a 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|kr(td|||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)paramZ param_shapenamerQ/var/www/html/assistant/venv/lib/python3.9/site-packages/sklearn/mixture/_base.py _check_shapes   rc@sveZdZUdZeeddddgeeddddgeeddddgeeddddgeeddddgehdgd gd gd geeddddgd Ze e d <ddZ e ddZ ddZe ddZdd&d'Zd(d)Zd*d+Zd?d,d-Zd.d/Ze d0d1Ze d2d3Zd4d5Zd6d7Z d8d9Z!d: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>randomrandom_from_datakmeans 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%r/r'r0rrr__init__@s zBaseMixture.__init__cCsdS)zCheck initial parameters of the derived class. Parameters ---------- X : array-like of shape (n_samples, n_features) Nrr3Xrrr_check_parametersXszBaseMixture._check_parameterscCs$|j\}}|jdkrRt||jf}tj|jd|d|j}d|t ||f<n|jdkr|j ||jfd}||j ddddtj f}n|jdkrt||jf}|j ||jd d }d||t |jf<nD|jd krt||jf}t||j|d \}}d||t |jf<|||dS) a?Initialize the model parameters. Parameters ---------- X : array-like of shape (n_samples, n_features) random_state : RandomState A random number generator instance that controls the random seed used for the method chosen to initialize the parameters. r#r)Z n_clustersr-r%r!sizeZaxisNr"F)r9replacer$)r%)rr.rZzerosr)r ZKMeansfitZlabels_Zarangeuniformsumnewaxischoicer _initialize)r3r6r% n_samples_resplabelindicesrrr_initialize_parametersbs8       z"BaseMixture._initialize_parameterscCsdS)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)r3r6rDrrrrAs 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_predictr3r6yrrrr<s zBaseMixture.fitT)Zprefer_skip_nested_validationcCst||tjtjgdd}|jd|jkrDtd|jd|jd|||jo\t |d }|rj|j nd}tj }d|_ t |j}|j\}}t|D]} || |r||||rtj n|j} |jdkr|} d} qd} td|jdD]Z}| }||\}}||||||} | |}|||t||jkrd } qPq|| | | |ksr|tj kr| }|} |} | |_ q|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)dtypeZensure_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:)"rrZfloat64Zfloat32rr)rr7r/hasattrr-infrLrr%range_print_verbose_msg_init_begrGZ lower_bound_r,_get_parameters_e_step_m_stepZ_compute_lower_bound_print_verbose_msg_iter_endabsr*_print_verbose_msg_init_endwarningswarnr_set_parametersZn_iter_argmax)r3r6rJZdo_initr-Zmax_lower_boundr%rBrCinit lower_boundZ best_paramsZ best_n_iter convergedn_iterZprev_lower_bound log_prob_normlog_respZchangerrrrHsf            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)r3r6r_r`rrrrR!szBaseMixture._e_stepcCsdS)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)r3r6r`rrrrS4s zBaseMixture._m_stepcCsdSr2rr3rrrrQBszBaseMixture._get_parameterscCsdSr2r)r3paramsrrrrYFszBaseMixture._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_probr5rrr 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. )rhrbrIrrrscore]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. Frerr:)rrrgrZr5rrrpredictpszBaseMixture.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. Fre)rrrarexp)r3r6rCr`rrr predict_probaszBaseMixture.predict_probacst|dkrtdjjj\}tj|j}j dkrrt fddt jj |D}nTj dkrt fddt j|D}n&t 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 . 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Returns ------- log_weight : array, shape (n_components, ) Nrrcrrrr}sz!BaseMixture._estimate_log_weightscCsdS)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) Nrr5rrrr|szBaseMixture._estimate_log_probcCs`||}t|dd}tjdd&||ddtjf}Wdn1sN0Y||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)ZunderN)rgrrZerrstater?)r3r6Zweighted_log_probr_r`rrrras   4z#BaseMixture._estimate_log_prob_respcCsB|jdkrtd|n&|jdkr>td|t|_|j|_dS)(Print verbose message on initialization.rzInitialization %drN)r'printr_init_prev_time_iter_prev_time)r3r-rrrrPs    z'BaseMixture._print_verbose_msg_init_begcCsX||jdkrT|jdkr&td|n.|jdkrTt}td|||j|f||_dS)rrrz Iteration %drz0 Iteration %d time lapse %.5fs ll change %.5fN)r0r'rrr)r3r^Zdiff_llZcur_timerrrrTs  z'BaseMixture._print_verbose_msg_iter_endcCsb|rdnd}|jdkr(td|dn6|jdkr^t|j}td|d|dd |ddd S) z.Print verbose message on the end of iteration.r]zdid not convergerzInitialization .rz . time lapse z.5fzs lower bound N)r'rrr)r3ZlbZinit_has_convergedZ converged_msgtrrrrV*s    z'BaseMixture._print_verbose_msg_init_end)N)N)N)r)#__name__ __module__ __qualname____doc__rrrrr1dict__annotations__r4rr7rGrAr<r rHrRrSrQrYrhrirjrlrrrgr}r|rarPrTrVrrrrr*sV   +  i     >     r) metaclass) rrWabcrrnumbersrrrnumpyrZ scipy.specialrr baser r r r exceptionsrutilsrZutils._param_validationrrZutils.validationrrrrrrrrs