tgalery / knn

K Nearest Neighbor Classifier in Clojure

knn

knn is a k nearest neighbor classifier library written in Clojure. It supports a variety of distance functions out of the box, listed below. It has full test coverage.

Usage

(use ‘[knn.core :refer :all])
(use ‘[knn.distance :refer :all])
(def neighbors 3)
; After loading training data and test data into vectors(the observation vectors need to be same size)
; Predictions are the vector that has the class predictions for each observation
(def predictions (predict training-data test-data manhattan-distance neighbors)

Documentation

Documentation Page

Generating Documentation

After cloning the repo, in order to generate the documentation:

; For single page documentation(uberdoc.html)
lein marg
; For multi page documentation(one page per module)
lein marg --multi

License

Copyright © 2014 Bugra Akyildiz

Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.

Future

• I am planning to support PigPen in future.
• KD -Trees integration in order to improve the speed even further.

Supported Distance Functions and Formulas

Adapted from Distances.jl

Distance name	Syntax	math definition
Euclidean (L_2)	(euclidean-distance x y)	sqrt(sum((x - y) .^ 2))
Squared Euclidean	(squared-euclidean-distance x y)	sum((x - y).^2)
Manhattan (L_1)	(manhattan-distance x y)	sum(abs(x - y))
Chebyshev	(chebyshev-distance x y)	max(abs(x - y))
Minkowski (L_P)	(minkowski-distance x y p)	sum(abs(x - y).^p) ^ (1/p)
Hamming	(hamming-distance x y)	sum(x .!= y)
Cosine	(cosine-distance x y)	1 - dot(x, y) / (norm(x) * norm(y))
Correlation	(correlation-distance x y)	cosine_dist(x - mean(x), y - mean(y))
Chi Squared	(chi-squared-distance x y)	sum((x - y).^2 / (x + y))
KLDivergence	(kl-divergence x y)	sum(p .* log(p ./ q))
JSDivergence	(js-divergence x y)	KL(x, m) / 2 + KL(y, m) / 2 with m = (x + y) / 2
SpanNormDist	(span-norm-distance x y)	max(x - y) - min(x - y )
BhattacharyyaDist	(bhattacharyya-distance x y)	-log(sum(sqrt(x .* y) / sqrt(sum(x) * sum(y)))
HellingerDist	(hellinger-distance x y)	sqrt(1 - sum(sqrt(x .* y) / sqrt(sum(x) * sum(y))))
WeightedMinkowski	(weighted-minkowski-distance x y w p)	sum(abs(x - y).^p .* w) ^ (1/p)