Accord.Statistics.Models.Markov.ContinuousHiddenMarkovModel.Decode C# (CSharp) Method

        public int[] Decode(Array observations, bool logarithm, out double probability)
        {
            if (observations == null)
                throw new ArgumentNullException("observations");

            if (observations.Length == 0)
            {
                probability = 0.0;
                return new int[0];
            }

            if (!(observations is double[][] || observations is double[]))
                throw new ArgumentException("Argument should be either of type " +
                                            "double[] (for univariate observation) or double[][] (for " +
                                            "multivariate observation).", "observations");


            double[][] x = convert(observations);


            // Viterbi-forward algorithm.
            int T = x.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].ProbabilityFunction(x[0]));

            // Induction
            for (int t = 1; t < T; t++)
            {
                double[] observation = x[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].ProbabilityFunction(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;
        }