a ;ZavG@sdZddlZddlmZmZddlmZddlZddlm Z ddl m Z ddl m Z dd l mZdd lmZdd lmZdd lmZd dZGdddee edZdS)zBase class for mixture models.N)ABCMetaabstractmethod)time) logsumexp)cluster) BaseEstimator) DensityMixin)ConvergenceWarning)check_random_state)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)npZarrayshape ValueError)ZparamZ param_shapenamer4lib/python3.9/site-packages/sklearn/mixture/_base.py _check_shapes   rc@seZdZdZddZddZeddZdd Zed d Z d2d dZ d3ddZ ddZ eddZ eddZeddZddZd4ddZddZdd Zd5d"d#Zd$d%Zed&d'Zed(d)Zd*d+Zd,d-Zd.d/Zd0d1Zd S)6 BaseMixturezBase class for mixture models. This abstract class specifies an interface for all mixture classes and provides basic common methods for mixture models. c Cs@||_||_||_||_||_||_||_||_| |_| |_ dSN) n_componentstol reg_covarmax_itern_init init_params random_state warm_startverboseverbose_interval) selfrrrrrrrrrrrrr__init__0s zBaseMixture.__init__cCs|jdkrtd|j|jdkr0td|j|jdkrHtd|j|jdkr`td|j|jdkrxtd|j||dS) zCheck values of the basic parameters. Parameters ---------- X : array-like of shape (n_samples, n_features) zOInvalid value for 'n_components': %d Estimation requires at least one componentgzKInvalid value for 'tol': %.5f Tolerance used by the EM must be non-negativezCInvalid value for 'n_init': %d Estimation requires at least one runzKInvalid value for 'max_iter': %d Estimation requires at least one iterationzUInvalid value for 'reg_covar': %.5f regularization on covariance must be non-negativeN)rrrrrr_check_parametersr Xrrr_check_initial_parametersHs>     z%BaseMixture._check_initial_parameterscCsdS)zCheck initial parameters of the derived class. Parameters ---------- X : array-like of shape (n_samples, n_features) Nrr$rrrr#uszBaseMixture._check_parameterscCs|j\}}|jdkrRt||jf}tj|jd|d|j}d|t ||f<nF|jdkr| ||j}||j ddddtj f}nt d|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. Zkmeansr")Z n_clustersrrZrandomZaxisNz(Unimplemented initialization method '%s')rrr ZzerosrrZKMeansfitZlabels_ZarangeZrandsumnewaxisr _initialize)r r%r n_samples_respZlabelrrr_initialize_parameterss"    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)r r%r.rrrr+s 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|jkrDtd|jd|jd|||jo\t |d }|rj|j nd}tj }d|_ t |j}|j\}}t|D]} || |r||||rtj n|j} td|jdD]\} | } ||\} }|||||| } | | }|| |t||jkrd |_ q6q|| | |ksV|tj kr| }|}| }q|j std | 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_r"FTzzInitialization %d did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.r')"_validate_datar Zfloat64Zfloat32rrrr&rhasattrrinfr4r rrange_print_verbose_msg_init_begr/Z lower_bound_r_e_step_m_stepZ_compute_lower_bound_print_verbose_msg_iter_endabsr_print_verbose_msg_init_end_get_parameterswarningswarnr _set_parametersZn_iter_argmax)r r%r2Zdo_initrZmax_lower_boundrr,r-initZ lower_boundn_iterZprev_lower_bound log_prob_normlog_respZchangeZ best_paramsZ best_n_iterrrrr0s`           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_respr mean)r r%rFrGrrrr:(szBaseMixture._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)r r%rGrrrr;;s zBaseMixture._m_stepcCsdSrrr rrrr?IszBaseMixture._get_parameterscCsdSrr)r paramsrrrrBMszBaseMixture._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"r')r r5r_estimate_weighted_log_probr$rrr score_samplesQszBaseMixture.score_samplescCs||S)aCompute the per-sample average log-likelihood of the given data X. Parameters ---------- X : array-like of shape (n_samples, n_dimensions) List of n_features-dimensional data points. Each row corresponds to a single data point. y : Ignored Not used, present for API consistency by convention. Returns ------- log_likelihood : float Log-likelihood of `X` under the Gaussian mixture model. )rOrIr1rrrscoredszBaseMixture.scorecCs(t||j|dd}||jddS)aPredict the labels for the data samples in X using trained model. Parameters ---------- X : array-like of shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. Returns ------- labels : array, shape (n_samples,) Component labels. FrLr"r')r r5rNrCr$rrrpredictwszBaseMixture.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. FrL)r r5rHr Zexp)r r%r-rGrrr predict_probaszBaseMixture.predict_probar"cst|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. r"zNInvalid value for 'n_samples': %d . The sampling requires at least one sample.fullcs$g|]\}}}||t|qSr)multivariate_normalint.0rIZ covariancesample)rngrr sz&BaseMixture.sample..Ztiedcs$g|]\}}|jt|qSr)rT covariances_rU)rWrIrX)rYr rrrZscs,g|]$\}}}||t|qSr)Zrandnr ZsqrtrV) n_featuresrYrrrZscSs g|]\}}tj||tdqS))r3)r rSrU)rWjrXrrrrZ)r rrZmeans_rr rZ multinomialZweights_Zcovariance_typer Zvstackzipr[Z concatenate enumerate)r r,r-Zn_samples_compr%r2r)r\rYr rrXsF           zBaseMixture.samplecCs|||S)aEstimate the weighted log-probabilities, log P(X | Z) + log weights. Parameters ---------- X : array-like of shape (n_samples, n_features) Returns ------- weighted_log_prob : array, shape (n_samples, n_component) )_estimate_log_prob_estimate_log_weightsr$rrrrNs 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, ) NrrJrrrrbsz!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) Nrr$rrrraszBaseMixture._estimate_log_probcCs`||}t|dd}tjdd&||ddtjf}Wdn1sN0Y||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"r'ignore)ZunderN)rNrr Zerrstater*)r r%Zweighted_log_probrFrGrrrrHs   4z#BaseMixture._estimate_log_prob_respcCsB|jdkrtd|n&|jdkr>td|t|_|j|_dS)(Print verbose message on initialization.r"zInitialization %drN)rprintr_init_prev_time_iter_prev_time)r rrrrr9s    z'BaseMixture._print_verbose_msg_init_begcCsX||jdkrT|jdkr&td|n.|jdkrTt}td|||j|f||_dS)rdrr"z Iteration %drz0 Iteration %d time lapse %.5fs ll change %.5fN)rrrerrg)r rEZdiff_llZcur_timerrrr<"s  z'BaseMixture._print_verbose_msg_iter_endcCsD|jdkrtd|jn&|jdkr@td|jt|j|fdS)z.Print verbose message on the end of iteration.r"zInitialization converged: %srz7Initialization converged: %s time lapse %.5fs ll %.5fN)rrer4rrf)r Zllrrrr>/s  z'BaseMixture._print_verbose_msg_init_end)N)N)N)r")__name__ __module__ __qualname____doc__r!r&rr#r/r+r(r0r:r;r?rBrOrPrQrRrXrNrbrarHr9r<r>rrrrr)s>- !  _     <     r) metaclass)rkr@abcrrrZnumpyr Z scipy.specialrrbaserr exceptionsr Zutilsr Zutils.validationr rrrrrrs