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* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.mahout.math.random;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.MahoutTestCase;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.stats.OnlineSummarizer;
import org.junit.Before;
import org.junit.Test;
public class MultiNormalTest extends MahoutTestCase {
@Override
@Before
public void setUp() {
RandomUtils.useTestSeed();
}
@Test
public void testDiagonal() {
DenseVector offset = new DenseVector(new double[]{6, 3, 0});
MultiNormal n = new MultiNormal(
new DenseVector(new double[]{1, 2, 5}), offset);
OnlineSummarizer[] s = {
new OnlineSummarizer(),
new OnlineSummarizer(),
new OnlineSummarizer()
};
OnlineSummarizer[] cross = {
new OnlineSummarizer(),
new OnlineSummarizer(),
new OnlineSummarizer()
};
for (int i = 0; i < 10000; i++) {
Vector v = n.sample();
for (int j = 0; j < 3; j++) {
s[j].add(v.get(j) - offset.get(j));
int k1 = j % 2;
int k2 = (j + 1) / 2 + 1;
cross[j].add((v.get(k1) - offset.get(k1)) * (v.get(k2) - offset.get(k2)));
}
}
for (int j = 0; j < 3; j++) {
assertEquals(0, s[j].getMean() / s[j].getSD(), 0.04);
assertEquals(0, cross[j].getMean() / cross[j].getSD(), 0.04);
}
}
@Test
public void testRadius() {
MultiNormal gen = new MultiNormal(0.1, new DenseVector(10));
OnlineSummarizer s = new OnlineSummarizer();
for (int i = 0; i < 10000; i++) {
double x = gen.sample().norm(2) / Math.sqrt(10);
s.add(x);
}
assertEquals(0.1, s.getMean(), 0.01);
}
}