| public Decode ( int observations, bool logarithm, double &probability ) : int[] | ||
| observations | int | A sequence of observations. |
| logarithm | bool | True to return the log-likelihood, false to return /// the likelihood. Default is false (default is to return the likelihood). |
| probability | double | The state optimized probability. |
| Результат | int[] | |
public int[] Decode(int[] observations, bool logarithm, out double probability)
{
if (observations == null)
throw new ArgumentNullException("observations");
if (observations.Length == 0)
{
probability = 0.0;
return new int[0];
}
// Viterbi-forward algorithm.
int T = observations.Length;
int states = States;
int minState;
double minWeight;
double weight;
double[] pi = Probabilities;
double[,] A = Transitions;
var s = new int[states,T];
var a = new double[states,T];
// Base
for (int i = 0; i < states; i++)
a[i, 0] = -System.Math.Log(pi[i]) - System.Math.Log(B[i, observations[0]]);
// Induction
for (int t = 1; t < T; t++)
{
int observation = observations[t];
for (int j = 0; j < states; j++)
{
minState = 0;
minWeight = a[0, t - 1] - System.Math.Log(A[0, j]);
for (int i = 1; i < states; i++)
{
weight = a[i, t - 1] - System.Math.Log(A[i, j]);
if (weight < minWeight)
{
minState = i;
minWeight = weight;
}
}
a[j, t] = minWeight - System.Math.Log(B[j, observation]);
s[j, t] = minState;
}
}
// Find minimum value for time T-1
minState = 0;
minWeight = a[0, T - 1];
for (int i = 1; i < states; i++)
{
if (a[i, T - 1] < minWeight)
{
minState = i;
minWeight = a[i, T - 1];
}
}
// Trackback
var path = new int[T];
path[T - 1] = minState;
for (int t = T - 2; t >= 0; t--)
path[t] = s[path[t + 1], t + 1];
// Returns the sequence probability as an out parameter
probability = logarithm ? -minWeight : System.Math.Exp(-minWeight);
// Returns the most likely (Viterbi path) for the given sequence
return path;
}
| HiddenMarkovModel::Decode ( int observations, double &probability ) : int[] |
private void btnModel_Click(object sender, EventArgs e)
{
var transition = new double[,]
{
{2.0/8, 1.0/8, 2.0/8, 3.0/8},
{0, 0, 0, 1.0/8},
{0, 0, 0, 0},
{1, 0, 0, 0},
};
var emission = new[,]
{
{2.0/8, 0, 0, 0, 0},
{0, 0, 0, 0, 0},
{0, 0, 1, 0, 0},
{0, 1, 0, 1, 0},
};
var start = new double[] {1, 0, 0, 0};
var hmm = new HiddenMarkovModel(transition, emission, start, false);
var liklyhood = 0d;
var x = hmm.Decode(new[] {1}, out liklyhood);
}
