sklearn.linear_model.OrthogonalMatchingPursuit¶
- class sklearn.linear_model.OrthogonalMatchingPursuit(copy_X=None, copy_Gram=None, copy_Xy=None, n_nonzero_coefs=None, tol=None, fit_intercept=True, normalize=True, precompute='auto', precompute_gram=None)¶
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 : {True, False, ‘auto’}, 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.
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. WARNING : will be deprecated in 0.15
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. WARNING : will be deprecated in 0.15
copy_Xy : bool, optional
Whether the covariance vector Xy must be copied by the algorithm. If False, it may be overwritten. WARNING : will be deprecated in 0.15
Attributes: `coef_` : array, shape (n_features,) or (n_features, n_targets)
parameter vector (w in the formula)
`intercept_` : float or array, shape (n_targets,)
independent term in decision function.
See also
orthogonal_mp, orthogonal_mp_gram, lars_path, Lars, LassoLars, decomposition.sparse_encode
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
Methods
decision_function(X) Decision function of the linear model. fit(X, y[, Gram, Xy]) 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_X=None, copy_Gram=None, copy_Xy=None, n_nonzero_coefs=None, tol=None, fit_intercept=True, normalize=True, precompute='auto', precompute_gram=None)¶
- decision_function(X)¶
Decision function of the linear model.
Parameters: X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Samples.
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 WARNING : will be deprecated in 0.15
Xy : array-like, shape (n_features,) or (n_features, n_targets)
(optional) Input targets multiplied by X: X.T * y WARNING : will be deprecated in 0.15
Returns: self: object :
returns an instance of self.
- get_params(deep=True)¶
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)¶
Predict using the linear model
Parameters: X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Samples.
Returns: C : array, shape = (n_samples,)
Returns predicted values.
- score(X, y, sample_weight=None)¶
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_true - 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)
Test samples.
y : array-like, shape = (n_samples,)
True values for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
Returns: score : float
R^2 of self.predict(X) wrt. y.
- set_params(**params)¶
Set the parameters of this 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 :
