private void JacobianIteration(MatrixEquasion <Double> eq, Matrix <Double> oldMatrix, Matrix <Double> newMatrix, int i)
{
IMatrixDataType <Double> x = new MatrixDouble(0);
for (int j = 0; j < eq.A.ColCount; j++)
{
if (i != j)
{
x = (x.Add(eq.A.ValueMatrix[i][j].Multiply(oldMatrix.ValueMatrix[j][0])));
}
}
x = x.Multiply(MatrixDouble.MINUSONE);
x = x.Add(eq.B.ValueMatrix[i][0]);
x = x.Divide(eq.A.ValueMatrix[i][i]);
newMatrix.ValueMatrix[i][0] = x;
}
private void GaussSeidelIteration(MatrixEquasion <Double> eq, Matrix <Double> newVector, int i)
{
IMatrixDataType <double> x = new MatrixDouble(0);
for (int j = 0; j < eq.A.ColCount; j++)
{
if (i != j)
{
x = (x.Add(eq.A.ValueMatrix[i][j].Multiply(newVector.ValueMatrix[j][0])));
}
}
x = x.Multiply(MatrixDouble.MINUSONE);
x = x.Add(eq.B.ValueMatrix[i][0]);
x = x.Divide(eq.A.ValueMatrix[i][i]);
newVector.ValueMatrix[i][0] = x;
}
public Result Perform(MatrixEquasion <double> eq)
{
int NOfEquasions = 0;
for (int i = 0; i <= NOfAgents; i++)
{
for (int j = 0; j <= NOfAgents - i; j++)
{
NOfEquasions++;
}
}
MatrixDouble[][] raw = new MatrixDouble[NOfEquasions][];
for (int i = 0; i < NOfEquasions; i++)
{
raw[i] = new MatrixDouble[1];
}
Stopwatch st = new Stopwatch();
int iteration = 0;
st.Start();
for (int i = 0; i <= NOfAgents; i++)
{
for (int j = 0; j <= NOfAgents - i; j++)
{
raw[iteration][0] = new MatrixDouble(GetProbability(i, j));
iteration++;
}
}
st.Stop();
Matrix <double> probVector = new Matrix <double>(raw);
double error = Matrix <double> .GetNormOfDiffrence(eq.A.Multiply(probVector), eq.B);
long time = st.ElapsedMilliseconds;
return(new Result("MonteCarlo", error, time, 100000, probVector));
}
