com.analog.lyric.dimple.factorfunctions.MultinomialEnergyParameters.java Source code

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/*******************************************************************************
*   Copyright 2013 Analog Devices, Inc.
*
*   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 com.analog.lyric.dimple.factorfunctions;

import com.analog.lyric.dimple.exceptions.DimpleException;
import com.analog.lyric.dimple.factorfunctions.core.FactorFunction;
import com.analog.lyric.dimple.factorfunctions.core.FactorFunctionUtilities;
import com.analog.lyric.dimple.model.values.Value;

/**
 * Parameterized multinomial distribution, which corresponds to p(x | N, alpha),
 * where x is a vector of discrete count variables, N is the total count across all categories, and
 * where alpha is a vector of real probability parameters (not necessarily normalized).
 * <p>
 * The domain of each x variable must be a zero-based integer with maximum value N (domain 0 through N).
 * If N is a variable rather than a constant, then the domain of each x must be range from 0 through
 * the maximum value in the domain of N.
 * <p>
 * Representing alpha as described, the conjugate prior for alpha is such that
 * each entry of alpha is independently distributed according to
 * a negative exp-Gamma distribution, all with a common Beta parameter.
 * Depending on the solver, it may or may not be necessary to use a
 * conjugate prior (for the Gibbs solver, for example, it is not).
 * <p>
 * The variables in the argument list are ordered as follows:
 * 
 * <ol>
 * <li> N: Parameter indicating the total count
 * <li> dimension+1) Alpha: A number of Real variables containing energy values, where
 *      the number of variables must equal the specified dimension
 * <li> dimension+2...2*dimension+1) x: A number of discrete count variable, where the number of variables
 *    must equal the specified dimension
 * </ol>
 * 
 * N parameter may optionally be specified as a constant in the constructor.
 * In this case, N is not included in the list of arguments. *
 * <p>
 * @since 0.06
 */
public class MultinomialEnergyParameters extends FactorFunction {
    private int _dimension;
    protected int _N;
    protected double _negativeLogFactorialN;
    protected boolean _NParameterConstant = false;
    private double[] _alpha;
    private int _firstDirectedToIndex;

    public MultinomialEnergyParameters(int dimension) // Variable N
    {
        super();
        _dimension = dimension;
        _NParameterConstant = false;
        _firstDirectedToIndex = dimension + 1;
        _alpha = new double[dimension];
    }

    public MultinomialEnergyParameters(int dimension, int N) // Fixed N
    {
        super();
        _dimension = dimension;
        _N = N;
        if (_N < 0)
            throw new DimpleException("N must be a non-negative value.");
        _negativeLogFactorialN = -org.apache.commons.math3.special.Gamma.logGamma(_N + 1);
        _NParameterConstant = true;
        _firstDirectedToIndex = dimension;
        _alpha = new double[dimension];
    }

    @Override
    public final double evalEnergy(Value[] arguments) {
        int index = 0;
        if (!_NParameterConstant) {
            _N = arguments[index++].getInt(); // First argument is N parameter
            if (_N < 0)
                return Double.POSITIVE_INFINITY;
            _negativeLogFactorialN = -org.apache.commons.math3.special.Gamma.logGamma(_N + 1);
        }

        for (int i = 0; i < _dimension; i++)
            _alpha[i] = arguments[index++].getDouble(); // Next _dimension arguments are vector of Alpha parameters

        if (arguments.length - index != _dimension)
            throw new DimpleException(
                    "Number of count variables must equal the dimension of the parameter vector.");

        int countSum = 0;
        double parameterSum = 0;
        double sum = _negativeLogFactorialN;
        for (int i = 0; i < _dimension; i++) {
            final double alphai = _alpha[i];
            parameterSum += Math.exp(-alphai);

            final int x = arguments[index++].getInt(); // Remaining arguments are discrete count variables
            if (x < 0)
                return Double.POSITIVE_INFINITY;
            countSum += x;

            sum += x * alphai + org.apache.commons.math3.special.Gamma.logGamma(x + 1);
        }
        if (countSum != _N)
            return Double.POSITIVE_INFINITY;

        final double energy = sum + _N * Math.log(parameterSum);
        if (energy != energy) // Faster isNaN
            return Double.POSITIVE_INFINITY;

        return energy;
    }

    @Override
    public final boolean isDirected() {
        return true;
    }

    @Override
    public final int[] getDirectedToIndices(int numEdges) {
        // All edges except the parameter edges (if present) are directed-to edges
        return FactorFunctionUtilities.getListOfIndices(_firstDirectedToIndex, numEdges - 1);
    }

    // Factor-specific methods
    public final int getDimension() {
        return _dimension;
    }

    public final boolean hasConstantNParameter() {
        return _NParameterConstant;
    }

    public final int getN() {
        return _N;
    }
}