:iISrSSKrSSKJrJr SSKJrJr SSKJr SSK r SSK J r SSK Jr SS KJrJrJr SS KJr SS KJr SS KJr SS KJrJr SSKJr 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_fittedc[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 X/srv/projetos/modelo_ml_acdoc/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.r/r0r1r2r)r3r+r4s r__init__BaseMixture.__init__As:)"  &($ 0rcg)zwCheck initial parameters of the derived class. Parameters ---------- X : array-like of shape (n_samples, n_features) Nr8Xs r_check_parametersBaseMixture._check_parametersY rcURup4URS:Xaw[R"X0R45n[ R "URSUS9RU5RnSU[R"U5U4'GO-URS:XaAURX0R4S9nXURSS9SS2[R4-nOURS:Xa`[R"X0R45nURX0RS S 9nSXW[R"UR54'OlURS :Xa\[R"X0R45n[UURUS 9upGSXW[R"UR54'URUW5 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%r") n_clustersr1r)r&sizeaxisNr'F)rEreplacer()r))rr2rzerosr-r KMeansfitlabels_arangeuniformsumnewaxischoicer _initialize)r8r>r) n_samples_resplabelindicess r_initialize_parameters"BaseMixture._initialize_parameterscsww   x '88Y(9(9:;D#00Q 12D9%u, -    )''i9J9J-K'LD HH!H$Q ]3 3D   !3 388Y(9(9:;D")) 1 15*G;rUs rrRBaseMixture._initializes rc(URX5 U$)aXEstimate 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_predictr8r>ys rrKBaseMixture.fits6  rT)prefer_skip_nested_validationcURU[R[R/SS9nURSUR :a([ SUR SURS35eURU5 UR=(a [US5(+nU(a UROSn[R*nSUl [UR5nURupx[U5GHLn UR!U 5 U(aUR#X5 U(a[R*O UR$n UR&S:XaUR)5n Sn MuSn [SUR&S-5HpnU nUR+U5unnUR-UU5 UR/UU5n X- nUR1UU5 [3U5UR4:dMnS n O UR7X5 X:dU[R*:XdGM2U nUR)5n Wn Xl GMO UR(d+UR&S:a:"S [<5 UR?W 5 W Ul XPlUR+U5unnURCSS 9$) 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 )dtypeensure_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.rF)"_validate_datarfloat64float32rr-rr?r3hasattrr1infrerr)range_print_verbose_msg_init_begrX lower_bound_r0_get_parameters_e_step_m_step_compute_lower_bound_print_verbose_msg_iter_endabsr._print_verbose_msg_init_endwarningswarnr_set_parametersn_iter_argmax)r8r>r_do_initr1max_lower_boundr)rSrTinit lower_bound best_params best_n_iter convergedn_iterprev_lower_bound log_prob_normlog_respchanges rr]BaseMixture.fit_predictsm8   "**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-$( :00H0Ow4N&1O"&"6"6"8K"(K&/OC"L4==1#4 MM9#   [)" + ll1o 8A&&rcXURU5up#[R"U5U4$)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)r8r>rrs rroBaseMixture._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<)r8r>rs rrpBaseMixture._m_step5s rcgr7r<r8s rrnBaseMixture._get_parametersC rcgr7r<)r8paramss rrwBaseMixture._set_parametersGrrcj[U5 URUSS9n[URU5SS9$)aKCompute the log-likelihood of each sample. Parameters ---------- X : array-like of shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. Returns ------- log_prob : array, shape (n_samples,) Log-likelihood of each sample in `X` under the current model. Fresetr"rF)rrfr_estimate_weighted_log_probr=s r score_samplesBaseMixture.score_samplesKs9      /99!<1EErc@URU5R5$)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. )rrr^s rscoreBaseMixture.score^s"!!!$))++rct[U5 URUSS9nURU5RSS9$)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"rF)rrfrryr=s rpredictBaseMixture.predictqs@      ///299q9AArc[U5 URUSS9nURU5up#[R"U5$)aVEvaluate the components' density for each sample. Parameters ---------- X : array-like of shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. Returns ------- resp : array, shape (n_samples, n_components) Density of each Gaussian component for each sample in X. Fr)rrfrrexp)r8r>rTrs r predict_probaBaseMixture.predict_probasB      /2215 vvhrcr[U5 US:a[SUR-5eURRup#[ UR 5nURXR5nURS:Xag[R"[URURU5VVVs/sH!upgnURXg[U55PM# snnn5n OURS:Xac[R"[URU5VVs/sH*uphURX`R[U55PM, snn5n Ou[R"[URURU5VVVs/sH0upgnUUR!X4S9[R""U5--PM2 snnn5n [R$"['U5V Vs/sHup[R("X[S9PM snn 5n X4$s snnnfs snnfs snnnfs snn f)a!Generate random samples from the fitted Gaussian distribution. Parameters ---------- n_samples : int, default=1 Number of samples to generate. Returns ------- X : array, shape (n_samples, n_features) Randomly generated sample. y : array, shape (nsamples,) Component labels. r"zNInvalid value for 'n_samples': %d . The sampling requires at least one sample.fulltiedrD)rc)rrr-means_rrr) multinomialweights_covariance_typervstackzip covariances_multivariate_normalintstandard_normalsqrt concatenate enumerater) r8rSrT n_featuresrngn_samples_compr covariancesampler>jr_s rrBaseMixture.samples  q=$'+'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 NNweighted_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)r8lbinit_has_converged converged_msgts rrt'BaseMixture._print_verbose_msg_init_end+sw'9 ?Q <<1  OM?!4 5 \\Q ---A !- aWEs81 r)rrrer2rmr0r-r1rxr)r/r.r+r4r3r7)r")%__name__ __module__ __qualname____firstlineno____doc__rrrrr5dict__annotations__r9rr?rXrRrKr r]rorprnrwrrrrrrrrrrlrrrt__static_attributes__r<rrr r +s"(AtFCDsD89tS$v>?h4?@Haf=> L M (( k;%h4GH $D 10  )"V    <5g'6g'R0&        F&,&B$ &<| I      '48 0 rr ) metaclass)rruabcrrnumbersrrrnumpyr scipy.specialrr baser r r r exceptionsrutilsrutils._param_validationrrutils.validationrrr r<rrrsK$ '"#<<%+&:. &J, Jr