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__ =



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 Aug 25 13:15:03 2016  http://epydoc.sourceforge.net 