public void Training(IResults results, int start, int end)
{
for (int scanNum = start; scanNum <= end; scanNum++)
{
if (results.Contains(scanNum))
{
List <IScore> scores = results.GetResult(scanNum);
foreach (IScore score in scores)
{
double y = (score as FDRScoreProxy).IsDecoy() ? 0 : 1;
List <SVMNode> X = new List <SVMNode>();
// store score value in X
int idx = 0;
foreach (MassType type in types)
{
SVMNode node = new SVMNode();
node.Index = idx;
node.Value = score.GetScore(type);
X.Add(node);
idx++;
}
problem.Add(X.ToArray(), y);
}
}
}
// training
SVMParameter parameter = new SVMParameter();
parameter.Probability = true;
model = problem.Train(parameter);
}
public void Report(IResults results, int start, int end)
{
if (Init())
{
// training svm
Training(results, start, end);
// predicting probability
List <IProbScoreProxy> probabilities = new List <IProbScoreProxy>();
for (int scanNum = start; scanNum <= end; scanNum++)
{
if (results.Contains(scanNum))
{
probabilities.AddRange(Predicting(results, scanNum));
}
}
double scoreCutoff = GetScoreCutoff(probabilities, start, end);
// report
//List<IProbScoreProxy> scores = probabilities.
// Where(score => !(score as IFDRScoreProxy).IsDecoy()).ToList();
List <IProbScoreProxy> scores = probabilities;
ReportLines(scores, scoreCutoff);
Exit();
}
}
public virtual void Report(IResults results, int start, int end)
{
if (InitSucess(scoreCutoff))
{
for (int scanNum = start; scanNum <= end; scanNum++)
{
if (results.Contains(scanNum))
{
ReportLines(results.GetSpectrum(scanNum), results.GetResult(scanNum), scoreCutoff);
}
}
Exit();
}
}
public override void Report(IResults results, int start, int end)
{
//scoreCutoff = GetScoreCutoff(results, start, end);
scoreCutoff = 0;
if (InitSucess(scoreCutoff))
{
for (int scanNum = start; scanNum <= end; scanNum++)
{
if (results.Contains(scanNum))
{
List <IScore> scores = results.GetResult(scanNum);
//List<IScore> scores = results.GetResult(scanNum).Where(score => !(score as IFDRScoreProxy).IsDecoy()).ToList();
ReportLines(results.GetSpectrum(scanNum), scores, scoreCutoff);
}
}
Exit();
}
}
public void Report(IResults results, int start, int end)
{
if (Init())
{
// training svm
Training(results, start, end);
// predict and report
for (int scanNum = start; scanNum <= end; scanNum++)
{
if (results.Contains(scanNum))
{
List <IScore> scores = results.GetResult(scanNum);
//List<IScore> scores = results.GetResult(scanNum).
// Where(score => !(score as IFDRScoreProxy).IsDecoy()).ToList();
ReportLines(results.GetSpectrum(scanNum), scores);
}
}
Exit();
}
}
protected double GetScoreCutoff(IResults results, int start, int end)
{
// init
double cutoff = 0;
// get score values for true and decoy results
List <double> targets = new List <double>();
List <double> decoys = new List <double>();
for (int scan = start; scan <= end; scan++)
{
if (results.Contains(scan))
{
foreach (IScore score in results.GetResult(scan))
{
if (!(score as IFDRScoreProxy).IsDecoy())
{
targets.Add(score.GetScore());
break;
}
}
foreach (IScore score in results.GetResult(scan))
{
if ((score as IFDRScoreProxy).IsDecoy())
{
decoys.Add(score.GetScore());
cutoff = Math.Max(cutoff, score.GetScore());
break;
}
}
}
}
targets.Sort((a, b) => - a.CompareTo(b)); //descending
decoys.Sort((a, b) => - a.CompareTo(b));
// compare and compute
if (decoys.Count * 1.0 / (decoys.Count + targets.Count) < fdr) //trivial case
{
return(0);
}
int i = 0, j = 0;
while (i < targets.Count && j < decoys.Count)
{
if (targets[i] > decoys[j])
{
double rate = j * 1.0 / (i + j + 1);
if (rate <= fdr)
{
cutoff = Math.Min(cutoff, decoys[j]);
}
i++;
}
else
{
j++;
}
}
return(cutoff);
}
