o \hI@sdZddlZddlmZmZddlmZmZddlmZddl Z ddl m Z ddl mZdd lmZmZmZdd lmZdd lmZdd lmZdd lmZmZddlmZmZddZGdddeeedZdS)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_datacCs,t|}|j|krtd|||jfdS)zValidate the shape of the input parameter 'param'. Parameters ---------- param : array param_shape : tuple name : str z:The parameter '%s' should have the shape of %s, but got %sN)nparrayshape ValueError)param param_shapenamerX/home/air/segue/gemini/backup/venv/lib/python3.10/site-packages/sklearn/mixture/_base.py _check_shapes  rc@sveZdZUdZeeddddgeeddddgeeddddgeeddddgeeddddgehdgd gd gd geeddddgd Ze e d <ddZ e ddZ ddZe ddZd 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 Cs@||_||_||_||_||_||_||_||_| |_| |_ dSNr)) selfr*r+r,r-r.r/r&r0r(r1rrr__init__@s  zBaseMixture.__init__cCdS)zCheck initial parameters of the derived class. Parameters ---------- X : array-like of shape (n_samples, n_features) Nrr4Xrrr_check_parametersXzBaseMixture._check_parameterscCs"|j\}}|jdkr)t||jf}tj|jd|d|j}d|t ||f<n`|jdkrG|j ||jfd}||j ddddtj f}nB|jdkrht||jf}|j ||jd d }d||t |jf<n!|jd krt||jf}t||j|d \}}d||t |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_clustersr.r&r#sizeaxisNr$F)r=replacer%)r&)rr/rzerosr*r KMeansfitlabels_arangeuniformsumnewaxischoicer _initialize)r4r8r& n_samples_resplabelindicesrrr_initialize_parametersbs8      z"BaseMixture._initialize_parameterscCr6)zInitialize the model parameters of the derived class. Parameters ---------- X : array-like of shape (n_samples, n_features) resp : array-like of shape (n_samples, n_components) Nr)r4r8rMrrrrJs zBaseMixture._initializecCs||||S)aEstimate 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_predictr4r8yrrrrCs zBaseMixture.fitT)prefer_skip_nested_validationcCst||tjtjgdd}|jd|jkr"td|jd|jd|||jo.t |d }|r5|j nd}tj }d|_ t |j}|j\}}t|D]v} || |r[||||ratj n|j} |jdkrp|} d} qLd} td|jdD],}| }||\}}||||||} | |}|||t||jkrd } nqz|| | | |ks|tj kr| }|} |} | |_ qL|j s|jdkrtd t|| | |_ ||_||\}}|j!dd S) aEstimate 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_rFTzBest performing initialization did not converge. Try different init parameters, or increase max_iter, tol, or check for degenerate data.r>)"rrfloat64float32rr*rr9r0hasattrr.infrWrr&range_print_verbose_msg_init_begrP 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)r4r8rSdo_initr.max_lower_boundr&rKrLinit lower_bound best_params best_n_iter convergedn_iterprev_lower_bound log_prob_normlog_respchangerrrrQsj             zBaseMixture.fit_predictcCs||\}}t||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)r4r8rtrurrrr`!szBaseMixture._e_stepcCr6)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)r4r8rurrrra4s zBaseMixture._m_stepcCdSr3rr4rrrr_BzBaseMixture._get_parameterscCryr3r)r4paramsrrrrhFr{zBaseMixture._set_parameterscCs(t|t||dd}t||ddS)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. Fresetrr>)rrr_estimate_weighted_log_probr7rrr score_samplesJszBaseMixture.score_samplescCs||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. )rrxrRrrrscore]szBaseMixture.scorecCs(t|t||dd}||jddS)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. Fr}rr>)rrrrjr7rrrpredictpszBaseMixture.predictcCs.t|t||dd}||\}}t|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})rrrwrexp)r4r8rLrurrr predict_probas zBaseMixture.predict_probacst|dkrtdjjj\}tj|j}j dkr9t fddt jj |D}n*j dkrPt fddt j|D}nt fddt jj |D}t d dt|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. rzNInvalid value for 'n_samples': %d . The sampling requires at least one sample.fullcs$g|]\}}}||t|qSr)multivariate_normalint.0rx covariancesample)rngrr sz&BaseMixture.sample..tiedcs$g|]\}}|jt|qSr)r covariances_r)rrxr)rr4rrrscs0g|]\}}}|j|fdt|qS)r<)standard_normalrsqrtr) n_featuresrrrrscSs g|] \}}tj||tdqS))rU)rrr)rjrrrrrs )rrr*means_rrr& multinomialweights_covariance_typervstackzipr concatenate enumerate)r4rKrLn_samples_compr8rSr)rrr4rrsF           zBaseMixture.samplecCs|||S)aEstimate the weighted log-probabilities, log P(X | Z) + log weights. Parameters ---------- X : array-like of shape (n_samples, n_features) Returns ------- weighted_log_prob : array, shape (n_samples, n_component) )_estimate_log_prob_estimate_log_weightsr7rrrrs z'BaseMixture._estimate_weighted_log_probcCr6)zEstimate log-weights in EM algorithm, E[ log pi ] in VB algorithm. Returns ------- log_weight : array, shape (n_components, ) Nrrzrrrrr:z!BaseMixture._estimate_log_weightscCr6)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) Nrr7rrrrszBaseMixture._estimate_log_probcCsf||}t|dd}tjdd||ddtjf}Wd||fS1s*wY||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 rr>ignore)underN)rrrerrstaterH)r4r8weighted_log_probrtrurrrrws   z#BaseMixture._estimate_log_prob_respcCsH|jdkr td|dS|jdkr"td|t|_|j|_dSdS)(Print verbose message on initialization.rzInitialization %drN)r(printr_init_prev_time_iter_prev_time)r4r.rrrr]s    z'BaseMixture._print_verbose_msg_init_begcCsb||jdkr-|jdkrtd|dS|jdkr/t}td|||j|f||_dSdSdS)rrrz Iteration %drz0 Iteration %d time lapse %.5fs ll change %.5fN)r1r(rrr)r4rrdiff_llcur_timerrrrcs   z'BaseMixture._print_verbose_msg_iter_endcCsh|rdnd}|jdkrtd|dd S|jdkr2t|j}td|d|dd |ddd Sd S) z.Print verbose message on the end of iteration.rqzdid not convergerzInitialization .rz . time lapse z.5fzs lower bound N)r(rrr)r4lbinit_has_converged converged_msgtrrrre*s    z'BaseMixture._print_verbose_msg_init_endr3)r)#__name__ __module__ __qualname____doc__rrrrr2dict__annotations__r5rr9rPrJrCr rQr`rar_rhrrrrrrrrrwr]rcrerrrrr*sX    +   i     >   r) metaclass) rrfabcrrnumbersrrrnumpyr scipy.specialrr baser r r r exceptionsrutilsrutils._param_validationrrutils.validationrrrrrrrrs