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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.
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package org.apache.mahout.math.jet.random;
import com.google.common.base.Charsets;
import com.google.common.base.Splitter;
import com.google.common.collect.Iterables;
import com.google.common.io.CharStreams;
import com.google.common.io.InputSupplier;
import com.google.common.io.Resources;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.math.MahoutTestCase;
import org.junit.Test;
import java.io.InputStreamReader;
public final class NegativeBinomialTest extends MahoutTestCase {
private static final Splitter onComma = Splitter.on(",").trimResults();
//private static final int N = 10000;
@Test
public void testDistributionFunctions() throws Exception {
InputSupplier<InputStreamReader> input =
Resources.newReaderSupplier(Resources.getResource("negative-binomial-test-data.csv"), Charsets.UTF_8);
boolean header = true;
for (String line : CharStreams.readLines(input)) {
if (header) {
// skip
header = false;
} else {
Iterable<String> values = onComma.split(line);
int k = Integer.parseInt(Iterables.get(values, 0));
double p = Double.parseDouble(Iterables.get(values, 1));
int r = Integer.parseInt(Iterables.get(values, 2));
double density = Double.parseDouble(Iterables.get(values, 3));
double cume = Double.parseDouble(Iterables.get(values, 4));
NegativeBinomial nb = new NegativeBinomial(r, p, RandomUtils.getRandom());
assertEquals("cumulative " + k + ',' + p + ',' + r, cume, nb.cdf(k), cume * 1.0e-5);
assertEquals("density " + k + ',' + p + ',' + r, density, nb.pdf(k), density * 1.0e-5);
}
}
}
}