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

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Here is the source code for edu.byu.nlp.stats.DirichletTestUtils.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 java.util.List;

import org.apache.commons.math3.random.RandomGenerator;

import com.google.common.collect.Lists;

import edu.byu.nlp.util.DoubleArrays;

/**
 * @author plf1
 *
 */
public class DirichletTestUtils {

    private DirichletTestUtils() {
    }

    public static double[][][] sampleDirichletMultinomialMatrixDataset(double diagonalAlpha,
            double offdiagonalAlpha, int matrixSize, int numDataPoints, int datumSize, RandomGenerator rnd) {
        double[][] alpha = new double[matrixSize][matrixSize];
        for (int r = 0; r < matrixSize; r++) {
            for (int c = 0; c < matrixSize; c++) {
                alpha[r][c] = (r == c) ? diagonalAlpha : offdiagonalAlpha;
            }
        }
        List<double[][]> result = Lists.newArrayList();
        for (int i = 0; i < numDataPoints; i++) {
            result.add(sampleDirichletMultinomialMatrixDataset(alpha, datumSize, rnd));
        }
        return result.toArray(new double[0][0][0]);
    }

    public static double[][] sampleDirichletMultinomialMatrixDataset(double[][] alpha, int datumSize,
            RandomGenerator rnd) {
        double[][] result = new double[alpha.length][];
        for (int r = 0; r < alpha.length; r++) {
            result[r] = sampleMultinomialDataset(alpha[r], 1, datumSize, rnd)[0];
        }
        return result;
    }

    public static double[][] sampleMultinomialDataset(double[] alpha, int numDataPoints, int datumSize,
            RandomGenerator rnd) {
        double[][] dataset = new double[numDataPoints][];
        for (int i = 0; i < numDataPoints; i++) {
            double[] p = DirichletDistribution.sample(alpha, rnd);
            dataset[i] = RandomGenerators.nextVectorUnnormalizedProbs(rnd, p, datumSize);
        }
        return dataset;
    }

    /**
     * Returns a 3 x 3 matrix of log probability vectors.
     */
    public static double[][] sampleDataset() {
        double[][] sampleData = new double[][] { { 0.7, 0.2, 0.1 }, { 0.3, 0.4, 0.3 }, { 0.6, 0.3, 0.1 } };
        for (int i = 0; i < sampleData.length; i++) {
            DoubleArrays.logToSelf(sampleData[i]);
        }
        return sampleData;
    }

}