protected double calculateLogFieldLikelihood() {
if (!intervalsKnown) {
// intervalsKnown -> false when handleModelChanged event occurs in super.
wrapSetupIntervals();
setupGMRFWeights();
intervalsKnown = true;
}
double currentLike = 0;
DenseVector diagonal1 = new DenseVector(fieldLength);
DenseVector currentGamma = new DenseVector(popSizeParameter.getParameterValues());
SymmTridiagMatrix currentQ =
getScaledWeightMatrix(
precisionParameter.getParameterValue(0), lambdaParameter.getParameterValue(0));
currentQ.mult(currentGamma, diagonal1);
// currentLike += 0.5 * logGeneralizedDeterminant(currentQ) - 0.5 *
// currentGamma.dot(diagonal1);
currentLike +=
0.5 * (fieldLength - 1) * Math.log(precisionParameter.getParameterValue(0))
- 0.5 * currentGamma.dot(diagonal1);
if (lambdaParameter.getParameterValue(0) == 1) {
currentLike -= (fieldLength - 1) / 2.0 * LOG_TWO_TIMES_PI;
} else {
currentLike -= fieldLength / 2.0 * LOG_TWO_TIMES_PI;
}
return currentLike;
}
public SymmTridiagMatrix getStoredScaledWeightMatrix(double precision) {
SymmTridiagMatrix a = storedWeightMatrix.copy();
for (int i = 0; i < a.numRows() - 1; i++) {
a.set(i, i, a.get(i, i) * precision);
a.set(i + 1, i, a.get(i + 1, i) * precision);
}
a.set(fieldLength - 1, fieldLength - 1, a.get(fieldLength - 1, fieldLength - 1) * precision);
return a;
}
protected void storeState() {
super.storeState();
System.arraycopy(
coalescentIntervals, 0, storedCoalescentIntervals, 0, coalescentIntervals.length);
System.arraycopy(
sufficientStatistics, 0, storedSufficientStatistics, 0, sufficientStatistics.length);
storedWeightMatrix = weightMatrix.copy();
storedLogFieldLikelihood = logFieldLikelihood;
}
public SymmTridiagMatrix getStoredScaledWeightMatrix(double precision, double lambda) {
if (lambda == 1) return getStoredScaledWeightMatrix(precision);
SymmTridiagMatrix a = storedWeightMatrix.copy();
for (int i = 0; i < a.numRows() - 1; i++) {
a.set(i, i, precision * (1 - lambda + lambda * a.get(i, i)));
a.set(i + 1, i, a.get(i + 1, i) * precision * lambda);
}
a.set(
fieldLength - 1,
fieldLength - 1,
precision * (1 - lambda + lambda * a.get(fieldLength - 1, fieldLength - 1)));
return a;
}
public static double logGeneralizedDeterminant(SymmTridiagMatrix X) {
// Set up the eigenvalue solver
SymmTridiagEVD eigen = new SymmTridiagEVD(X.numRows(), false);
// Solve for the eigenvalues
try {
eigen.factor(X);
} catch (NotConvergedException e) {
throw new RuntimeException(
"Not converged error in generalized determinate calculation.\n" + e.getMessage());
}
// Get the eigenvalues
double[] x = eigen.getEigenvalues();
double a = 0;
for (double d : x) {
if (d > 0.00001) a += Math.log(d);
}
return a;
}
public SymmTridiagMatrix getWeightMatrix() {
return weightMatrix.copy();
}
