U _{jI@sdZddlZddlmZmZddlmZmZddlmZddl Z ddl m Z ddl mZdd lmZmZmZdd lmZdd lmZdd lmZdd lmZmZddlmZddZGdddeeedZdS)zBase class for mixture models.N)ABCMetaabstractmethod)IntegralReal)time) logsumexp)cluster) BaseEstimator DensityMixin _fit_context)kmeans_plusplus)ConvergenceWarning)check_random_state)Interval StrOptions)check_is_fittedcCs,t|}|j|kr(td|||jfdS)zValidate the shape of the input parameter 'param'. Parameters ---------- param : array param_shape : tuple name : str z:The parameter '%s' should have the shape of %s, but got %sN)nparrayshape ValueError)paramZ param_shapenamerT/var/www/html/bwave/python/venv/lib/python3.8/site-packages/sklearn/mixture/_base.py _check_shapes   rc@szeZdZUdZeeddddgeeddddgeeddddgeeddddgeeddddgedd d d hgd gd gdgeeddddgd Ze e d<ddZ e ddZ ddZe ddZd?ddZeddd@ddZdd Ze d!d"Ze d#d$Ze d%d&Zd'd(ZdAd)d*Zd+d,Zd-d.ZdBd/d0Zd1d2Ze d3d4Ze d5d6Zd7d8Zd9d:Z d;d<Z!d=d>Z"dS)C BaseMixturezBase class for mixture models. This abstract class specifies an interface for all mixture classes and provides basic common methods for mixture models. Nleft)closedgrkmeansrandomrandom_from_data k-means++ random_statebooleanverbose n_componentstol reg_covarmax_itern_init init_paramsr$ warm_startr&verbose_interval_parameter_constraintsc Cs@||_||_||_||_||_||_||_||_| |_| |_ dSNr') selfr(r)r*r+r,r-r$r.r&r/rrr__init__As zBaseMixture.__init__cCsdS)zCheck initial parameters of the derived class. Parameters ---------- X : array-like of shape (n_samples, n_features) Nrr2Xrrr_check_parametersYszBaseMixture._check_parameterscCs$|j\}}|jdkrRt||jf}tj|jd|d|j}d|t ||f<n|jdkr|j ||jfd}||j ddddtj f}n|jdkrt||jf}|j ||jd d }d||t |jf<nD|jd krt||jf}t||j|d \}}d||t |jf<|||dS) a?Initialize the model parameters. Parameters ---------- X : array-like of shape (n_samples, n_features) random_state : RandomState A random number generator instance that controls the random seed used for the method chosen to initialize the parameters. r r)Z n_clustersr,r$r!sizeZaxisNr"F)r8replacer#)r$)rr-rZzerosr(r ZKMeansfitZlabels_Zarangeuniformsumnewaxischoicer _initialize)r2r5r$ n_samples_resplabelindicesrrr_initialize_parameterscs@       z"BaseMixture._initialize_parameterscCsdS)zInitialize the model parameters of the derived class. Parameters ---------- X : array-like of shape (n_samples, n_features) resp : array-like of shape (n_samples, n_components) Nr)r2r5rCrrrr@s zBaseMixture._initializecCs||||S)aEstimate model parameters with the EM algorithm. The method fits the model ``n_init`` times and sets the parameters with which the model has the largest likelihood or lower bound. Within each trial, the method iterates between E-step and M-step for ``max_iter`` times until the change of likelihood or lower bound is less than ``tol``, otherwise, a ``ConvergenceWarning`` is raised. If ``warm_start`` is ``True``, then ``n_init`` is ignored and a single initialization is performed upon the first call. Upon consecutive calls, training starts where it left off. Parameters ---------- X : array-like of shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. y : Ignored Not used, present for API consistency by convention. Returns ------- self : object The fitted mixture. ) fit_predictr2r5yrrrr;s zBaseMixture.fitT)Zprefer_skip_nested_validationcCs|j|tjtjgdd}|jd|jkrDtd|jd|jd|||jo\t |d }|rj|j nd}tj }d|_ t |j}|j\}}t|D]} || |r||||rtj n|j} |jdkr|} d} qtd|jdD]\} | }||\}}||||||} | |}|| |t||jkrd |_ qNq|| | |ksn|tj kr| }|} | } q|j s|jdkrtd | dt|| | |_ ||_||\}}|j!dd S) aEstimate model parameters using X and predict the labels for X. The method fits the model n_init times and sets the parameters with which the model has the largest likelihood or lower bound. Within each trial, the method iterates between E-step and M-step for `max_iter` times until the change of likelihood or lower bound is less than `tol`, otherwise, a :class:`~sklearn.exceptions.ConvergenceWarning` is raised. After fitting, it predicts the most probable label for the input data points. .. versionadded:: 0.20 Parameters ---------- X : array-like of shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. y : Ignored Not used, present for API consistency by convention. Returns ------- labels : array, shape (n_samples,) Component labels. r)dtypeZensure_min_samplesrz:Expected n_samples >= n_components but got n_components = z, n_samples = converged_rFTzzInitialization %d did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.r9)"_validate_datarZfloat64Zfloat32rr(rr6r.hasattrr,infrKrr$range_print_verbose_msg_init_begrFZ lower_bound_r+_get_parameters_e_step_m_stepZ_compute_lower_bound_print_verbose_msg_iter_endabsr)_print_verbose_msg_init_endwarningswarnr_set_parametersZn_iter_argmax)r2r5rIZdo_initr,Zmax_lower_boundr$rArBinit lower_boundZ best_paramsZ best_n_itern_iterZprev_lower_bound log_prob_normlog_respZchangerrrrGs^            zBaseMixture.fit_predictcCs||\}}t||fS)aE step. Parameters ---------- X : array-like of shape (n_samples, n_features) Returns ------- log_prob_norm : float Mean of the logarithms of the probabilities of each sample in X log_responsibility : array, shape (n_samples, n_components) Logarithm of the posterior probabilities (or responsibilities) of the point of each sample in X. )_estimate_log_prob_resprmean)r2r5r^r_rrrrRszBaseMixture._e_stepcCsdS)a*M step. Parameters ---------- X : array-like of shape (n_samples, n_features) log_resp : array-like of shape (n_samples, n_components) Logarithm of the posterior probabilities (or responsibilities) of the point of each sample in X. Nr)r2r5r_rrrrS2s zBaseMixture._m_stepcCsdSr1rr2rrrrQ@szBaseMixture._get_parameterscCsdSr1r)r2paramsrrrrYDszBaseMixture._set_parameterscCs(t||j|dd}t||ddS)aCompute the log-likelihood of each sample. Parameters ---------- X : array-like of shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. Returns ------- log_prob : array, shape (n_samples,) Log-likelihood of each sample in `X` under the current model. Fresetrr9)rrLr_estimate_weighted_log_probr4rrr score_samplesHszBaseMixture.score_samplescCs||S)aCompute the per-sample average log-likelihood of the given data X. Parameters ---------- X : array-like of shape (n_samples, n_dimensions) List of n_features-dimensional data points. Each row corresponds to a single data point. y : Ignored Not used, present for API consistency by convention. Returns ------- log_likelihood : float Log-likelihood of `X` under the Gaussian mixture model. )rgrarHrrrscore[szBaseMixture.scorecCs(t||j|dd}||jddS)aPredict the labels for the data samples in X using trained model. Parameters ---------- X : array-like of shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. Returns ------- labels : array, shape (n_samples,) Component labels. Frdrr9)rrLrfrZr4rrrpredictnszBaseMixture.predictcCs.t||j|dd}||\}}t|S)aEvaluate the components' density for each sample. Parameters ---------- X : array-like of shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. Returns ------- resp : array, shape (n_samples, n_components) Density of each Gaussian component for each sample in X. Frd)rrLr`rexp)r2r5rBr_rrr predict_probaszBaseMixture.predict_probacst|dkrtdjjj\}tj|j}j dkrrt fddt jj |D}nTj dkrt fddt j|D}n&t fddt jj |D}t d dt|D}||fS) ayGenerate random samples from the fitted Gaussian distribution. Parameters ---------- n_samples : int, default=1 Number of samples to generate. Returns ------- X : array, shape (n_samples, n_features) Randomly generated sample. y : array, shape (nsamples,) Component labels. rzNInvalid value for 'n_samples': %d . The sampling requires at least one sample.fullcs$g|]\}}}||t|qSr)multivariate_normalint.0raZ covariancesample)rngrr sz&BaseMixture.sample..Ztiedcs$g|]\}}|jt|qSr)rm covariances_rn)rprarq)rrr2rrrsscs0g|](\}}}|j|fdt|qS)r7)Zstandard_normalrsqrtro) n_featuresrrrrrss cSs g|]\}}tj||tdqS))rJ)rrlrn)rpjrqrrrrss)rrr(Zmeans_rrr$Z multinomialZweights_Zcovariance_typerZvstackziprtZ concatenate enumerate)r2rArBZn_samples_compr5rIr)rvrrr2rrqsN         zBaseMixture.samplecCs|||S)aEstimate the weighted log-probabilities, log P(X | Z) + log weights. Parameters ---------- X : array-like of shape (n_samples, n_features) Returns ------- weighted_log_prob : array, shape (n_samples, n_component) )_estimate_log_prob_estimate_log_weightsr4rrrrfs z'BaseMixture._estimate_weighted_log_probcCsdS)zEstimate log-weights in EM algorithm, E[ log pi ] in VB algorithm. Returns ------- log_weight : array, shape (n_components, ) Nrrbrrrr{sz!BaseMixture._estimate_log_weightscCsdS)a9Estimate the log-probabilities log P(X | Z). Compute the log-probabilities per each component for each sample. Parameters ---------- X : array-like of shape (n_samples, n_features) Returns ------- log_prob : array, shape (n_samples, n_component) Nrr4rrrrzszBaseMixture._estimate_log_probc CsL||}t|dd}tjdd||ddtjf}W5QRX||fS)a@Estimate log probabilities and responsibilities for each sample. Compute the log probabilities, weighted log probabilities per component and responsibilities for each sample in X with respect to the current state of the model. 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