class sklearn.neural_network.BernoulliRBM(n_components=256, learning_rate=0.1, batch_size=10, n_iter=10, verbose=0, random_state=None)
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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.
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>>> 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(batch_size=10, learning_rate=0.1, n_components=2, n_iter=10, random_state=None, verbose=0)
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). |
__init__(n_components=256, learning_rate=0.1, batch_size=10, n_iter=10, verbose=0, random_state=None)
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fit(X, y=None)
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Fit the model to the data X.
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fit_transform(X, y=None, **fit_params)
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Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
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get_params(deep=True)
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Get parameters for this estimator.
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gibbs(v)
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Perform one Gibbs sampling step.
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partial_fit(X, y=None)
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Fit the model to the data X which should contain a partial segment of the data.
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score_samples(X)
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Compute the pseudo-likelihood of X.
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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)
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Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
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transform(X)
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Compute the hidden layer activation probabilities, P(h=1|v=X).
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sklearn.neural_network.BernoulliRBM
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.BernoulliRBM.html