-i)KSrSSKrSSKJrJr SSKJrJr SSKJr SSK r SSK J r SSK Jr SS KJrJrJr SS KJr SS KJr SS KJr SS KJrJr SSKJrJr Sr"SS\\\S9rg)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_datac[R"U5nURU:wa![SU<SU<SUR<35eg)zyValidate the shape of the input parameter 'param'. Parameters ---------- param : array param_shape : tuple name : str zThe parameter 'z' should have the shape of z , but got N)nparrayshape ValueError)param param_shapenames H/var/www/html/venv/lib/python3.13/site-packages/sklearn/mixture/_base.py _check_shapers? HHUOE {{k![%++ /  "c\rSrSr%Sr\"\SSSS9/\"\SSSS9/\"\SSSS9/\"\SSSS9/\"\SSSS9/\"1S k5/S /S /S /\"\SSSS9/S . r \ \ S'Sr \ S5rSr\ S5rS(Sjr\"SS9S(Sj5rSr\ S5r\ S5r\ S5rSrS(SjrSrSrS)SjrS r\ S!5r\ S"5rS#r S$r!S%r"S&r#S'r$g)* BaseMixture*zBase class for mixture models. This abstract class specifies an interface for all mixture classes and provides basic common methods for mixture models. Nleft)closedgr>kmeansrandomrandom_from_data k-means++ random_statebooleanverbose n_componentstol reg_covarmax_itern_init init_paramsr* warm_startr,verbose_interval_parameter_constraintsc |XlX lX0lX@lXPlX`lXplXlXlXl gNr-) selfr.r/r0r1r2r3r*r4r,r5s r__init__BaseMixture.__init__@s:)"  &($ 0rcg)zwCheck initial parameters of the derived class. Parameters ---------- X : array-like of shape (n_samples, n_features) Nr9Xs r_check_parametersBaseMixture._check_parametersX rcURup4URS:Xa[R"X0R4UR S9n[ R"URSUS9RU5RnSU[R"U5U4'GO\URS:Xa^[R"URX0R4S9UR S9nXURSS9SS2[R4-nOURS :Xai[R"X0R4UR S9nURX0RS S 9nSXW[R"UR54'OuURS :Xae[R"X0R4UR S9n[!UURUS 9upGSXW[R"UR54'UR#UW5 g)aInitialize 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#) n_clustersr2r*r'sizeaxisNr(F)rHreplacer))r*)rr3rzerosr.rEr KMeansfitlabels_arangeasarrayuniformsumnewaxischoicer _initialize)r9r?r* n_samples_resplabelindicess r_initialize_parameters"BaseMixture._initialize_parametersbsww   x '88Y(9(9:!''JD#00Q 12D9%u, -    )::$$96G6G*H$IQRQXQXD HH!H$Q ]3 3D   !3 388Y(9(9:!''JD")) 1 15*G;5 URAW 5 WUl!XPlX`l"UR+U5un nURGSS 9$) a>Estimate model parameters using X and predict the labels for X. The method fits the model n_init times and sets the parameters with which the model has the largest likelihood or lower bound. Within each trial, the method iterates between E-step and M-step for `max_iter` times until the change of likelihood or lower bound is less than `tol`, otherwise, a :class:`~sklearn.exceptions.ConvergenceWarning` is raised. After fitting, it predicts the most probable label for the input data points. .. versionadded:: 0.20 Parameters ---------- X : array-like of shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. y : Ignored Not used, present for API consistency by convention. Returns ------- labels : array, shape (n_samples,) Component labels. r )rEensure_min_samplesrz:Expected n_samples >= n_components but got n_components = z, n_samples = converged_r#FTzBest performing initialization did not converge. Try different init parameters, or increase max_iter, tol, or check for degenerate data.rI)$rrfloat64float32rr.rr@r4hasattrr2infrhrr*range_print_verbose_msg_init_begr\ lower_bound_r1_get_parameters_e_step_m_step_compute_lower_boundappend_print_verbose_msg_iter_endabsr/_print_verbose_msg_init_endwarningswarnr_set_parametersn_iter_ lower_bounds_argmax)r9r?rcdo_initr2max_lower_boundbest_lower_boundsr*rWrXinit lower_boundcurrent_lower_bounds best_params best_n_iter convergedn_iterprev_lower_bound log_prob_normlog_respchanges rraBaseMixture.fit_predicts8 $"**bjj)AVW X 771:)) )**.*;*;)<= wwqzl,  q!F74+FG 'Q66')$*;*;< ww &MD  , ,T 2++A<%,266'$2C2CK#% }}!"224  ! #At}}q'89F'2$.2ll1o+M8LLH-"&";";Hm"TK(// <(+;;F44VVD6{TXX-$( :00H0Ow4N&1O"&"6"6"8K"(K(<%&/OI"R4==1#4 MM9#   [)" +. ll1o 8A&&rcXURU5up#[R"U5U4$)a`E 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)r9r?rrs rrqBaseMixture._e_step(s+ #'">">q"A ww}%x//rcg)zM 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=)r9r?rs rrrBaseMixture._m_step;s rcgr8r=r9s rrpBaseMixture._get_parametersI rcgr8r=)r9paramss rrzBaseMixture._set_parametersMrrc^[U5 [XSS9n[URU5SS9$)aKCompute 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#rI)rrr_estimate_weighted_log_probr>s r score_samplesBaseMixture.score_samplesQs0  $ /99!<1EErc@URU5R5$)aCompute the per-sample average log-likelihood of the given data X. Parameters ---------- X : array-like of shape (n_samples, n_dimensions) List of n_features-dimensional data points. Each row corresponds to a single data point. y : Ignored Not used, present for API consistency by convention. Returns ------- log_likelihood : float Log-likelihood of `X` under the Gaussian mixture model. )rrrbs rscoreBaseMixture.scoreds"!!!$))++rch[U5 [XSS9nURU5RSS9$)a4Predict 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. Frr#rI)rrrr}r>s rpredictBaseMixture.predictws7  $ ///299q9AArc~[U5 [XSS9nURU5up#[R"U5$)aVEvaluate 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. Fr)rrrrexp)r9r?rXrs r predict_probaBaseMixture.predict_probas9  $ /2215 vvhrcr[U5 US:a[SUR-5eURRup#[ UR 5nURXR5nURS:Xag[R"[URURU5VVVs/sH!upgnURXg[U55PM# snnn5n OURS:Xac[R"[URU5VVs/sH*uphURX`R[U55PM, snn5n Ou[R"[URURU5VVVs/sH0upgnUUR!X4S9[R""U5--PM2 snnn5n [R$"['U5V Vs/sHup[R("X[S9PM snn 5n X4$s snnnfs snnfs snnnfs snn f)a!Generate 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.fulltiedrGrD)rrr.means_rrr* multinomialweights_covariance_typervstackzip covariances_multivariate_normalintstandard_normalsqrt concatenate enumerater) r9rWrX n_featuresrngn_samples_compr covariancesampler?jrcs rrBaseMixture.samples  q=$'+'8'8:   ))  !2!23MMB   6 ) 7: T%6%67726++Dc&kJ7A ! !V + +.dkk>*J*J++D2C2CS[Q*JA 7: T%6%67 726))/C)Dggj)**7  A NNs rr'BaseMixture._estimate_weighted_log_probs#&&q)D,F,F,HHHrcg)zEstimate log-weights in EM algorithm, E[ log pi ] in VB algorithm. Returns ------- log_weight : array, shape (n_components, ) Nr=rs rr!BaseMixture._estimate_log_weightsrBrcg)zEstimate the log-probabilities log P(X | Z). Compute the log-probabilities per each component for each sample. Parameters ---------- X : array-like of shape (n_samples, n_features) Returns ------- log_prob : array, shape (n_samples, n_component) Nr=r>s rrBaseMixture._estimate_log_probs rcURU5n[USS9n[R"SS9 X#SS2[R4- nSSS5 X44$!,(df  UW4$=f)aEstimate log probabilities and responsibilities for each sample. Compute the log probabilities, weighted log probabilities per component and responsibilities for each sample in X with respect to the current state of the model. Parameters ---------- X : array-like of shape (n_samples, n_features) Returns ------- log_prob_norm : array, shape (n_samples,) log p(X) log_responsibilities : array, shape (n_samples, n_components) logarithm of the responsibilities r#rIignore)underN)rrrerrstaterT)r9r?weighted_log_probrrs rr#BaseMixture._estimate_log_prob_respsi&!<*>>HI(0$ # /rc U(aSOSnURS:Xa[SUS35 g URS:a/[5UR- n[SUSUSS USS35 g g ) z.Print verbose message on the end of iteration.rzdid not converger#zInitialization .r z . time lapse z.5fzs lower bound N)r,rrr)r9lbinit_has_converged converged_msgts rrw'BaseMixture._print_verbose_msg_init_end1sw'9 ?Q <<1  OM?!4 5 \\Q ---A !- aWEs81 r)rrrhr3ror|r1r.r2r{r*r0r/r,r5r4r8)r#)%__name__ __module__ __qualname____firstlineno____doc__rrrrr6dict__annotations__r:rr@r\rVrNr rarqrrrprzrrrrrrrrrrnrurw__static_attributes__r=rrr!r!*s"(AtFCDsD89tS$v>?h4?@Haf=> L M (( k;%h4GH $D 10  +"Z    <5l'6l'\0&        F&,&B$ &<| I      '48 0 rr!) metaclass) rrxabcrrnumbersrrrnumpyr scipy.specialrr baser r r r exceptionsrutilsrutils._param_validationrrutils.validationrrrr!r=rrrsK$ '"#<<%+&:= &Q, Qr