sklearn.linear_model.OrthogonalMatchingPursuitCV¶
- class sklearn.linear_model.OrthogonalMatchingPursuitCV(copy=True, fit_intercept=True, normalize=True, max_iter=None, cv=None, n_jobs=1, verbose=False)¶
Cross-validated Orthogonal Mathching Pursuit model (OMP)
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 Fortran-ordered, 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
If False, the regressors X are assumed to be already normalized.
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 : cross-validation generator, optional
see sklearn.cross_validation. If None is passed, default to a 5-fold strategy
n_jobs : integer, optional
Number of CPUs to use during the cross validation. If -1, use all the CPUs
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_features, n_targets)
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.
See also
orthogonal_mp, orthogonal_mp_gram, lars_path, Lars, LassoLars, OrthogonalMatchingPursuit, LarsCV, LassoLarsCV, decomposition.sparse_encode
Methods
decision_function(X) Decision function of the linear model. 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=None, n_jobs=1, verbose=False)¶
- 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)¶
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]
Target values.
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 :
