lale.lib.autogen.additive_chi2_sampler module¶
- class lale.lib.autogen.additive_chi2_sampler.AdditiveChi2Sampler(*, sample_steps=2, sample_interval=None)¶
Bases:
PlannedIndividualOp
Combined schema for expected data and hyperparameters.
This documentation is auto-generated from JSON schemas.
- Parameters
sample_steps (integer, >=1 for optimizer, <=5 for optimizer, uniform distribution, default 2) –
Gives the number of (complex) sampling points.
See also constraint-1.
sample_interval (union type, default None) –
Sampling interval
float, >=0.1 for optimizer, <=1.0 for optimizer, uniform distribution
or None
See also constraint-1.
Notes
constraint-1 : union type
From /kernel_approximation.py:AdditiveChi2Sampler:fit, Exception: raise ValueError( ‘If sample_steps is not in [1, 2, 3], you need to provide sample_interval’)
sample_interval : negated type of None
or sample_steps : 1, 2, or 3
- 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, where n_samples in the number of samples and n_features is the number of features.
- transform(X, y=None)¶
Transform the data.
Note: The transform method is not available until this operator is trained.
Once this method is available, it will have the following signature:
- Parameters
X (array of items : array of items : float) –
- Returns
result – Whether the return value is an array of sparse matrix depends on the type of the input X.
- Return type
Any