private void MakeDendrogs(AglomerativeType linkage)
{
ClusterOutput outCl;
hierarchicalCluster dendrog = new hierarchicalCluster(dMeasure, input, dirName);
currentV = 0;
maxV = leaves.Count + 1;
double remProgress = currentProgress;
for (int i = 0; i < leaves.Count; i++)
{
HClusterNode c = leaves[i];
dendrog.mustRefStructure = c.setStruct[0];
outCl = dendrog.HierarchicalClustering(c.setStruct);
dendrogList.Add(c);
c.levelDist = outCl.hNode.levelDist;
c.realDist = dMeasure.GetRealValue(c.levelDist);
c.refStructure = outCl.hNode.refStructure;
if (outCl.hNode.joined != null)
{
c.joined = new List <HClusterNode>();
foreach (var item in outCl.hNode.joined)
{
c.joined.Add(item);
}
}
currentV++;
currentProgress = remProgress + 1.0 / maxProgress * (double)currentV / maxV;
}
maxV = currentV;
currentProgress = remProgress;
}
private void RunHierarchicalCluster(string name, string dirName, string alignFile = null, DCDFile dcd = null)
{
DateTime cpuPart1 = DateTime.Now;
DistanceMeasure distance = null;
//distance.CalcDistMatrix(distance.structNames);
// opt.hierarchical.atoms = PDB.PDBMODE.ALL_ATOMS;
if (dcd != null)
{
distance = CreateMeasureForDCD(dcd, opt.hierarchical.distance, opt.hierarchical.atoms, opt.hierarchical.reference1DjuryAglom,
opt.hierarchical.alignmentFileName, opt.hierarchical.hammingProfile, opt.hierarchical.jury1DProfileAglom);
}
else
{
distance = CreateMeasure(name, dirName, opt.hierarchical.distance, opt.hierarchical.atoms, opt.hierarchical.reference1DjuryAglom,
alignFile, opt.hierarchical.hammingProfile, opt.hierarchical.jury1DProfileAglom);
}
DebugClass.WriteMessage("Measure Created");
hierarchicalCluster hk = new hierarchicalCluster(distance, opt.hierarchical, dirName);
if (beginJob != null)
{
beginJob(currentProcessName, hk.ToString(), dirName, distance.ToString());
}
clType = hk.ToString();
ClusterOutput output;
progressDic.Add(name, hk);
distance.InitMeasure();
DateTime cpuPart2 = DateTime.Now;
output = hk.HierarchicalClustering(new List <string>(distance.structNames.Keys));
UpdateOutput(name, dirName, alignFile, output, distance.ToString(), cpuPart1, cpuPart2, hk);
}
public ClusterOutput DendrogUsingMeasures(List <string> structures)
{
jury1D juryLocal = new jury1D();
juryLocal.PrepareJury(al);
ClusterOutput outC = null;
Dictionary <string, List <int> > dic;
//Console.WriteLine("Start after jury " + Process.GetCurrentProcess().PeakWorkingSet64);
maxV = refPoints * 20 * 4;
currentV = 0;
dic = PrepareKeys(structures, false);
//DebugClass.DebugOn();
// input.relClusters = input.reqClusters;
// input.perData = 90;
if (dic.Count > input.relClusters)
{
if (!input.combine)
{
dic = HashEntropyCombine(dic, structures, input.relClusters);
}
else
{
dic = Rpart(dic, structures, false);
}
//dic = FastCombineKeysNew(dic, structures, false);
}
Dictionary <string, int> xx = ReadLeafs();
dic = SelectClusters(xx, dic);
maxV = 3;
currentV = 1;
//Console.WriteLine("Entropy ready after jury " + Process.GetCurrentProcess().PeakWorkingSet64);
DebugClass.WriteMessage("Entropy ready");
//Alternative way to start of UQclust Tree must be finished
//input.relClusters = 10000;
//dic = FastCombineKeys(dic, structures, true);
DebugClass.WriteMessage("dic size" + dic.Count);
currentV++;
//Console.WriteLine("Combine ready after jury " + Process.GetCurrentProcess().PeakWorkingSet64);
DebugClass.WriteMessage("Combine Keys ready");
Dictionary <string, string> translateToCluster = new Dictionary <string, string>(dic.Count);
List <string> structuresToDendrogram = new List <string>(dic.Count);
List <string> structuresFullPath = new List <string>(dic.Count);
DebugClass.WriteMessage("Number of clusters: " + dic.Count);
int cc = 0;
List <string> order = new List <string>(dic.Keys);
order.Sort(delegate(string a, string b)
{
if (dic[b].Count == dic[a].Count)
{
for (int i = 0; i < a.Length; i++)
{
if (a[i] != b[i])
{
if (a[i] == '0')
{
return(-1);
}
else
{
return(1);
}
}
}
}
return(dic[b].Count.CompareTo(dic[a].Count));
});
foreach (var item in order)
{
if (dic[item].Count > 2)
{
List <string> cluster = new List <string>(dic[item].Count);
foreach (var str in dic[item])
{
cluster.Add(structures[str]);
}
ClusterOutput output = juryLocal.JuryOptWeights(cluster);
structuresToDendrogram.Add(output.juryLike[0].Key);
if (alignFile == null)
{
structuresFullPath.Add(dirName + Path.DirectorySeparatorChar + output.juryLike[0].Key);
}
else
{
structuresFullPath.Add(output.juryLike[0].Key);
}
translateToCluster.Add(output.juryLike[0].Key, item);
}
else
{
structuresToDendrogram.Add(structures[dic[item][0]]);
if (alignFile == null)
{
structuresFullPath.Add(dirName + Path.DirectorySeparatorChar + structures[dic[item][0]]);
}
else
{
structuresFullPath.Add(structures[dic[item][0]]);
}
translateToCluster.Add(structures[dic[item][0]], item);
}
cc++;
}
currentV++;
DebugClass.WriteMessage("Jury finished");
switch (dMeasure)
{
case DistanceMeasures.HAMMING:
if (refJuryProfile == null || !jury1d)
{
throw new Exception("Sorry but for jury measure you have to define 1djury profile to find reference structure");
}
else
{
dist = new JuryDistance(structuresFullPath, alignFile, true, profileName, refJuryProfile);
}
break;
case DistanceMeasures.COSINE:
dist = new CosineDistance(structuresFullPath, alignFile, jury1d, profileName, refJuryProfile);
break;
case DistanceMeasures.RMSD:
dist = new Rmsd(structuresFullPath, "", jury1d, atoms, refJuryProfile);
break;
case DistanceMeasures.MAXSUB:
dist = new MaxSub(structuresFullPath, "", jury1d, refJuryProfile);
break;
}
// return new ClusterOutput();
DebugClass.WriteMessage("Start hierarchical");
//Console.WriteLine("Start hierarchical " + Process.GetCurrentProcess().PeakWorkingSet64);
currentV = maxV;
hk = new hierarchicalCluster(dist, hier, dirName);
dist.InitMeasure();
//Now just add strctures to the leaves
outC = hk.HierarchicalClustering(structuresToDendrogram);
DebugClass.WriteMessage("Stop hierarchical");
List <HClusterNode> hLeaves = outC.hNode.GetLeaves();
foreach (var item in hLeaves)
{
if (translateToCluster.ContainsKey(item.setStruct[0]))
{
foreach (var str in dic[translateToCluster[item.setStruct[0]]])
{
if (item.setStruct[0] != structures[str])
{
item.setStruct.Add(structures[str]);
}
}
item.consistency = CalcClusterConsistency(item.setStruct);
}
else
{
throw new Exception("Cannot add structure. Something is wrong");
}
}
outC.hNode.RedoSetStructures();
outC.runParameters = hier.GetVitalParameters();
outC.runParameters += input.GetVitalParameters();
return(outC);
}