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);
    }
  }