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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);
}
}
}
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