ClusterOutput DivideSpace1DJury(List <string> list)
{
ClusterOutput outC, finalOut = new ClusterOutput();
outC = jury.JuryOptWeights(list);
double dev = CalcStandDev(outC);
List <string> clust1 = new List <string>();
List <string> clust2 = new List <string>();
dev += dev;
if (useKMeans)
{
clust1.Add(outC.juryLike[0].Key);
clust1.Add(outC.juryLike[outC.juryLike.Count - 1].Key);
kMeans km = new kMeans(dMeasure);
return(km.kMeansRun(kMeansIter, list, clust1));
}
else
{
for (int i = 0; i < outC.juryLike.Count; i++)
{
if (i < outC.juryLike.Count / 2)
{
clust1.Add(outC.juryLike[i].Key);
}
else
{
clust2.Add(outC.juryLike[i].Key);
}
}
finalOut.clusters = new List <List <string> >();
finalOut.clusters.Add(clust1);
finalOut.clusters.Add(clust2);
}
return(finalOut);
}
ClusterOutput DivideSpaceHamming(List <string> list)
{
ClusterOutput output = new ClusterOutput();
Dictionary <string, int> aux = new Dictionary <string, int>();
ClusterOutput outC = jury.JuryOptWeights(list);
if (outC == null)
{
return(null);
}
List <string> clust1 = new List <string>();
List <string> clust2 = new List <string>();
//consensus.ToConsensusStates(list, jury.GetStructureStates(outC.juryLike[0].Key));
consensus.ToConsensusStates(list, outC.juryLike[0].Key);
foreach (var item in list)
{
aux.Add(item, consensus.distanceOrdered[item]);
}
if (useKMeans)
{
clust1.Add(outC.juryLike[0].Key);
var item = aux.OrderByDescending(j => j.Value);
int dist = item.First().Value;
foreach (var ll in item)
{
if (dist != ll.Value)
{
break;
}
else
{
clust2.Add(ll.Key);
}
}
Dictionary <string, double> dic = new Dictionary <string, double>();
foreach (var it in outC.juryLike)
{
dic.Add(it.Key, it.Value);
}
double min = Double.MaxValue;
string rem = "";
foreach (var it in clust2)
{
if (dic[it] < min)
{
min = dic[it];
rem = it;
}
}
clust1.Add(rem);
kMeans km = new kMeans(dMeasure);
return(km.kMeansRun(kMeansIter, list, clust1));
}
else
{
int i = 0;
foreach (var item in aux.OrderBy(j => j.Value))
{
if (i < list.Count() / 2)
{
clust1.Add(item.Key);
}
else
{
clust2.Add(item.Key);
}
i++;
}
output.clusters = new List <List <string> >();
output.clusters.Add(clust1);
output.clusters.Add(clust2);
}
return(output);
}
ClusterOutput DivideSpaceHamming(List<string> list)
{
ClusterOutput output=new ClusterOutput();
Dictionary<string, int> aux = new Dictionary<string, int>();
ClusterOutput outC = jury.JuryOptWeights(list);
if (outC == null)
return null;
List<string> clust1 = new List<string>();
List<string> clust2 = new List<string>();
//consensus.ToConsensusStates(list, jury.GetStructureStates(outC.juryLike[0].Key));
consensus.ToConsensusStates(list,outC.juryLike[0].Key);
foreach (var item in list)
aux.Add(item, consensus.distanceOrdered[item]);
if (useKMeans)
{
clust1.Add(outC.juryLike[0].Key);
var item = aux.OrderByDescending(j => j.Value);
int dist = item.First().Value;
foreach (var ll in item)
{
if (dist != ll.Value)
break;
else
clust2.Add(ll.Key);
}
Dictionary<string, double> dic = new Dictionary<string, double>();
foreach (var it in outC.juryLike)
dic.Add(it.Key, it.Value);
double min = Double.MaxValue;
string rem = "";
foreach (var it in clust2)
{
if (dic[it] < min)
{
min = dic[it];
rem = it;
}
}
clust1.Add(rem);
kMeans km = new kMeans(dMeasure);
return km.kMeansRun(kMeansIter,list, clust1);
}
else
{
int i = 0;
foreach (var item in aux.OrderBy(j => j.Value))
{
if (i < list.Count() / 2)
clust1.Add(item.Key);
else
clust2.Add(item.Key);
i++;
}
output.clusters = new List<List<string>>();
output.clusters.Add(clust1);
output.clusters.Add(clust2);
}
return output;
}
ClusterOutput DivideSpace1DJury(List<string> list)
{
ClusterOutput outC,finalOut=new ClusterOutput();
outC=jury.JuryOptWeights(list);
double dev = CalcStandDev(outC);
List<string> clust1 = new List<string>();
List<string> clust2 = new List<string>();
dev += dev;
if (useKMeans)
{
clust1.Add(outC.juryLike[0].Key);
clust1.Add(outC.juryLike[outC.juryLike.Count - 1].Key);
kMeans km = new kMeans(dMeasure);
return km.kMeansRun(kMeansIter,list, clust1);
}
else
{
for (int i = 0; i < outC.juryLike.Count; i++)
if(i<outC.juryLike.Count / 2)
clust1.Add(outC.juryLike[i].Key);
else
clust2.Add(outC.juryLike[i].Key);
finalOut.clusters = new List<List<string>>();
finalOut.clusters.Add(clust1);
finalOut.clusters.Add(clust2);
}
return finalOut;
}
