Accord.Statistics.Models.Markov.HiddenMarkovModel.Predict C# (CSharp) Метод

        public int Predict(int[] observations, out double[] probabilities)
        {
            double[][] prob;
            double probability;
            int prediction = Predict(observations, 1, false, out probability, out prob)[0];
            probabilities = prob[0];
            return prediction;
        }

        public HMMGenerator(PatchNames instrument)
        {
            this.book = new Codebook<Note>();
            this.instrument = instrument;

            DotNetLearn.Data.SampleSet asdasd;

            Accord.Math.Tools.SetupGenerator(10);

            // Consider some phrases:
            //
            string[][] phrases =
            {
            "The Big Brown Fox Jumps Over the Ugly Dog".Split(new char[]{' '},  StringSplitOptions.RemoveEmptyEntries),
            "This is too hot to handle".Split(new char[]{' '},  StringSplitOptions.RemoveEmptyEntries),
            "I am flying away like a gold eagle".Split(new char[]{' '},  StringSplitOptions.RemoveEmptyEntries),
            "Onamae wa nan desu ka".Split(new char[]{' '},  StringSplitOptions.RemoveEmptyEntries),
            "And then she asked, why is it so small?".Split(new char[]{' '},  StringSplitOptions.RemoveEmptyEntries),
            "Great stuff John! Now you will surely be promoted".Split(new char[]{' '},  StringSplitOptions.RemoveEmptyEntries),
            "Jayne was taken aback when she found out her son was gay".Split(new char[]{' '},  StringSplitOptions.RemoveEmptyEntries),
            };

            // Let's begin by transforming them to sequence of
            // integer labels using a codification codebook:
            var codebook = new Codification("Words", phrases);

            // Now we can create the training data for the models:
            int[][] sequence = codebook.Translate("Words", phrases);

            // To create the models, we will specify a forward topology,
            // as the sequences have definite start and ending points.
            //
            var topology = new Forward(states: codebook["Words"].Symbols);
            int symbols = codebook["Words"].Symbols; // We have 7 different words

            // Create the hidden Markov model
            HiddenMarkovModel hmm = new HiddenMarkovModel(topology, symbols);

            // Create the learning algorithm
            var teacher = new ViterbiLearning(hmm);

            // Teach the model about the phrases
            double error = teacher.Run(sequence);

            // Now, we can ask the model to generate new samples
            // from the word distributions it has just learned:
            //
            List<int> sample = new List<int>();
            int count = 10;
            sample.Add(hmm.Generate(1)[0]);
            while(sample.Count < count)
            {
                var k = hmm.Predict(sample.ToArray(), 1);
                sample.AddRange(k);
            }

            // And the result will be: "those", "are", "words".
            string[] result = codebook.Translate("Words", sample.ToArray());
        }