K Nearest Neighbor Classifier in Clojure
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Latest commit 676054e Dec 26, 2014


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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.


(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 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


Copyright © 2014 Bugra Akyildiz

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


  • 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)