Class OrthogonalMatchingPursuitCVScikitsLearnNode

Cross-validated Orthogonal Matching Pursuit model (OMP)

This node has been automatically generated by wrapping the ``sklearn.linear_model.omp.OrthogonalMatchingPursuitCV`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

**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 : int, cross-validation generator or an iterable, optional
    Determines the cross-validation splitting strategy.
    Possible inputs for cv are:


    - None, to use the default 3-fold cross-validation,
    - integer, to specify the number of folds.
    - An object to be used as a cross-validation generator.
    - An iterable yielding train/test splits.

    For integer/None inputs, :class:`KFold` is used.

    Refer :ref:`User Guide <cross_validation>` for the various
    cross-validation strategies that can be used here.

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

Read more in the :ref:`User Guide <omp>`.

**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.

``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
decomposition.sparse_encode

Overrides: object.__init__

Predict using the linear model

This node has been automatically generated by wrapping the sklearn.linear_model.omp.OrthogonalMatchingPursuitCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape = (n_samples, n_features)

Samples.

Returns

C : array, shape = (n_samples,)

Returns predicted values.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit the model using X, y as training data.

This node has been automatically generated by wrapping the sklearn.linear_model.omp.OrthogonalMatchingPursuitCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

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.

Overrides: Node.stop_training