sklearn rbm

Sklearn doesn't provide an implementation of Restricted Boltzmann Machines (RBM) in its core library. However, you can use the `BernoulliRBM` class from the `sklearn.neural_network` module to create an RBM.

Here is an example code snippet to train an RBM using Scikit-learn:

from sklearn.neural_network import BernoulliRBM
from sklearn.pipeline import Pipeline

# Create an RBM object
rbm = BernoulliRBM(n_components=100, learning_rate=0.01, n_iter=10)

# Create a pipeline with RBM and any desired classifier
# For example, you can use Logistic Regression as the classifier
pipeline = Pipeline(steps=[('rbm', rbm), ('classifier', LogisticRegression())])

# Train the RBM
pipeline.fit(X_train, y_train)

In the above code, `n_components` is the number of hidden units in the RBM, `learning_rate` controls the learning rate of the RBM, and `n_iter` specifies the number of iterations during training.

Please note that this code assumes you have already loaded your input data `X_train` and corresponding target labels `y_train` for training the RBM.