/// <summary>
/// Each row should contain the correction factors in numbers that range from 1 to 100
/// The value for the monoisotopic should be entered as -1
/// </summary>
/// <param name="correctionTable"></param>
/// <returns></returns>
public static PatternTools.CSML.Matrix GenerateInverseCorrectionMatrix(List <List <double> > correctionTable)
{
//We now need to normalize the -1 so the sum adds to 100
foreach (List <double> l in correctionTable)
{
double sum = l.Sum() + 1;
int index = l.FindIndex(a => a == -1);
l[index] = 100 - sum;
}
//Generate Correction Matrix
//Prepare the coeficient matrix
double[,] coeficientMatrix = new double[correctionTable.Count, correctionTable.Count];
int counter = -1;
for (int i = 0; i < correctionTable.Count; i++)
{
counter++;
for (int j = 0; j < 5; j++)
{
try
{
coeficientMatrix[j - 2 + counter, i] = correctionTable[i][j];
}
catch (Exception e)
{
Console.WriteLine(e.Message + "\n i = {0}, j = {1}", i, j);
}
}
}
//Mcorrected = c^-1 * signal
PatternTools.CSML.Matrix c = new PatternTools.CSML.Matrix(coeficientMatrix);
PatternTools.CSML.Matrix cInverse = c.Inverse();
cInverse *= 100;
return(cInverse);
}
/// <summary>
/// The method returns unstable dims that were elmininated from the sparse matrix and should be eliminated from future input vectors used for classification
/// </summary>
/// <param name="useRobustStatistics"></param>
/// <param name="regularizationFactor"></param>
/// <param name="saveMatrices"></param>
/// <returns></returns>
public List <int> ComputeMatrices(bool useRobustStatistics, bool saveMatrices)
{
//Get the mean vector
double[,] covMatrix;
if (useRobustStatistics)
{
meanVector = new PatternTools.CSML.Matrix(matrix.GetMedianVector().ToArray());
covMatrix = PatternTools.SparseMatrix.CovarianceMatrix(matrix.ToDoubleArrayMatrix(), matrix.GetMedianVector().ToArray());
}
else
{
meanVector = new PatternTools.CSML.Matrix(matrix.GetMeanVector().ToArray());
covMatrix = PatternTools.SparseMatrix.CovarianceMatrix(matrix.ToDoubleArrayMatrix(), matrix.GetMeanVector().ToArray());
}
unstableDims = checkStability(covMatrix);
if (unstableDims.Count > 0)
{
unstableDims.Sort((a, b) => b.CompareTo(a));
double perturbationCounter = 0;
if (unstableDims.Count > 0)
{
Console.WriteLine("**Warning:: I am adding a perturbation to the convariance matrix for stability reasons!!!");
foreach (int d in unstableDims)
{
foreach (sparseMatrixRow r in matrix.theMatrixInRows)
{
perturbationCounter += 0.0005;
r.Values[d] += perturbationCounter;
if (perturbationCounter > 0.05)
{
perturbationCounter = 0;
}
}
}
covMatrix = PatternTools.SparseMatrix.CovarianceMatrix(matrix.ToDoubleArrayMatrix(), matrix.GetMedianVector().ToArray());
}
if (useRobustStatistics)
{
covMatrix = PatternTools.SparseMatrix.CovarianceMatrix(matrix.ToDoubleArrayMatrix(), matrix.GetMedianVector().ToArray());
}
else
{
covMatrix = PatternTools.SparseMatrix.CovarianceMatrix(matrix.ToDoubleArrayMatrix(), matrix.GetMeanVector().ToArray());
}
}
covarianceMatrix = new CSML.Matrix(covMatrix);
inverseCovarianceMatrix = covarianceMatrix.Inverse();
if (saveMatrices)
{
matrix.saveMatrix(matrix.theMatrixInRows[0].Lable + "FullMatrix.txt");
StreamWriter sr = new StreamWriter(matrix.theMatrixInRows[0].Lable + "_covMatrix.txt");
int top = (int)Math.Sqrt(covMatrix.Length);
for (int i = 0; i < top; i++)
{
for (int j = 0; j < top; j++)
{
sr.Write(covMatrix[i, j] + "\t");
}
sr.WriteLine("");
}
sr.Close();
Console.WriteLine("Saving variance matrix and its inverse to disk.");
}
return(unstableDims);
}
