public void rowSigmaNormalization(int sigmaMultiplier)
{
PatternTools.pTools ptool = new PatternTools.pTools();
foreach (sparseMatrixRow row in matrix)
{
//Calculate the row sigma
List <double> rowValues = new List <double>();
foreach (double v in row.Values)
{
rowValues.Add(v);
}
double rowSigma = Math.Sqrt(PatternTools.pTools.variance(rowValues, true));
double rowAverage = PatternTools.pTools.Average(rowValues);
double sigmaFactor = rowSigma * sigmaMultiplier;
//divide each item in the row
for (int dimIndex = 0; dimIndex < row.Values.Count; dimIndex++)
{
row.Values[dimIndex] /= (sigmaFactor + rowAverage);
}
}
}
public void zNormalization()
{
List <int> allDims = this.allDims();
PatternTools.pTools ptool = new PatternTools.pTools();
for (int dimIndex = 0; dimIndex < allDims.Count; dimIndex++)
{
//Calculate mean and standard deviation
List <double> theNumbers = this.ExtractDimValues(allDims[dimIndex], 0, true);
double mean = PatternTools.pTools.Average(theNumbers);
double stdDev = Math.Sqrt(PatternTools.pTools.variance(theNumbers, true));
//Finaly Manipulate the matrix
foreach (sparseMatrixRow row in matrix)
{
//find in what index our value is located
int index = getDimIndexFromValue(row, allDims[dimIndex]);
//MessageBox.Show("Working on row " + matrix.IndexOf(row) + " the index is " + index );
//modify the value
if (index >= 0)
{
double value = (row.Values[index] - mean) / stdDev;
if (value.Equals(double.NaN))
{
row.Values[index] = 0;
}
else
{
row.Values[index] = value;
}
}
}
}
}
