/*
* 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 com.mapr.stats.random;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.function.Functions;
import org.apache.mahout.math.jet.random.AbstractContinousDistribution;
import org.junit.Test;
import java.util.Arrays;
import java.util.Random;
import static org.junit.Assert.assertEquals;
public class GammaNormalDistributionTest {
@Test
public void testEstimation() {
final Random gen = new Random(1);
GammaNormalDistribution gnd = new GammaNormalDistribution(0, 1, 1, gen);
for (int i = 0; i < 10000; i++) {
gnd.add(gen.nextGaussian() * 2 + 1);
}
assertEquals(1.0, gnd.nextMean(), 0.05);
assertEquals(2.0, gnd.nextSD(), 0.1);
double[] x = new double[10000];
double[] y = new double[10000];
double[] z = new double[10000];
AbstractContinousDistribution dist = gnd.posteriorDistribution();
for (int i = 0; i < 10000; i++) {
x[i] = gnd.nextDouble();
y[i] = dist.nextDouble();
z[i] = gen.nextGaussian() * 2 + 1;
}
Arrays.sort(x);
Arrays.sort(y);
Arrays.sort(z);
final Vector xv = new DenseVector(x).viewPart(1000, 8000);
final Vector yv = new DenseVector(y).viewPart(1000, 8000);
final Vector zv = new DenseVector(z).viewPart(1000, 8000);
final double diffX = xv.minus(zv).assign(Functions.ABS).maxValue();
final double diffY = yv.minus(zv).assign(Functions.ABS).maxValue();
assertEquals(0, diffX, 0.13);
assertEquals(0, diffY, 0.13);
}
}