/*
* AbstractARGLikelihood.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
*/
/*
* AbstractARGLikelihood.java
*
* (c) 2002-2005 BEAST Development Core Team
*
* This package may be distributed under the
* Lesser Gnu Public Licence (LGPL)
*/
package dr.evomodel.arg.likelihood;
import dr.evolution.alignment.PatternList;
import dr.evolution.datatype.DataType;
import dr.evolution.tree.NodeRef;
import dr.evomodel.arg.ARGModel;
import dr.oldevomodel.treelikelihood.LikelihoodCore;
import dr.inference.model.*;
/**
* AbstractTreeLikelihood - a base class for likelihood calculators of sites on a tree.
*
* @author Andrew Rambaut
* @version $Id: AbstractARGLikelihood.java,v 1.1 2006/10/10 22:57:55 msuchard Exp $
*/
public abstract class AbstractARGLikelihood extends AbstractModelLikelihood implements ParallelLikelihood {
public AbstractARGLikelihood(String name, PatternList patternList,
ARGModel treeModel) {
super(name);
this.patternList = patternList;
this.dataType = patternList.getDataType();
patternCount = patternList.getPatternCount();
stateCount = dataType.getStateCount();
patternWeights = patternList.getPatternWeights();
this.treeModel = treeModel;
addModel(treeModel);
nodeCount = treeModel.getNodeCount();
updateNode = new boolean[nodeCount];
for (int i = 0; i < nodeCount; i++) {
updateNode[i] = true;
}
}
/**
* Sets the partials from a sequence in an alignment.
*/
protected final void setStates(LikelihoodCore likelihoodCore, PatternList patternList,
int sequenceIndex, int nodeIndex) {
int i;
int[] states = new int[patternCount];
for (i = 0; i < patternCount; i++) {
states[i] = patternList.getPatternState(sequenceIndex, i);
//System.err.print(states[i]+" ");
}
likelihoodCore.setNodeStates(nodeIndex, states);
}
/**
* Sets the partials from a sequence in an alignment.
*/
protected final void setPartials(LikelihoodCore likelihoodCore, PatternList patternList,
int categoryCount,
int sequenceIndex, int nodeIndex) {
int i, j;
double[] partials = new double[patternCount * stateCount];
boolean[] stateSet;
int v = 0;
for (i = 0; i < patternCount; i++) {
int state = patternList.getPatternState(sequenceIndex, i);
stateSet = dataType.getStateSet(state);
for (j = 0; j < stateCount; j++) {
if (stateSet[j]) {
partials[v] = 1.0;
} else {
partials[v] = 0.0;
}
v++;
}
}
likelihoodCore.setNodePartials(nodeIndex, partials);
}
/**
* Set update flag for a node and its children
*/
protected void updateNode(NodeRef node) {
updateNode[node.getNumber()] = true;
likelihoodKnown = false;
}
/**
* Set update flag for a node and its children
*/
protected void updateNodeAndChildren(NodeRef node) {
updateNode[node.getNumber()] = true;
for (int i = 0; i < treeModel.getChildCount(node); i++) {
NodeRef child = treeModel.getChild(node, i);
updateNode[child.getNumber()] = true;
}
likelihoodKnown = false;
}
/**
* Set update flag for a node and its children
*/
protected void updateNodeAndDescendents(NodeRef node) {
updateNode[node.getNumber()] = true;
for (int i = 0; i < treeModel.getChildCount(node); i++) {
NodeRef child = treeModel.getChild(node, i);
updateNodeAndDescendents(child);
}
likelihoodKnown = false;
}
/**
* Set update flag for all nodes
*/
protected void updateAllNodes() {
for (int i = 0; i < nodeCount; i++) {
updateNode[i] = true;
}
likelihoodKnown = false;
}
/**
* Set update flag for a pattern
*/
protected void updatePattern(int i) {
if (updatePattern != null) {
updatePattern[i] = true;
}
likelihoodKnown = false;
}
/**
* Set update flag for all patterns
*/
protected void updateAllPatterns() {
if (updatePattern != null) {
for (int i = 0; i < patternCount; i++) {
updatePattern[i] = true;
}
}
likelihoodKnown = false;
}
public final double[] getPatternWeights() {
return patternWeights;
}
// **************************************************************
// VariableListener IMPLEMENTATION
// **************************************************************
protected void handleVariableChangedEvent(Variable variable, int index, Parameter.ChangeType type) {
// do nothing
}
// **************************************************************
// Model IMPLEMENTATION
// **************************************************************
protected void handleModelChangedEvent(Model model, Object object, int index) {
likelihoodKnown = false;
}
/**
* Stores the additional state other than model components
*/
protected void storeState() {
storedLikelihoodKnown = likelihoodKnown;
storedLogLikelihood = logLikelihood;
}
/**
* Restore the additional stored state
*/
protected void restoreState() {
likelihoodKnown = storedLikelihoodKnown;
logLikelihood = storedLogLikelihood;
}
protected void acceptState() {
} // nothing to do
// **************************************************************
// Likelihood IMPLEMENTATION
// **************************************************************
public final Model getModel() {
return this;
}
public final double getLogLikelihood() {
if (!likelihoodKnown) {
logLikelihood = calculateLogLikelihood();
likelihoodKnown = true;
}
return logLikelihood;
}
/**
* Forces a complete recalculation of the likelihood next time getLikelihood is called
*/
public void makeDirty() {
likelihoodKnown = false;
updateAllNodes();
updateAllPatterns();
}
public void setLikelihood(double likelihood) {
this.logLikelihood = likelihood;
likelihoodKnown = true;
}
public boolean getLikelihoodKnown() {
return likelihoodKnown;
}
protected abstract double calculateLogLikelihood();
public String toString() {
return Double.toString(getLogLikelihood());
}
// **************************************************************
// INSTANCE VARIABLES
// **************************************************************
/**
* the tree
*/
protected ARGModel treeModel = null;
/**
* the partition *
*/
protected int partition;
/**
* the patternList
*/
protected PatternList patternList = null;
protected DataType dataType = null;
/**
* the pattern weights
*/
protected double[] patternWeights;
/**
* the number of patterns
*/
protected int patternCount;
/**
* the number of states in the data
*/
protected int stateCount;
/**
* the number of nodes in the tree
*/
protected int nodeCount;
/**
* Flags to specify which patterns are to be updated
*/
protected boolean[] updatePattern = null;
/**
* Flags to specify which nodes are to be updated
*/
protected boolean[] updateNode;
private double logLikelihood;
private double storedLogLikelihood;
private boolean likelihoodKnown = false;
private boolean storedLikelihoodKnown = false;
}