Test for clustering result

jplag/src/test/java/de/jplag/clustering/ClusteringResultTest.java

@@ -0,0 +1,56 @@
+package de.jplag.clustering;
+
+import static org.junit.Assert.assertEquals;
+
+import java.util.List;
+
+import org.apache.commons.math3.linear.Array2DRowRealMatrix;
+import org.apache.commons.math3.linear.RealMatrix;
+import org.junit.Test;
+
+public class ClusteringResultTest {
+
+    @Test
+    public void perfectClustering() {
+        RealMatrix similarity = new Array2DRowRealMatrix(4, 4);
+
+        // These are similar
+        setEntries(similarity, 0, 1, 1f);
+        setEntries(similarity, 2, 3, 1f);
+
+        // Others are dissimilar
+
+        ClusteringResult<Integer> result = ClusteringResult.fromIntegerCollections(List.of(List.of(0, 1), List.of(2, 3)), similarity);
+
+        // The maximum of the metric is 1 - 1/k for k clusters
+        assertEquals(0.5, result.getCommunityStrength(), 0.00001);
+    }
+
+    @Test
+    public void uniformClustering() {
+        RealMatrix similarity = new Array2DRowRealMatrix(4, 4);
+
+        // We'd obtain such weights by pre-selecting the clusters,
+        // then randomly picking two clusters and adding weight between random of each
+        // cluster
+
+        // These are similar
+        setEntries(similarity, 0, 1, 0.1f);
+        setEntries(similarity, 2, 3, 0.1f);
+
+        // Others are dissimilar
+        setEntries(similarity, 0, 2, 0.05f);
+        setEntries(similarity, 0, 3, 0.05f);
+        setEntries(similarity, 1, 2, 0.05f);
+        setEntries(similarity, 1, 3, 0.05f);
+
+        ClusteringResult<Integer> result = ClusteringResult.fromIntegerCollections(List.of(List.of(0, 1), List.of(2, 3)), similarity);
+
+        assertEquals(0.0, result.getCommunityStrength(), 0.00001);
+    }
+
+    private static void setEntries(RealMatrix matrix, int i, int j, double value) {
+        matrix.setEntry(i, j, value);
+        matrix.setEntry(j, i, value);
+    }
+}