8.14.1.14. sklearn.linear_model.OrthogonalMatchingPursuit

class sklearn.linear_model.OrthogonalMatchingPursuit(copy_X=True, copy_Gram=True, copy_Xy=True, n_nonzero_coefs=None, tol=None, fit_intercept=True, normalize=True, precompute_gram=False)

Orthogonal Mathching Pursuit model (OMP)

Parameters :

n_nonzero_coefs : int, optional

Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features.

tol : float, optional

Maximum norm of the residual. If not None, overrides n_nonzero_coefs.

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

If False, the regressors X are assumed to be already normalized.

precompute_gram : {True, False, ‘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.

copy_X : bool, optional

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.

copy_Gram : bool, optional

Whether the gram matrix must be copied by the algorithm. A false value is only helpful if X is already Fortran-ordered, otherwise a copy is made anyway.

copy_Xy : bool, optional

Whether the covariance vector Xy must be copied by the algorithm. If False, it may be overwritten.

See also

orthogonal_mp, orthogonal_mp_gram, lars_path, Lars, LassoLars, decomposition.sparse_encode, decomposition.sparse_encode_parallel

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. (http://blanche.polytechnique.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. http://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf

Attributes

coef_	array, shape = (n_features,) or (n_features, n_targets)	parameter vector (w in the fomulation formula)
intercept_	float or array, shape =(n_targets,)	independent term in decision function.

Methods

decision_function(X)	Decision function of the linear model
fit(X, y[, Gram, Xy])	Fit the model using X, y as training data.
predict(X)	Predict using the linear model
score(X, y)	Returns the coefficient of determination R^2 of the prediction.
set_params(**params)	Set the parameters of the estimator.

__init__(copy_X=True, copy_Gram=True, copy_Xy=True, n_nonzero_coefs=None, tol=None, fit_intercept=True, normalize=True, precompute_gram=False)

decision_function(X)

Decision function of the linear model

Parameters :

X : numpy array of shape [n_samples, n_features]

Returns :

C : array, shape = [n_samples]

Returns predicted values.

fit(X, y, Gram=None, Xy=None)

Fit the model using X, y as training data.

Parameters :

X: array-like, shape = (n_samples, n_features) :

Training data.

y: array-like, shape = (n_samples,) or (n_samples, n_targets) :

Target values.

Gram: array-like, shape = (n_features, n_features) (optional) :

Gram matrix of the input data: X.T * X

Xy: array-like, shape = (n_features,) or (n_features, n_targets) :

(optional) Input targets multiplied by X: X.T * y

Returns :

self: object :

returns an instance of self.

predict(X)

Predict using the linear model

Parameters :

X : numpy array of shape [n_samples, n_features]

Returns :

C : array, shape = [n_samples]

Returns predicted values.

score(X, y)

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.

Parameters :

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples]

Returns :

z : float

set_params(**params)

Set the parameters of the estimator.

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

Returns :self :