public void forwardIBD() {
int numNodes = treeModel.getNodeCount();
int stateCount = substitutionModel.getStateCount();
getDiagonalRates(diag);
for (int nodeId = 0; nodeId < numNodes; ++nodeId) {
NodeRef node = treeModel.getNode(nodeId);
NodeRef parent = treeModel.getParent(node);
if (parent == null) { // handle the root
} else if (treeModel.isExternal(node)) { // Handle the tip
double branchTime =
branchRateModel.getBranchRate(treeModel, node)
* (treeModel.getNodeHeight(parent) - treeModel.getNodeHeight(node));
for (int state = 0; state < stateCount; ++state) {
ibdForward[nodeId][state] = Math.exp(-diag[state] * branchTime);
}
} else { // Handle internal node
double branchTime =
branchRateModel.getBranchRate(treeModel, node)
* (treeModel.getNodeHeight(parent) - treeModel.getNodeHeight(node));
int childCount = treeModel.getChildCount(node);
for (int state = 0; state < stateCount; ++state) {
ibdForward[nodeId][state] = 0;
for (int child = 0; child < childCount; ++child) {
int childNodeId = treeModel.getChild(node, child).getNumber();
ibdForward[nodeId][state] += ibdForward[childNodeId][state];
}
ibdForward[nodeId][state] *= Math.exp(-diag[state] * branchTime);
}
}
}
}
public void backwardIBD(NodeRef node) {
int stateCount = substitutionModel.getStateCount();
if (node == null) {
node = treeModel.getRoot();
int nodeId = node.getNumber();
for (int state = 0; state < stateCount; ++state) {
ibdBackward[nodeId][state] = 0;
}
}
getDiagonalRates(diag);
int childCount = treeModel.getChildCount(node);
int nodeId = node.getNumber();
for (int child = 0; child < childCount; ++child) {
NodeRef childNode = treeModel.getChild(node, child);
int childNodeId = childNode.getNumber();
double branchTime =
branchRateModel.getBranchRate(treeModel, childNode)
* (treeModel.getNodeHeight(node) - treeModel.getNodeHeight(childNode));
for (int state = 0; state < stateCount; ++state) {
ibdBackward[childNodeId][state] = ibdBackward[nodeId][state];
for (int sibling = 0; sibling < childCount; ++sibling) {
if (sibling != child) {
int siblingId = treeModel.getChild(node, sibling).getNumber();
ibdBackward[childNodeId][state] += ibdForward[siblingId][state];
}
}
ibdBackward[childNodeId][state] *= Math.exp(-diag[state] * branchTime);
}
}
for (int child = 0; child < childCount; ++child) {
NodeRef childNode = treeModel.getChild(node, child);
backwardIBD(childNode);
}
}
public static void main(String[] args) {
final double l1 = -10;
final double l2 = -2;
AbstractModelLikelihood like1 =
new AbstractModelLikelihood("dummy") {
public Model getModel() {
return null;
}
public double getLogLikelihood() {
return l1;
}
public void makeDirty() {}
public String prettyName() {
return null;
}
public boolean isUsed() {
return false;
}
@Override
protected void handleModelChangedEvent(Model model, Object object, int index) {}
@Override
protected void handleVariableChangedEvent(
Variable variable, int index, Variable.ChangeType type) {}
@Override
protected void storeState() {}
@Override
protected void restoreState() {}
@Override
protected void acceptState() {}
public void setUsed() {}
public LogColumn[] getColumns() {
return new LogColumn[0];
}
public String getId() {
return null;
}
public void setId(String id) {}
};
AbstractModelLikelihood like2 =
new AbstractModelLikelihood("dummy") {
public Model getModel() {
return null;
}
public double getLogLikelihood() {
return l2;
}
public void makeDirty() {}
public String prettyName() {
return null;
}
public boolean isUsed() {
return false;
}
@Override
protected void handleModelChangedEvent(Model model, Object object, int index) {}
@Override
protected void handleVariableChangedEvent(
Variable variable, int index, Variable.ChangeType type) {}
@Override
protected void storeState() {}
@Override
protected void restoreState() {}
@Override
protected void acceptState() {}
public void setUsed() {}
public LogColumn[] getColumns() {
return new LogColumn[0];
}
public String getId() {
return null;
}
public void setId(String id) {}
};
List<AbstractModelLikelihood> likelihoodList = new ArrayList<AbstractModelLikelihood>();
likelihoodList.add(like1);
likelihoodList.add(like2);
Parameter weights = new Parameter.Default(2);
double p1 = 0.05;
weights.setParameterValue(0, p1);
weights.setParameterValue(1, 1.0 - p1);
WeightedMixtureModel mixture = new WeightedMixtureModel(likelihoodList, weights);
System.err.println("getLogLikelihood() = " + mixture.getLogLikelihood());
double test = Math.log(p1 * Math.exp(l1) + (1.0 - p1) * Math.exp(l2));
System.err.println("correct = " + test);
}
public void setCoercableParameter(double value) {
scaleFactor = Math.exp(value);
}
private void exp(double[] logX) {
for (int i = 0; i < logX.length; ++i) {
logX[i] = Math.exp(logX[i]);
// if(logX[i]<1E-5){logX[i]=0;}
}
}