-ix:SrSSKrSSKJrJr SSKrSSKJr SSK J r SSK J r JrJrJr SSKJrJr SSKJr SS KJr SS KJrJr "S S \\\ 5rg) zRestricted Boltzmann MachineN)IntegralReal)expit) BaseEstimatorClassNamePrefixFeaturesOutMixinTransformerMixin _fit_context)check_random_stategen_even_slices)Interval)safe_sparse_dot)check_is_fitted validate_datac ^\rSrSr%Sr\"\SSSS9/\"\SSSS9/\"\SSSS9/\"\SSSS9/S /S /S .r\ \ S 'SS SSSSS.Sjjr Sr Sr SrSrSrSr\"SS9S Sj5rSrSr\"SS9S Sj5rU4SjrSrU=r$)! BernoulliRBMa 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 :ref:`User Guide `. Parameters ---------- n_components : int, default=256 Number of binary hidden units. learning_rate : float, 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_size : int, default=10 Number of examples per minibatch. n_iter : int, default=10 Number of iterations/sweeps over the training dataset to perform during training. verbose : int, default=0 The verbosity level. The default, zero, means silent mode. Range of values is [0, inf]. random_state : int, 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 :term:`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 and `n_components` is the number of hidden units. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- sklearn.neural_network.MLPRegressor : Multi-layer Perceptron regressor. sklearn.neural_network.MLPClassifier : Multi-layer Perceptron classifier. sklearn.decomposition.PCA : An unsupervised linear dimensionality reduction model. 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, see :ref:`sphx_glr_auto_examples_neural_networks_plot_rbm_logistic_classification.py`. Nleft)closedrneitherverbose random_state n_components learning_rate batch_sizen_iterrr_parameter_constraintsg? )rrrrrcLXlX lX0lX@lXPlX`lgNr)selfrrrrrrs N/var/www/html/venv/lib/python3.13/site-packages/sklearn/neural_network/_rbm.py__init__BernoulliRBM.__init__s%)*$  (c[U5 [XSS[R[R4S9nUR U5$)aCompute the hidden layer activation probabilities, P(h=1|v=X). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data to be transformed. Returns ------- h : ndarray of shape (n_samples, n_components) Latent representations of the data. csrF) accept_sparseresetdtype)rrnpfloat64float32 _mean_hiddens)r#Xs r$ transformBernoulliRBM.transforms?   5bjj"**=U !!!$$r'cn[XRR5nX R- n[ X"S9$)aComputes the probabilities P(h=1|v). Parameters ---------- v : ndarray of shape (n_samples, n_features) Values of the visible layer. Returns ------- h : ndarray of shape (n_samples, n_components) Corresponding mean field values for the hidden layer. out)r components_Tintercept_hidden_r)r#vps r$r0BernoulliRBM._mean_hiddenss2 A//11 2 # ##Qr'c\URU5nURURS9U:$)a9Sample from the distribution P(h|v). Parameters ---------- v : ndarray of shape (n_samples, n_features) Values of the visible layer to sample from. rng : RandomState instance Random number generator to use. Returns ------- h : ndarray of shape (n_samples, n_components) Values of the hidden layer. size)r0uniformshape)r#r:rngr;s r$_sample_hiddensBernoulliRBM._sample_hiddenss.   q !{{{(1,,r'c[R"XR5nX0R- n[ X3S9 UR UR S9U:$)a9Sample from the distribution P(v|h). Parameters ---------- h : ndarray of shape (n_samples, n_components) Values of the hidden layer to sample from. rng : RandomState instance Random number generator to use. Returns ------- v : ndarray of shape (n_samples, n_features) Values of the visible layer. r5r>)r-dotr7intercept_visible_rr@rA)r#hrBr;s r$_sample_visiblesBernoulliRBM._sample_visiblessG FF1&& ' $ $$ a{{{(1,,r'c[XR5*[R"S[XRR 5UR -5RSS9- $)zComputes the free energy F(v) = - log sum_h exp(-E(v,h)). Parameters ---------- v : ndarray of shape (n_samples, n_features) Values of the visible layer. Returns ------- free_energy : ndarray of shape (n_samples,) The value of the free energy. rraxis)rrGr- logaddexpr7r8r9sum)r#r:s r$ _free_energyBernoulliRBM._free_energysV #:#:;;bll q"2"2"4"458N8NN? #1#+ r'c[U5 [US5(d[UR5UlUR XR5nUR X R5nU$)a Perform one Gibbs sampling step. Parameters ---------- v : ndarray of shape (n_samples, n_features) Values of the visible layer to start from. Returns ------- v_new : ndarray of shape (n_samples, n_features) Values of the visible layer after one Gibbs step. random_state_)rhasattrr rrSrCrI)r#r:h_v_s r$gibbsBernoulliRBM.gibbss] t_--!3D4E4E!FD   ! !!%7%7 8  " "2'9'9 : r'T)prefer_skip_nested_validationc B[US5(+n[XS[RUS9n[US5(d[ UR 5Ul[US5(dl[R"UR RSSURURS45SS 9Ul URRSUl [US 5(d%[R"UR5Ul[US 5(d([R"URS5Ul[US 5(d1[R"UR UR45UlUR%XR 5 g )aOFit the model to the partial segment of the data X. Parameters ---------- X : ndarray of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None Target values (None for unsupervised transformations). Returns ------- self : BernoulliRBM The fitted model. r7r))r*r,r+rSr{Gz?rF)orderr9rG h_samples_N)rTrr-r.r rrSasarraynormalrrAr7_n_features_outzerosr9rGrr^_fit)r#r1y first_passs r$ partial_fitBernoulliRBM.partial_fitsG"!}55  5 * t_--!3D4E4E!FD t]++!zz""))!TD4E4Eqwwqz3RS D $(#3#3#9#9!#N)r0rIr^floatrrArr8r-rFr7r9rOrGr_squeezer@floor)r#v_posrBh_posv_negh_neglrupdates r$rcBernoulliRBM._fit:s@""5)%%doos;""5) 4%% &Q 7 %dCEE"&&%(( BK' " q (9EII1I>;?D$ ** 'V WGGAJ  !2!23:: JJq$!2!2AGGAJ ? @''  $//55a8!#$*;*;177!K"$((1771:QWW"E((DOOT5F5F#GqwwWi 04?? 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