/**
* 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.stats;
import org.apache.mahout.common.MahoutTestCase;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Vector;
import org.junit.Test;
public class SamplerTest extends MahoutTestCase {
@Test
public void testDiscreteSampler() {
Vector distribution = new DenseVector(new double[] {1, 0, 2, 3, 5, 0});
Sampler sampler = new Sampler(RandomUtils.getRandom(), distribution);
Vector sampledDistribution = distribution.like();
int i = 0;
while (i < 100000) {
int index = sampler.sample();
sampledDistribution.set(index, sampledDistribution.get(index) + 1);
i++;
}
assertTrue("sampled distribution is far from the original",
l1Dist(distribution, sampledDistribution) < 1.0e-2);
}
private static double l1Dist(Vector v, Vector w) {
return v.normalize(1.0).minus(w.normalize(1)).norm(1.0);
}
}