3.2.4.1.8. sklearn.linear_model.OrthogonalMatchingPursuitCV

class sklearn.linear_model.OrthogonalMatchingPursuitCV(copy=True, fit_intercept=True, normalize=True, max_iter=None, cv=’warn’, n_jobs=None, verbose=False)
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Crossvalidated Orthogonal Matching Pursuit model (OMP)
Read more in the User Guide.
Parameters: 

copy : bool, optional 
Whether the design matrix X must be copied by the algorithm. A false value is only helpful if X is already Fortranordered, otherwise a copy is made anyway. 
fit_intercept : boolean, optional 
whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). 
normalize : boolean, optional, 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 l2norm. If you wish to standardize, please use sklearn.preprocessing.StandardScaler before calling fit on an estimator with normalize=False . 
max_iter : integer, optional 
Maximum numbers of iterations to perform, therefore maximum features to include. 10% of n_features but at least 5 if available. 
cv : int, crossvalidation generator or an iterable, optional 
Determines the crossvalidation splitting strategy. Possible inputs for cv are:  None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs, KFold is used. Refer User Guide for the various crossvalidation strategies that can be used here. Changed in version 0.20: cv default value if None will change from 3fold to 5fold in v0.22. 
n_jobs : int or None, optional (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. 
verbose : boolean or integer, optional 
Sets the verbosity amount 
Attributes: 

intercept_ : float or array, shape (n_targets,) 
Independent term in decision function. 
coef_ : array, shape (n_features,) or (n_targets, n_features) 
Parameter vector (w in the problem formulation). 
n_nonzero_coefs_ : int 
Estimated number of nonzero coefficients giving the best mean squared error over the crossvalidation folds. 
n_iter_ : int or arraylike 
Number of active features across every target for the model refit with the best hyperparameters got by crossvalidating across all folds. 
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])  Returns the coefficient of determination R^2 of the prediction. 
set_params (**params)  Set the parameters of this estimator. 

__init__(copy=True, fit_intercept=True, normalize=True, max_iter=None, cv=’warn’, n_jobs=None, verbose=False)
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fit(X, y)
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Fit the model using X, y as training data.
Parameters: 

X : arraylike, shape [n_samples, n_features] 
Training data. 
y : arraylike, shape [n_samples] 
Target values. Will be cast to X’s dtype if necessary 
Returns: 

self : object 
returns an instance of self. 

get_params(deep=True)
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Get parameters for this estimator.
Parameters: 

deep : boolean, optional 
If True, will return the parameters for this estimator and contained subobjects that are estimators. 
Returns: 

params : mapping of string to any 
Parameter names mapped to their values. 

predict(X)
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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)
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Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1  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 : arraylike, shape = (n_samples, n_features) 
Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator. 
y : arraylike, shape = (n_samples) or (n_samples, n_outputs) 
True values for X. 
sample_weight : arraylike, shape = [n_samples], optional 
Sample weights. 
Returns: 

score : float 
R^2 of self.predict(X) wrt. y. 

set_params(**params)
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Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
3.2.4.1.8.1. Examples using sklearn.linear_model.OrthogonalMatchingPursuitCV