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 percentagevar similarity = 100 - dissimilarity; // change dissimilarity to similarity (Bray-Curtis index)
var BrayCurtis = ;
var nn = BrayCurtis(vectors)
Create a new Bray Curtis nearest neighbor instance.
vector is an an array (neighbors) of arrays (neighbor features).
var neighbors = nn.knn(subject, k);
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 = ;var vectors =26 4 13 11 00 10 9 8 00 0 15 3 013 5 3 10 731 21 13 16 59 6 0 11 232 26 0 23 032 21 0 10 224 17 0 25 616 3 12 20 211 0 7 8 024 37 5 18 1;var nn = vectors;var subject = 32 26 0 23 0;var mostSimilar = 32 21 0 10 2;var neighbors = nn;var neighbor = neighbors1; // 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 elementneighbors0dshould;neighborvectorshould;
(The MIT License)
Copyright (c) 2014 Nick Poorman email@example.com
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