/*
* MixtureModelBranchRates.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.branchratemodel;
import dr.evolution.tree.NodeRef;
import dr.evolution.tree.SimpleTree;
import dr.evolution.tree.Tree;
import dr.evomodel.tree.TreeModel;
import dr.evomodel.tree.TreeParameterModel;
import dr.evomodelxml.branchratemodel.MixtureModelBranchRatesParser;
import dr.inference.distribution.ParametricDistributionModel;
import dr.inference.model.*;
/**
* @author Wai Lok Sibon Li
* @version $Id: DiscretizedBranchRates.java,v 1.11 2009/12/01 17:44:30 rambaut Exp $
*/
public class MixtureModelBranchRates extends AbstractBranchRateModel {
private final ParametricDistributionModel[] distributionModels;
// The rate categories of each branch
final TreeParameterModel rateCategoryQuantiles;
private Parameter distributionIndexParameter;
private final double[] rates;
private boolean useQuantilesForRates = true;
private boolean normalize = false;
private double normalizeBranchRateTo = Double.NaN;
private double scaleFactor = 1.0;
private TreeModel treeModel;
private Tree tree;
public MixtureModelBranchRates(
TreeModel tree,
Parameter rateCategoryQuantilesParameter,
ParametricDistributionModel[] models,
Parameter distributionIndexParameter) {
this(tree, rateCategoryQuantilesParameter, models, distributionIndexParameter, true, false, Double.NaN);
}
public MixtureModelBranchRates(
TreeModel tree,
Parameter rateCategoryQuantilesParameter,
ParametricDistributionModel[] models,
Parameter distributionIndexParameter,
boolean normalize,
double normalizeBranchRateTo) {
this(tree, rateCategoryQuantilesParameter, models, distributionIndexParameter, true, normalize, normalizeBranchRateTo);
}
public MixtureModelBranchRates(
TreeModel tree,
Parameter rateCategoryQuantilesParameter,
ParametricDistributionModel[] models,
Parameter distributionIndexParameter,
boolean useQuantilesForRates) {
this(tree, rateCategoryQuantilesParameter, models, distributionIndexParameter, useQuantilesForRates, false, Double.NaN);
}
public MixtureModelBranchRates(
TreeModel tree,
Parameter rateCategoryQuantilesParameter,
ParametricDistributionModel[] models,
Parameter distributionIndexParameter,
boolean useQuantilesForRates,
boolean normalize,
double normalizeBranchRateTo) {
super(MixtureModelBranchRatesParser.MIXTURE_MODEL_BRANCH_RATES);
this.useQuantilesForRates = useQuantilesForRates;
this.rateCategoryQuantiles = new TreeParameterModel(tree, rateCategoryQuantilesParameter, false);
rates = new double[tree.getNodeCount()];
this.normalize = normalize;
this.treeModel = tree;
this.distributionModels = models;
this.normalizeBranchRateTo = normalizeBranchRateTo;
this.tree = new SimpleTree(tree);
this.distributionIndexParameter = distributionIndexParameter;
addVariable(this.distributionIndexParameter);
//Force the boundaries of rateCategoryParameter to match the category count
//d Parameter.DefaultBounds bound = new Parameter.DefaultBounds(categoryCount - 1, 0, rateCategoryParameter.getDimension());
//d rateCategoryParameter.addBounds(bound);
//rateCategoryQuantilesParameter.;
Parameter.DefaultBounds bound = new Parameter.DefaultBounds(1.0, 0.0, rateCategoryQuantilesParameter.getDimension());
rateCategoryQuantilesParameter.addBounds(bound);
Parameter.DefaultBounds bound2 = new Parameter.DefaultBounds(models.length - 1, 0.0, 1);
distributionIndexParameter.addBounds(bound2);
distributionIndexParameter.setParameterValue(0, 0);
//Parameter distributionIndexParameter;
for (ParametricDistributionModel distributionModel : distributionModels) {
addModel(distributionModel);
}
// AR - commented out: changes to the tree are handled by model changed events fired by rateCategories
// addModel(tree);
//d addModel(rateCategories);
addModel(rateCategoryQuantiles);
//addModel(treeModel); // Maybe
// AR - commented out: changes to rateCategoryParameter are handled by model changed events fired by rateCategories
// addVariable(rateCategoryParameter);
if (normalize) {
tree.addModelListener(new ModelListener() {
public void modelChangedEvent(Model model, Object object, int index) {
computeFactor();
}
public void modelRestored(Model model) {
computeFactor();
}
});
}
setupRates();
}
// compute scale factor
private void computeFactor() {
//scale mean rate to 1.0 or separate parameter
double treeRate = 0.0;
double treeTime = 0.0;
//normalizeBranchRateTo = 1.0;
for (int i = 0; i < treeModel.getNodeCount(); i++) {
NodeRef node = treeModel.getNode(i);
if (!treeModel.isRoot(node)) {
//d int rateCategory = (int) Math.round(rateCategories.getNodeValue(treeModel, node));
//d treeRate += rates[rateCategory] * treeModel.getBranchLength(node);
treeTime += treeModel.getBranchLength(node);
// d System.out.println("rates and time\t" + rates[rateCategory] + "\t" + treeModel.getBranchLength(node));
}
}
//treeRate /= treeTime;
scaleFactor = normalizeBranchRateTo / (treeRate / treeTime);
System.out.println("scaleFactor\t\t\t\t\t" + scaleFactor);
}
public void handleModelChangedEvent(Model model, Object object, int index) {
//System.out.println("if you dont know: " + model.getClass().getName());
for (ParametricDistributionModel distributionModel : distributionModels) {
if (model == distributionModel) {
setupRates();
fireModelChanged();
} //else if (model == rateCategories) {
// AR - commented out: if just the rate categories have changed the rates will be the same
// setupRates();
// fireModelChanged(null, index);
//}
}
if (model == rateCategoryQuantiles) {
setupRates(); // Maybe
//rateCategories.fireModelChanged();
fireModelChanged(null, index);
}
/*else if(model == distributionIndexParameter) { // Not a model
setupRates();
}*/
/*else if (model == treeModel) {
setupRates(); // Maybe
}*/
}
protected final void handleVariableChangedEvent(Variable variable, int index, Parameter.ChangeType type) {
if(variable==distributionIndexParameter) {
//System.out.print(Math.round(distributionIndexParameter.getValue(0)));
setupRates();
fireModelChanged();
}
// AR - commented out: changes to rateCategoryParameter are handled by model changed events
//setupRates(); // Maybe
}
protected void storeState() {
//setupRates(); // Maybe
}
protected void restoreState() {
setupRates();
}
protected void acceptState() {
//setupRates(); // Maybe
}
public double getBranchRate(final Tree tree, final NodeRef node) {
assert !tree.isRoot(node) : "root node doesn't have a rate!";
//int rateCategory = (int) Math.round(rateCategories.getNodeValue(tree, node));
return rates[node.getNumber()] * scaleFactor;
}
/**
* Calculates the actual rates corresponding to the category indices.
*/
protected void setupRates() {
//System.out.println("BRRRTTZZZ " + distributionIndexParameter.getValue(0));
for (int i = 0; i < tree.getNodeCount(); i++) {
//rates[i] = distributionModel.quantile(rateCategoryQuantiles.getNodeValue(
// rateCategoryQuantiles.getTreeModel(), rateCategoryQuantiles.getTreeModel().getNode(i) ));
if (!tree.isRoot(tree.getNode(i))) {
if(useQuantilesForRates) { /* Using quantiles to represent rates */
rates[tree.getNode(i).getNumber()] = distributionModels[(int) Math.round(distributionIndexParameter.getValue(0))]
.quantile(rateCategoryQuantiles.getNodeValue(tree, tree.getNode(i)));
}
else { /* Not using quantiles to represent rates. This is practically useless for anything else other than simulation */
rates[tree.getNode(i).getNumber()] = rateCategoryQuantiles.getNodeValue(tree, tree.getNode(i));
}
}
}
/*System.out.print(distributionModels[(int) Math.round(distributionIndexParameter.getValue(0))].getClass().getName() + "\t" + (int) Math.round(distributionIndexParameter.getValue(0)) + "\t" + rates[1] + "\t" + rateCategoryQuantiles.getNodeValue(tree, tree.getNode(1)));// + "\t" + distributionModels[(int) Math.round(distributionIndexParameter.getValue(0))].);
if(distributionModels[(int) Math.round(distributionIndexParameter.getValue(0))].getClass().getName().equals("dr.inference.distribution.LogNormalDistributionModel")) {
LogNormalDistributionModel lndm = (LogNormalDistributionModel) distributionModels[(int) Math.round(distributionIndexParameter.getValue(0))];
System.out.println("\t" + lndm.getS());
}
else if (distributionModels[(int) Math.round(distributionIndexParameter.getValue(0))].getClass().getName().equals("dr.inference.distribution.InverseGaussianDistributionModel")) {
InverseGaussianDistributionModel lndm = (InverseGaussianDistributionModel) distributionModels[(int) Math.round(distributionIndexParameter.getValue(0))];
System.out.println("\t" + lndm.getS());
}*/
if (normalize) computeFactor();
}
}