static public NeuralNet.Sample GetSampleFromModel(FrequencyModel model)
        {
            double[] data = new double[model.Data.Count];
            int      i    = 0;

            foreach (var x in model.GetHighestData())
            {
                data[i] = x.Value;
                i++;
            }

            return(new NeuralNet.Sample(data, new double[61]));
        }
        static public NeuralNet.Sample GetSampleFromModel(FrequencyModel model, Note n)
        {
            double[] data = new double[model.Data.Count];
            int      i    = 0;

            foreach (var x in model.GetHighestData())
            {
                data[i] = x.Value;
                i++;
            }

            double[] pred = new double[61];
            pred[n.ToInt()] = 1;

            return(new NeuralNet.Sample(data, pred));
        }
        private void Capturer_NewPick(object sender, AudioPickEventArgs e)
        {
            if (DateTime.Now - lastSound > new TimeSpan(0, 0, 0, 0, 150))
            {
                var            t0   = DateTime.Now;
                FrequencyModel test = new FrequencyModel(new Dictionary <float, float>());
                Task           t    = Task.Factory.StartNew(() => { test = SoundCalculator.GetFrequencyModel(e.pickData); });
                renderer.Render(e.pickData, 0);
                t.Wait();
                appInfo.SetFrequency(test.FirstTone);
                Note n = noteFinder.getNoteFromModel(test);
                appInfo.SetNote(n);

                testSong.AddNote(n, t0);
            }
            lastSound = DateTime.Now;
        }
 public Note getNoteFromModel(FrequencyModel model)
 {
     return((Note)(m_noteNetwork.Predict(SoundCalculator.GetSampleFromModel(model))));
 }