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);
}
}
