public void AddEValueDist(ScoreDistribution otherDistribution, int deltaScore, double weight)
{
if (otherDistribution == null) return;
for (var index = Math.Max(otherDistribution.MinScore, MinScore - deltaScore); index < otherDistribution.MaxScore; index++)
{
var delEValue = otherDistribution._eValueDistribution[index - otherDistribution.MinScore]*weight;
_eValueDistribution[index + deltaScore - MinScore] += delEValue;
}
}
private ScoreDistribution GetScoreDistribution(int nodeIndex, ScoreDistribution[] gfTable)
{
var curNodeScore = _graph.GetNodeScore(nodeIndex);
// determine MinScore and MaxScore
var maxScore = (double)int.MinValue;
var minScore = (double)int.MaxValue;
var hasValidEdge = false;
foreach (var edge in _graph.GetEdges(nodeIndex))
{
var prevNodeIndex = edge.PrevNodeIndex;
var prevScoreDistribution = gfTable[prevNodeIndex];
if (prevScoreDistribution == null)
{
continue;
}
var combinedScore = curNodeScore + _graph.GetEdgeScore(prevNodeIndex, nodeIndex);
if (prevScoreDistribution.MaxScore + combinedScore > maxScore)
{
maxScore = prevScoreDistribution.MaxScore + combinedScore;
}
if (prevScoreDistribution.MinScore + combinedScore < minScore)
{
minScore = prevScoreDistribution.MinScore + combinedScore;
}
hasValidEdge = true;
}
if (!hasValidEdge)
{
return(null); //new ScoreDistribution(0, 1);
}
// considering Target score for fast computing.....max 2 times faster but not useful at this point Scoredistriubtion
//minScore = Math.Max(_targetScore - _maxAchievableScore[nodeIndex], minScore);
// Compute scoring distribution for the current node
var scoringDistribution = new ScoreDistribution((int)Math.Floor(minScore), (int)Math.Ceiling(maxScore));
foreach (var edge in _graph.GetEdges(nodeIndex))
{
var prevScoreDistribution = gfTable[edge.PrevNodeIndex];
if (prevScoreDistribution == null)
{
continue;
}
var combinedScore = curNodeScore + _graph.GetEdgeScore(edge.PrevNodeIndex, nodeIndex);
scoringDistribution.AddEValueDist(prevScoreDistribution, (int)Math.Round(combinedScore), edge.Weight);
}
return(scoringDistribution);
}
public void AddEValueDist(ScoreDistribution otherDistribution, int deltaScore, double weight)
{
if (otherDistribution == null)
{
return;
}
for (var index = Math.Max(otherDistribution.MinScore, MinScore - deltaScore); index < otherDistribution.MaxScore; index++)
{
var delEValue = otherDistribution._eValueDistribution[index - otherDistribution.MinScore] * weight;
_eValueDistribution[index + deltaScore - MinScore] += delEValue;
}
}
private ScoreDistribution GetScoreDistribution(int nodeIndex, ScoreDistribution[] gfTable)
{
var curNodeScore = _graph.GetNodeScore(nodeIndex);
// determine MinScore and MaxScore
var maxScore = (double) int.MinValue;
var minScore = (double) int.MaxValue;
var hasValidEdge = false;
foreach (var edge in _graph.GetEdges(nodeIndex))
{
var prevNodeIndex = edge.PrevNodeIndex;
var prevScoreDistribution = gfTable[prevNodeIndex];
if (prevScoreDistribution == null) continue;
var combinedScore = curNodeScore + _graph.GetEdgeScore(prevNodeIndex, nodeIndex);
if (prevScoreDistribution.MaxScore + combinedScore > maxScore)
{
maxScore = prevScoreDistribution.MaxScore + combinedScore;
}
if (prevScoreDistribution.MinScore + combinedScore < minScore)
{
minScore = prevScoreDistribution.MinScore + combinedScore;
}
hasValidEdge = true;
}
if (!hasValidEdge) return null; //new ScoreDistribution(0, 1);
// considering Target score for fast computing.....max 2 times faster but not useful at this point Scoredistriubtion
//minScore = Math.Max(_targetScore - _maxAchievableScore[nodeIndex], minScore);
// Compute scoring distribution for the current node
var scoringDistribution = new ScoreDistribution((int)Math.Floor(minScore), (int)Math.Ceiling(maxScore));
foreach (var edge in _graph.GetEdges(nodeIndex))
{
var prevScoreDistribution = gfTable[edge.PrevNodeIndex];
if (prevScoreDistribution == null) continue;
var combinedScore = curNodeScore + _graph.GetEdgeScore(edge.PrevNodeIndex, nodeIndex);
scoringDistribution.AddEValueDist(prevScoreDistribution, (int)Math.Round(combinedScore), edge.Weight);
}
return scoringDistribution;
}
//private int _targetScore;
//private readonly int[] _maxAchievableScore;
public void ComputeGeneratingFunction()//int targetScore = 0)
{
//_targetScore = targetScore;
var gfTable = new ScoreDistribution[_graph.GetNumNodes()];
// Source
var sourceDist = new ScoreDistribution(0, 1);
sourceDist.SetEValue(0, 1);
gfTable[0] = sourceDist;
// All the other nodes
for (var nodeIndex = 1; nodeIndex < _graph.GetNumNodes(); nodeIndex++)
{
gfTable[nodeIndex] = GetScoreDistribution(nodeIndex, gfTable);
}
// Sink
// TODO: adjusting the distribution depending on neighboring amino acid (e.g. R.PEPTIDEK. vs A.PEPTIDEK)
_scoreDistribution = gfTable[gfTable.Length - 1];
}
//private int _targetScore;
//private readonly int[] _maxAchievableScore;
public void ComputeGeneratingFunction()//int targetScore = 0)
{
//_targetScore = targetScore;
var gfTable = new ScoreDistribution[_graph.GetNumNodes()];
// Source
var sourceDist = new ScoreDistribution(0, 1);
sourceDist.SetEValue(0, 1);
gfTable[0] = sourceDist;
// All the other nodes
for (var nodeIndex = 1; nodeIndex < _graph.GetNumNodes(); nodeIndex++)
{
gfTable[nodeIndex] = GetScoreDistribution(nodeIndex, gfTable);
}
// Sink
// TODO: adjusting the distribution depending on neighboring amino acid (e.g. R.PEPTIDEK. vs A.PEPTIDEK)
_scoreDistribution = gfTable[gfTable.Length - 1];
}
