lale.lib.autogen.skewed_chi2_sampler module

class lale.lib.autogen.skewed_chi2_sampler.SkewedChi2Sampler(*, skewedness=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

• skewedness (float, not for optimizer, default 1.0) – “skewedness” parameter of the kernel

• 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

Notes

constraint-1 : negated type of ‘X/isSparse’

A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array.

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