protected double getRawBranchRate(final Tree tree, final NodeRef node) {
double rate = 0.0;
double[] processValues = getProcessValues(tree, node);
double totalTime = 0;
if (indicatorParameter != null) {
double absRate = rateParameter.getParameterValue(0);
for (int i = 0; i < indicatorParameter.getDimension(); i++) {
int index = (int) indicatorParameter.getParameterValue(i);
if (index == 0) {
rate += absRate * processValues[i];
} else {
rate +=
(absRate * (1.0 + relativeRatesParameter.getParameterValue(index - 1)))
* processValues[i];
}
totalTime += processValues[i];
}
} else {
for (int i = 0; i < rateParameter.getDimension(); i++) {
rate += rateParameter.getParameterValue(i) * processValues[i];
totalTime += processValues[i];
}
}
rate /= totalTime;
return rate;
}
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 double calculateWeightedSSE() {
double weightedSSE = 0;
double currentPopSize = popSizeParameter.getParameterValue(0);
double currentInterval = coalescentIntervals[0];
for (int j = 1; j < fieldLength; j++) {
double nextPopSize = popSizeParameter.getParameterValue(j);
double nextInterval = coalescentIntervals[j];
double delta = nextPopSize - currentPopSize;
double weight = (currentInterval + nextInterval) / 2.0;
weightedSSE += delta * delta / weight;
currentPopSize = nextPopSize;
currentInterval = nextInterval;
}
return weightedSSE;
}
/** @return the lower limit of this hypervolume in the given dimension. */
public Double getLowerLimit(int dimension) {
if (dimension != 0)
throw new RuntimeException("omega parameters have wrong dimension " + dimension);
if (lowerOmega == null) return OMEGA_MIN_VALUE;
else return lowerOmega.getParameterValue(dimension);
}
private void printInformtion(Parameter par) {
StringBuffer sb = new StringBuffer("parameter \n");
for (int j = 0; j < par.getDimension(); j++) {
sb.append(par.getParameterValue(j));
}
Logger.getLogger("dr.evomodel").info(sb.toString());
};
public int[] connected(int i, Parameter clusteringParameter) {
int n = clusteringParameter.getDimension();
int[] visited = new int[n + 1];
visited[0] = i + 1;
int tv = 1;
for (int j = 0; j < n; j++) {
if (visited[j] != 0) {
int curr = visited[j] - 1;
/*look forward
*/
int forward = (int) clusteringParameter.getParameterValue(curr);
visited[tv] = forward + 1;
tv++;
// Check to see if is isn't already on the list
for (int ii = 0; ii < tv - 1; ii++) {
if (visited[ii] == forward + 1) {
tv--;
visited[tv] = 0;
}
}
/*look back
*/
for (int jj = 0; jj < n; jj++) {
if ((int) clusteringParameter.getParameterValue(jj) == curr) {
visited[tv] = jj + 1;
tv++;
for (int ii = 0; ii < tv - 1; ii++) {
if (visited[ii] == jj + 1) {
tv--;
visited[tv] = 0;
}
}
}
}
}
}
return visited;
}
public double getStatisticValue(int dim) {
double val;
// x is location count, y is location dimension
int x = getColumnIndex(dim);
int y = getRowIndex(dim);
Parameter loc = locationsParameter.getParameter(x);
if (y == 0) {
val =
loc.getParameterValue(y)
+ locationDriftParameter.getParameterValue(0) * offsetsParameter.getParameterValue(x);
} else {
val = loc.getParameterValue(y);
}
return val;
}
protected void getDiagonalRates(double[] diagonalRates) {
double kappa = substitutionModel.getKappa();
double[] freq = substitutionModel.getFrequencyModel().getFrequencies();
double mutationRate = mutationParameter.getParameterValue(0);
double beta =
0.5
/ ((freq[0] + freq[2]) * (freq[1] + freq[3])
+ kappa * (freq[0] * freq[2] + freq[1] * freq[3]));
diagonalRates[0] = ((freq[1] + freq[3]) + freq[2] * kappa) * mutationRate * beta;
diagonalRates[1] = ((freq[0] + freq[2]) + freq[3] * kappa) * mutationRate * beta;
diagonalRates[2] = ((freq[1] + freq[3]) + freq[0] * kappa) * mutationRate * beta;
diagonalRates[3] = ((freq[0] + freq[2]) + freq[1] * kappa) * mutationRate * beta;
}
public double logLhoodAllGeneTreesInSpeciesTree() {
double[] llhoodcpts = new double[priorComponents.length];
double totalweight = 0.0;
for (int i = 0; i < priorComponents.length; i++) {
totalweight += priorComponents[i].weight;
}
for (int i = 0; i < priorComponents.length; i++) {
llhoodcpts[i] = Math.log(priorComponents[i].weight / totalweight);
double sigma = popPriorScale.getParameterValue(0);
double alpha = priorComponents[i].alpha;
double beta = sigma * priorComponents[i].beta;
llhoodcpts[i] += logLhoodAllGeneTreesInSpeciesSubtree(pionodes[rootn], alpha, beta);
}
return logsumexp(llhoodcpts);
}
/** change the parameter and return the hastings ratio. */
public double doOperation() throws OperatorFailedException {
double[] mean = sccs.getMode();
double[] currentValue = parameter.getParameterValues();
double[] newValue = new double[dim];
Set<Integer> updateSet = new HashSet<Integer>();
if (setSizeMean != -1.0) {
final int listLength = Poisson.nextPoisson(setSizeMean);
while (updateSet.size() < listLength) {
int newInt = MathUtils.nextInt(parameter.getDimension());
if (!updateSet.contains(newInt)) {
updateSet.add(newInt);
}
}
} else {
for (int i = 0; i < dim; ++i) {
updateSet.add(i);
}
}
double logq = 0;
for (Integer i : updateSet) {
newValue[i] = mean[i] + scaleFactor * MathUtils.nextGaussian();
if (UPDATE_ALL) {
parameter.setParameterValueQuietly(i, newValue[i]);
} else {
parameter.setParameterValue(i, newValue[i]);
}
logq +=
(NormalDistribution.logPdf(currentValue[i], mean[i], scaleFactor)
- NormalDistribution.logPdf(newValue[i], mean[i], scaleFactor));
}
// for (Integer i : updateSet) {
// parameter.setParameterValueQuietly(i, newValue[i]);
// }
if (UPDATE_ALL) {
parameter.setParameterValueNotifyChangedAll(0, parameter.getParameterValue(0));
}
return logq;
}
public double getBranchRate(final Tree tree, final NodeRef node) {
return rateParameter.getParameterValue(0);
}
/** change the parameter and return the hastings ratio. */
public final double doOperation() {
int index = MathUtils.nextInt(links.getDimension());
int oldGroup = (int) assignments.getParameterValue(index);
/*
* Set index customer link to index and all connected to it to a new assignment (min value empty)
*/
int minEmp = minEmpty(modelLikelihood.getLogLikelihoodsVector());
links.setParameterValue(index, index);
int[] visited = connected(index, links);
int ii = 0;
while (visited[ii] != 0) {
assignments.setParameterValue(visited[ii] - 1, minEmp);
ii++;
}
/*
* Adjust likvector for group separated
*/
modelLikelihood.setLogLikelihoodsVector(oldGroup, getLogLikGroup(oldGroup));
modelLikelihood.setLogLikelihoodsVector(minEmp, getLogLikGroup(minEmp));
int maxFull = maxFull(modelLikelihood.getLogLikelihoodsVector());
double[] liks = modelLikelihood.getLogLikelihoodsVector();
/*
* computing likelihoods of joint groups
*/
double[] crossedLiks = new double[maxFull + 1];
for (int ll = 0; ll < maxFull + 1; ll++) {
if (ll != minEmp) {
crossedLiks[ll] = getLogLik2Group(ll, minEmp);
}
}
/*
* Add logPrior
*/
double[] logP = new double[links.getDimension()];
for (int jj = 0; jj < links.getDimension(); jj++) {
logP[jj] += depMatrix[index][jj];
int n = (int) assignments.getParameterValue(jj);
if (n != minEmp) {
logP[jj] += crossedLiks[n] - liks[n] - liks[minEmp];
}
}
logP[index] = Math.log(chiParameter.getParameterValue(0));
/*
* possibilidade de mandar p zero as probs muito pequenas
*/
/*
* Gibbs sampling
*/
this.rescale(logP); // Improve numerical stability
this.exp(logP); // Transform back to probability-scale
int k = MathUtils.randomChoicePDF(logP);
links.setParameterValue(index, k);
int newGroup = (int) assignments.getParameterValue(k);
ii = 0;
while (visited[ii] != 0) {
assignments.setParameterValue(visited[ii] - 1, newGroup);
ii++;
}
/*
* updating conditional likelihood vector
*/
modelLikelihood.setLogLikelihoodsVector(newGroup, getLogLikGroup(newGroup));
if (newGroup != minEmp) {
modelLikelihood.setLogLikelihoodsVector(minEmp, 0);
}
sampleMeans(maxFull);
return 0.0;
}
public void sampleMeans(int maxFull) {
double[][] means = new double[maxFull + 2][2];
// sample mean vector for each cluster
for (int i = 0; i < maxFull + 1; i++) {
// Find all elements in cluster
int ngroup = 0;
for (int ii = 0; ii < assignments.getDimension(); ii++) {
if ((int) assignments.getParameterValue(ii) == i) {
ngroup++;
}
}
if (ngroup != 0) {
double[][] group = new double[ngroup][2];
double[] groupMean = new double[2];
int count = 0;
for (int ii = 0; ii < assignments.getDimension(); ii++) {
if ((int) assignments.getParameterValue(ii) == i) {
group[count][0] = modelLikelihood.getData()[ii][0];
group[count][1] = modelLikelihood.getData()[ii][1];
groupMean[0] += group[count][0];
groupMean[1] += group[count][1];
count += 1;
}
}
groupMean[0] /= ngroup;
groupMean[1] /= ngroup;
double kn = k0 + ngroup;
double vn = v0 + ngroup;
double[][] sumdif = new double[2][2];
for (int jj = 0; jj < ngroup; jj++) {
sumdif[0][0] += (group[jj][0] - groupMean[0]) * (group[jj][0] - groupMean[0]);
sumdif[0][1] += (group[jj][0] - groupMean[0]) * (group[jj][1] - groupMean[1]);
sumdif[1][0] += (group[jj][0] - groupMean[0]) * (group[jj][1] - groupMean[1]);
sumdif[1][1] += (group[jj][1] - groupMean[1]) * (group[jj][1] - groupMean[1]);
}
double[][] TnInv = new double[2][2];
TnInv[0][0] =
T0Inv[0][0]
+ ngroup * (k0 / kn) * (groupMean[0] - m[0]) * (groupMean[0] - m[0])
+ sumdif[0][0];
TnInv[0][1] =
T0Inv[0][1]
+ ngroup * (k0 / kn) * (groupMean[1] - m[1]) * (groupMean[0] - m[0])
+ sumdif[0][1];
TnInv[1][0] =
T0Inv[1][0]
+ ngroup * (k0 / kn) * (groupMean[0] - m[0]) * (groupMean[1] - m[1])
+ sumdif[1][0];
TnInv[1][1] =
T0Inv[1][1]
+ ngroup * (k0 / kn) * (groupMean[1] - m[1]) * (groupMean[1] - m[1])
+ sumdif[1][1];
Matrix Tn = new SymmetricMatrix(TnInv).inverse();
double[] posteriorMean = new double[2];
// compute posterior mean
posteriorMean[0] = (k0 * m[0] + ngroup * groupMean[0]) / (k0 + ngroup);
posteriorMean[1] = (k0 * m[1] + ngroup * groupMean[1]) / (k0 + ngroup);
// compute posterior Precision
double[][] posteriorPrecision =
new WishartDistribution(vn, Tn.toComponents()).nextWishart();
posteriorPrecision[0][0] *= kn;
posteriorPrecision[1][0] *= kn;
posteriorPrecision[0][1] *= kn;
posteriorPrecision[1][1] *= kn;
double[] sample =
new MultivariateNormalDistribution(posteriorMean, posteriorPrecision)
.nextMultivariateNormal();
means[i][0] = sample[0];
means[i][1] = sample[1];
}
}
// Fill in cluster means for each observation
for (int j = 0; j < assignments.getDimension(); j++) {
double[] group = new double[2];
group[0] = means[(int) assignments.getParameterValue(j)][0];
group[1] = means[(int) assignments.getParameterValue(j)][1];
modelLikelihood.setMeans(j, group);
}
}
public double getLogLik2Group(int group1, int group2) {
double L = 0.0;
int ngroup1 = 0;
for (int i = 0; i < assignments.getDimension(); i++) {
if ((int) assignments.getParameterValue(i) == group1) {
ngroup1++;
}
}
int ngroup2 = 0;
for (int i = 0; i < assignments.getDimension(); i++) {
if ((int) assignments.getParameterValue(i) == group2) {
ngroup2++;
}
}
int ngroup = (ngroup1 + ngroup2);
if (ngroup != 0) {
double[][] group = new double[ngroup][2];
double mean[] = new double[2];
int count = 0;
for (int i = 0; i < assignments.getDimension(); i++) {
if ((int) assignments.getParameterValue(i) == group1) {
group[count][0] = modelLikelihood.getData()[i][0];
group[count][1] = modelLikelihood.getData()[i][1];
mean[0] += group[count][0];
mean[1] += group[count][1];
count += 1;
}
}
for (int i = 0; i < assignments.getDimension(); i++) {
if ((int) assignments.getParameterValue(i) == group2) {
group[count][0] = modelLikelihood.getData()[i][0];
group[count][1] = modelLikelihood.getData()[i][1];
mean[0] += group[count][0];
mean[1] += group[count][1];
count += 1;
}
}
mean[0] /= ngroup;
mean[1] /= ngroup;
double kn = k0 + ngroup;
double vn = v0 + ngroup;
double[][] sumdif = new double[2][2];
for (int i = 0; i < ngroup; i++) {
sumdif[0][0] += (group[i][0] - mean[0]) * (group[i][0] - mean[0]);
sumdif[0][1] += (group[i][0] - mean[0]) * (group[i][1] - mean[1]);
sumdif[1][0] += (group[i][0] - mean[0]) * (group[i][1] - mean[1]);
sumdif[1][1] += (group[i][1] - mean[1]) * (group[i][1] - mean[1]);
}
double[][] TnInv = new double[2][2];
TnInv[0][0] =
T0Inv[0][0] + ngroup * (k0 / kn) * (mean[0] - m[0]) * (mean[0] - m[0]) + sumdif[0][0];
TnInv[0][1] =
T0Inv[0][1] + ngroup * (k0 / kn) * (mean[1] - m[1]) * (mean[0] - m[0]) + sumdif[0][1];
TnInv[1][0] =
T0Inv[1][0] + ngroup * (k0 / kn) * (mean[0] - m[0]) * (mean[1] - m[1]) + sumdif[1][0];
TnInv[1][1] =
T0Inv[1][1] + ngroup * (k0 / kn) * (mean[1] - m[1]) * (mean[1] - m[1]) + sumdif[1][1];
double logDetTn = -Math.log(TnInv[0][0] * TnInv[1][1] - TnInv[0][1] * TnInv[1][0]);
L += -(ngroup) * Math.log(Math.PI);
L += Math.log(k0) - Math.log(kn);
L += (vn / 2) * logDetTn - (v0 / 2) * logDetT0;
L += GammaFunction.lnGamma(vn / 2) + GammaFunction.lnGamma((vn / 2) - 0.5);
L += -GammaFunction.lnGamma(v0 / 2) - GammaFunction.lnGamma((v0 / 2) - 0.5);
}
return L;
}
