EmpiricalPiecewiseModel.java

/*
 * EmpiricalPiecewiseModel.java
 *
 * Copyright (c) 2002-2015 Alexei Drummond, Andrew Rambaut and Marc Suchard
 *
 * This file is part of BEAST.
 * See the NOTICE file distributed with this work for additional
 * information regarding copyright ownership and licensing.
 *
 * BEAST is free software; you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as
 * published by the Free Software Foundation; either version 2
 * of the License, or (at your option) any later version.
 *
 *  BEAST is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with BEAST; if not, write to the
 * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
 * Boston, MA  02110-1301  USA
 */

package dr.evomodel.coalescent;

import dr.evolution.coalescent.DemographicFunction;
import dr.evolution.coalescent.EmpiricalPiecewiseConstant;
import dr.evomodelxml.coalescent.EmpiricalPiecewiseModelParser;
import dr.inference.model.Model;
import dr.inference.model.Parameter;

/**
 * @author Alexei Drummond
 * @author Andrew Rambaut
 * @version $Id: EmpiricalPiecewiseModel.java,v 1.4 2005/04/11 11:24:39 alexei Exp $
 */
public class EmpiricalPiecewiseModel extends DemographicModel {
    //
    // Public stuff
    //
    /**
     * Construct demographic model with default settings
     */
    public EmpiricalPiecewiseModel(double[] intervalWidths, Parameter populationSizesParameter, Parameter tauParameter, Parameter bParameter, Parameter lagParameter, Type units) {

        this(EmpiricalPiecewiseModelParser.EMPIRICAL_PIECEWISE, intervalWidths, populationSizesParameter, tauParameter, bParameter, lagParameter, units);

    }

    /**
     * Construct demographic model with default settings
     */
    public EmpiricalPiecewiseModel(String name, double[] intervalWidths, Parameter populationSizesParameter, Parameter tauParameter, Parameter bParameter, Parameter lagParameter, Type units) {

        super(name);

        //System.out.println("intervalWidths.length=" + intervalWidths.length);
        //System.out.println("populationSizes.dimension=" + populationSizesParameter.getDimension());

        if (intervalWidths.length == 1) {
            double[] newIntervalWidths = new double[populationSizesParameter.getDimension() - 1];
            for (int i = 0; i < newIntervalWidths.length; i++) {
                newIntervalWidths[i] = intervalWidths[0];
            }
            intervalWidths = newIntervalWidths;
        }
        //System.out.println("new intervalWidths.length=" + intervalWidths.length);

        if (populationSizesParameter.getDimension() != (intervalWidths.length + 1)) {
            throw new IllegalArgumentException(
                    "interval widths array must have either 1 or " + (populationSizesParameter.getDimension() - 1) +
                            " elements, but instead it has " + intervalWidths.length + "."
            );
        }

        this.tauParameter = tauParameter;
        this.lagParameter = lagParameter;
        this.bParameter = bParameter;

        this.intervalWidths = intervalWidths;
        this.populationSizesParameter = populationSizesParameter;

        addVariable(tauParameter);
        addVariable(lagParameter);
        addVariable(bParameter);
        addVariable(populationSizesParameter);
        tauParameter.addBounds(new Parameter.DefaultBounds(Double.MAX_VALUE, 0.0, tauParameter.getDimension()));
        lagParameter.addBounds(new Parameter.DefaultBounds(Double.MAX_VALUE, 0.0, lagParameter.getDimension()));
        bParameter.addBounds(new Parameter.DefaultBounds(Double.MAX_VALUE, 0.0, bParameter.getDimension()));
        populationSizesParameter.addBounds(new Parameter.DefaultBounds(Double.MAX_VALUE, 0.0, populationSizesParameter.getDimension()));

        setUnits(units);

        piecewiseFunction = new EmpiricalPiecewiseConstant(intervalWidths, calculatePopSizes(),
                lagParameter.getParameterValue(0), units);
    }

    /**
     *
     */
    public DemographicFunction getDemographicFunction() {
        piecewiseFunction.setLag(lagParameter.getParameterValue(0));
        piecewiseFunction.setPopulationSizes(calculatePopSizes());

        return piecewiseFunction;
    }

    private double[] calculatePopSizes() {
        double m = tauParameter.getParameterValue(0);
        double c = bParameter.getParameterValue(0);

        double[] popSizes = new double[populationSizesParameter.getDimension()];
        for (int i = 0; i < popSizes.length; i++) {
            popSizes[i] = m * populationSizesParameter.getParameterValue(i) + c;
        }
        return popSizes;
    }

    // **************************************************************
    // Model IMPLEMENTATION
    // **************************************************************

    protected void handleModelChangedEvent(Model model, Object object, int index) {
        // no intermediates need to be recalculated...
    }

//	protected void handleVariableChangedEvent(Parameter parameter, int index) {
//
//		// no intermediates need to be recalculated...
//	}
//

    // todo: why override?

    protected void storeState() {
    } // no additional state needs storing

    protected void restoreState() {
    } // no additional state needs restoring

    protected void acceptState() {
    } // no additional state needs accepting

    //
    // protected stuff
    //

    Parameter tauParameter;
    Parameter lagParameter;
    Parameter bParameter;
    Parameter populationSizesParameter;
    double[] intervalWidths;
    EmpiricalPiecewiseConstant piecewiseFunction = null;
}