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