public ClusterOutput Run(List <string> structs)
{
maxProgress = 5;
currentProgress = 0;
if (hConcensus)
{
maxProgress++;
consensus = new HammingConsensus(dMeasure.dirName, null, false, input.consensusProfile);
progressObject = consensus;
consensus.InitMeasure();
currentProgress += 1.0 / maxProgress;
}
jury = new jury1D();
progressObject = jury;
currentProgress += 1.0 / maxProgress;
progressObject = null;
jury.PrepareJury(dMeasure.dirName, dMeasure.alignFile, input.jury1DProfileFast);
currentProgress += 1.0 / maxProgress;
ClusterOutput clOut = new ClusterOutput();
root.setStruct = structs;
// if(hConcensus)
// consensus.ToConsensusStates(structs);
FastCluster(root);
maxV = initNodesNum;
while (st.Count > 0 && (leaves.Count + st.Count) < initNodesNum)
{
st.Sort(
delegate(HClusterNode p1, HClusterNode p2)
{
return(p2.setStruct.Count.CompareTo(p1.setStruct.Count));
}
);
HClusterNode node = st[0];
st.RemoveAt(0);
FastCluster(node);
currentV += leaves.Count + st.Count;
}
currentV = maxV;
currentProgress += 1.0 / maxProgress;
while (st.Count > 0)
{
HClusterNode node = st[0];
st.RemoveAt(0);
leaves.Add(node);
}
MakeDendrogs(linkage);
currentProgress += 1.0 / maxProgress;
PrepareList();
root = ConnectDendrogs(linkage);
root.levelDist = root.SearchMaxDist();
root.realDist = dMeasure.GetRealValue(root.levelDist);
//CheckRefDistances();
//dendrogList = RearangeDendrogram(root);
//root = ConnectDendrogs();
clOut.hNode = root;
currentProgress += 1.0 / maxProgress;
return(clOut);
}
