/// <summary>
/// Generates the influence matrix based on a given <see cref="T:IFiniteStateMachine`1"/> and an index
/// that is part of a strongly connected group in the finite state machine.
/// </summary>
/// <returns>A 2d-matrix that contains the influence matrix of the strongly connected group.</returns>
/// <param name="fsm">The given finite state machine to generate the influence matrix from.</param>
/// <param name="strongIndex">The initial index to generate an influence matrix from.</param>
/// <remarks>
/// <para>The given initial index must be part of a strongly connected group. If the index is not part,
/// the algorithm will loop.</para>
/// </remarks>
public void GenerateInfluenceMatrix(IFiniteStateMachine <int> fsm, int strongIndex)
{
int n = fsm.Length;
int s = this.Length;
int o = this.OutputSize;
double[] trans = new double[s * s];
double[,] a = this.a, b = this.b;
double[] ch1 = new double[s], ch2 = new double[s], cht;
int idx, otp, j, i;
double sum;
for (int ori = 0x00; ori < s; ori++)
{
for (j = 0x00; j < ori; j++)
{
ch2 [j] = 0.0d;
}
ch2 [j] = 1.0d;
for (j++; j < s; j++)
{
ch2 [j] = 0.0d;
}
idx = strongIndex;
do
{
cht = ch1;
ch1 = ch2;
ch2 = cht;
otp = fsm.GetOutput(idx);
for (j = 0x00; j < s; j++)
{
sum = 0.0d;
for (i = 0x00; i < s; i++)
{
sum += ch1 [i] * a [i, j];
}
sum *= b [j, otp];
ch2 [j] = sum;
}
idx = fsm.GetTransitionOfIndex(idx);
} while(idx != strongIndex);
for (j = 0x00, i = ori; j < s; i += s, j++)
{
trans [i] = ch2 [j];
}
}
}
/// <summary>
/// Train this <see cref="IHiddenMarkovModel"/> by using the given <see cref="T:IFiniteStateMachine`1"/>
/// model. Since such finite state machine generates infinite sequences of output, training that sequence
/// would result in an underdetermined system. The sample length must thefore be specified.
/// </summary>
/// <returns>The error on the training. In other words how different the original Hidden Markov model is from the
/// given finite state machine.</returns>
/// <param name="fsm">The finite state machine from which the <see cref="IHiddenMarkovModel"/> learns.</param>
/// <param name="initialDistribution"> the initial distribution for the states of the finite state machine.</param>
/// <param name="sampleLength">The length of the samples, has impact on the trained model.</param>
public double Train(IFiniteStateMachine <int> fsm, IList <int> initialDistribution, int sampleLength)
{
int N = fsm.Length;
int S = this.Length;
int O = this.OutputSize;
int T = sampleLength;
int T1 = T - 0x01;
double[] p = this.Probabilities;
double[,] a = this.a, b = this.b;
double[,] alpha = new double[T, S], beta = new double[T, S], gamma = new double[T, S], bhat = new double[S, O], ahat = new double[S, S];
double[,,] epsilon = new double[T, S, S];
double[] asc = new double[S], bsc = new double[S], phat = new double[S];
for (int si = 0x00; si < S; si++)
{
beta [T1, si] = 1.0d;
}
double norm, sum, ssum, tmp;
int[] os = new int[sampleLength];
double sprob = 0.0d, prob;
int stok = 0x00;
for (int ni = 0x00; ni < N; ni++)
{
stok += initialDistribution [ni];
}
for (int ni = 0x00; ni < N; ni++) //TODO: extremely optimize this
{
int tok = initialDistribution [ni];
if (tok > 0x00)
{
int n = ni;
#region Forward algorithm
prob = 1.0d;
int o = fsm.GetOutput(n);
os [0x00] = o;
ssum = 0.0d;
for (int si = 0x00; si < S; si++)
{
sum = p [si] * b [si, o];
alpha [0x00, si] = sum;
ssum += sum;
}
n = fsm.GetTransitionOfIndex(n);
norm = 1.0d / ssum;
for (int t = 0x00; t < T1; t++)
{
o = fsm.GetOutput(n);
os [t + 0x01] = o;
ssum = 0.0d;
for (int sj = 0x00; sj < S; sj++)
{
sum = 0.0d;
for (int si = 0x00; si < S; si++)
{
sum += a [si, sj] * alpha [t, si];
}
sum *= norm * b [sj, o];
ssum += sum;
alpha [t + 0x01, sj] = sum;
}
n = fsm.GetTransitionOfIndex(n);
prob *= ssum;
norm = 1.0d / ssum;
}
sprob += Math.Abs((double)tok / stok - prob);
#endregion
#region Backward algorithm
norm = 1.0d;
for (int t = sampleLength - 0x01; t > 0x00; t--)
{
o = os [t]; //OST: soundtrack
ssum = 0.0d;
for (int si = S - 0x01; si >= 0x00; si--)
{
sum = 0.0d;
for (int sj = S - 0x01; sj >= 0x00; sj--)
{
sum += a [si, sj] * beta [t, sj] * b [sj, o];
}
sum *= norm;
ssum += sum;
beta [t - 0x01, si] = sum;
}
norm = 1.0d / ssum;
}
#endregion
#region Gamma/Xi values
for (int t = 0x00; t < T; t++)
{
sum = 0.0d;
o = os [t];
for (int si = 0x00; si < S; si++)
{
tmp = alpha [t, si] * beta [t, si];
sum += tmp;
gamma [t, si] = tmp;
}
sum = 1.0d / sum;
for (int si = 0x00; si < S; si++)
{
tmp = gamma [t, si];
tmp *= sum;
gamma [t, si] = tmp;
asc [si] += tok * tmp;
bhat [si, o] += tok * tmp;
}
}
for (int si = 0x00; si < S; si++)
{
phat [si] = tok * gamma [0x00, si];
bsc [si] += tok * gamma [T1, si];
}
for (int t = 0x00; t < T1; t++)
{
o = os [t + 0x01];
for (int si = 0x00; si < S; si++)
{
sum = 0.0d;
for (int sj = 0x00; sj < S; sj++)
{
tmp = alpha [t, si] * beta [t + 0x01, sj] * a [si, sj] * b [sj, o];
sum += tmp;
epsilon [t, si, sj] = tmp;
}
sum = gamma [t, si] / sum;
for (int sj = 0x00; sj < S; sj++)
{
tmp = epsilon [t, si, sj];
tmp *= sum;
ahat [si, sj] += tok * tmp;
epsilon [t, si, sj] = tmp;
}
}
}
#endregion
}
}
#region Updates (should be moved after tokenselect)
sum = 1.0d / stok;
for (int si = 0x00; si < S; si++)
{
p [si] = phat [si] * stok;
tmp = 1.0d / asc [si];
for (int o = 0x00; o < O; o++)
{
b [si, o] = bhat [si, o] * tmp;
}
tmp = 1.0d / (asc [si] - bsc [si]);
for (int sj = 0x00; sj < S; sj++)
{
a [si, sj] = ahat [si, sj] * tmp;
}
}
#endregion
return(sprob / stok);
}
/// <summary>
/// Train this <see cref="IHiddenMarkovModel"/> by using the given <see cref="T:IFiniteStateMachine`1"/>
/// model. Since such finite state machine generates infinite sequences of output, training that sequence
/// would result in an underdetermined system. The sample length must thefore be specified.
/// </summary>
/// <returns>The error on the training. In other words how different the original Hidden Markov model is from the
/// given finite state machine.</returns>
/// <param name="fsm">The finite state machine from which the <see cref="IHiddenMarkovModel"/> learns.</param>
/// <param name="initialDistribution"> the initial distribution for the states of the finite state machine.</param>
/// <param name="sampleLength">The length of the samples, has impact on the trained model.</param>
public double Train(IFiniteStateMachine<int> fsm, IList<int> initialDistribution, int sampleLength)
{
int N = fsm.Length;
int S = this.Length;
int O = this.OutputSize;
int T = sampleLength;
int T1 = T - 0x01;
double[] p = this.Probabilities;
double[,] a = this.a, b = this.b;
double[,] alpha = new double[T, S], beta = new double[T, S], gamma = new double[T, S], bhat = new double[S, O], ahat = new double[S, S];
double[,,] epsilon = new double[T, S, S];
double[] asc = new double[S], bsc = new double[S], phat = new double[S];
for (int si = 0x00; si < S; si++) {
beta [T1, si] = 1.0d;
}
double norm, sum, ssum, tmp;
int[] os = new int[sampleLength];
double sprob = 0.0d, prob;
int stok = 0x00;
for (int ni = 0x00; ni < N; ni++) {
stok += initialDistribution [ni];
}
for (int ni = 0x00; ni < N; ni++) {//TODO: extremely optimize this
int tok = initialDistribution [ni];
if (tok > 0x00) {
int n = ni;
#region Forward algorithm
prob = 1.0d;
int o = fsm.GetOutput (n);
os [0x00] = o;
ssum = 0.0d;
for (int si = 0x00; si < S; si++) {
sum = p [si] * b [si, o];
alpha [0x00, si] = sum;
ssum += sum;
}
n = fsm.GetTransitionOfIndex (n);
norm = 1.0d / ssum;
for (int t = 0x00; t < T1; t++) {
o = fsm.GetOutput (n);
os [t + 0x01] = o;
ssum = 0.0d;
for (int sj = 0x00; sj < S; sj++) {
sum = 0.0d;
for (int si = 0x00; si < S; si++) {
sum += a [si, sj] * alpha [t, si];
}
sum *= norm * b [sj, o];
ssum += sum;
alpha [t + 0x01, sj] = sum;
}
n = fsm.GetTransitionOfIndex (n);
prob *= ssum;
norm = 1.0d / ssum;
}
sprob += Math.Abs ((double)tok / stok - prob);
#endregion
#region Backward algorithm
norm = 1.0d;
for (int t = sampleLength-0x01; t > 0x00; t--) {
o = os [t];//OST: soundtrack
ssum = 0.0d;
for (int si = S-0x01; si >= 0x00; si--) {
sum = 0.0d;
for (int sj = S-0x01; sj >= 0x00; sj--) {
sum += a [si, sj] * beta [t, sj] * b [sj, o];
}
sum *= norm;
ssum += sum;
beta [t - 0x01, si] = sum;
}
norm = 1.0d / ssum;
}
#endregion
#region Gamma/Xi values
for (int t = 0x00; t < T; t++) {
sum = 0.0d;
o = os [t];
for (int si = 0x00; si < S; si++) {
tmp = alpha [t, si] * beta [t, si];
sum += tmp;
gamma [t, si] = tmp;
}
sum = 1.0d / sum;
for (int si = 0x00; si < S; si++) {
tmp = gamma [t, si];
tmp *= sum;
gamma [t, si] = tmp;
asc [si] += tok * tmp;
bhat [si, o] += tok * tmp;
}
}
for (int si = 0x00; si < S; si++) {
phat [si] = tok * gamma [0x00, si];
bsc [si] += tok * gamma [T1, si];
}
for (int t = 0x00; t < T1; t++) {
o = os [t + 0x01];
for (int si = 0x00; si < S; si++) {
sum = 0.0d;
for (int sj = 0x00; sj < S; sj++) {
tmp = alpha [t, si] * beta [t + 0x01, sj] * a [si, sj] * b [sj, o];
sum += tmp;
epsilon [t, si, sj] = tmp;
}
sum = gamma [t, si] / sum;
for (int sj = 0x00; sj < S; sj++) {
tmp = epsilon [t, si, sj];
tmp *= sum;
ahat [si, sj] += tok * tmp;
epsilon [t, si, sj] = tmp;
}
}
}
#endregion
}
}
#region Updates (should be moved after tokenselect)
sum = 1.0d / stok;
for (int si = 0x00; si < S; si++) {
p [si] = phat [si] * stok;
tmp = 1.0d / asc [si];
for (int o = 0x00; o < O; o++) {
b [si, o] = bhat [si, o] * tmp;
}
tmp = 1.0d / (asc [si] - bsc [si]);
for (int sj = 0x00; sj < S; sj++) {
a [si, sj] = ahat [si, sj] * tmp;
}
}
#endregion
return sprob / stok;
}
/// <summary>
/// Generates the influence matrix based on a given <see cref="T:IFiniteStateMachine`1"/> and an index
/// that is part of a strongly connected group in the finite state machine.
/// </summary>
/// <returns>A 2d-matrix that contains the influence matrix of the strongly connected group.</returns>
/// <param name="fsm">The given finite state machine to generate the influence matrix from.</param>
/// <param name="strongIndex">The initial index to generate an influence matrix from.</param>
/// <remarks>
/// <para>The given initial index must be part of a strongly connected group. If the index is not part,
/// the algorithm will loop.</para>
/// </remarks>
public void GenerateInfluenceMatrix(IFiniteStateMachine<int> fsm, int strongIndex)
{
int n = fsm.Length;
int s = this.Length;
int o = this.OutputSize;
double[] trans = new double[s * s];
double[,] a = this.a, b = this.b;
double[] ch1 = new double[s], ch2 = new double[s], cht;
int idx, otp, j, i;
double sum;
for (int ori = 0x00; ori < s; ori++) {
for (j = 0x00; j < ori; j++) {
ch2 [j] = 0.0d;
}
ch2 [j] = 1.0d;
for (j++; j < s; j++) {
ch2 [j] = 0.0d;
}
idx = strongIndex;
do {
cht = ch1;
ch1 = ch2;
ch2 = cht;
otp = fsm.GetOutput (idx);
for (j = 0x00; j < s; j++) {
sum = 0.0d;
for (i = 0x00; i < s; i++) {
sum += ch1 [i] * a [i, j];
}
sum *= b [j, otp];
ch2 [j] = sum;
}
idx = fsm.GetTransitionOfIndex (idx);
} while(idx != strongIndex);
for (j = 0x00, i = ori; j < s; i += s, j++) {
trans [i] = ch2 [j];
}
}
}
