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