unit testing for single linkage clustering

Author: chen0040

src/main/java/com/github/chen0040/clustering/onelink/SingleLinkageClustering.java

@@ -16,7 +16,7 @@
  */
 @Getter
 @Setter
-public class SingleLinkageClustering{
+public class SingleLinkageClustering {
     private int clusterCount = 10;
     private BiFunction<DataRow, double[], Double> distanceMeasure;
 

src/test/java/com/github/chen0040/clustering/onelink/SingleLinkageClusteringUnitTest.java

@@ -0,0 +1,82 @@
+package com.github.chen0040.clustering.onelink;
+
+
+import com.github.chen0040.clustering.density.DBSCAN;
+import com.github.chen0040.data.frame.DataFrame;
+import com.github.chen0040.data.frame.DataQuery;
+import com.github.chen0040.data.frame.DataRow;
+import com.github.chen0040.data.frame.Sampler;
+import org.testng.annotations.Test;
+
+import java.util.Random;
+
+import static org.testng.Assert.*;
+
+
+/**
+ * Created by xschen on 2/6/2017.
+ */
+public class SingleLinkageClusteringUnitTest {
+   private static Random random = new Random();
+
+   public static double rand(){
+      return random.nextDouble();
+   }
+
+   public static double rand(double lower, double upper){
+      return rand() * (upper - lower) + lower;
+   }
+
+   public static double randn(){
+      double u1 = rand();
+      double u2 = rand();
+      double r = Math.sqrt(-2.0 * Math.log(u1));
+      double theta = 2.0 * Math.PI * u2;
+      return r * Math.sin(theta);
+   }
+
+
+   // unit testing based on example from http://scikit-learn.org/stable/auto_examples/svm/plot_oneclass.html#
+   @Test
+   public void testSimple(){
+
+
+      DataQuery.DataFrameQueryBuilder schema = DataQuery.blank()
+              .newInput("c1")
+              .newInput("c2")
+              .newOutput("designed")
+              .end();
+
+      Sampler.DataSampleBuilder negativeSampler = new Sampler()
+              .forColumn("c1").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
+              .forColumn("c2").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
+              .forColumn("designed").generate((name, index) -> 0.0)
+              .end();
+
+      Sampler.DataSampleBuilder positiveSampler = new Sampler()
+              .forColumn("c1").generate((name, index) -> rand(-4, -2))
+              .forColumn("c2").generate((name, index) -> rand(-2, -4))
+              .forColumn("designed").generate((name, index) -> 1.0)
+              .end();
+
+      DataFrame data = schema.build();
+
+      data = negativeSampler.sample(data, 50);
+      data = positiveSampler.sample(data, 50);
+
+      System.out.println(data.head(10));
+
+      SingleLinkageClustering algorithm = new SingleLinkageClustering();
+      algorithm.setClusterCount(2);
+
+      DataFrame learnedData = algorithm.fitAndTransform(data);
+
+      for(int i = 0; i < learnedData.rowCount(); ++i){
+         DataRow tuple = learnedData.row(i);
+         String clusterId = tuple.getCategoricalTargetCell("cluster");
+         System.out.println("learned: " + clusterId +"\tknown: "+tuple.target());
+      }
+
+
+   }
+}