class sklearn.neural_network.BernoulliRBM(n_components=256, *, learning_rate=0.1, batch_size=10, n_iter=10, verbose=0, random_state=None) [source]
 
Bernoulli Restricted Boltzmann Machine (RBM). A Restricted Boltzmann Machine with binary visible units and binary hidden units. Parameters are estimated using Stochastic Maximum Likelihood (SML), also known as Persistent Contrastive Divergence (PCD) [2]. The time complexity of this implementation is O(d ** 2) assuming d ~ n_features ~ n_components. Read more in the User Guide.  Parameters 
 
n_componentsint, default=256 

Number of binary hidden units.  
learning_ratefloat, default=0.1 

The learning rate for weight updates. It is highly recommended to tune this hyper-parameter. Reasonable values are in the 10**[0., -3.] range.  
batch_sizeint, default=10 

Number of examples per minibatch.  
n_iterint, default=10 

Number of iterations/sweeps over the training dataset to perform during training.  
verboseint, default=0 

The verbosity level. The default, zero, means silent mode.  
random_stateint, RandomState instance or None, default=None 

Determines random number generation for:  Gibbs sampling from visible and hidden layers. Initializing components, sampling from layers during fit. Corrupting the data when scoring samples.  Pass an int for reproducible results across multiple function calls. See Glossary.    Attributes 
 
intercept_hidden_array-like of shape (n_components,) 

Biases of the hidden units.  
intercept_visible_array-like of shape (n_features,) 

Biases of the visible units.  
components_array-like of shape (n_components, n_features) 

Weight matrix, where n_features in the number of visible units and n_components is the number of hidden units.  
h_samples_array-like of shape (batch_size, n_components) 

Hidden Activation sampled from the model distribution, where batch_size in the number of examples per minibatch and n_components is the number of hidden units.     References  [1] Hinton, G. E., Osindero, S. and Teh, Y. A fast learning algorithm for

deep belief nets. Neural Computation 18, pp 1527-1554. https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf  [2] Tieleman, T. Training Restricted Boltzmann Machines using

Approximations to the Likelihood Gradient. International Conference on Machine Learning (ICML) 2008   Examples >>> import numpy as np
>>> from sklearn.neural_network import BernoulliRBM
>>> X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
>>> model = BernoulliRBM(n_components=2)
>>> model.fit(X)
BernoulliRBM(n_components=2)
 Methods  
fit(X[, y]) Fit the model to the data X.  
fit_transform(X[, y]) Fit to data, then transform it.  
get_params([deep]) Get parameters for this estimator.  
gibbs(v) Perform one Gibbs sampling step.  
partial_fit(X[, y]) Fit the model to the data X which should contain a partial segment of the data.  
score_samples(X) Compute the pseudo-likelihood of X.  
set_params(**params) Set the parameters of this estimator.  
transform(X) Compute the hidden layer activation probabilities, P(h=1|v=X).    
fit(X, y=None) [source]
 
Fit the model to the data X.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

Training data.    Returns 
 
selfBernoulliRBM 

The fitted model.     
  
fit_transform(X, y=None, **fit_params) [source]
 
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Input samples.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None 

Target values (None for unsupervised transformations).  
**fit_paramsdict 

Additional fit parameters.    Returns 
 
X_newndarray array of shape (n_samples, n_features_new) 

Transformed array.     
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

If True, will return the parameters for this estimator and contained subobjects that are estimators.    Returns 
 
paramsdict 

Parameter names mapped to their values.     
  
gibbs(v) [source]
 
Perform one Gibbs sampling step.  Parameters 
 
vndarray of shape (n_samples, n_features) 

Values of the visible layer to start from.    Returns 
 
v_newndarray of shape (n_samples, n_features) 

Values of the visible layer after one Gibbs step.     
  
partial_fit(X, y=None) [source]
 
Fit the model to the data X which should contain a partial segment of the data.  Parameters 
 
Xndarray of shape (n_samples, n_features) 

Training data.    Returns 
 
selfBernoulliRBM 

The fitted model.     
  
score_samples(X) [source]
 
Compute the pseudo-likelihood of X.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

Values of the visible layer. Must be all-boolean (not checked).    Returns 
 
pseudo_likelihoodndarray of shape (n_samples,) 

Value of the pseudo-likelihood (proxy for likelihood).     Notes This method is not deterministic: it computes a quantity called the free energy on X, then on a randomly corrupted version of X, and returns the log of the logistic function of the difference. 
  
set_params(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
  
transform(X) [source]
 
Compute the hidden layer activation probabilities, P(h=1|v=X).  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

The data to be transformed.    Returns 
 
hndarray of shape (n_samples, n_components) 

Latent representations of the data.