KiRRSrSSKrSSKJrJr SSKJr SSKJrJ r SSK J r SSK r SSK Jr SSKJrJrJr SS KJr SS KJr SS KJr SS KJrJrJrJrJrJr SS K J!r!J"r" SSK#J$r$J%r% Sr&"SS\\\S9r'g)zBase class for mixture models.N)ABCMetaabstractmethod) nullcontext)IntegralReal)time)cluster) BaseEstimator DensityMixin _fit_context)kmeans_plusplus)ConvergenceWarning)check_random_state)_convert_to_numpy_is_numpy_namespace _logsumexp_max_precision_float_dtype get_namespaceget_namespace_and_device)Interval StrOptions)check_is_fitted validate_datacfURU: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)shape ValueError)param param_shapenames W/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/sklearn/mixture/_base.py _check_shaper!s2 {{k![%++ /  "c\rSrSr%Sr\"\SSSS9/\"\SSSS9/\"\SSSS9/\"\SSSS9/\"\SSSS9/\"1S k5/S /S /S /\"\SSSS9/S . r \ \ S'Sr \ S(Sj5rS(Sjr\ S5rS(Sjr\"SS9S(Sj5rS(Sjr\ S5r\ S5r\ S5rSrS(SjrSrSrS)SjrS(S jr\ S(S!j5r\ S(S"j5rS(S#jr S$r!S%r"S&r#S'r$g)* BaseMixture1zBase 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 gNr0) selfr1r2r3r4r5r6r-r7r/r8s r __init__BaseMixture.__init__Gs:)"  &($ 0r"cg)zwCheck initial parameters of the derived class. Parameters ---------- X : array-like of shape (n_samples, n_features) Nr<Xxps r _check_parametersBaseMixture._check_parameters_ r"c [XS9up4nURupdURS:Xa[R"X`R 4UR S9n[R"UR SUS9RU5RnSU[R"U5U4'GOGURS:XaWURURX`R 4S9UR US9nXsRUSS 9S S 2UR4-nOURS :Xa[UR X`R 4UR US9nUR!X`R S S 9n [#U 5H upSX{U 4'M OuURS:Xae[R"X`R 4UR S9n[%UUR US9upISXy[R"UR 54'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. rCr))dtyper&) n_clustersr5r-r*sizerIdeviceaxisNr+F)rLreplacer,)r-)rrr6npzerosr1rIr KMeansfitlabels_arangeasarrayuniformsumnewaxischoice enumerater _initialize) r<rBr-rC_rN n_samplesresplabelindicescolindexs r _initialize_parameters"BaseMixture._initialize_parametersis1: vww   x '88Y(9(9:!''JD#00Q 12D9%u, -    )::$$96G6G*H$IggD FF4aF(BJJ7 7D   !3 388--.aggfD#)) 1 15*G(0 #$CZ 1    ,88Y(9(9:!''JD(!!)JA ;5 URAWUS9 WUl!XplXl"UR+XS9unnURGUSS 9$) aBEstimate model parameters using X and predict the labels for X. The method fits the model ``n_init`` times and sets the parameters with which the model has the largest likelihood or lower bound. Within each trial, the method iterates between E-step and M-step for `max_iter` times until the change of likelihood or lower bound is less than `tol`, otherwise, a :class:`~sklearn.exceptions.ConvergenceWarning` is raised. After fitting, it predicts the most probable label for the input data points. .. versionadded:: 0.20 Parameters ---------- X : array-like of shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. y : Ignored Not used, present for API consistency by convention. Returns ------- labels : array, shape (n_samples,) Component labels. )rIensure_min_samplesrz:Expected n_samples >= n_components but got n_components = z, n_samples = rH converged_r&FTzBest performing initialization did not converge. Try different init parameters, or increase max_iter, tol, or check for degenerate data.rO)$rrfloat64float32rr1rrDr7hasattrr5infrrrr-range_print_verbose_msg_init_begrf lower_bound_r4_get_parameters_e_step_m_step_compute_lower_boundappend_print_verbose_msg_iter_endabsr2_print_verbose_msg_init_endwarningswarnr_set_parametersn_iter_ lower_bounds_argmax)r<rBrlrCr_do_initr5max_lower_boundbest_lower_boundsr-r`init lower_boundcurrent_lower_bounds best_params best_n_iter convergedn_iterprev_lower_bound log_prob_normlog_respchanges r rkBaseMixture.fit_predicts8a  $"**bjj)AVW X 771:)) )**.*;*;)<= wwqzl,  q(F74+FG 'Q66')$*;*;< ww &MD  , ,T 2++A+C%,266'$2C2CK#% }}!"224  ! #At}}q'89F'2$.2ll1l.D+M8LLHL4"&";";Hm"TK(// <(+;;F44VVD6{TXX-$( :00iH0Ow4N&1O"&"6"6"8K"(K(<%&/DOI"R4==1#4 MM9#   [R0" +. ll1l, 8yyy**r"c`[XS9up#URXS9upEURU5U4$)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. rH)r_estimate_log_prob_respmean)r<rBrCr_rrs r r{BaseMixture._e_step:s: a'"&">">q">"H ww}%x//r"cg)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@)r<rBrs r r|BaseMixture._m_stepNs r"cgr;r@)r<s r rzBaseMixture._get_parameters\ r"cgr;r@)r<paramss r rBaseMixture._set_parameters`rr"c^[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&rO)rrr_estimate_weighted_log_prob)r<rBs r score_samplesBaseMixture.score_samplesds0  $ /$::1=AFFr"cn[U5up4[URURU555$)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. )rfloatrr)r<rBrlrCr_s r scoreBaseMixture.scorews/"a RWWT//2344r"c[U5 [U5up#[XSS9nURUR U5SS9$)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&rO)rrrrr)r<rBrCr_s r predictBaseMixture.predictsB a  $ /yy99!<1yEEr"c[U5 [XSS9n[U5up#URXS9up4UR 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. FrrH)rrrrexp)r<rBrCr_rs r predict_probaBaseMixture.predict_probasH  $ /a 2212< vvhr"c|[U5 [UR5up#nUS:a[SUR-5eURR up5[ UR5nURU[URU55nURS:Xa|[R"[[URU5[URU5U5VV V s/sH!upn UR!X[#U 55PM# sn n n5n GOURS:Xax[R"[[URU5U5VV s/sH5upUR!U[URU5[#U 55PM7 sn n5n O[R"[[URU5[URU5U5VV V s/sH0upn UUR%X4S9[R&"U 5--PM2 sn n n5n UR)[+[-U55V s/sH)n UR/[#X|5XR0US9PM+ sn 5n [3X$S9nUR5XUS9U 4$s sn n nfs sn nfs sn n nfs sn 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.fulltiedrKrM)rCrN)rrmeans_rr1rrr- multinomialrweights_covariance_typerRvstackzip covariances_multivariate_normalintstandard_normalsqrtconcatrwlenrint64rrX)r<r`rCr_device_ n_featuresrngn_samples_compr covariancesamplerBirlmax_float_dtypes r rBaseMixture.samples 1$++>w q=$'+'8'8:   ))  !2!23 (;    6 ) 7:)$++r:)$*;*;R@&7726++Dc&kJ7 A ! !V + +.)$++r:N+ +++/0A0A2FF +  A 7:)$++r:)$*;*;R@&7 726))/C)Dggj)**7  A IIs>23 3AN-.'R3  5Kzz!7zCQFFU  s.(J% $*>>HI(0$ # /r"c 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 .rpz . time lapse z.5fzs lower bound N)r/rrr)r<lbinit_has_converged converged_msgts r r'BaseMixture._print_verbose_msg_init_end\sw'9 ?Q <<1  OM?!4 5 \\Q ---A !- aWEs81 r")rrrrr6ryrr4r1r5rr-r3r2r/r8r7r;)r&)%__name__ __module__ __qualname____firstlineno____doc__rrrrr9dict__annotations__r=rrDrfr^rUr rkr{r|rzrrrrrrrrrrrxrr__static_attributes__r@r"r r$r$1s"(AtFCDsD89tS$v>?h4?@Haf=> L M (( k;%h4GH $D 10  5"n    <5m+6m+^0(        G&5(F& (KGZ U      '@8 0 r"r$) metaclass)(rrabcrr contextlibrnumbersrrrnumpyrRsklearnr sklearn.baser r r sklearn.clusterr sklearn.exceptionsr sklearn.utilsrsklearn.utils._array_apirrrrrrsklearn.utils._param_validationrrsklearn.utils.validationrrr!r$r@r"r rsX$ '""BB+1,AC $u, ur"