lale.lib.autogen.bernoulli_rbm module

class lale.lib.autogen.bernoulli_rbm.BernoulliRBM(*, n_components=256, learning_rate=0.1, batch_size=10, n_iter=10, verbose=0, random_state=33)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

• n_components (integer, >=2 for optimizer, <=256 for optimizer, uniform distribution, default 256) – Number of binary hidden units.

• learning_rate (float, not for optimizer, default 0.1) – The learning rate for weight updates

• batch_size (integer, >=3 for optimizer, <=128 for optimizer, uniform distribution, default 10) – Number of examples per minibatch.

• n_iter (integer, >=5 for optimizer, <=1000 for optimizer, uniform distribution, default 10) – Number of iterations/sweeps over the training dataset to perform during training.

• verbose (integer, not for optimizer, default 0) – The verbosity level

• random_state (union type, not for optimizer, default 33) –

  A random number generator instance to define the state of the random permutations generator

  • integer

  • or numpy.random.RandomState

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.

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) – The data to be transformed.

Returns

result – Latent representations of the data.

Return type

array of items : array of items : float