source]
 
Cross-validated Orthogonal Matching Pursuit model (OMP). See glossary entry for cross-validation estimator. Read more in the User Guide.  Parameters 
 
copybool, 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_interceptbool, 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).  
normalizebool, default=True 

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.  
max_iterint, default=None 

Maximum numbers of iterations to perform, therefore maximum features to include. 10% of n_features but at least 5 if available.  
cvint, 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. 
CV splitter, An iterable yielding (train, test) splits as arrays of indices.  For integer/None inputs, KFold is used. Refer User Guide for the various cross-validation strategies that can be used here.  Changed in version 0.22: cv default value if None changed from 3-fold to 5-fold.   
n_jobsint, default=None 

Number of CPUs to use during the cross validation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.  
verbosebool 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.      See also  
orthogonal_mp


orthogonal_mp_gram


lars_path


Lars


LassoLars


OrthogonalMatchingPursuit


LarsCV


LassoLarsCV


sklearn.decomposition.sparse_encode

  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...])
 Methods  
fit(X, y) Fit the model using X, y as training data.  
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 \(R^2\) of the prediction.  
set_params(**params) Set the parameters of this estimator.    
fit(X, y) [source]
 
Fit the model using X, y as training data.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Training data.  
yarray-like of shape (n_samples,) 

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

returns an instance of self.     
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

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

Parameter names mapped to their values.     
  
predict(X) [source]
 
Predict using the linear model.  Parameters 
 
Xarray-like or sparse matrix, shape (n_samples, n_features) 

Samples.    Returns 
 
Carray, shape (n_samples,) 

Returns predicted values.     
  
score(X, y, sample_weight=None) [source]
 
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) 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 
 
Xarray-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.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs) 

True values for X.  
sample_weightarray-like of shape (n_samples,), default=None 

Sample weights.    Returns 
 
scorefloat 

\(R^2\) of self.predict(X) wrt. 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) [source]
 
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 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.