-i2SrSSKrSSKJr SSKrSSKJr SSK J r J r J r SSK Jr SSKJrJr SS KJr SS KJr SS KJr SS KJrJr SS KJr SSKJr SSKJ r J!r! "SS\\ \ 5r"g)z! Nearest Centroid Classification N)Real)sparse) BaseEstimatorClassifierMixin _fit_context)#DiscriminantAnalysisPredictionMixin)pairwise_distancespairwise_distances_argmin) LabelEncoder)get_tags) available_if)Interval StrOptions)check_classification_targets)csc_median_axis_0)check_is_fitted validate_datacR^\rSrSr%Sr\"SS15/\"\SSSS9S/S \"S S 15/S .r\ \ S 'SSS S.Sjjr \ "SS9S5r U4SjrSrSr\"\5"\R&5r\"\5"\R(5r\"\5"\R*5rU4SjrSrU=r$)NearestCentroidav Nearest centroid classifier. Each class is represented by its centroid, with test samples classified to the class with the nearest centroid. Read more in the :ref:`User Guide `. Parameters ---------- metric : {"euclidean", "manhattan"}, default="euclidean" Metric to use for distance computation. If `metric="euclidean"`, the centroid for the samples corresponding to each class is the arithmetic mean, which minimizes the sum of squared L1 distances. If `metric="manhattan"`, the centroid is the feature-wise median, which minimizes the sum of L1 distances. .. versionchanged:: 1.5 All metrics but `"euclidean"` and `"manhattan"` were deprecated and now raise an error. .. versionchanged:: 0.19 `metric='precomputed'` was deprecated and now raises an error shrink_threshold : float, default=None Threshold for shrinking centroids to remove features. priors : {"uniform", "empirical"} or array-like of shape (n_classes,), default="uniform" The class prior probabilities. By default, the class proportions are inferred from the training data. .. versionadded:: 1.6 Attributes ---------- centroids_ : array-like of shape (n_classes, n_features) Centroid of each class. classes_ : array of shape (n_classes,) The unique classes labels. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 deviations_ : ndarray of shape (n_classes, n_features) Deviations (or shrinkages) of the centroids of each class from the overall centroid. Equal to eq. (18.4) if `shrink_threshold=None`, else (18.5) p. 653 of [2]. Can be used to identify features used for classification. .. versionadded:: 1.6 within_class_std_dev_ : ndarray of shape (n_features,) Pooled or within-class standard deviation of input data. .. versionadded:: 1.6 class_prior_ : ndarray of shape (n_classes,) The class prior probabilities. .. versionadded:: 1.6 See Also -------- KNeighborsClassifier : Nearest neighbors classifier. Notes ----- When used for text classification with tf-idf vectors, this classifier is also known as the Rocchio classifier. References ---------- [1] Tibshirani, R., Hastie, T., Narasimhan, B., & Chu, G. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proceedings of the National Academy of Sciences of the United States of America, 99(10), 6567-6572. The National Academy of Sciences. [2] Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning Data Mining, Inference, and Prediction. 2nd Edition. New York, Springer. Examples -------- >>> from sklearn.neighbors import NearestCentroid >>> import numpy as np >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> y = np.array([1, 1, 1, 2, 2, 2]) >>> clf = NearestCentroid() >>> clf.fit(X, y) NearestCentroid() >>> print(clf.predict([[-0.8, -1]])) [1] manhattan euclideanrNneither)closedz array-like empiricaluniformmetricshrink_thresholdpriors_parameter_constraints)r r!c(XlX lX0lg)Nr)selfrr r!s V/var/www/html/venv/lib/python3.13/site-packages/sklearn/neighbors/_nearest_centroid.py__init__NearestCentroid.__init__s 0 T)prefer_skip_nested_validationc URS:Xa[XUS/S9upO9[U5RR(aSOSn[UUUUSS/S9up[ R "U5n[U5 URupV[5nURU5nUR=Ul n U Rn U S:a[S U -5eURS :XaH[R "USS 9up[R""U 5[%['U55- UlOXURS :Xa#[R*"S U - /U -5UlO%[R*"UR5UlUR(S:R-5(a [S5e[R."UR(R15S5(dG[2R4"S[65 UR(UR(R15- Ul[R8"X4[R:S9Ul[R>"U 5n [AU 5HnX:Hn[R0"U5X'U(a[RB"U5SnURS:XaIU(d&[RD"XSS9UR<U'M|[GX5UR<U'MXRISS9UR<U'M [RJ"XR<U- SS9S-n[RJ"[RL"UR1SS9XZ- - 5SS9Ul'[-URNS:H5(a[2R4"S5 SnU(aT[RP"URSSS9URUSS9- RW5S:H5(a [U5eU(d<[RP"[RX"USS9S:H5(a [U5eURISS9n[RL"SU - SU- - 5nURN[RD"URN5-nUR[['U5S 5nUU-n[RJ"UR<U- U- SS9Ul.UR^(a[R`"UR\5n[Rb"UR\5UR^- Ul.[Rd"UR\SSUR\S9 U=R\U-sl.UUR\-n[RJ"UU-SS9UlU$)a Fit the NearestCentroid model according to the given training data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vector, where `n_samples` is the number of samples and `n_features` is the number of features. Note that centroid shrinking cannot be used with sparse matrices. y : array-like of shape (n_samples,) Target values. Returns ------- self : object Fitted estimator. rcsc) accept_sparse allow-nanTcsr)ensure_all_finiter,rz>The number of classes has to be greater than one; got %d classr)return_inverserrzpriors must be non-negativeg?zAThe priors do not sum to 1. Normalizing such that it sums to one.dtype)axisF)copyzself.within_class_std_dev_ has at least 1 zero standard deviation.Inputs within the same classes for at least 1 feature are identical.z2All features have zero variance. Division by zero.N)out)3rrr input_tags allow_nanspissparsershaper fit_transformclasses_size ValueErrorr!npuniquebincountfloatlen class_prior_asarrayanyisclosesumwarningswarn UserWarningemptyfloat64 centroids_zerosrangewheremedianrmeanarraysqrtwithin_class_std_dev_allmaxmintoarrayptpreshape deviations_r signabsclip)r$Xyr/ is_X_sparse n_samples n_featuresley_indclasses n_classes_ class_countsnk cur_class center_maskvarianceerr_msgdataset_centroid_msmmmssignsmsds r%fitNearestCentroid.fits* ;;+ % !E7CDAq (~88BB  !"3$en DAkk!n $Q' ! ^  #"$++- LL q=P  ;;+ % ii$?OA " L 9E#a&M ID  [[I % " A M?Y+F GD  " 4;; 7D     ! & & ( (:; ;zz$++//1377 MMS !% 1 1D4E4E4I4I4K KD ((I#:"**MXXi y)I,KFF;/BM hh{3A6 {{k)"131>PQ1RDOOI.1B1>1RDOOI.-.^-@-@a-@-H ** 88A 66UCqH%'XX GGHLLaL(I,AB C%& " t))Q. / / MMW  G 266155a5=155a5=#@"I"I"Kq"PQQW% %qq(9Q(>!?!?W% %FFFN GGS2X# /2 3  & &43M3M)N N YYs1vq ! !V88 __0 0B 6U   GGD,,-E!vvd&6&67$:O:OOD  GGD$$a43C3C D    % t'''C hh'83'>UKDO r(c>[U5 [R"URS[ UR 5- 5R 5(a`[U5RR(aSOSn[UUUSSS9nUR [XRURS9$[TU]=U5$)a0Perform classification on an array of test vectors `X`. The predicted class `C` for each sample in `X` is returned. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Input data. Returns ------- y_pred : ndarray of shape (n_samples,) The predicted classes. r1r-Tr.F)r/r,resetr)rr@rHrErDr=rXr r7r8rr rOrsuperpredict)r$rbr/ __class__s r%rNearestCentroid.predicts  ::d''S-?)? @ D D F F (~88BB  "3# A==)!__T[[Q 7?1% %r(c [US5 [XSSS[RS9n[R"UR SUR R4[RS9nURS:gnUSS2U4==URU-ss'URR5nUSS2U4==URU-ss'[UR R5Hsn[X%U/URS9R5nUS -n[R"U*S [R "UR"U5--5USS2U4'Mu U$) NrOTFr.)r5r|r,r3rr2r}rg@)rrr@rNrMr;r=r>rWrOr5rQr rravelsqueezelogrE)r$rb X_normalizeddiscriminant_scoremaskcentroids_normalized class_idx distancess r%_decision_function"NearestCentroid._decision_function8sGl+$ $e5   XX    "DMM$6$6 7rzz ))Q.QW!;!;D!AA#335QW%)C)CD)II%t}}112I*I;? eg  !OI/1zz S266$*;*;I*F#GGG0 q)| , 3"!r(c URS:H$)Nrr})r$s r%_check_euclidean_metric'NearestCentroid._check_euclidean_metricTs{{k))r(c>[TU]5nURS:HURlSURlU$)N nan_euclideanT)r~__sklearn_tags__rr7r8r)r$tagsrs r%r NearestCentroid.__sklearn_tags__cs6w')$(KK?$B!!% r()rOrEr=r^rr!r rW)r)__name__ __module__ __qualname____firstlineno____doc__rrrr"dict__annotations__r&rryrrrrr decision_function predict_probapredict_log_probar__static_attributes__ __classcell__)rs@r%rrseP{K89:%dAtIFM[),D!EF$D  5{6{z &D"8*%%<=+==!!89+99M%%<=+==r(r)#rrJnumbersrnumpyr@scipyrr9baserrrdiscriminant_analysisr metrics.pairwiser r preprocessingr utilsr utils._available_ifrutils._param_validationrrutils.multiclassrutils.sparsefuncsrutils.validationrrrr(r%rsP??G).:;1=J'-Jr(