B 0d 7@sdZddlZddlZddlmZddlmZddl m Z ddl m Z ddl m Z ddl mZdd lmZdd lmZdd lmZGd d d e e ZdS)zRestricted Boltzmann Machine N)expit) BaseEstimator)TransformerMixin)check_random_state)gen_even_slices)safe_sparse_dot) log_logistic)check_is_fittedc@seZdZdZd dddddddd Zd d Zd d ZddZddZddZ ddZ d!ddZ ddZ ddZ d"ddZddZdS)# 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) g? rN) learning_rate batch_sizen_iterverbose random_statecCs(||_||_||_||_||_||_dS)N) n_componentsrrrrr)selfrrrrrrrM/var/www/html/venv/lib/python3.7/site-packages/sklearn/neural_network/_rbm.py__init__s zBernoulliRBM.__init__cCs,t||j|ddtjtjfd}||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)r _validate_datanpfloat64float32 _mean_hiddens)rXrrr transforms zBernoulliRBM.transformcCs$t||jj}||j7}t||dS)aLComputes the probabilities P(h=1|v). Parameters ---------- v : ndarray of shape (n_samples, n_features) Values of the visible layer. Returns ------- h : ndarray of shape (n_samples, n_components) Corresponding mean field values for the hidden layer. )out)r components_Tintercept_hidden_r)rvprrrr s  zBernoulliRBM._mean_hiddenscCs||}|j|jd|kS)aSample from the distribution P(h|v). Parameters ---------- v : ndarray of shape (n_samples, n_features) Values of the visible layer to sample from. rng : RandomState instance Random number generator to use. Returns ------- h : ndarray of shape (n_samples, n_components) Values of the hidden layer. )size)r random_sampleshape)rr'rngr(rrr_sample_hiddenss zBernoulliRBM._sample_hiddenscCs6t||j}||j7}t||d|j|jd|kS)aSample from the distribution P(v|h). Parameters ---------- h : ndarray of shape (n_samples, n_components) Values of the hidden layer to sample from. rng : RandomState instance Random number generator to use. Returns ------- v : ndarray of shape (n_samples, n_features) Values of the visible layer. )r#)r))rdotr$intercept_visible_rr*r+)rhr,r(rrr_sample_visibless  zBernoulliRBM._sample_visiblescCs2t||j tdt||jj|jjddS)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. r)axis)rr/rZ logaddexpr$r%r&sum)rr'rrr _free_energys zBernoulliRBM._free_energycCs>t|t|dst|j|_|||j}|||j}|S)aTPerform one Gibbs sampling step. Parameters ---------- v : ndarray of shape (n_samples, n_features) Values of the visible layer to start from. Returns ------- v_new : ndarray of shape (n_samples, n_features) Values of the visible layer after one Gibbs step. random_state_)r hasattrrrr6r-r1)rr'Zh_v_rrrgibbss   zBernoulliRBM.gibbscCst|d }|j|dtj|d}t|ds6t|j|_t|dshtj|jdd|j |j dfdd |_ t|d st |j |_ t|d st |j d|_t|d st |j|j f|_|||jd S)aFit the model to the partial segment of the data X. Parameters ---------- X : ndarray of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None Target values (None for unsupervised transformations). Returns ------- self : BernoulliRBM The fitted model. r$r)rrrr6rg{Gz?r2F)orderr&r/ h_samples_N)r7rrrrrr6asarraynormalrr+r$zerosr&r/rr<_fit)rr!yZ first_passrrr partial_fits$         zBernoulliRBM.partial_fitcCs||}||j|}||}t|j|jd}t|j|ddj}|t |j|8}|j ||7_ |j ||j dd|j dd7_ |j |t|j dd|j dd7_ d||j|jd|k<t|||_dS)aInner fit for one mini-batch. Adjust the parameters to maximize the likelihood of v using Stochastic Maximum Likelihood (SML). Parameters ---------- v_pos : ndarray of shape (n_samples, n_features) The data to use for training. rng : RandomState instance Random number generator to use for sampling. rT)Z dense_output)r3g?)r)N)r r1r<floatrr+rr%rr.r$r&r4r/r=Zsqueezeuniformfloor)rZv_posr,Zh_posZv_negZh_neglrupdaterrrr@,s  &(zBernoulliRBM._fitc Cst||j|ddd}t|j}t|jd|d|jd|jdf}t |rd||d}|tj |j |f|jd}n| }d||||<||}||}|jdt||S)a|Compute the pseudo-likelihood of X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Values of the visible layer. Must be all-boolean (not checked). Returns ------- pseudo_likelihood : ndarray of shape (n_samples,) Value of the pseudo-likelihood (proxy for likelihood). Notes ----- This method is not deterministic: it computes a quantity called the free energy on X, then on a randomly corrupted version of X, and returns the log of the logistic function of the difference. rF)rrrr2)r+)r rrrrZaranger+randintspissparseZ csr_matrixAZravelcopyr5r ) rr!r'r,inddatar8ZfeZfe_rrr score_samplesJs *    zBernoulliRBM.score_samplesc CsN|j|dtjtjfd}|jd}t|j}tj|dd|j |jdfd|j d|_ tj |j |j d|_ tj |jd|j d|_tj |j|j f|j d|_ttt||j}tt||j||d }|j}t}xntd|jdD]Z} x|D]} ||| |qW|rt} td t|j| ||| |f| }qW|S) aFit the model to the data X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None Target values (None for unsupervised transformations). Returns ------- self : BernoulliRBM The fitted model. r)rrrg{Gz?r2r:)r;r)r) n_samplesz9[%s] Iteration %d, pseudo-likelihood = %.2f, time = %.2fs) rrrrr+rrr=r>rrr$r?r&r/rr<intceilrClistrrtimerangerr@printtype__name__rPZmean) rr!rArQr,Z n_batchesZ batch_slicesrbegin iterationZ batch_sliceendrrrfitos8     zBernoulliRBM.fitcCsddddiS)NZ _xfail_checksz&fails for the decision_function methodz"fails for the score_samples method)Zcheck_methods_subset_invarianceZ%check_methods_sample_order_invariancer)rrrr _more_tagsszBernoulliRBM._more_tags)r )N)N)rY __module__ __qualname____doc__rr"r r-r1r5r9rBr@rPr]r^rrrrr s$e  (% 6r )rarUnumpyrZ scipy.sparsesparserJZ scipy.specialrbaserrutilsrrZ utils.extmathrr Zutils.validationr r rrrrs