Designed with making an API in mind, this simple library find the K Nearest Neighbors for associative arrays.
Let's look at these passing tests! clj-knn takes an array of maps as training data and a test vector. It returns the k of the nearest training data records with appended distances.
(import '[clj-knn.core :as clj-knn])
(def training-data
[{:id 1111 :feature1 "black" :feature2 "manual" :feature3 "earth" :feature4 "no"}
{:id 1112 :feature1 "black" :feature2 "automatic" :feature3 "earth" :feature4 "no"}
{:id 1114 :feature1 "green" :feature2 "manual" :feature3 "wind" :feature4 "yes"}
{:id 1113 :feature1 "yellow" :feature2 "automatic" :feature3 "earth" :feature4 "no"}
{:id 1118 :feature1 "blue" :feature2 "manual" :feature3 "fire" :feature4 "yes"}
{:id 1119 :feature1 "red" :feature2 "manual" :feature3 "water" :feature4 "yes"}
{:id 1116 :feature1 "red" :feature2 "automatic" :feature3 "wind" :feature4 "yes"}
{:id 1117 :feature1 "white" :feature2 "autonomous" :feature3 "wind" :feature4 "no"}
{:id 1115 :feature1 "green" :feature2 "manual" :feature3 "wind" :feature4 "no"}
{:id 1120 :feature1 "red" :feature2 "automatic" :feature3 "fire" :feature4 "no"}])
(deftest end-to-end-test
(testing "should find the match with identical specs, id 1111"
(let [test-vector {:id 1111 :feature1 "black" :feature2 "manual" :feature3 "earth" :feature4 "no"}]
(is (= (assoc test-vector :distance 0) (first (clj-knn/knn test-vector training-data 1))))))
(testing "should find the match with identical specs, id 1111"
(let [test-vector {:id 1111 :feature1 "black" :feature2 "ONE DIFFERENCE" :feature3 "TWO DIFFERENCE" :feature4 "no"}]
(is (= 2 (:distance (first (clj-knn/knn test-vector training-data 1))))))))git clone https://github.com/emilyagras/clj-knncd clj-knnlein test