public static double[][] ConvertToNestedArray(double[,] coeffs)
{
double[,] treesOfCoeffs = Haar.ListOfCoeffs(coeffs);
double[][] Data = new double[treesOfCoeffs.GetLength(0)][];
for (int i = 0; i < treesOfCoeffs.GetLength(0); i++)
{
double[] row = new double[5];
for (int j = 0; j < treesOfCoeffs.GetLength(1); j++)
{
row[j] = treesOfCoeffs[i, j];
}
Data[i] = row;
}
return(Data);
}
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);
}
public static double[] Means(double[,] coeffs, int m)
{
double[] mean = new double[5]; //5 node each tree
double[,] treesOfcoeffs = Haar.ListOfCoeffs(coeffs);
double[,] hiddenstates = GetHiddenStateValue(coeffs);
double[,] treesOfHiddenStates = Haar.ListOfCoeffs(hiddenstates);
//P(m)
double[] rootpmf = ParentStateProbability(hiddenstates);
//P(n|m)
double transitionn1m1 = TransitionProbability(hiddenstates, 1, 1);
double transitionn2m1 = TransitionProbability(hiddenstates, 2, 1);
double transitionn1m2 = TransitionProbability(hiddenstates, 1, 2);
double transitionn2m2 = TransitionProbability(hiddenstates, 2, 2);
//P(n)
double[] childpmf = ChildStateProbability(hiddenstates);
if (m == 1)
{
double sum = 0;
double sum2 = 0;
double sum3 = 0;
double sum4 = 0;
double sum5 = 0;
// Parent Node
for (int j = 0; j < treesOfcoeffs.GetLength(0); j++)
{
sum += treesOfcoeffs[j, 0] * rootpmf[0];
}
double mean1 = (double)sum / ((double)treesOfcoeffs.GetLength(0) * (double)rootpmf[0]);
// Child node 1
for (int k = 0; k < treesOfcoeffs.GetLength(0); k++)
{
sum2 += treesOfcoeffs[k, 1] * childpmf[0];
}
double mean2 = (double)sum2 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[0]);
// Child node 2
for (int l = 0; l < treesOfcoeffs.GetLength(0); l++)
{
sum3 += treesOfcoeffs[l, 2] * childpmf[0];
}
double mean3 = (double)sum3 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[0]);
// Child node 3
for (int o = 0; o < treesOfcoeffs.GetLength(0); o++)
{
sum4 += treesOfcoeffs[o, 3] * childpmf[0];
}
double mean4 = (double)sum4 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[0]);
// Child node 4
for (int p = 0; p < treesOfcoeffs.GetLength(0); p++)
{
sum5 += treesOfcoeffs[p, 4] * childpmf[0];
}
double mean5 = (double)sum5 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[0]);
mean[0] = mean1;
mean[1] = mean2;
mean[2] = mean3;
mean[3] = mean4;
mean[4] = mean5;
}
else
{
double sum = 0;
double sum2 = 0;
double sum3 = 0;
double sum4 = 0;
double sum5 = 0;
// Parent Node
for (int j = 0; j < treesOfcoeffs.GetLength(0); j++)
{
sum += treesOfcoeffs[j, 0] * rootpmf[1];
}
double mean1 = (double)sum / ((double)treesOfcoeffs.GetLength(0) * (double)rootpmf[1]);
// Child node 1
for (int k = 0; k < treesOfcoeffs.GetLength(0); k++)
{
sum2 += treesOfcoeffs[k, 1] * childpmf[1];
}
double mean2 = (double)sum2 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[1]);
// Child node 2
for (int l = 0; l < treesOfcoeffs.GetLength(0); l++)
{
sum3 += treesOfcoeffs[l, 2] * childpmf[1];
}
double mean3 = (double)sum3 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[1]);
// Child node 3
for (int o = 0; o < treesOfcoeffs.GetLength(0); o++)
{
sum4 += treesOfcoeffs[o, 3] * childpmf[1];
}
double mean4 = (double)sum4 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[1]);
// Child node 4
for (int p = 0; p < treesOfcoeffs.GetLength(0); p++)
{
sum5 += treesOfcoeffs[p, 4] * childpmf[1];
}
double mean5 = (double)sum5 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[1]);
mean[0] = mean1;
mean[1] = mean2;
mean[2] = mean3;
mean[3] = mean4;
mean[4] = mean5;
}
return(mean);
}
public static double TransitionStateProbability(double[,] coeffs, int n, int m) //P(n|m)
{
int size = ((coeffs.GetLength(0) * coeffs.GetLength(1)) / 16) * 3;
double[,] hiddenstates = GetHiddenStateValue(coeffs);
double[] transitionProb = new double[size];
double[,] trees = Haar.ListOfCoeffs(hiddenstates);
int n1m1 = 0;
int n2m1 = 0;
int n1m2 = 0;
int n2m2 = 0;
if (n == 1 && m == 1)
{
for (int i = 0; i < trees.GetLength(0); i++)
{
n1m1 = 0;
for (int j = 1; j < trees.GetLength(1); j++)
{
if (trees[i, j] == 1 && trees[i, 0] == 1)
{
n1m1++;
}
}
transitionProb[i] = (double)n1m1 / (double)4;
}
double sum = transitionProb.Sum();
double[] rootpmf = ParentStateProbability(coeffs);
double pmf1 = rootpmf[0];
double transitionn1m1 = (double)sum / ((double)trees.GetLength(0) * (double)pmf1);
return(transitionn1m1);
}
else if (n == 2 && m == 1)
{
for (int i = 0; i < trees.GetLength(0); i++)
{
n2m1 = 0;
for (int j = 1; j < trees.GetLength(1); j++)
{
if (trees[i, j] == 2 && trees[i, 0] == 1)
{
n2m1++;
}
}
transitionProb[i] = (double)n2m1 / (double)4;
}
double sum = transitionProb.Sum();
double[] rootpmf = ParentStateProbability(coeffs);
double pmf1 = rootpmf[0];
double transitionn2m1 = (double)sum / ((double)trees.GetLength(0) * (double)pmf1);
return(transitionn2m1);
}
else if (n == 1 && m == 2)
{
for (int i = 0; i < trees.GetLength(0); i++)
{
n1m2 = 0;
for (int j = 1; j < trees.GetLength(1); j++)
{
if (trees[i, j] == 1 && trees[i, 0] == 2)
{
n1m2++;
}
}
transitionProb[i] = (double)n1m2 / (double)4;
}
double sum = transitionProb.Sum();
double[] rootpmf = ParentStateProbability(coeffs);
double pmf2 = rootpmf[1];
double transitionn1m2 = (double)sum / ((double)trees.GetLength(0) * (double)pmf2);
return(transitionn1m2);
}
else
{
for (int i = 0; i < trees.GetLength(0); i++)
{
n2m2 = 0;
for (int j = 1; j < trees.GetLength(1); j++)
{
if (trees[i, j] == 2 && trees[i, 0] == 2)
{
n2m2++;
}
}
transitionProb[i] = (double)n2m2 / (double)4;
}
double sum = transitionProb.Sum();
double[] rootpmf = ParentStateProbability(coeffs);
double pmf2 = rootpmf[1];
double transitionn2m2 = (double)sum / ((double)trees.GetLength(0) * (double)pmf2);
return(transitionn2m2);
}
}
/// <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);
}
}
public static double[] Variances(double[,] coeffs, int m)
{
double[] variances = new double[5]; //5 node each tree
double[,] treesOfcoeffs = Haar.ListOfCoeffs(coeffs);
double[,] hiddenstates = GetHiddenStateValue(coeffs);
double[,] treesOfHiddenStates = Haar.ListOfCoeffs(hiddenstates);
//P(m)
double[] rootpmf = ParentStateProbability(hiddenstates);
//P(n|m)
double transitionn1m1 = TransitionProbability(hiddenstates, 1, 1);
double transitionn2m1 = TransitionProbability(hiddenstates, 2, 1);
double transitionn1m2 = TransitionProbability(hiddenstates, 1, 2);
double transitionn2m2 = TransitionProbability(hiddenstates, 2, 2);
//P(n)
double[] childpmf = ChildStateProbability(hiddenstates);
double[] mean1 = Means(coeffs, 1);
double[] mean2 = Means(coeffs, 2);
if (m == 1)
{
double sum = 0;
double sum2 = 0;
double sum3 = 0;
double sum4 = 0;
double sum5 = 0;
// Parent Node
for (int j = 0; j < treesOfcoeffs.GetLength(0); j++)
{
sum += Math.Pow((treesOfcoeffs[j, 0] - mean1[0]), 2) * rootpmf[0];
}
double variance1 = (double)sum / ((double)treesOfcoeffs.GetLength(0) * (double)rootpmf[0]);
// Child node 1
for (int k = 0; k < treesOfcoeffs.GetLength(0); k++)
{
sum2 += Math.Pow((treesOfcoeffs[k, 1] - mean1[1]), 2) * childpmf[0];
}
double variance2 = (double)sum2 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[0]);
// Child node 2
for (int l = 0; l < treesOfcoeffs.GetLength(0); l++)
{
sum3 += Math.Pow((treesOfcoeffs[l, 2] - mean1[2]), 2) * childpmf[0];
}
double variance3 = (double)sum3 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[0]);
// Child node 3
for (int o = 0; o < treesOfcoeffs.GetLength(0); o++)
{
sum4 += Math.Pow((treesOfcoeffs[o, 3] - mean1[3]), 2) * childpmf[0];
}
double variance4 = (double)sum4 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[0]);
// Child node 4
for (int p = 0; p < treesOfcoeffs.GetLength(0); p++)
{
sum5 += Math.Pow((treesOfcoeffs[p, 4] - mean1[4]), 2) * childpmf[0];
}
double variance5 = (double)sum5 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[0]);
variances[0] = variance1;
variances[1] = variance2;
variances[2] = variance3;
variances[3] = variance4;
variances[4] = variance5;
}
else
{
double sum = 0;
double sum2 = 0;
double sum3 = 0;
double sum4 = 0;
double sum5 = 0;
// Parent Node
for (int j = 0; j < treesOfcoeffs.GetLength(0); j++)
{
sum += Math.Pow((treesOfcoeffs[j, 0] - mean2[0]), 2) * rootpmf[1];
}
double variance1 = (double)sum / ((double)treesOfcoeffs.GetLength(0) * (double)rootpmf[1]);
// Child node 1
for (int k = 0; k < treesOfcoeffs.GetLength(0); k++)
{
sum2 += Math.Pow((treesOfcoeffs[k, 1] - mean2[1]), 2) * childpmf[1];
}
double variance2 = (double)sum2 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[1]);
// Child node 2
for (int l = 0; l < treesOfcoeffs.GetLength(0); l++)
{
sum3 += Math.Pow((treesOfcoeffs[l, 2] - mean2[2]), 2) * childpmf[1];
}
double variance3 = (double)sum3 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[1]);
// Child node 3
for (int o = 0; o < treesOfcoeffs.GetLength(0); o++)
{
sum4 += Math.Pow((treesOfcoeffs[o, 3] - mean2[3]), 2) * childpmf[1];
}
double variance4 = (double)sum4 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[1]);
// Child node 4
for (int p = 0; p < treesOfcoeffs.GetLength(0); p++)
{
sum5 += Math.Pow((treesOfcoeffs[p, 4] - mean2[4]), 2) * childpmf[1];
}
double variance5 = (double)sum5 / ((double)treesOfcoeffs.GetLength(0) * (double)childpmf[1]);
variances[0] = variance1;
variances[1] = variance2;
variances[2] = variance3;
variances[3] = variance4;
variances[4] = variance5;
}
return(variances);
}
