o tBhI@sdZddlZddlZddlmZmZddlmZddlZddl m Z ddl m Z ddl m Z dd lmZdd lmZdd lmZdd lmZmZdd lmZddZGdddeeedZdS)zBase class for mixture models.N)ABCMetaabstractmethod)time) logsumexp)cluster)kmeans_plusplus) BaseEstimator) DensityMixin)ConvergenceWarning)check_random_state check_scalar)check_is_fittedcCs,t|}|j|krtd|||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)param param_shapenamerl/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/sklearn/mixture/_base.py _check_shapes  rc@seZdZdZddZddZeddZdd Zed d Z d2d dZ d2ddZ ddZ eddZ eddZeddZddZd2ddZddZdd Zd3d"d#Zd$d%Zed&d'Zed(d)Zd*d+Zd,d-Zd.d/Zd0d1Zd S)4 BaseMixturezBase class for mixture models. This abstract class specifies an interface for all mixture classes and provides basic common methods for mixture models. c Cs@||_||_||_||_||_||_||_||_| |_| |_ dSN) n_componentstol reg_covarmax_itern_init init_params random_state warm_startverboseverbose_interval) selfrrrrrr r!r"r#r$rrr__init__2s  zBaseMixture.__init__cCsrt|jdtjddt|jdtjddt|jdtjddt|jdtjddt|jd tjdd| |d S) zCheck values of the basic parameters. Parameters ---------- X : array-like of shape (n_samples, n_features) r)r target_typemin_valrgrrrrN) r rnumbersIntegralrRealrrr_check_parametersr%Xrrr_check_initial_parametersJs"   z%BaseMixture._check_initial_parameterscCdS)zCheck initial parameters of the derived class. Parameters ---------- X : array-like of shape (n_samples, n_features) Nrr.rrrr-izBaseMixture._check_parameterscCs2|j\}}|jdkr)t||jf}tj|jd|d|j}d|t ||f<nh|jdkrG|j ||jfd}||j ddddtj f}nJ|jdkrht||jf}|j ||jd d }d||t |jf<n)|jd krt||jf}t||j|d \}}d||t |jf<ntd |j|||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. kmeansr') n_clustersrr!randomsizeaxisNrandom_from_dataF)r7replacez k-means++)r!z(Unimplemented initialization method '%s')rr rzerosrrKMeansfitlabels_arangeuniformsumnewaxischoicerr _initialize)r%r/r! n_samples_resplabelindicesrrr_initialize_parametersss>      z"BaseMixture._initialize_parameterscCr1)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)r%r/rHrrrrEs zBaseMixture._initializeNcCs||||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_predictr%r/yrrrr>s zBaseMixture.fitcCs|j|tjtjgdd}|jd|jkr"td|jd|jd|||jo.t |d }|r5|j nd}tj }d|_ t |j}|j\}}t|D]q} || |r[||||ratj n|j} |jdkrp|} d} qLtd|jdD]-} | }||\}}||||||} | |}|| |t||jkrd |_ nqx|| | |ks|tj kr| }|} | } qL|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)dtypeensure_min_samplesrz:Expected n_samples >= n_components but got n_components = z, n_samples = converged_r'FTzzInitialization %d did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.r8)"_validate_datarfloat64float32rrrr0r"hasattrrinfrQr r!range_print_verbose_msg_init_begrK lower_bound_r_get_parameters_e_step_m_step_compute_lower_bound_print_verbose_msg_iter_endabsr_print_verbose_msg_init_endwarningswarnr _set_parametersn_iter_argmax)r%r/rNdo_initrmax_lower_boundr!rFrGinit lower_bound best_params best_n_itern_iterprev_lower_bound log_prob_normlog_respchangerrrrLsj             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)r%r/rnrorrrr[1szBaseMixture._e_stepcCr1)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)r%r/rorrrr\Ds zBaseMixture._m_stepcCdSrrr%rrrrZRzBaseMixture._get_parameterscCrsrr)r%paramsrrrrcVruzBaseMixture._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. Fresetr'r8)rrRr_estimate_weighted_log_probr.rrr score_samplesZszBaseMixture.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. )rzrrrMrrrscoremszBaseMixture.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. Frwr'r8)rrRryrer.rrrpredictszBaseMixture.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. Frw)rrRrqrexp)r%r/rGrorrr predict_probas zBaseMixture.predict_probar'cst|dkrtdjjj\}tj|j}j dkr9t fddt jj |D}n*j dkrPt fddt j|D}nt 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. r'zNInvalid value for 'n_samples': %d . The sampling requires at least one sample.fullcs$g|]\}}}||t|qSr)multivariate_normalint.0rr covariancesample)rngrr sz&BaseMixture.sample..tiedcs$g|]\}}|jt|qSr)r covariances_r)rrrr)rr%rrrscs0g|]\}}}|j|fdt|qS)r6)standard_normalrsqrtr) n_featuresrrrrscSs g|] \}}tj||tdqS))rO)rrr)rjrrrrrs )rrrmeans_rr r! multinomialweights_covariance_typervstackzipr concatenate enumerate)r%rFrGn_samples_compr/rNr)rrr%rrsF           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_weightsr.rrrrys z'BaseMixture._estimate_weighted_log_probcCr1)zEstimate log-weights in EM algorithm, E[ log pi ] in VB algorithm. Returns ------- log_weight : array, shape (n_components, ) Nrrtrrrrr2z!BaseMixture._estimate_log_weightscCr1)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) Nrr.rrrrszBaseMixture._estimate_log_probcCsf||}t|dd}tjdd||ddtjf}Wd||fS1s*wY||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. Parameters ---------- X : array-like of shape (n_samples, n_features) Returns ------- log_prob_norm : array, shape (n_samples,) log p(X) log_responsibilities : array, shape (n_samples, n_components) logarithm of the responsibilities r'r8ignore)underN)ryrrerrstaterC)r%r/weighted_log_probrnrorrrrq s   z#BaseMixture._estimate_log_prob_respcCsH|jdkr td|dS|jdkr"td|t|_|j|_dSdS)(Print verbose message on initialization.r'zInitialization %drN)r#printr_init_prev_time_iter_prev_time)r%rrrrrX$s    z'BaseMixture._print_verbose_msg_init_begcCsb||jdkr-|jdkrtd|dS|jdkr/t}td|||j|f||_dSdSdS)rrr'z Iteration %drz0 Iteration %d time lapse %.5fs ll change %.5fN)r$r#rrr)r%rldiff_llcur_timerrrr^-s   z'BaseMixture._print_verbose_msg_iter_endcCsJ|jdkrtd|jdS|jdkr#td|jt|j|fdSdS)z.Print verbose message on the end of iteration.r'zInitialization converged: %srz7Initialization converged: %s time lapse %.5fs ll %.5fN)r#rrQrr)r%llrrrr`:s  z'BaseMixture._print_verbose_msg_init_endr)r')__name__ __module__ __qualname____doc__r&r0rr-rKrEr>rLr[r\rZrcrzr{r|r~rryrrrqrXr^r`rrrrr+s@  /  f     >   r) metaclass)rr*raabcrrrnumpyr scipy.specialrrrbaser r exceptionsr utilsr r utils.validationrrrrrrrs