rtj:dZddlZddlmZmZddlZddlmZ ddl m Z ddl m Z mZmZmZddlmZmZddlmZddlmZdd lmZmZGd d eee ZdS) zRestricted Boltzmann MachineN)IntegralReal)expit) BaseEstimatorClassNamePrefixFeaturesOutMixinTransformerMixin _fit_context)check_random_stategen_even_slices)Interval)safe_sparse_dot)check_is_fitted validate_datac TeZdZUdZeedddgeedddgeedddgeedddgdgd gd Zee d < dd ddddddZ dZ dZ dZ dZdZdZedddZdZdZedddZfdZxZS) BernoulliRBMaBernoulli 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? )rrrrrcZ||_||_||_||_||_||_dSNr)selfrrrrrrs Y/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/neural_network/_rbm.py__init__zBernoulliRBM.__init__s7)*$  (ct|t||ddtjtjf}||S)agCompute 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# transformzBernoulliRBM.transformsP   !5bj"*=U   !!!$$$r%clt||jj}||jz }t ||S)aLComputes the probabilities P(h=1|v). Parameters ---------- v : ndarray of shape (n_samples, n_features) Values of the visible layer. Returns ------- h : ndarray of shape (n_samples, n_components) Corresponding mean field values for the hidden layer. out)r components_Tintercept_hidden_r)r"vps r#r.zBernoulliRBM._mean_hiddenss6 At/1 2 2 T ##QAr%cj||}||j|kS)aSample from the distribution P(h|v). Parameters ---------- v : ndarray of shape (n_samples, n_features) Values of the visible layer to sample from. rng : RandomState instance Random number generator to use. Returns ------- h : ndarray of shape (n_samples, n_components) Values of the hidden layer. size)r.uniformshape)r"r7rngr8s r#_sample_hiddenszBernoulliRBM._sample_hiddenss2   q ! !{{{((1,,r%ctj||j}||jz }t ||||j|kS)aSample from the distribution P(v|h). Parameters ---------- h : ndarray of shape (n_samples, n_components) Values of the hidden layer to sample from. rng : RandomState instance Random number generator to use. Returns ------- v : ndarray of shape (n_samples, n_features) Values of the visible layer. r2r:)r+dotr4intercept_visible_rr<r=)r"hr>r8s r#_sample_visibleszBernoulliRBM._sample_visiblessO F1d& ' ' T $$ aQ{{{((1,,r%ct||j tjdt||jj|jzdz S)aFComputes 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)r rBr+ logaddexpr4r5r6sum)r"r7s r# _free_energyzBernoulliRBM._free_energysY 4#:;;;bl q$"2"4558NN? ? #1#++ r%ct|t|dst|j|_|||j}|||j}|S)aTPerform one Gibbs sampling step. Parameters ---------- v : ndarray of shape (n_samples, n_features) Values of the visible layer to start from. Returns ------- v_new : ndarray of shape (n_samples, n_features) Values of the visible layer after one Gibbs step. random_state_)rhasattrr rrLr?rD)r"r7h_v_s r#gibbszBernoulliRBM.gibbssm t_-- G!3D4E!F!FD   ! !!T%7 8 8  " "2t'9 : : r%T)prefer_skip_nested_validationc t|d }t||dtj|}t|dst |j|_t|ds^tj|jdd|j |j dfd |_ |j j d|_ t|d stj |j |_t|d s$tj |j d|_t|d s%tj |j|j f|_|||jd S)aFit 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. r4r')r(r*r)rLr{Gz?rF)orderr6rB h_samples_N)rMrr+r,r rrLasarraynormalrr=r4_n_features_outzerosr6rBrrV_fit)r"r/y first_passs r# partial_fitzBernoulliRBM.partial_fitst"!}555  !5 *   t_-- G!3D4E!F!FD t]++ =!z"))!TD4Eqwqz3RSS   D $(#3#9!#h_posv_negh_neglrupdates r#r[zBernoulliRBM._fit:sl""5))%%dos;;""5)) 4% & &Q 7 %dCCCE"&%((( BK' " q (9(9EII1IinddatarOfefe_s r# score_sampleszBernoulliRBM.score_samplesXse&  $e D D D !233y$$ckk!QWQZ&L&LM ;q>> "#;?D$ ** J ' n_batches batch_slicesrbegin iteration batch_sliceends r#fitzBernoulliRBM.fits" $rz2:>V W W WGAJ  !233: JJq$!2AGAJ ? @ @'    $/5a8!#$*;17!K!K!K"$(171:QW"E"E"E(DOT5F#GqwWWWi 0 04? 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