public void testCorrectness(
int i,
int cluster,
int clusterIndex1,
int clusterIndex2,
int[] shuffle,
int[] mergedClusterSites,
double[] lik1,
double[] lik2)
throws Exception {
// System.out.println("Hi!");
int[] tempWeights = new int[tempLikelihood.dataInput.get().getPatternCount()];
tempWeights[tempLikelihood.dataInput.get().getPatternIndex(mergedClusterSites[shuffle[i]])] = 1;
tempLikelihood.setPatternWeights(tempWeights);
double temp1 =
Math.exp(
tempLikelihood
.calculateLogP(
alphaList.getParameter(clusterIndex1).getValue(),
invPrList.getParameter(clusterIndex1).getValue(),
ratesList.getParameter(clusterIndex1).getValue(),
siteModelList.getParameter(clusterIndex1).getValue(),
new int[] {mergedClusterSites[shuffle[i]]})[0]);
double temp2 =
Math.exp(
tempLikelihood
.calculateLogP(
alphaList.getParameter(clusterIndex2).getValue(),
invPrList.getParameter(clusterIndex2).getValue(),
ratesList.getParameter(clusterIndex2).getValue(),
siteModelList.getParameter(clusterIndex2).getValue(),
new int[] {mergedClusterSites[shuffle[i]]})[0]);
if (temp1 != lik1[shuffle[i]] || temp2 != lik2[shuffle[i]]) {
System.out.println("temp1");
System.out.println("shuffle_i: " + shuffle[i]);
System.out.println("mergedClusterSites[shuffle]: " + mergedClusterSites[shuffle[i]]);
System.out.println("cluster: " + cluster);
System.out.println(+mergedClusterSites.length + " " + lik1.length);
for (int j = 0; j < lik1.length; j++) {
System.out.println("merged lik1: " + mergedClusterSites[j] + " " + lik1[j]);
}
for (int j = 0; j < lik2.length; j++) {
System.out.println("merged lik2: " + mergedClusterSites[j] + " " + lik2[j]);
}
throw new RuntimeException(
temp1 + " " + lik1[shuffle[i]] + " " + temp2 + " " + lik2[shuffle[i]]);
}
}
public double merge(
int index1,
int index2,
int clusterIndex1,
int clusterIndex2,
int[] cluster1Sites,
int[] cluster2Sites) {
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);
QuietRealParameter mergedRates = ratesList.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++) {
// Point every member in cluster 1 to cluster 2
// paramPointers.point(cluster1Sites[i],mergedParam);
// modelPointers.point(cluster1Sites[i],mergedModel);
// freqsPointers.point(cluster1Sites[i],mergedFreqs);
// ratesPointers.point(cluster1Sites[i],mergedRates);
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);
mergedClusterSites[k++] = cluster2Sites[i];
}
}
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,
mergedRates,
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, mergedRates, 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);
RealParameter removedParam = paramList.getParameter(clusterIndex1);
RealParameter removedModel = modelList.getParameter(clusterIndex1);
RealParameter removedFreqs = freqsList.getParameter(clusterIndex1);
RealParameter removedRates = ratesList.getParameter(clusterIndex1);
double[] cluster2SitesCluster1ParamLogLik = tempLikelihood.calculateLogP(
removedParam,
removedModel,
removedFreqs,
removedRates,
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);
RealParameter removedParam = paramList.getParameter(clusterIndex1);
RealParameter removedModel = modelList.getParameter(clusterIndex1);
RealParameter removedFreqs = freqsList.getParameter(clusterIndex1);
RealParameter removedRates = ratesList.getParameter(clusterIndex1);
double[] cluster2SitesCluster1ParamLogLik =
tempLikelihood.calculateLogP(
removedParam, removedModel, removedFreqs, removedRates, 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]);
}
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]);
}
double[] lik1 = new double[logLik1.length];
double[] lik2 = new double[logLik2.length];
// scale it so it may be more accuate
double minLog;
/*for(int i = 0; i < logLik1.length; i++){
minLog = Math.min(logLik1[i],logLik2[i]);
if(minLog == logLik1[i]){
lik1[i] = 1.0;
lik2[i] = Math.exp(logLik2[i] - minLog);
}else{
lik1[i] = Math.exp(logLik1[i] - minLog);
lik2[i] = 1.0;
}
}*/
for (int i = 0; i < logLik1.length; i++) {
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]];
/*testCorrectness(i,cluster,
clusterIndex1,clusterIndex2,shuffle, mergedClusterSites,
lik1,lik2);*/
cluster1Prob = psi1 / (psi1 + psi2);
// System.out.println(cluster1Prob);
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)
mergeValue(removedParam, mergedParam, paramBaseDistr)
// + modelBaseDistr.calcLogP(removedModel)
+ mergeDiscreteValue(removedModel, mergedModel, modelDistrInput.get())
+ freqsBaseDistr.calcLogP(removedFreqs)
// + ratesBaseDistr.calcLogP(removedRates);
+ mergeValueInLogSpace(removedRates, mergedRates, ratesBaseDistr);
if (logqMerge > Double.NEGATIVE_INFINITY) {
paramList.mergeParameter(clusterIndex1, clusterIndex2);
modelList.mergeParameter(clusterIndex1, clusterIndex2);
freqsList.mergeParameter(clusterIndex1, clusterIndex2);
ratesList.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);
ratesPointers.point(cluster1Sites[i], mergedRates);
}
}
} 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 newParam = new QuietRealParameter(new Double[5]);
// logqSplit += proposeNewValue(newParam, paramBaseDistr, paramList.getUpper(),
// paramList.getLower());
double[] oldParamValues = new double[5];
for (int i = 0; i < oldParamValues.length; i++) {
oldParamValues[i] = paramList.getValue(clusterIndex, i);
}
logqSplit +=
proposeNewValue2(
newParam, oldParamValues, paramBaseDistr, paramList.getUpper(), paramList.getLower());
// QuietRealParameter newModel = getSample(modelBaseDistr, modelList.getUpper(),
// modelList.getLower());
QuietRealParameter newModel = new QuietRealParameter(new Double[1]);
logqSplit +=
proposeDiscreteValue(
newModel,
modelList.getValue(clusterIndex, 0),
modelDistrInput.get(),
modelList.getUpper(),
modelList.getLower());
QuietRealParameter newFreqs =
getSample(freqsBaseDistr, freqsList.getUpper(), freqsList.getLower());
// QuietRealParameter newRates = getSample(ratesBaseDistr, ratesList.getUpper(),
// ratesList.getLower());
QuietRealParameter newRates = new QuietRealParameter(new Double[1]);
logqSplit +=
proposalValueInLogSpace(
newRates,
ratesList.getValue(clusterIndex, 0),
ratesBaseDistr,
ratesList.getUpper(),
ratesList.getLower());
// 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);
ratesPointers.point(index1, newRates);
// 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.setPatternWeights(tempWeights);*/
tempLikelihood.setupPatternWeightsFromSites(clusterSites);
// Site log likelihoods in the order of the shuffled sites
double[] logLik1 =
tempLikelihood.calculateLogP(newParam, newModel, newFreqs, newRates, clusterSites);
double[] logLik2 = new double[clusterSites.length];
for (int i = 0; i < logLik2.length; i++) {
logLik2[i] = dpTreeLikelihood.getSiteLogLikelihood(clusterIndex, clusterSites[i]);
}
double[] lik1 = new double[logLik1.length];
double[] lik2 = new double[logLik2.length];
double minLog;
// scale it so it may be more accurate
/*for(int i = 0; i < logLik1.length; i++){
minLog = Math.min(logLik1[i],logLik2[i]);
if(minLog == logLik1[i]){
lik1[i] = 1.0;
lik2[i] = Math.exp(logLik2[i] - minLog);
}else{
lik1[i] = Math.exp(logLik1[i] - minLog);
lik2[i] = 1.0;
}
}*/
for (int i = 0; i < logLik1.length; i++) {
lik1[i] = Math.exp(logLik1[i]);
lik2[i] = Math.exp(logLik2[i]);
// System.out.println(lik1[i]+" "+lik2[i]);
}
/*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;
int[] sitesInCluster1 = new int[initClusterSites.length];
sitesInCluster1[0] = index1;
// 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;
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]);
sitesInCluster1[cluster1Count] = clusterSites[i];
// paramPointers.point(clusterSites[i],newParam);
// modelPointers.point(clusterSites[i],newModel);
// freqsPointers.point(clusterSites[i],newFreqs);
// ratesPointers.point(clusterSites[i],newRates);
logqSplit += Math.log(newClusterProb);
cluster1Count++;
} else {
logqSplit += Math.log(1.0 - newClusterProb);
cluster2Count++;
}
}
// logqSplit += paramBaseDistr.calcLogP(newParam)
logqSplit += // modelBaseDistr.calcLogP(newModel) +
freqsBaseDistr.calcLogP(newFreqs)
// + ratesBaseDistr.calcLogP(newRates)
;
// Perform a split
paramList = paramListInput.get(this);
modelList = modelListInput.get(this);
freqsList = freqsListInput.get(this);
ratesList = ratesListInput.get(this);
paramPointers = paramPointersInput.get(this);
modelPointers = modelPointersInput.get(this);
freqsPointers = freqsPointersInput.get(this);
ratesPointers = ratesPointersInput.get(this);
paramList.splitParameter(clusterIndex, newParam);
modelList.splitParameter(clusterIndex, newModel);
freqsList.splitParameter(clusterIndex, newFreqs);
ratesList.splitParameter(clusterIndex, newRates);
// Form a new cluster with index 1
paramPointers = paramPointersInput.get(this);
modelPointers = modelPointersInput.get(this);
freqsPointers = freqsPointersInput.get(this);
ratesPointers = ratesPointersInput.get(this);
for (int i = 0; i < cluster1Count; i++) {
paramPointers.point(sitesInCluster1[i], newParam);
modelPointers.point(sitesInCluster1[i], newModel);
freqsPointers.point(sitesInCluster1[i], newFreqs);
ratesPointers.point(sitesInCluster1[i], newRates);
}
return -logqSplit;
} catch (Exception e) {
throw new RuntimeException(e);
}
}
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 newModel =
getSample(modelBaseDistr, modelList.getUpper(), modelList.getLower());
QuietRealParameter newFreqs =
getSample(freqsBaseDistr, freqsList.getUpper(), freqsList.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(newParam, newModel, newFreqs, 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(
paramList.getParameter(clusterIndex).getIDNumber(), 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.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]);
}
}
/*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 < newParam.getDimension();i++){
System.out.print(newParam.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++;
}
}
// System.out.println("halfway: "+logqSplit);
logqSplit +=
paramBaseDistr.calcLogP(newParam)
+ modelBaseDistr.calcLogP(newModel)
+ freqsBaseDistr.calcLogP(newFreqs);
if (-logqSplit > Double.NEGATIVE_INFINITY) {
paramList = paramListInput.get(this);
modelList = modelListInput.get(this);
freqsList = freqsListInput.get(this);
paramPointers = paramPointersInput.get(this);
modelPointers = modelPointersInput.get(this);
freqsPointers = freqsPointersInput.get(this);
// Perform a split
paramList.splitParameter(clusterIndex, newParam);
modelList.splitParameter(clusterIndex, newModel);
freqsList.splitParameter(clusterIndex, newFreqs);
// Form a new cluster with index 1
paramPointers.point(index1, newParam);
modelPointers.point(index1, newModel);
freqsPointers.point(index1, newFreqs);
for (int i = 0; i < (cluster1Count - 1); i++) {
paramPointers.point(newAssignment[i], newParam);
modelPointers.point(newAssignment[i], newModel);
freqsPointers.point(newAssignment[i], newFreqs);
}
}
return -logqSplit;
} catch (Exception e) {
// freqsBaseDistr.printDetails();
throw new RuntimeException(e);
}
}