void postOrderTraverse(
MultivariateTraitTree treeModel,
NodeRef node,
double[][] precisionMatrix,
double logDetPrecisionMatrix,
boolean cacheOuterProducts) {
final int thisNumber = node.getNumber();
if (treeModel.isExternal(node)) {
// Fill in precision scalar, traitValues already filled in
if (missingTraits.isCompletelyMissing(thisNumber)) {
upperPrecisionCache[thisNumber] = 0;
lowerPrecisionCache[thisNumber] = 0; // Needed in the pre-order traversal
} else { // not missing tip trait
upperPrecisionCache[thisNumber] =
(1.0 / getRescaledBranchLengthForPrecision(node))
* Math.pow(cacheHelper.getOUFactor(node), 2);
lowerPrecisionCache[thisNumber] = Double.POSITIVE_INFINITY;
}
return;
}
final NodeRef childNode0 = treeModel.getChild(node, 0);
final NodeRef childNode1 = treeModel.getChild(node, 1);
postOrderTraverse(
treeModel, childNode0, precisionMatrix, logDetPrecisionMatrix, cacheOuterProducts);
postOrderTraverse(
treeModel, childNode1, precisionMatrix, logDetPrecisionMatrix, cacheOuterProducts);
final int childNumber0 = childNode0.getNumber();
final int childNumber1 = childNode1.getNumber();
final int meanOffset0 = dim * childNumber0;
final int meanOffset1 = dim * childNumber1;
final int meanThisOffset = dim * thisNumber;
final double precision0 = upperPrecisionCache[childNumber0];
final double precision1 = upperPrecisionCache[childNumber1];
final double totalPrecision = precision0 + precision1;
lowerPrecisionCache[thisNumber] = totalPrecision;
// Multiply child0 and child1 densities
// Delegate this!
cacheHelper.computeMeanCaches(
meanThisOffset,
meanOffset0,
meanOffset1,
totalPrecision,
precision0,
precision1,
missingTraits,
node,
childNode0,
childNode1);
// if (totalPrecision == 0) {
// System.arraycopy(zeroDimVector, 0, meanCache, meanThisOffset, dim);
// } else {
// // Delegate in case either child is partially missing
// // computeCorrectedWeightedAverage
// missingTraits.computeWeightedAverage(meanCache,
// meanOffset0, precision0,
// meanOffset1, precision1,
// meanThisOffset, dim);
// }
// In this delegation, you can call
// getShiftForBranchLength(node);
if (!treeModel.isRoot(node)) {
// Integrate out trait value at this node
double thisPrecision = 1.0 / getRescaledBranchLengthForPrecision(node);
if (Double.isInfinite(thisPrecision)) {
upperPrecisionCache[thisNumber] = totalPrecision;
} else {
upperPrecisionCache[thisNumber] =
(totalPrecision * thisPrecision / (totalPrecision + thisPrecision))
* Math.pow(cacheHelper.getOUFactor(node), 2);
}
}
// Compute logRemainderDensity
logRemainderDensityCache[thisNumber] = 0;
if (precision0 != 0 && precision1 != 0) {
incrementRemainderDensities(
precisionMatrix,
logDetPrecisionMatrix,
thisNumber,
meanThisOffset,
meanOffset0,
meanOffset1,
precision0,
precision1,
cacheHelper.getOUFactor(childNode0),
cacheHelper.getOUFactor(childNode1),
cacheOuterProducts);
}
}
private void preOrderTraverseSample(
MultivariateTraitTree treeModel,
NodeRef node,
int parentIndex,
double[][] treePrecision,
double[][] treeVariance) {
final int thisIndex = node.getNumber();
if (treeModel.isRoot(node)) {
// draw root
double[] rootMean = new double[dimTrait];
final int rootIndex = treeModel.getRoot().getNumber();
double rootPrecision = lowerPrecisionCache[rootIndex];
for (int datum = 0; datum < numData; datum++) {
// System.arraycopy(meanCache, thisIndex * dim + datum * dimTrait, rootMean, 0, dimTrait);
System.arraycopy(
cacheHelper.getMeanCache(), thisIndex * dim + datum * dimTrait, rootMean, 0, dimTrait);
double[][] variance =
computeMarginalRootMeanAndVariance(
rootMean, treePrecision, treeVariance, rootPrecision);
double[] draw =
MultivariateNormalDistribution.nextMultivariateNormalVariance(rootMean, variance);
if (DEBUG_PREORDER) {
Arrays.fill(draw, 1.0);
}
System.arraycopy(draw, 0, drawnStates, rootIndex * dim + datum * dimTrait, dimTrait);
if (DEBUG) {
System.err.println("Root mean: " + new Vector(rootMean));
System.err.println("Root var : " + new Matrix(variance));
System.err.println("Root draw: " + new Vector(draw));
}
}
} else { // draw conditional on parentState
if (!missingTraits.isCompletelyMissing(thisIndex)
&& !missingTraits.isPartiallyMissing(thisIndex)) {
// System.arraycopy(meanCache, thisIndex * dim, drawnStates, thisIndex * dim, dim);
System.arraycopy(
cacheHelper.getMeanCache(), thisIndex * dim, drawnStates, thisIndex * dim, dim);
} else {
if (missingTraits.isPartiallyMissing(thisIndex)) {
throw new RuntimeException("Partially missing values are not yet implemented");
}
// This code should work for sampling a missing tip trait as well, but needs testing
// parent trait at drawnStates[parentOffset]
double precisionToParent = 1.0 / getRescaledBranchLengthForPrecision(node);
double precisionOfNode = lowerPrecisionCache[thisIndex];
double totalPrecision = precisionOfNode + precisionToParent;
double[] mean = Ay; // temporary storage
double[][] var = tmpM; // temporary storage
for (int datum = 0; datum < numData; datum++) {
int parentOffset = parentIndex * dim + datum * dimTrait;
int thisOffset = thisIndex * dim + datum * dimTrait;
if (DEBUG) {
double[] parentValue = new double[dimTrait];
System.arraycopy(drawnStates, parentOffset, parentValue, 0, dimTrait);
System.err.println("Parent draw: " + new Vector(parentValue));
if (parentValue[0] != drawnStates[parentOffset]) {
throw new RuntimeException("Error in setting indices");
}
}
for (int i = 0; i < dimTrait; i++) {
mean[i] =
(drawnStates[parentOffset + i] * precisionToParent
// + meanCache[thisOffset + i] * precisionOfNode) / totalPrecision;
+ cacheHelper.getMeanCache()[thisOffset + i] * precisionOfNode)
/ totalPrecision;
for (int j = 0; j < dimTrait; j++) {
var[i][j] = treeVariance[i][j] / totalPrecision;
}
}
double[] draw = MultivariateNormalDistribution.nextMultivariateNormalVariance(mean, var);
System.arraycopy(draw, 0, drawnStates, thisOffset, dimTrait);
if (DEBUG) {
System.err.println("Int prec: " + totalPrecision);
System.err.println("Int mean: " + new Vector(mean));
System.err.println("Int var : " + new Matrix(var));
System.err.println("Int draw: " + new Vector(draw));
System.err.println("");
}
}
}
}
if (peel() && !treeModel.isExternal(node)) {
preOrderTraverseSample(
treeModel, treeModel.getChild(node, 0), thisIndex, treePrecision, treeVariance);
preOrderTraverseSample(
treeModel, treeModel.getChild(node, 1), thisIndex, treePrecision, treeVariance);
}
}
public IntegratedMultivariateTraitLikelihood(
String traitName,
MultivariateTraitTree treeModel,
MultivariateDiffusionModel diffusionModel,
CompoundParameter traitParameter,
Parameter deltaParameter,
List<Integer> missingIndices,
boolean cacheBranches,
boolean scaleByTime,
boolean useTreeLength,
BranchRateModel rateModel,
List<BranchRateModel> optimalValues,
BranchRateModel strengthOfSelection,
Model samplingDensity,
boolean reportAsMultivariate,
boolean reciprocalRates) {
super(
traitName,
treeModel,
diffusionModel,
traitParameter,
deltaParameter,
missingIndices,
cacheBranches,
scaleByTime,
useTreeLength,
rateModel,
optimalValues,
strengthOfSelection,
samplingDensity,
reportAsMultivariate,
reciprocalRates);
// Delegate caches to helper
meanCache = new double[dim * treeModel.getNodeCount()];
if (optimalValues != null) {
cacheHelper =
new OUCacheHelper(
dim * treeModel.getNodeCount(), cacheBranches); // new DriftCacheHelper ....
} else {
cacheHelper = new CacheHelper(dim * treeModel.getNodeCount(), cacheBranches);
}
drawnStates = new double[dim * treeModel.getNodeCount()];
upperPrecisionCache = new double[treeModel.getNodeCount()];
lowerPrecisionCache = new double[treeModel.getNodeCount()];
logRemainderDensityCache = new double[treeModel.getNodeCount()];
if (cacheBranches) {
storedMeanCache = new double[dim * treeModel.getNodeCount()];
storedUpperPrecisionCache = new double[treeModel.getNodeCount()];
storedLowerPrecisionCache = new double[treeModel.getNodeCount()];
storedLogRemainderDensityCache = new double[treeModel.getNodeCount()];
}
// Set up reusable temporary storage
Ay = new double[dimTrait];
tmpM = new double[dimTrait][dimTrait];
tmp2 = new double[dimTrait];
zeroDimVector = new double[dim];
missingTraits = new MissingTraits.CompletelyMissing(treeModel, missingIndices, dim);
setTipDataValuesForAllNodes();
}