lale.lib.autogen.orthogonal_matching_pursuit module

class lale.lib.autogen.orthogonal_matching_pursuit.OrthogonalMatchingPursuit(*, n_nonzero_coefs=None, tol=None, fit_intercept=True, precompute='auto')

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • n_nonzero_coefs (union type, default None) –

    Desired number of non-zero entries in the solution

    • integer, >=500 for optimizer, <=501 for optimizer, uniform distribution

    • or None

  • tol (union type, default None) –

    Maximum norm of the residual

    • float, >=1e-08 for optimizer, <=0.01 for optimizer

    • or None

  • fit_intercept (boolean, default True) – whether to calculate the intercept for this model

  • precompute (True, False, or ‘auto’, default ‘auto’) – Whether to use a precomputed Gram and Xy matrix to speed up calculations

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

  • X (array of items : array of items : float) – Training data.

  • y (union type) –

    Target values

    • array of items : float

    • or array of items : array of items : float

predict(X, **predict_params)

Make predictions.

Note: The predict method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (union type) –

Samples.

  • array of items : Any

  • or array of items : array of items : float

Returns

result – Returns predicted values.

Return type

array of items : float