public static double[,] ListOfCoeffs(double[,] coeffs)
{
double[] Scale2 = Haar.Scale2To1DCoeff(coeffs);
double[,] Scale1 = Haar.Scale1To1DCoeff(coeffs);
double[,] Scale = new double[Scale2.Length, 5];
for (int i = 0; i < Scale2.Length; i++)
{
Scale[i, 0] = Scale2[i];
Scale[i, 1] = Scale1[i, 0];
Scale[i, 2] = Scale1[i, 1];
Scale[i, 3] = Scale1[i, 2];
Scale[i, 4] = Scale1[i, 3];
}
return(Scale);
}
/// <summary>
/// Calculate likelihood probability of child node in state n given the parent node in state m.
/// P(n|m) , n = child node and m = parent node.
/// Create A Matrix that contain:
/// [P(n=1|m=1) P(n=2|m=1)] -> [ pos=1 pos=2 ]
/// [P(n=1|m=2) P(n=2|m=2)] [ pos=3 pos=4 ]
/// The nature of Child Hidden States are that 1 & 3 always same, and 2 & 4 always same.(still in doubt)
/// </summary>
/// <param name="coeffs">Wavelet Coefficients</param>
/// <param name="pos">Position of child node for each parent -> pos ={1,2,3,4}</param>
/// <param name="m">Hidden state of Parent node</param>
/// <param name="n">Hidden state of Child node</param>
/// <returns></returns>
public static double TransitionProbability(double[,] coeffs, /*int pos,*/ int n, int m)
{
double[,] hiddenstates = GetHiddenStateValue(coeffs);
// Calculate number of parent node with state 1 or 2
double rootpmf1 = StateProbability2(hiddenstates, 2, 1);
double rootpmf2 = StateProbability2(hiddenstates, 2, 2);
double NumOfRootm1 = (double)rootpmf1 * (double)(((coeffs.GetLength(0) * coeffs.GetLength(1)) / 16) * 3); //Number of parent node whose m = 1
double NumOfRootm2 = (double)rootpmf2 * (double)(((coeffs.GetLength(0) * coeffs.GetLength(1)) / 16) * 3); //Number of parent node whose m = 2
double[] Scale2 = Haar.Scale2To1DCoeff(hiddenstates);
double[,] Scale1 = Haar.Scale1To1DCoeff(hiddenstates);
double[,] Scale = new double[Scale2.Length, 5];
for (int i = 0; i < Scale2.Length; i++)
{
Scale[i, 0] = Scale2[i];
Scale[i, 1] = Scale1[i, 0];
Scale[i, 2] = Scale1[i, 1];
Scale[i, 3] = Scale1[i, 2];
Scale[i, 4] = Scale1[i, 3];
}
///Calculating Transition probability
int c1 = 0;
int c2 = 0;
int c3 = 3;
int c4 = 4;
if (n == 1 && m == 1)
{
for (int i = 0; i < Scale.GetLength(0); i++)
{
for (int j = 1; j < Scale.GetLength(1); j++)
{
if (Scale[i, j] == 1 && Scale[i, 0] == 1)
{
c1++;
}
}
}
double transitionProb = (double)c1 / (double)NumOfRootm1;
return(transitionProb);
}
else if (n == 2 && m == 1)
{
for (int i = 0; i < Scale.GetLength(0); i++)
{
for (int j = 1; j < Scale.GetLength(1); j++)
{
if (Scale[i, j] == 2 && Scale[i, 0] == 1)
{
c2++;
}
}
}
double transitionProb = (double)c2 / (double)NumOfRootm1;
return(transitionProb);
}
else if (n == 1 && m == 2)
{
for (int i = 0; i < Scale.GetLength(0); i++)
{
for (int j = 1; j < Scale.GetLength(1); j++)
{
if (Scale[i, j] == 1 && Scale[i, 0] == 2)
{
c3++;
}
}
}
double transtitionProb = (double)c3 / (double)NumOfRootm2;
return(transtitionProb);
}
else
{
for (int i = 0; i < Scale.GetLength(0); i++)
{
for (int j = 1; j < Scale.GetLength(1); j++)
{
if (Scale[i, j] == 2 && Scale[i, 0] == 2)
{
c4++;
}
}
}
double transtitionProb = (double)c4 / (double)NumOfRootm2;
return(transtitionProb);
}
}