/*
* 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.commons.math3.distribution.NormalDistribution;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.math.MahoutTestCase;
import org.apache.mahout.math.stats.OnlineSummarizer;
import org.junit.Before;
import org.junit.Test;
import java.util.Arrays;
public final class NormalTest extends MahoutTestCase {
@Override
@Before
public void setUp() {
RandomUtils.useTestSeed();
}
@Test
public void testOffset() {
OnlineSummarizer s = new OnlineSummarizer();
Sampler<Double> sampler = new Normal(2, 5);
for (int i = 0; i < 10001; i++) {
s.add(sampler.sample());
}
assertEquals(String.format("m = %.3f, sd = %.3f", s.getMean(), s.getSD()), 2, s.getMean(), 0.04 * s.getSD());
assertEquals(5, s.getSD(), 0.12);
}
@Test
public void testSample() throws Exception {
double[] data = new double[10001];
Sampler<Double> sampler = new Normal();
for (int i = 0; i < data.length; i++) {
data[i] = sampler.sample();
}
Arrays.sort(data);
NormalDistribution reference = new NormalDistribution(RandomUtils.getRandom().getRandomGenerator(),
0, 1,
NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
assertEquals("Median", reference.inverseCumulativeProbability(0.5), data[5000], 0.04);
}
}