0PhIdZddlZddlmZmZddlmZmZddlmZddl Z ddl m Z ddl mZdd lmZmZmZdd lmZdd lmZdd lmZdd lmZmZddlmZmZdZGddeeeZdS)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~tj|}|j|krtd|d|d|jdS)zValidate 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 U/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/mixture/_base.py _check_shapersR HUOOE {k!!jtt[[[%++ /   "!ceZdZUdZeedddgeedddgeedddgeedddgeedddgehdgd gd gd geedddgd Ze e d <dZ e dZ dZe dZd&dZedd&dZdZe dZe dZe dZdZd&dZdZdZd'dZdZe d Ze d!Zd"Zd#Z d$Z!d%Z"dS)( BaseMixturezBase class for mixture models. This abstract class specifies an interface for all mixture classes and provides basic common methods for mixture models. Nleft)closedgr>kmeansrandomrandom_from_data k-means++ random_statebooleanverbose n_componentstol reg_covarmax_itern_init init_paramsr) warm_startr+verbose_interval_parameter_constraintsc ||_||_||_||_||_||_||_||_| |_| |_ dSNr,) selfr-r.r/r0r1r2r)r3r+r4s r__init__zBaseMixture.__init__@sT)"   &($ 0rcdS)zCheck initial parameters of the derived class. Parameters ---------- X : array-like of shape (n_samples, n_features) Nr8Xs r_check_parameterszBaseMixture._check_parametersX  rc\|j\}}|jdkrjtj||jf}t j|jd||j}d|tj ||f<n|jdkrK| ||jf}|| dddtj fz}n|jdkrWtj||jf}| ||jd }d||tj |jf<n^|jd krStj||jf}t||j| \}}d||tj |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%r") n_clustersr1r)r&sizeaxisNr'F)rCreplacer()r))rr2rzerosr-r KMeansfitlabels_arangeuniformsumnewaxischoicer _initialize)r8r=r) n_samples_resplabelindicess r_initialize_parametersz"BaseMixture._initialize_parametersbsw 1  x ' '8Y(9:;;D#0Q 12D9%%u, - -   ) )''i9J-K'LLD DHH!H$$QQQ ]3 3DD  !3 3 38Y(9:;;D")) 15*G;= 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.rD)"rrfloat64float32rr-rr>r3hasattrr1infr`rr)range_print_verbose_msg_init_begrV 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)rQrRinit lower_bound best_params best_n_iter convergedn_iterprev_lower_bound log_prob_normlog_respchanges rrYzBaseMixture.fit_predicts8 $"*bj)AVW X X X 71:) ) ),*.*;,, wqz,,  q!!!F74+F+FG '.Q6')$*;<< w 1&MM! 0! 0D  , ,T 2 2 2 =++A|<<<%,C26''$2CK}!!"2244  ! #At}q'899  F'2$.2ll1oo+M8LLH---"&";";Hm"T"TK(+;;F44VVDDD6{{TX--$( .00iHHH00Ow4N4N&1O"&"6"6"8"8K"(K&/DO  4=1#4#4 M9#     [)))" + ll1oo 8A&&&rc^||\}}tj||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)r8r=r}r~s rrizBaseMixture._e_step!s1 #'">">q"A"A xw}%%x//rcdS)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;)r8r=r~s rrjzBaseMixture._m_step4s  rcdSr7r;r8s rrhzBaseMixture._get_parametersB rcdSr7r;)r8paramss rrqzBaseMixture._set_parametersFrrct|t||d}t||dS)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"rD)rrr_estimate_weighted_log_probr<s r score_sampleszBaseMixture.score_samplesJsG  $ / / /99!<<1EEEErcP||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. )rrrZs rscorezBaseMixture.score]s$"!!!$$))+++rct|t||d}||dS)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. Frr"rD)rrrrsr<s rpredictzBaseMixture.predictpsK  $ / / ///2299q9AAArct|t||d}||\}}tj|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. Fr)rrrrexp)r8r=rRr~s r predict_probazBaseMixture.predict_probasM  $ / / /22155 8vhrct|dkrtdjzjj\}t j|j}j dkr:tj fdtjj |D}nzj dkr5tj fdtj|D}n:tj fdtjj |D}tjdt|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.fullc `g|]*\}}}||t|+Sr;)multivariate_normalint).0r covariancesamplerngs r z&BaseMixture.sample..sG2z6++D*c&kkJJrtiedc hg|].\}}|jt|/Sr;)r covariances_r)rrrrr8s rrz&BaseMixture.sample..sH&v++D$2CS[[QQrcxg|]6\}}}||ftj|zz7S)rB)standard_normalrsqrt)rrrr n_featuresrs rrz&BaseMixture.sample..sa3z6)) /C)DDgj))**rcLg|]!\}}tj||t"S))r^)rrr)rjrs rrz&BaseMixture.sample..s- V V Vyq&RWVQc * * * V V Vr)rrr-means_rrr) multinomialweights_covariance_typervstackzipr concatenate enumerate)r8rQrRn_samples_compr=r[rrs` @@rrzBaseMixture.samples  q==$'+'8:   ) : !233DMBB  6 ) ) 69 T%677AA !V + + *-dk>*J*JAA 7: T%677   A N V VIn>HI(0$$$ / .#"rc |rdnd}|jdkrtd|dd S|jdkr3t|jz }td|d|dd |ddd Sd S) z.Print verbose message on the end of iteration.rzzdid not converger"zInitialization .r z . time lapse z.5fzs lower bound N)r+rrr)r8lbinit_has_converged converged_msgts rrnz'BaseMixture._print_verbose_msg_init_end*s'9Q ?Q <1   4M444 5 5 5 5 5 \Q  --A -aT       rr7)r")#__name__ __module__ __qualname____doc__rrrrr5dict__annotations__r9rr>rVrPrIr rYrirjrhrqrrrrrrrrrrfrlrnr;rrr!r!*s"(AtFCCCDsD8889htS$v>>>?Xh4???@8Haf===> JLLL M M (( k;%Xh4GGGH $ $D   1110  ^ )")")"V   ^  <\555g'g'g'65g'R000&   ^    ^   ^ FFF&,,,,&BBB$   &<<<<| I I I  ^    ^  '''4888 0 0 0     rr!) metaclass) rroabcrrnumbersrrrnumpyr scipy.specialrr baser r r r exceptionsrutilsrutils._param_validationrrutils.validationrrrr!r;rrrsc$$ ''''''''""""""""######<<<<<<<<<<%%%%%%++++++&&&&&&::::::::========   &JJJJJ, JJJJJJr