U md:@sdZddlZddlmZmZddlZddlmZ ddl m Z ddl m Z ddl mZddl mZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZGdddeee ZdS)zRestricted Boltzmann Machine N)IntegralReal)expit) BaseEstimator)TransformerMixin)ClassNamePrefixFeaturesOutMixin)check_random_state)gen_even_slices)safe_sparse_dot) log_logistic)check_is_fitted)Intervalc @seZdZUdZeeddddgeeddddgeeddddgeeddddgdgd gd Zee d <d(d dddddddZ ddZ ddZ ddZ ddZddZddZd)ddZd d!Zd"d#Zd*d$d%Zd&d'ZdS)+ 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) Nleft)closedrZneitherverbose random_state n_components learning_rate batch_sizen_iterrr_parameter_constraintsg? )rrrrrcCs(||_||_||_||_||_||_dS)Nr)selfrrrrrrrT/home/sam/Atlas/atlas_env/lib/python3.8/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)uniformshape)rr1rngr2rrr_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,r3)r&dotr.intercept_visible_rr5r6)rhr7r2rrr_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. rrZaxis)r r:r&Z logaddexpr.r/r0sum)rr1rrr _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 hasattrr rr@r8r<)rr1Zh_v_rrrgibbss   zBernoulliRBM.gibbscCs|t|d }|j|dtj|d}t|ds>t|j|_t|ds~tj|j dd|j |j dfdd |_ |j j d|_ 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!)r"r$r#r@r{Gz?rF)orderr0r: h_samples_N)_validate_paramsrAr%r&r'r rr@asarraynormalrr6r._n_features_outzerosr0r:rrG_fit)rr*yZ first_passrrr partial_fits6       zBernoulliRBM.partial_fitcCs||}||j|}||}t|j|jd}t|j|ddj}|t |j|8}|j ||7_ |j ||j dd|j dd7_ |j |t|j dd|j dd7_ d||j|jd|k<t|||_dS)aInner fit for one mini-batch. Adjust the parameters to maximize the likelihood of v using Stochastic Maximum Likelihood (SML). Parameters ---------- v_pos : ndarray of shape (n_samples, n_features) The data to use for training. rng : RandomState instance Random number generator to use for sampling. rT)Z dense_outputr=g?r3N)r)r<rGfloatrr6r r/r&r9r.r0r>r:rIZsqueezer5floor)rZv_posr7Zh_posZv_negZh_neglrupdaterrrrM<s  & zBernoulliRBM._fitc Cst||j|ddd}t|j}t|jd|d|jd|jdf}t |rd||d}|tj |j |f|jd}n| }d||||<||}||}|jdt||S)a|Compute the pseudo-likelihood of X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Values of the visible layer. Must be all-boolean (not checked). Returns ------- pseudo_likelihood : ndarray of shape (n_samples,) Value of the pseudo-likelihood (proxy for likelihood). Notes ----- This method is not deterministic: it computes a quantity called the free energy on X, then on a randomly corrupted version of X, and returns the log of the logistic function of the difference. r!F)r"r#rr)r6)r r%r rr&Zaranger6randintspissparseZ csr_matrixAZravelcopyr?r ) rr*r1r7inddatarBZfeZfe_rrr score_samplesZs *    zBernoulliRBM.score_samplesc Csb||j|dtjtjfd}|jd}t|j}tj| dd|j |jdfd|j d|_ |j jd|_ tj|j |j d|_tj|jd|j d|_tj|j|j f|j d|_ttt||j}tt||j||d }|j}t}td|jdD]\} |D]} ||| |q|rt} td t|j| | |!| |f| }q|S) aFit the model to the data X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None Target values (None for unsupervised transformations). Returns ------- self : BernoulliRBM The fitted model. r!)r"r$rrDrrE)rFr$)r$) n_samplesz9[%s] Iteration %d, pseudo-likelihood = %.2f, time = %.2fs)"rHr%r&r'r(r6r rrIrJrr$r.rKrLr0r:rrGintceilrPlistr rtimerangerrMprinttype__name__r\Zmean) rr*rNr]r7Z n_batchesZ batch_slicesrbegin iterationZ batch_sliceendrrrfitsF    zBernoulliRBM.fitcCsdddtjtjgdS)Nz&fails for the decision_function methodz"fails for the score_samples method)Zcheck_methods_subset_invarianceZ%check_methods_sample_order_invariance)Z _xfail_checksZpreserves_dtype)r&r'r()rrrr _more_tagss  zBernoulliRBM._more_tags)r)N)N)re __module__ __qualname____doc__rrrrdict__annotations__r r+r)r8r<r?rCrOrMr\rirjrrrrrs6 g   ,% :r)rmranumbersrrnumpyr&Z scipy.sparsesparserVZ scipy.specialrbaserrrutilsr r Z utils.extmathr r Zutils.validationr Zutils._param_validationrrrrrrs