UgIdZddlZddlmZmZddlmZmZddlmZddl Z ddl m Z ddl mZdd lmZmZmZdd lmZdd lmZdd lmZdd lmZmZddlmZdZGddeeeZdS)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~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 T/var/www/surfInsights/venv3-11/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$Z!d%Z"dS)( BaseMixturezBase class for mixture models. This abstract class specifies an interface for all mixture classes and provides basic common methods for mixture models. Nleft)closedgr>kmeansrandomrandom_from_data k-means++ random_statebooleanverbose n_componentstol reg_covarmax_itern_init init_paramsr( warm_startr*verbose_interval_parameter_constraintsc ||_||_||_||_||_||_||_||_| |_| |_ dSNr+) selfr,r-r.r/r0r1r(r2r*r3s r__init__zBaseMixture.__init__AsT)"   &($ 0rcdS)zCheck initial parameters of the derived class. Parameters ---------- X : array-like of shape (n_samples, n_features) Nr7Xs r_check_parameterszBaseMixture._check_parametersY  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_clustersr0r(r%sizeaxisNr&F)rBreplacer')r()rr1rzerosr,r KMeansfitlabels_arangeuniformsumnewaxischoicer _initialize)r7r<r( n_samples_resplabelindicess r_initialize_parametersz"BaseMixture._initialize_parameterscsw 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.rC)"_validate_datarfloat64float32rr,rr=r2hasattrr0infr_rr(range_print_verbose_msg_init_begrU lower_bound_r/_get_parameters_e_step_m_step_compute_lower_bound_print_verbose_msg_iter_endabsr-_print_verbose_msg_init_endwarningswarnr_set_parametersn_iter_argmax)r7r<rZdo_initr0max_lower_boundr(rPrQinit lower_bound best_params best_n_iter convergedn_iterprev_lower_bound log_prob_normlog_respchanges rrXzBaseMixture.fit_predicts8   "*bj)AVW  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)r7r<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:)r7r<r~s rrjzBaseMixture._m_step5s  rcdSr6r:r7s rrhzBaseMixture._get_parametersC rcdSr6r:)r7paramss rrqzBaseMixture._set_parametersGrrct|||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!rC)rr`r_estimate_weighted_log_probr;s r score_sampleszBaseMixture.score_samplesKsK      / /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. )rrrYs rscorezBaseMixture.score^s$"!!!$$))+++rct|||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!rC)rr`rrsr;s rpredictzBaseMixture.predictqsO      / ///2299q9AAArct|||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)rr`rrexp)r7r<rQr~s r predict_probazBaseMixture.predict_probasQ      / /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)rrrrr7s rrz&BaseMixture.sample..sH&v++D$2CS[[QQrcxg|]6\}}}||ftj|zz7S)rA)standard_normalrsqrt)rrrr n_featuresrs rrz&BaseMixture.sample..sa3z6)) /C)DDgj))**rcLg|]!\}}tj||t"S))r])rrr)rjrs rrz&BaseMixture.sample..s- V V Vyq&RWVQc * * * V V Vr)rrr,means_rrr( multinomialweights_covariance_typervstackzipr concatenate enumerate)r7rPrQn_samples_compr<rZrrs` @@rrzBaseMixture.samples  q==$'+'8:   ) : !233DMBB  6 ) ) 69 T%677AA !V + + *-dk>*J*JAA 7: T%677   A N V VInrcdS)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) Nr:r;s rrzBaseMixture._estimate_log_probs  rc||}t|d}tjd5||ddtjfz }dddn #1swxYwY||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 r!rCignore)underN)rrrerrstaterM)r7r<weighted_log_probr}r~s rrz#BaseMixture._estimate_log_prob_resps&!<>HI(0$$$ / .#"rc |rdnd}|jdkrtd|dd S|jdkr3t|jz }td|d|dd |ddd Sd S) z.Print verbose message on the end of iteration.rzzdid not converger!zInitialization .r z . time lapse z.5fzs lower bound N)r*rrr)r7lbinit_has_converged converged_msgts rrnz'BaseMixture._print_verbose_msg_init_end+s'9Q ?Q <1   4M444 5 5 5 5 5 \Q  --A -aT       rr6)r!)#__name__ __module__ __qualname____doc__rrrrr4dict__annotations__r8rr=rUrOrHr rXrirjrhrqrrrrrrrrrrfrlrnr:rrr r +s"(AtFCCCDsD8889htS$v>>>?Xh4???@8Haf===> JLLL M M (( k;%Xh4GGGH $ $D   1110  ^ )")")"V   ^  <\555g'g'g'65g'R000&   ^    ^   ^ FFF&,,,,&BBB$   &<<<<| I I I  ^    ^  '''4888 0 0 0     rr ) metaclass)rroabcrrnumbersrrrnumpyr scipy.specialrr baser r r r exceptionsrutilsrutils._param_validationrrutils.validationrrr r:rrrs]$$ ''''''''""""""""######<<<<<<<<<<%%%%%%++++++&&&&&&::::::::......   &JJJJJ, JJJJJJr