o i)K@sdZddlZddlmZmZddlmZmZddlmZddl Z ddl m Z ddl mZdd lmZmZmZdd lmZdd lmZdd lmZdd lmZmZddlmZmZddZGdddeeedZdS)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_fitted validate_datacCs,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)paramZ param_shapenamerd/var/www/html/eduruby.in/lip-sync/lip-sync-env/lib/python3.10/site-packages/sklearn/mixture/_base.py _check_shapes  rc@sveZdZUdZeeddddgeeddddgeeddddgeeddddgeeddddgehdgd gd gd geeddddgd Ze e d <ddZ e ddZ ddZe ddZd BaseMixturezBase class for mixture models. This abstract class specifies an interface for all mixture classes and provides basic common methods for mixture models. Nleft)closedgr>randomkmeans k-means++random_from_data 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'r0rrr__init__@s  zBaseMixture.__init__cCdS)zCheck initial parameters of the derived class. Parameters ---------- X : array-like of shape (n_samples, n_features) Nrr3Xrrr_check_parametersXzBaseMixture._check_parameterscCs@|j\}}|jdkr,tj||jf|jd}tj|jd|d|j }d|t ||f<nl|jdkrPtj |j ||jfd|jd}||j ddddtjf}nH|jd krttj||jf|jd}|j||jd d }d||t |jf<n$|jd krtj||jf|jd}t||j|d \}}d||t |jf<|||dS)a?Initialize the model parameters. Parameters ---------- X : array-like of shape (n_samples, n_features) random_state : RandomState A random number generator instance that controls the random seed used for the method chosen to initialize the parameters. r"dtyper)Z n_clustersr-r%r!sizeZaxisNr$F)r=replacer#)r%)rr.rZzerosr)r;r ZKMeansfitZlabels_ZarangeZasarrayuniformsumnewaxischoicer _initialize)r3r7r% n_samples_resplabelindicesrrr_initialize_parametersbs<      z"BaseMixture._initialize_parameterscCr5)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)r3r7rHrrrrEs 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_predictr3r7yrrrr@s zBaseMixture.fitT)Zprefer_skip_nested_validationcCst||tjtjgdd}|jd|jkr"td|jd|jd|||jo.t |d }|r5|j nd}tj }g}d|_ t |j}|j\}} t|D]} || |r]||||rctj n|j} g} |jdkrt|} d}qNd}td|jdD]1}| }||\}}||||||} | | | |}|||t||jkrd }nq~|| || |ks|tj kr| }|} |}| }||_ qN|j s|jdkrtd t| | ||_!||_||_"||\} }|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)r;Zensure_min_samplesrz:Expected n_samples >= n_components but got n_components = z, n_samples = converged_rFTzBest performing initialization did not converge. Try different init parameters, or increase max_iter, tol, or check for degenerate data.r>)$rrZfloat64Zfloat32rr)rr8r/hasattrr-infrOrr%range_print_verbose_msg_init_begrKZ lower_bound_r,_get_parameters_e_step_m_stepZ_compute_lower_boundappend_print_verbose_msg_iter_endabsr*_print_verbose_msg_init_endwarningswarnr_set_parametersZn_iter_Z lower_bounds_argmax)r3r7rNZdo_initr-Zmax_lower_boundZbest_lower_boundsr%rFrGinit lower_boundZcurrent_lower_boundsZ best_paramsZ best_n_iter convergedn_iterZprev_lower_bound log_prob_normlog_respZchangerrrrLst              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)r3r7rcrdrrrrU(szBaseMixture._e_stepcCr5)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)r3r7rdrrrrV;s zBaseMixture._m_stepcCdSr2rr3rrrrTIzBaseMixture._get_parameterscCrgr2r)r3paramsrrrr]MrizBaseMixture._set_parameterscCs(t|t||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. Fresetrr>)rrr_estimate_weighted_log_probr6rrr 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. )rnrfrMrrrscoredszBaseMixture.scorecCs(t|t||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. Frkrr>)rrrmr^r6rrrpredictwszBaseMixture.predictcCs.t|t||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. Frk)rrrerexp)r3r7rGrdrrr predict_probas zBaseMixture.predict_probacst|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. rzNInvalid value for 'n_samples': %d . The sampling requires at least one sample.fullcs$g|]\}}}||t|qSr)multivariate_normalint.0rfZ covariancesample)rngrr sz&BaseMixture.sample..Ztiedcs$g|]\}}|jt|qSr)rt covariances_ru)rwrfrx)ryr3rrrzscs0g|]\}}}|j|fdt|qS)r<)Zstandard_normalrsqrtrv) n_featuresryrrrzscSs g|] \}}tj||tdqS)r:)rrsru)rwjrxrrrrzs )rrr)Zmeans_rrr%Z multinomialZweights_Zcovariance_typerZvstackzipr{Z concatenate enumerate)r3rFrGZn_samples_compr7rNr)r}ryr3rrxsF           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_weightsr6rrrrms z'BaseMixture._estimate_weighted_log_probcCr5)zEstimate log-weights in EM algorithm, E[ log pi ] in VB algorithm. Returns ------- log_weight : array, shape (n_components, ) Nrrhrrrrr9z!BaseMixture._estimate_log_weightscCr5)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) Nrr6rrrrszBaseMixture._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 rr>ignore)ZunderN)rmrrZerrstaterC)r3r7Zweighted_log_probrcrdrrrres   z#BaseMixture._estimate_log_prob_respcCsH|jdkr td|dS|jdkr"td|t|_|j|_dSdS)(Print verbose message on initialization.rzInitialization %drN)r'printr_init_prev_time_iter_prev_time)r3r-rrrrSs    z'BaseMixture._print_verbose_msg_init_begcCsb||jdkr-|jdkrtd|dS|jdkr/t}td|||j|f||_dSdSdS)rrrz Iteration %drz0 Iteration %d time lapse %.5fs ll change %.5fN)r0r'rrr)r3rbZdiff_llZcur_timerrrrX$s   z'BaseMixture._print_verbose_msg_iter_endcCsh|rdnd}|jdkrtd|dd S|jdkr2t|j}td|d|dd |ddd Sd S) z.Print verbose message on the end of iteration.razdid not convergerzInitialization .rz . time lapse z.5fzs lower bound N)r'rrr)r3ZlbZinit_has_convergedZ converged_msgtrrrrZ1s    z'BaseMixture._print_verbose_msg_init_endr2)r)#__name__ __module__ __qualname____doc__rrrrr1dict__annotations__r4rr8rKrEr@r rLrUrVrTr]rnrorprrrxrmrrrerSrXrZrrrrr*sX    -   n     >   r) metaclass) rr[abcrrnumbersrrrnumpyrZ scipy.specialrr baser r r r exceptionsrutilsrZutils._param_validationrrZutils.validationrrrrrrrrs