public void initAndValidate() {
    paramList = paramListInput.get();
    modelList = modelListInput.get();
    freqsList = freqsListInput.get();
    ratesList = ratesListInput.get();

    paramPointers = paramPointersInput.get();
    modelPointers = modelPointersInput.get();
    freqsPointers = freqsPointersInput.get();
    ratesPointers = ratesPointersInput.get();

    pointerCount = paramPointers.getDimension();

    dp = dpInput.get();
    List<ParametricDistribution> distrs = dp.getBaseDistributions();
    paramBaseDistr = paramDistrInput.get();
    // paramBaseDistr = distrs.get(0);
    modelBaseDistr = distrs.get(1);
    // modelBaseDistr = modelDistrInput.get();
    freqsBaseDistr = distrs.get(2);
    // ratesBaseDistr = distrs.get(3);
    ratesBaseDistr = rateDistrInput.get();

    tempLikelihood = tempLikelihoodInput.get();
    dpTreeLikelihood = dpTreeLikelihoodInput.get();

    modelNetworkMap.put(1.0, new double[] {3.0});
    modelNetworkMap.put(2.0, new double[] {3.0});
    modelNetworkMap.put(3.0, new double[] {1.0, 2.0, 4.0});
    modelNetworkMap.put(4.0, new double[] {3.0, 5.0});
    modelNetworkMap.put(5.0, new double[] {4.0});
    // System.out.println("is null? "+(modelNetworkMap.get(5.0) == null));

  }
  public void initAndValidate() {
    testCorrect = testCorrectInput.get();
    ratesList = ratesListInput.get();
    alphaList = alphaListInput.get();
    invPrList = invPrListInput.get();
    siteModelList = siteModelListInput.get();

    ratesPointers = ratesPointersInput.get();

    pointerCount = ratesPointers.getDimension();

    ratesBaseDistr = ratesDistrInput.get();
    alphaBaseDistr = alphaDistrInput.get();
    invPrBaseDistr = invPrDistrInput.get();
    siteModelBaseDistr = siteModelDistrInput.get();

    tempLikelihood = tempLikelihoodInput.get();
    dpTreeLikelihood = dpTreeLikelihoodInput.get();

    modelNetworkMap.put(1.0, new double[] {3.0});
    modelNetworkMap.put(2.0, new double[] {3.0});
    modelNetworkMap.put(3.0, new double[] {1.0, 2.0, 4.0});
    modelNetworkMap.put(4.0, new double[] {3.0, 5.0});
    modelNetworkMap.put(5.0, new double[] {4.0});
    // System.out.println("is null? "+(modelNetworkMap.get(5.0) == null));

  }
  public double proposal() {
    double logq = 0.0;
    // Pick two indcies at random
    int index1 = Randomizer.nextInt(pointerCount);
    int index2 = index1;
    while (index2 == index1) {
      index2 = Randomizer.nextInt(pointerCount);
    }

    int clusterIndex1 = paramPointers.indexInList(index1, paramList);
    int clusterIndex2 = paramPointers.indexInList(index2, paramList);

    // If the randomly draw sites are from the same cluster, perform a split-move.
    if (clusterIndex1 == clusterIndex2) {

      int[] clusterSites = dpValuableInput.get().getClusterSites(clusterIndex1);

      double temp = split(index1, index2, clusterIndex1, clusterSites);
      // System.out.println("split: "+temp);
      logq += temp;

      // System.out.println("split: "+temp);

    } else {
      // If the the two randomly drawn sites are not from the same cluster, perform a merge-move.

      int[] cluster1Sites = dpValuableInput.get().getClusterSites(clusterIndex1);
      int[] cluster2Sites = dpValuableInput.get().getClusterSites(clusterIndex2);

      // logq = merge(index1, index2,clusterIndex1,clusterIndex2,cluster1Sites,cluster2Sites);
      double temp =
          merge(index1, index2, clusterIndex1, clusterIndex2, cluster1Sites, cluster2Sites);

      // System.out.println("merge: "+temp);
      logq = temp;
    }
    return logq;
  }
  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);
    }
  }