From 6d5ccb91b96eda7248f99969e866f2f5e811b496 Mon Sep 17 00:00:00 2001
From: sbosse <sbosse@uni-bremen.de>
Date: Tue, 27 Aug 2024 00:15:36 +0200
Subject: [PATCH] Tue 27 Aug 00:14:56 CEST 2024

---
 test/test-kmn1.js | 30 ++++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)
 create mode 100644 test/test-kmn1.js

diff --git a/test/test-kmn1.js b/test/test-kmn1.js
new file mode 100644
index 0000000..9f6ebf7
--- /dev/null
+++ b/test/test-kmn1.js
@@ -0,0 +1,30 @@
+var data = [[1,0,1,0,1,1,1,0,0,0,0,0,1,0],
+            [1,1,1,1,1,1,1,0,0,0,0,0,1,0],
+            [1,1,1,0,1,1,1,0,1,0,0,0,1,0],
+            [1,0,1,1,1,1,1,1,0,0,0,0,1,0],
+            [1,1,1,1,1,1,1,0,0,0,0,0,1,1],
+            [0,0,1,0,0,1,0,0,1,0,1,1,1,0],
+            [0,0,0,0,0,0,1,1,1,0,1,1,1,0],
+            [0,0,0,0,0,1,1,1,0,1,0,1,1,0],
+            [0,0,1,0,1,0,1,1,1,1,0,1,1,1],
+            [0,0,0,0,0,0,1,1,1,1,1,1,1,1],
+            [1,0,1,0,0,1,1,1,1,1,0,0,1,0]
+           ];
+
+var model = ml.learn({
+    algorithm: ml.ML.KMN,
+    data : data,
+    k : 4,
+    epochs: 100,
+
+    distance : {type : "pearson"}
+    // default : {type : 'euclidean'}
+    // {type : 'pearson'}
+    // Or you can use your own distance function
+    // distance : function(vecx, vecy) {return Math.abs(dot(vecx,vecy));}
+});
+print(toJSON(model).length+' Bytes')
+
+print("clusters : ", model.clusters);
+print("means : ", model.means);
+print(ml.classify(model))