lale.lib.autogen.rbf_sampler module¶
- class lale.lib.autogen.rbf_sampler.RBFSampler(*, gamma=1.0, n_components=100, random_state=None)¶
Bases:
PlannedIndividualOp
Combined schema for expected data and hyperparameters.
This documentation is auto-generated from JSON schemas.
- Parameters
gamma (float, not for optimizer, default 1.0) – Parameter of RBF kernel: exp(-gamma * x^2)
n_components (integer, >=2 for optimizer, <=256 for optimizer, uniform distribution, default 100) – Number of Monte Carlo samples per original feature
random_state (union type, not for optimizer, default None) –
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
integer
or numpy.random.RandomState
or None
- 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) – New data, where n_samples in the number of samples and n_features is the number of features.
- Returns
result – Apply the approximate feature map to X.
- Return type
array of items : array of items : float