public QuietRealParameter getSample(ParametricDistribution distr, double upper, double lower)
throws Exception {
Double[][] sampleVals = distr.sample(1);
QuietRealParameter sampleParameter = new QuietRealParameter(sampleVals[0]);
sampleParameter.setUpper(upper);
sampleParameter.setLower(lower);
return sampleParameter;
}
public double proposeDiscreteValue(
QuietRealParameter proposal,
double oldValue,
ConditionalCategoricalDistribution distr,
double upper,
double lower) {
int oldModel = (int) oldValue;
double newValue =
Randomizer.randomChoicePDF(distr.conditionalDensities(oldModel)) + distr.getOffset();
proposal.setValueQuietly(0, newValue);
proposal.setBounds(lower, upper);
return distr.logConditionalDensity(oldModel, (int) newValue);
}
public double proposeNewValueInLogSpace(
QuietRealParameter proposal,
double oldValue,
ParametricDistribution distr,
double upper,
double lower)
throws Exception {
double sampleVal = distr.sample(1)[0][0];
double newValue = Math.exp(sampleVal + Math.log(oldValue));
proposal.setValueQuietly(0, newValue);
proposal.setBounds(lower, upper);
return distr.calcLogP(new QuietRealParameter(new Double[] {sampleVal})) - Math.log(newValue);
}
private double proposeNewValue(
QuietRealParameter proposal,
Double[] oldValues,
ParametricDistribution distr,
double upper,
double lower)
throws Exception {
Double[] sampleVals = distr.sample(1)[0];
for (int i = 0; i < sampleVals.length; i++) {
// if(distr instanceof DiracDeltaDistribution)
// System.out.println(distr.getClass());
proposal.setValueQuietly(i, oldValues[i] + sampleVals[i]);
}
proposal.setUpper(upper);
proposal.setLower(lower);
return distr.calcLogP(new QuietRealParameter(sampleVals));
}
public double merge(
int index1,
int index2,
int clusterIndex1,
int clusterIndex2,
int[] cluster1Sites,
int[] cluster2Sites) {
/*if(Math.abs(modelList.getParameter(clusterIndex1).getValue() - modelList.getParameter(clusterIndex2).getValue()) > 1.0){
return Double.NEGATIVE_INFINITY;
}*/
double logqMerge = 0.0;
HashMap<Integer, Integer> siteMap = new HashMap<Integer, Integer>();
// The value of the merged cluster will have that of cluster 2 before the merge.
QuietRealParameter mergedParam = paramList.getParameter(clusterIndex2);
QuietRealParameter mergedModel = modelList.getParameter(clusterIndex2);
QuietRealParameter mergedFreqs = freqsList.getParameter(clusterIndex2);
// Create a vector that combines the site indices of the two clusters
int[] mergedClusterSites = new int[cluster1Sites.length + cluster2Sites.length - 2];
int k = 0;
for (int i = 0; i < cluster1Sites.length; i++) {
if (cluster1Sites[i] != index1) {
// For all members that are not index 1,
// record the cluster in which they have been before the merge,
// and assign them to the combined vector.
siteMap.put(cluster1Sites[i], clusterIndex1);
mergedClusterSites[k++] = cluster1Sites[i];
}
}
for (int i = 0; i < cluster2Sites.length; i++) {
// All members in cluster 2 remains in cluster2 so no new pointer assignments
if (cluster2Sites[i] != index2) {
// For all members that are not index 2,
// record the cluster in which they have been before the merge,
// and assign them to the combined vector.
siteMap.put(cluster2Sites[i], clusterIndex2);
try {
mergedClusterSites[k++] = cluster2Sites[i];
} catch (Exception e) {
System.out.println("k: " + k);
System.out.println("i: " + i);
System.out.println("cluster2Sites.length: " + cluster2Sites.length);
System.out.println("index2: " + index2);
for (int index : cluster2Sites) {
System.out.print(index + " ");
}
System.out.println();
throw new RuntimeException("");
}
}
}
try {
// Create a weight vector of patterns to inform the temporary tree likelihood
// which set of pattern likelihoods are to be computed.
// int[] tempWeights = dpTreeLikelihood.getClusterWeights(clusterIndex1);
/*int[] tempWeights = new int[tempLikelihood.m_data.get().getPatternCount()];
for(int i = 0; i < cluster1Sites.length; i++){
int patIndex = tempLikelihood.m_data.get().getPatternIndex(cluster1Sites[i]);
tempWeights[patIndex] = 1;
}
tempLikelihood.setPatternWeights(tempWeights);
double[] cluster1SitesCluster2ParamLogLik = tempLikelihood.calculateLogP(
mergedParam,
mergedModel,
mergedFreqs,
cluster1Sites,
index1
);*/
k = 0;
int[] sCluster1Sites = new int[cluster1Sites.length - 1];
for (int i = 0; i < cluster1Sites.length; i++) {
if (cluster1Sites[i] != index1) {
sCluster1Sites[k++] = cluster1Sites[i];
}
}
tempLikelihood.setupPatternWeightsFromSites(sCluster1Sites);
double[] cluster1SitesCluster2ParamLogLik =
tempLikelihood.calculateLogP(mergedParam, mergedModel, mergedFreqs, sCluster1Sites);
// tempWeights = dpTreeLikelihood.getClusterWeights(clusterIndex2);
/*tempWeights = new int[tempLikelihood.m_data.get().getPatternCount()];
for(int i = 0; i < cluster2Sites.length; i++){
int patIndex = tempLikelihood.m_data.get().getPatternIndex(cluster2Sites[i]);
tempWeights[patIndex] = 1;
}
tempLikelihood.setPatternWeights(tempWeights);
QuietRealParameter removedParam = paramList.getParameter(clusterIndex1);
QuietRealParameter removedModel = modelList.getParameter(clusterIndex1);
QuietRealParameter removedFreqs = freqsList.getParameter(clusterIndex1);
double[] cluster2SitesCluster1ParamLogLik = tempLikelihood.calculateLogP(
removedParam,
removedModel,
removedFreqs,
cluster2Sites,
index2
); */
k = 0;
int[] sCluster2Sites = new int[cluster2Sites.length - 1];
for (int i = 0; i < cluster2Sites.length; i++) {
if (cluster2Sites[i] != index2) {
sCluster2Sites[k++] = cluster2Sites[i];
}
}
tempLikelihood.setupPatternWeightsFromSites(sCluster2Sites);
QuietRealParameter removedParam = paramList.getParameter(clusterIndex1);
QuietRealParameter removedModel = modelList.getParameter(clusterIndex1);
QuietRealParameter removedFreqs = freqsList.getParameter(clusterIndex1);
double[] cluster2SitesCluster1ParamLogLik =
tempLikelihood.calculateLogP(removedParam, removedModel, removedFreqs, sCluster2Sites);
// System.out.println("populate logLik1:");
double[] logLik1 = new double[mergedClusterSites.length];
for (int i = 0; i < (cluster1Sites.length - 1); i++) {
// System.out.println(clusterIndex1+" "+mergedClusterSites[i]);
// logLik1[i] = dpTreeLikelihood.getSiteLogLikelihood(clusterIndex1,mergedClusterSites[i]);
logLik1[i] =
getSiteLogLikelihood(removedParam.getIDNumber(), clusterIndex1, mergedClusterSites[i]);
}
/*System.out.println(cluster2SitesCluster1ParamLogLik.length);
System.out.println(logLik1.length);
System.out.println(cluster1Sites.length-1);
System.out.println(cluster2SitesCluster1ParamLogLik.length);*/
System.arraycopy(
cluster2SitesCluster1ParamLogLik,
0,
logLik1,
cluster1Sites.length - 1,
cluster2SitesCluster1ParamLogLik.length);
double[] logLik2 = new double[mergedClusterSites.length];
System.arraycopy(
cluster1SitesCluster2ParamLogLik, 0, logLik2, 0, cluster1SitesCluster2ParamLogLik.length);
// System.out.println("populate logLik2:");
for (int i = cluster1SitesCluster2ParamLogLik.length; i < logLik2.length; i++) {
// System.out.println(clusterIndex2+"
// "+mergedClusterSites[i-cluster1SitesCluster2ParamLogLik.length]);
// logLik2[i] = dpTreeLikelihood.getSiteLogLikelihood(clusterIndex2,mergedClusterSites[i]);
logLik2[i] =
getSiteLogLikelihood(mergedParam.getIDNumber(), clusterIndex2, mergedClusterSites[i]);
}
double[] lik1 = new double[logLik1.length];
double[] lik2 = new double[logLik2.length];
// scale it so it may be more accuate
double maxLog;
// scale it so it may be more accurate
for (int i = 0; i < logLik1.length; i++) {
maxLog = Math.max(logLik1[i], logLik2[i]);
if (Math.exp(maxLog) < 1e-100) {
if (maxLog == logLik1[i]) {
lik1[i] = 1.0;
lik2[i] = Math.exp(logLik2[i] - maxLog);
} else {
lik1[i] = Math.exp(logLik1[i] - maxLog);
lik2[i] = 1.0;
}
} else {
lik1[i] = Math.exp(logLik1[i]);
lik2[i] = Math.exp(logLik2[i]);
}
}
/*for(int i = 0; i < logLik1.length; i++){
if(Double.isNaN(logLik1[i])){
//System.out.println("logLik1: "+logLik1[i]);
return Double.NEGATIVE_INFINITY;
}
if(Double.isNaN(logLik2[i])){
//System.out.println("logLik2: "+logLik2[i]);
return Double.NEGATIVE_INFINITY;
}
lik1[i] = Math.exp(logLik1[i]);
lik2[i] = Math.exp(logLik2[i]);
//System.out.println(lik1[i]+" "+lik2[i]);
} */
// Create a set of indices for random permutation
int[] shuffle = new int[mergedClusterSites.length];
for (int i = 0; i < shuffle.length; i++) {
shuffle[i] = i;
}
Randomizer.shuffle(shuffle);
int cluster1Count = 1;
int cluster2Count = 1;
int cluster;
double psi1, psi2, cluster1Prob;
for (int i = 0; i < mergedClusterSites.length; i++) {
cluster = siteMap.get(mergedClusterSites[shuffle[i]]);
psi1 = cluster1Count * lik1[shuffle[i]];
psi2 = cluster2Count * lik2[shuffle[i]];
if (testCorrect) {
testCorrectness(
i, cluster, clusterIndex1, clusterIndex2, shuffle, mergedClusterSites, lik1, lik2);
}
cluster1Prob = psi1 / (psi1 + psi2);
if (cluster == clusterIndex1) {
logqMerge += Math.log(cluster1Prob);
cluster1Count++;
} else if (cluster == clusterIndex2) {
logqMerge += Math.log(1 - cluster1Prob);
cluster2Count++;
} else {
throw new RuntimeException("Something is wrong.");
}
}
logqMerge +=
paramBaseDistr.calcLogP(removedParam)
+ modelBaseDistr.calcLogP(removedModel)
+ freqsBaseDistr.calcLogP(removedFreqs);
if (logqMerge > Double.NEGATIVE_INFINITY) {
paramList.mergeParameter(clusterIndex1, clusterIndex2);
modelList.mergeParameter(clusterIndex1, clusterIndex2);
freqsList.mergeParameter(clusterIndex1, clusterIndex2);
for (int i = 0; i < cluster1Sites.length; i++) {
// Point every member in cluster 1 to cluster 2
paramPointers.point(cluster1Sites[i], mergedParam);
modelPointers.point(cluster1Sites[i], mergedModel);
freqsPointers.point(cluster1Sites[i], mergedFreqs);
}
}
} catch (Exception e) {
throw new RuntimeException(e);
}
return logqMerge;
}
public double split(int index1, int index2, int clusterIndex, int[] initClusterSites) {
try {
double logqSplit = 0.0;
// Create a parameter by sampling from the prior
// QuietRealParameter newParam = getSample(paramBaseDistr, paramList.getUpper(),
// paramList.getLower());
QuietRealParameter newRates = new QuietRealParameter(new Double[1]);
logqSplit +=
proposeNewValueInLogSpace(
newRates,
ratesList.getValue(clusterIndex, 0),
ratesBaseDistr,
ratesList.getUpper(),
ratesList.getLower());
ratesList.getValues(clusterIndex);
// QuietRealParameter newModel = getSample(modelBaseDistr, modelList.getUpper(),
// modelList.getLower());
QuietRealParameter newAlpha = new QuietRealParameter(new Double[1]);
logqSplit +=
proposeNewValueInLogSpace(
newAlpha,
alphaList.getValue(clusterIndex, 0),
alphaBaseDistr,
alphaList.getUpper(),
alphaList.getLower());
// QuietRealParameter newFreqs = getSample(freqsBaseDistr, freqsList.getUpper(),
// freqsList.getLower());
QuietRealParameter newInvPr = new QuietRealParameter(new Double[1]);
logqSplit +=
proposeNewValue(
newInvPr,
invPrList.getValues(clusterIndex),
invPrBaseDistr,
invPrList.getUpper(),
invPrList.getLower());
QuietRealParameter newSiteModel = new QuietRealParameter(new Double[1]);
logqSplit +=
proposeDiscreteValue(
newSiteModel,
siteModelList.getValue(clusterIndex, 0),
siteModelBaseDistr,
siteModelList.getUpper(),
siteModelList.getLower());
// Perform a split
// paramList.splitParameter(clusterIndex,newParam);
// modelList.splitParameter(clusterIndex,newModel);
// freqsList.splitParameter(clusterIndex,newFreqs);
// Remove the index 1 and index 2 from the cluster
int[] clusterSites = new int[initClusterSites.length - 2];
int k = 0;
for (int i = 0; i < initClusterSites.length; i++) {
if (initClusterSites[i] != index1 && initClusterSites[i] != index2) {
clusterSites[k++] = initClusterSites[i];
}
}
// Form a new cluster with index 1
// paramPointers.point(index1,newParam);
// modelPointers.point(index1,newModel);
// freqsPointers.point(index1,newFreqs);
// Shuffle the cluster_-{index_1,index_2} to obtain a random permutation
Randomizer.shuffle(clusterSites);
// Create the weight vector of site patterns according to the order of the shuffled index.
/*int[] tempWeights = new int[tempLikelihood.m_data.get().getPatternCount()];
int patIndex;
for(int i = 0; i < clusterSites.length; i++){
patIndex = tempLikelihood.m_data.get().getPatternIndex(clusterSites[i]);
tempWeights[patIndex] = 1;
}*/
tempLikelihood.setupPatternWeightsFromSites(clusterSites);
// Site log likelihoods in the order of the shuffled sites
double[] logLik1 =
tempLikelihood.calculateLogP(
newAlpha.getValue(),
newInvPr.getValue(),
newRates.getValue(),
newSiteModel.getValue(),
clusterSites);
double[] logLik2 = new double[clusterSites.length];
for (int i = 0; i < logLik2.length; i++) {
// logLik2[i] = dpTreeLikelihood.getSiteLogLikelihood(clusterIndex,clusterSites[i]);
logLik2[i] =
getSiteLogLikelihood(
ratesList.getParameterIDNumber(clusterIndex), clusterIndex, clusterSites[i]);
}
double[] lik1 = new double[logLik1.length];
double[] lik2 = new double[logLik2.length];
double maxLog;
// scale it so it may be more accurate
for (int i = 0; i < logLik1.length; i++) {
maxLog = Math.min(logLik1[i], logLik2[i]);
if (Math.exp(maxLog) < 1e-100) {
if (maxLog == logLik1[i]) {
lik1[i] = 1.0;
lik2[i] = Math.exp(logLik2[i] - maxLog);
} else {
lik1[i] = Math.exp(logLik1[i] - maxLog);
lik2[i] = 1.0;
}
} else {
lik1[i] = Math.exp(logLik1[i]);
lik2[i] = Math.exp(logLik2[i]);
}
}
/*boolean ohCrap = false;
for(int i = 0; i < logLik1.length; i++){
if(Double.isNaN(logLik1[i])){
return Double.NEGATIVE_INFINITY;
//ohCrap = true;
//System.out.println("logLik1: "+logLik1);
//logLik1[i] = Double.NEGATIVE_INFINITY;
}
if(Double.isNaN(logLik2[i])){
return Double.NEGATIVE_INFINITY;
//ohCrap = true;
//System.out.println("logLik1: "+logLik2);
//logLik2[i] = Double.NEGATIVE_INFINITY;
}
lik1[i] = Math.exp(logLik1[i]);
lik2[i] = Math.exp(logLik2[i]);
//System.out.println(lik1[i]+" "+lik2[i]);
}
if(ohCrap){
for(int i = 0; i < newRates.getDimension();i++){
System.out.print(newRates.getValue(i)+" ");
}
System.out.println();
} */
/*for(int i = 0; i < clusterSites.length;i++){
System.out.println("clusterSites: "+clusterSites[i]);
}
System.out.println("index 1: "+index1+" index2: "+index2);*/
int cluster1Count = 1;
int cluster2Count = 1;
// Assign members of the existing cluster (except for indice 1 and 2) randomly
// to the existing and the new cluster
double psi1, psi2, newClusterProb, draw;
int[] newAssignment = new int[clusterSites.length];
for (int i = 0; i < clusterSites.length; i++) {
psi1 = cluster1Count * lik1[i];
psi2 = cluster2Count * lik2[i];
newClusterProb = psi1 / (psi1 + psi2);
draw = Randomizer.nextDouble();
if (draw < newClusterProb) {
// System.out.println("in new cluster: "+clusterSites[i]);
// paramPointers.point(clusterSites[i],newParam);
// modelPointers.point(clusterSites[i],newModel);
// freqsPointers.point(clusterSites[i],newFreqs);
newAssignment[cluster1Count - 1] = clusterSites[i];
logqSplit += Math.log(newClusterProb);
cluster1Count++;
} else {
logqSplit += Math.log(1.0 - newClusterProb);
cluster2Count++;
}
}
// logqSplit += //paramBaseDistr.calcLogP(newParam) +
// modelBaseDistr.calcLogP(newModel)+
// freqsBaseDistr.calcLogP(newFreqs);
if (-logqSplit > Double.NEGATIVE_INFINITY) {
ratesList = ratesListInput.get(this);
alphaList = alphaListInput.get(this);
invPrList = invPrListInput.get(this);
siteModelList = siteModelListInput.get(this);
ratesPointers = ratesPointersInput.get(this);
// Perform a split
ratesList.splitParameter(clusterIndex, newRates);
alphaList.splitParameter(clusterIndex, newAlpha);
invPrList.splitParameter(clusterIndex, newInvPr);
siteModelList.splitParameter(clusterIndex, newSiteModel);
// Form a new cluster with index 1
ratesPointers.point(index1, newRates);
for (int i = 0; i < (cluster1Count - 1); i++) {
ratesPointers.point(newAssignment[i], newRates);
}
}
return -logqSplit;
} catch (Exception e) {
// freqsBaseDistr.printDetails();
throw new RuntimeException(e);
}
}
