package hex.kmeans;

import org.junit.Assert;
import org.junit.BeforeClass;
import org.junit.Ignore;
import org.junit.Test;
import water.TestUtil;
import water.fvec.Frame;
import water.util.Log;

import java.util.Random;

public class KMeansRandomTest extends TestUtil {
  @BeforeClass()
  public static void setup() { stall_till_cloudsize(1); }

  @Test
  @Ignore
  public void run() {
    long seed = 0xDECAF;
    Random rng = new Random(seed);
    String[] datasets = new String[2];
    int[][] responses = new int[datasets.length][];
    datasets[0] = "smalldata/logreg/prostate.csv";
    responses[0] = new int[]{1, 2, 8}; //CAPSULE (binomial), AGE (regression), GLEASON (multi-class)
    datasets[1] = "smalldata/junit/iris.csv";
    responses[1] = new int[]{4}; //Iris-type (multi-class)


    int testcount = 0;
    int count = 0;
    for (int i = 0; i < datasets.length; ++i) {
      String dataset = datasets[i];
      Frame frame = parse_test_file(dataset);

      for (int clusters : new int[]{1,5,100}) {
        for (int max_iter : new int[]{1,10,100}) {
          for (boolean normalize : new boolean[]{false, true}) {
            for (KMeans.Initialization init : new KMeans.Initialization[]{
                    KMeans.Initialization.None,
                    KMeans.Initialization.Furthest,
                    KMeans.Initialization.PlusPlus,
            }) {

              count++;

              KMeansModel.KMeansParameters parms = new KMeansModel.KMeansParameters();
              parms._src = frame._key;
              parms._K = clusters;
              parms._seed = rng.nextLong();
              parms._max_iters = max_iter;
              parms._normalize = normalize;
              parms._init = init;

              KMeans job = new KMeans(parms);
              job.train();
              KMeansModel m = job.get();
              job.remove();
              for( int j=0; j<parms._K; j++ )
                Assert.assertTrue(m._output._rows[j] != 0);

              for (double d : m._output._mses) Assert.assertFalse(Double.isNaN(d));
              Assert.assertFalse(Double.isNaN(m._output._mse));
              for (long o : m._output._rows) Assert.assertTrue(o > 0); //have at least one point per centroid
              for (double[] dc : m._output._clusters) for (double d : dc) Assert.assertFalse(Double.isNaN(d));

              // make prediction (cluster assignment)
              Frame score = m.score(frame);
              for (long j=0; j<score.numRows(); ++j) Assert.assertTrue(score.anyVec().at8(j) >= 0 && score.anyVec().at8(j) < clusters);
              score.delete();

              Log.info("Parameters combination " + count + ": PASS");
              testcount++;

              m.delete();
            }
          }
        }
      }

      frame.delete();
    }
    Log.info("\n\n=============================================");
    Log.info("Tested " + testcount + " out of " + count + " parameter combinations.");
    Log.info("=============================================");
  }
}



@@ -73,33 +73,8 @@
 ss;%0A
-import org.junit.Ignore;%0A
 impo
@@ -308,17 +308,17 @@
 oudsize(
-1
+5
 ); %7D%0A%0A  
@@ -327,18 +327,8 @@
 est%0A
-  @Ignore%0A
   pu
@@ -1933,24 +1933,87 @@
 %5Bj%5D != 0);%0A%0A
+              Assert.assertTrue(m._output._iters %3C= max_iter);%0A
             

Enable KMeansRandomTest, now passes, use cloudsize 5.