Trees  Indices  Help 



This module provides code for doing knearestneighbors 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.


kNN Holds information necessary to do nearest neighbors classification. 


1 


kNN 







__package__ =


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.

calculate(knn, x[, weight_fn][, distance_fn]) > weight dict Calculate the probability for each class. knn is a kNN object. x is the observed data. weight_fn is an optional function that takes x and a training example, and returns a 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. Returns a dictionary of the class to the weight given to the class. 
classify(knn, x[, weight_fn][, distance_fn]) > class 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. 
Trees  Indices  Help 


Generated by Epydoc 3.0.1 on Thu May 29 11:45:02 2014  http://epydoc.sourceforge.net 