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Nearest neighbor using bray curtis dissimilarity.
This is a Bray Curtis implementation for nearest neighbor. More information can be found here.
"The Bray–Curtis dissimilarity is bound between 0 and 1, where 0 means the two sites have the same composition (that is they share all the species), and 1 means the two sites do not share any species."
Note: This implementation actually uses the Bray-Curtis index. This means the values are bound between 0 and 100, and 0 means the two sites do not share any species. This is implemented like so:
dissimilarity = dissimilarity * 100; // interpret dissimilarity as a percentage var similarity = 100 - dissimilarity; // change dissimilarity to similarity (Bray-Curtis index)
var BrayCurtis = require('bray-curtis');
Create a new Bray Curtis nearest neighbor instance.
vector is an an array (neighbors) of arrays (neighbor features).
k most nearest neighbors to
subject (the target neighbor).
k default is
neighbors is a sorted, (most similar first) array where each object has the following properties:
vector- the neighbor vector
d- the "Bray-Curtis index" score. ie. similarity score.
var BrayCurtis = require('bray-curtis'); var vectors = [ [26, 4, 13, 11, 0], [0, 10, 9, 8, 0], [0, 0, 15, 3, 0], [13, 5, 3, 10, 7], [31, 21, 13, 16, 5], [9, 6, 0, 11, 2], [32, 26, 0, 23, 0], [32, 21, 0, 10, 2], [24, 17, 0, 25, 6], [16, 3, 12, 20, 2], [11, 0, 7, 8, 0], [24, 37, 5, 18, 1] ]; var nn = new BrayCurtis(vectors); var subject = [32, 26, 0, 23, 0]; var mostSimilar = [32, 21, 0, 10, 2]; var neighbors = nn.knn(subject, 2); var neighbor = neighbors; // the subject itself should always be returned as the first element because the item will be most similar to itself, so we get the second element neighbors.d.should.eql(100); neighbor.vector.should.eql(mostSimilar);
(The MIT License)
Copyright (c) 2014 Nick Poorman firstname.lastname@example.org
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