edu.byu.nlp.stats.SymmetricDirichletMLEOptimizableTest.java Source code

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Here is the source code for edu.byu.nlp.stats.SymmetricDirichletMLEOptimizableTest.java

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/**
 * Copyright 2012 Brigham Young University
 *
 * Licensed 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 edu.byu.nlp.stats;

import static org.fest.assertions.Assertions.assertThat;

import org.apache.commons.math3.special.Gamma;
import org.fest.assertions.Delta;
import org.junit.Test;

import edu.byu.nlp.math.optimize.ValueAndObject;
import edu.byu.nlp.util.DoubleArrays;
import edu.byu.nlp.util.Matrices;

/**
 * @author rah67
 *
 */
public class SymmetricDirichletMLEOptimizableTest {

    private static final Delta delta = Delta.delta(1e-10);

    /**
     * Test method for {@link edu.byu.nlp.stats.SymmetricDirichletMLENROptimizable#computeNext(java.lang.Double)}.
     */
    @Test
    public void testComputeNext() {
        final double alpha = 2.13;

        final double[][] data = DirichletTestUtils.sampleDataset();
        SymmetricDirichletMLENROptimizable o = SymmetricDirichletMLENROptimizable.newOptimizable(data);

        ValueAndObject<Double> vao = o.computeNext(alpha);

        // Compute the update that should have happened
        final int N = data.length;
        final int K = data[0].length;
        double sumOfLogThetas = DoubleArrays.sum(Matrices.sumOverFirst(data));
        double value = N * Gamma.logGamma(K * alpha) - N * K * Gamma.logGamma(alpha) + (alpha - 1) * sumOfLogThetas;
        double firstDeriv = N * Gamma.digamma(K * alpha) - N * K * Gamma.digamma(alpha) + sumOfLogThetas;
        double secondDeriv = N * Gamma.trigamma(K * alpha) - N * K * Gamma.trigamma(alpha);
        double nextAlpha = alpha - firstDeriv / secondDeriv;

        // Ensure that the update was computed correctly
        assertThat(vao.getValue()).isEqualTo(value, delta);
        assertThat(vao.getObject().doubleValue()).isEqualTo(nextAlpha, delta);
    }

}