public void PrintProbabilities(int noOfGroups)
{
List <IntersectionResultAnalysis> iras = IntersectionResult.IntersectionResultAnalysis(intersectionResults, noOfGroups);
foreach (IntersectionResultAnalysis ira in iras)
{
Console.WriteLine("NoRep: {0}\t %: {1}\t groupSize: {2}\t Prob: {3}", ira.NoReplicatesAppeared, Math.Round(ira.PercentageOfUniqueness, 3), ira.NoCandidatesInGroup, Math.Round(ira.BayesianProbability, 3));
}
}
//------------------------------------------------------------------------------------------
public void CalculateProbabilitiesForClass(int classOfInterest, double minSignal, double maxSignal)
{
//First we generate a cloned sparse matrix containing only the vectors that belong to the class of interest.
SparseMatrix subSparseMatrix = new SparseMatrix();
foreach (sparseMatrixRow r in sparseMatrix.theMatrixInRows)
{
if (r.Lable == classOfInterest)
{
subSparseMatrix.addRow(r.Clone());
}
}
//Now we need to make sure that all the rows in the sparse matrix contain only signal within the predefined bounds
List <int> dimsToEliminate = new List <int>();
List <int> allDimsTmp = subSparseMatrix.allDims();
foreach (int dim in allDimsTmp)
{
double avgSignal = subSparseMatrix.ExtractDimValues(dim, 0, true).Average();
if (avgSignal < minSignal || avgSignal > maxSignal)
{
dimsToEliminate.Add(dim);
}
}
foreach (int dim in dimsToEliminate)
{
subSparseMatrix.eliminateDim(dim, 0, true);
}
//---
int noReplicates = subSparseMatrix.theMatrixInRows.Select(a => a.Lable = classOfInterest).ToList().Count;
List <int> allDims = subSparseMatrix.allDims();
if (noReplicates > 9)
{
throw new Exception("To many replicates for the combination engine");
}
if (noReplicates < 2)
{
throw new Exception("You need at least two repplicates for each class");
}
//Step 1, obtain a list of lists of all combination of the number of classes
List <List <int> > classCombinations = pTools.GetClassCombinations(noReplicates);
classCombinations.Sort((a, b) => a.Count.CompareTo(b.Count));
//Step 2 - Find the Intersection of all dims
List <int> intersectionOfAllDims = GetIntersection(classCombinations[classCombinations.Count - 1], subSparseMatrix.theMatrixInRows);
//Step 3 - Calculate all possible intersections
intersectionResults = new List <IntersectionResult>();
foreach (List <int> theRows in classCombinations)
{
List <int> intersectionOfDims = GetIntersection(theRows, subSparseMatrix.theMatrixInRows);
IntersectionResult ir = new IntersectionResult(theRows, intersectionOfDims);
intersectionResults.Add(ir);
}
//Step 4 - Calculate the percentage of uniqueness for each overlap
double noUniqueCount = 0;
foreach (IntersectionResult ir in intersectionResults)
{
//Find the union of all other assays not belonging to the one at hand
List <int> allOther = new List <int>();
for (int i = 0; i < noReplicates; i++)
{
if (!ir.TheSparseMatrixRows.Contains(i))
{
allOther.AddRange(subSparseMatrix.theMatrixInRows[i].Dims);
}
}
allOther = allOther.Distinct().ToList();
List <int> intersectOfThisWithAllOthers = ir.IntersectingDims.Intersect(allOther).ToList();
ir.NoUniqueComparedToTotal = ir.IntersectingDims.Count - intersectOfThisWithAllOthers.Count;
noUniqueCount += (double)ir.NoUniqueComparedToTotal;
double percentageOfUniqueness = (double)ir.NoUniqueComparedToTotal / (double)allDims.Count;
ir.PercentageUniqueness = percentageOfUniqueness;
}
}
public List <IntersectionResultAnalysis> GetIntersectionResultAnalysis(int noOfGroups)
{
return(IntersectionResult.IntersectionResultAnalysis(intersectionResults, noOfGroups));
}
