Import Debian changes 0.7.1-1

elki (0.7.1-1) unstable; urgency=low

  * New upstream release (mostly bug fixes)
  * Drop patches included upstream
  * Build-dep on default-jdk instead of openjdk7-jdk (Closes: #814163)

1 files changed, 91 insertions, 0 deletions

diff --git a/elki/src/test/java/de/lmu/ifi/dbs/elki/math/MeanVarianceTest.java b/elki/src/test/java/de/lmu/ifi/dbs/elki/math/MeanVarianceTest.java
new file mode 100644
index 00000000..76ae56a1
--- /dev/null
+++ b/elki/src/test/java/de/lmu/ifi/dbs/elki/math/MeanVarianceTest.java
@@ -0,0 +1,91 @@
+package de.lmu.ifi.dbs.elki.math;
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2015
+ Ludwig-Maximilians-Universität München
+ Lehr- und Forschungseinheit für Datenbanksysteme
+ ELKI Development Team
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU Affero General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ GNU Affero General Public License for more details.
+
+ You should have received a copy of the GNU Affero General Public License
+ along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
+import static org.junit.Assert.assertEquals;
+
+import java.util.Random;
+
+import org.junit.Test;
+
+import de.lmu.ifi.dbs.elki.JUnit4Test;
+
+/**
+ * Unit test {@link MeanVariance} with negative weights.
+ * 
+ * @author Erich Schubert
+ * @since 0.6.0
+ */
+public class MeanVarianceTest implements JUnit4Test {
+  /**
+   * Size of test data set.
+   */
+  private static final int SIZE = 100000;
+
+  /**
+   * Sliding window size.
+   */
+  private static final int WINDOWSIZE = 100;
+
+  @Test
+  public void testSlidingWindowVariance() {
+    MeanVariance mv = new MeanVariance();
+    MeanVariance mc = new MeanVariance();
+
+    Random r = new Random(0);
+    double[] data = new double[SIZE];
+    for(int i = 0; i < data.length; i++) {
+      data[i] = r.nextDouble();
+    }
+    // Arrays.sort(data);
+
+    // Pre-roll:
+    for(int i = 0; i < WINDOWSIZE; i++) {
+      mv.put(data[i]);
+    }
+    // Compare to window approach
+    for(int i = WINDOWSIZE; i < data.length; i++) {
+      mv.put(data[i - WINDOWSIZE], -1.); // Remove
+      mv.put(data[i]);
+
+      mc.reset(); // Reset statistics
+      for(int j = i + 1 - WINDOWSIZE; j <= i; j++) {
+        mc.put(data[j]);
+      }
+      // Fully manual statistics, exact two-pass algorithm:
+      double mean = 0.0;
+      for(int j = i + 1 - WINDOWSIZE; j <= i; j++) {
+        mean += data[j];
+      }
+      mean /= WINDOWSIZE;
+      double var = 0.0;
+      for(int j = i + 1 - WINDOWSIZE; j <= i; j++) {
+        double v = data[j] - mean;
+        var += v * v;
+      }
+      var /= (WINDOWSIZE - 1);
+      assertEquals("Variance does not agree at i=" + i, mv.getSampleVariance(), mc.getSampleVariance(), 1e-14);
+      assertEquals("Variance does not agree at i=" + i, mv.getSampleVariance(), var, 1e-14);
+    }
+  }
+}