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)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.
Adapted from Distances.jl
| type name | convenient syntax | math definition |
|---|---|---|
| Euclidean | euclidean(x, y) | sqrt(sum((x - y) .^ 2)) |
| SqEuclidean | sqeuclidean(x, y) | sum((x - y).^2) |
| Cityblock | cityblock(x, y) | sum(abs(x - y)) |
| Chebyshev | chebyshev(x, y) | max(abs(x - y)) |
| Minkowski | minkowski(x, y, p) | sum(abs(x - y).^p) ^ (1/p) |
| Hamming | hamming(x, y) | sum(x .!= y) |
| CosineDist | cosine_dist(x, y) | 1 - dot(x, y) / (norm(x) * norm(y)) |
| CorrDist | corr_dist(x, y) | cosine_dist(x - mean(x), y - mean(y)) |
| ChiSqDist | chisq_dist(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 | spannorm_dist(x, y) | max(x - y) - min(x - y ) |
| BhattacharyyaDist | bhattacharyya(x, y) | -log(sum(sqrt(x .* y) / sqrt(sum(x) * sum(y))) |
| HellingerDist | hellinger(x, y) | sqrt(1 - sum(sqrt(x .* y) / sqrt(sum(x) * sum(y)))) |
| WeightedMinkowski | minkowski(x, y, w, p) | sum(abs(x - y).^p .* w) ^ (1/p) |