0Ph2dZddlZddlmZddlZddlmZddl m Z m Z m Z ddl mZddlmZmZdd lmZdd lmZdd lmZdd lmZmZdd lmZddlmZddlm Z m!Z!Gddee e Z"dS)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_datacreZdZUdZeddhgeeddddgded d hgd Zee d < ddd d dZ e ddZ fdZ dZdZeeejZeeejZeeejZfdZxZS)NearestCentroida 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)rr c0||_||_||_dS)Nr)selfrrr s c/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/neighbors/_nearest_centroid.py__init__zNearestCentroid.__init__s 0 T)prefer_skip_nested_validationc" |jdkrt|||dg\}}n6t|jjrdnd}t||||ddg\}}t j|}t||j\}}t}| |}|j x|_ } | j } | dkrtd | z|jd krPtj|d \} } tj| t%t'|z |_nJ|jd kr!tjd | z g| z|_ntj|j|_|jdkrtdtj|jds@t3jdt6|j|jz |_tj| |ftj|_tj| } tA| D]}||k}tj|| |<|rtj!|d}|jdkrE|s%tj"||d|j|<mtG|||j|<||$d|j|<tj%||j|z ddz}tj%tj&|d|| z z d|_'t-|j'dkrt3jdd}|rdtj(|)d|*dz +dkrt||s;tj(tj,|ddkrt||$d}tj&d| z d|z z }|j'tj"|j'z}|-t'|d }||z}tj%|j|z |z d|_.|j/rtj0|j.}tj1|j.|j/z |_.tj2|j.dd|j.|xj.|zc_.||j.z}tj%||zd|_|S)a0 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_rsignabsclip)r#Xyr- is_X_sparse n_samples n_featuresley_indclasses n_classes_ class_countsnk cur_class center_maskvarianceerr_msgdataset_centroid_msmmmssignsmsds r$fitzNearestCentroid.fitsu* ;+ % % q!E7CCCDAqq (~~8BL  !"3$en DAqk!nn $Q''' ! : ^^  ##"$+- L q==P  ;+ % % i$???OA| " L 9 9E#a&&MM ID   [I % % " A M?Y+F G GD   " 4; 7 7D    ! & & ( ( <:;; ;z$+//11377 L MS   !% 1D4E4I4I4K4K KD (Iz#:"*MMMXi y)) I II9,KF;//ByM 7 h{33A6 {k))"S131[>PQ1R1R1RDOI..1B1[>1R1RDOI..-.{^-@-@a-@-H-H **8A 66UCCCqH%'X GHLLaL((I ,AB C C%& & & " t)Q. / /  MW    G  &26155a5==155a5==#@"I"I"K"Kq"PQQ &W%% % &qq(9(9(9Q(>!?!? &W%% %FFFNN GS2X# /2 3 3  &43M)N)N N YYs1vvq ! ! !V8 _0 0B 6U      LGD,--E!vd&677$:OOD  GD$a43C D D D D    %  t''C h'83'>UKKKDO r&ct|tj|jdt |jz rXt|jj rdnd}t|||dd}|jt||j |j St|S)aPerform classification on an array of test vectors `X`. 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