:>"^Íã@skdZddlZGdd„deƒZdd„Zddd„Zddd d „Zddd d „ZdS) aµCode for doing k-nearest-neighbors classification. k Nearest Neighbors is a supervised learning algorithm that classifies a new observation based the classes in its surrounding neighborhood. Glossary: - distance The distance between two points in the feature space. - weight The importance given to each point for classification. Classes: - kNN Holds information for a nearest neighbors classifier. Functions: - train Train a new kNN classifier. - calculate Calculate the probabilities of each class, given an observation. - classify Classify an observation into a class. Weighting Functions: - equal_weight Every example is given a weight of 1. éNc@s"eZdZdZdd„ZdS)ÚkNNaHolds information necessary to do nearest neighbors classification. Attribues: - classes Set of the possible classes. - xs List of the neighbors. - ys List of the classes that the neighbors belong to. - k Number of neighbors to look at. cCs+tƒ|_g|_g|_d|_dS)z Initialize.N)ÚsetÚclassesÚxsÚysÚk)Úself©r ú,/tmp/pip-build-ww9dw3qa/biopython/Bio/kNN.pyÚ__init__,s   z kNN.__init__N)Ú__name__Ú __module__Ú __qualname__Ú__doc__r r r r r r"s rcCsdS)z8Return integer one (dummy method for equally weighting).ér )ÚxÚyr r r Ú equal_weight4srcCsCtƒ}t|ƒ|_tj||ƒ|_||_||_|S)a(Train a k nearest neighbors classifier on a training set. xs is a list of observations and ys is a list of the class assignments. Thus, xs and ys should contain the same number of elements. k is the number of neighbors that should be examined when doing the classification. )rrrÚnumpyÚasarrayrrr)rrrÚtypecodeÚknnr r r Útrain:s    rc Cs||dkrt}tj|ƒ}g}|ryxÇtt|jƒƒD]/}|||j|ƒ}|j||fƒqCWn~tjt|ƒƒ}xftt|jƒƒD]O}||j||dd…|jƒD]0\}}|dksj||krF|}|}qFW|S)a%Classify an observation into a class. If not specified, weight_fn will give all neighbors equal weight. distance_fn is an optional function that takes two points and returns the distance between them. If distance_fn is None (the default), the Euclidean distance is used. Nrr )rr'Úitems) rrrr r%Z most_classZ most_weightr&Zweightr r r Úclassifyws  r))rrÚobjectrrrr'r)r r r r Ús   -