K Nearest Neighbours¶ ↑
Simple KNN Ruby implementation
Install¶ ↑
gem sources -a -http://gemcutter.org sudo gem install naive_bayes
How To Use¶ ↑
require 'rubygems' require 'knn' data = Array.new(100000) { Array.new(4) { rand } } knn = KNN.new(data) knn.nearest_neighbours([1,2,3,4], 4) # ([data], k's) #=> [[4837, 7.43033158269445, [0.966558570073977, 0.903158898673566, 0.954567901514261, 0.988114355901207]], ... # Data is returned in the format # [data index, distance to the input, [data points]] # So if we called queried the data array for 4837... data[4837] #=> [0.966558570073977, 0.903158898673566, 0.954567901514261, 0.988114355901207]
Distance Measurements¶ ↑
KNN uses the Distance Measures Gem (github.com/reddavis/Distance-Measures) so we get quite a range of distance measurements.
The measurements currently available are:
euclidean_distance cosine_similarity jaccard_index jaccard_distance binary_jaccard_index binary_jaccard_distance tanimoto_coefficient
To specify a particular one to use in the KNN algorithm, just provide it as an option:
KNN.new(@data, :distance_measure => :jaccard_index) KNN.new(@data, :distance_measure => :cosine_similarity) KNN.new(@data, :distance_measure => :tanimoto_coefficient)
Copyright¶ ↑
Copyright © 2009 Red Davis. See LICENSE for details.