BernoulliRBM (original) (raw)
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. Range of values is [0, inf].
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 is 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 is the number of examples per minibatch andn_components is the number of hidden units.
**n_features_in_**int
Number of features seen during fit.
Added in version 0.24.
**feature_names_in_**ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when Xhas feature names that are all strings.
Added in version 1.0.
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)
For a more detailed example usage, seeRestricted Boltzmann Machine features for digit classification.
Fit the model to the data X.
Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
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_paramsand 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_feature_names_out(input_features=None)[source]#
Get output feature names for transformation.
The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: ["class_name0", "class_name1", "class_name2"].
Parameters:
input_featuresarray-like of str or None, default=None
Only used to validate feature names with the names seen in fit.
Returns:
feature_names_outndarray of str objects
Transformed feature names.
get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Returns:
routingMetadataRequest
A MetadataRequest encapsulating routing information.
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.
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 partial segment of the data X.
Parameters:
Xndarray of shape (n_samples, n_features)
Training data.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
Returns:
selfBernoulliRBM
The fitted model.
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_output(*, transform=None)[source]#
Set output container.
See Introducing the set_output APIfor an example on how to use the API.
Parameters:
transform{“default”, “pandas”, “polars”}, default=None
Configure output of transform and fit_transform.
"default": Default output format of a transformer"pandas": DataFrame output"polars": Polars outputNone: Transform configuration is unchanged
Added in version 1.4: "polars" option was added.
Returns:
selfestimator instance
Estimator instance.
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.
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.