public MusicAnalyzer(List <string> contexts)
{
// _storedSongs = new double[MaxSongsAtOnce][];
// _storedTypes = new int[MaxSongsAtOnce][];
_allContexts = contexts;
// _currentIndex = 0;
// _network = new ActivationNetwork(new SigmoidFunction(), InputsSize, (int) ((InputsSize * (2.0/3.0)) + contexts.Count), (int)((InputsSize * (1.0 / 3.0)) + contexts.Count), contexts.Count);
int sum = (InputsSize + contexts.Count);
_network = new ActivationNetwork(new SigmoidFunction(), InputsSize, contexts.Count, contexts.Count);
// _network = new ActivationNetwork(new SigmoidFunction(), InputsSize, contexts.Count * 3, contexts.Count, contexts.Count);
_network.Randomize();
_learning = new BackPropagationLearning(_network)
{
LearningRate = 0.2,
Momentum = 0.5
};
_standard = new Standardizer();
// _learning = new ParallelResilientBackpropagationLearning(_network);
// learning.RunEpoch()
//_teacher = new MultilabelSupportVectorLearning<Linear>()
//{
// Learner = (p) => new SequentialMinimalOptimization<Linear>()
// {
// Complexity = 100.0
// }
//};
}
public unsafe double ProcessData(char *data, int size, string context)
{
if (!_allContexts.Contains(context))
{
throw new Exception(context + " context does not exist");
}
var arr = ByteArrToDoubleArr(data, size);
Standardizer.ProcessCalculate(arr);
double avg = 0;
for (int i = 0; i < arr.Length; i++)
{
avg += arr[i];
}
avg /= arr.Length;
Debug.WriteLine("Average: " + avg);
double[] types = TypesValues(context);
// Fourier.FFT(arr, FourierDirection.Forward);
//double[] input = new double[arr.Length];
//for(int i = 0; i < arr.Length; i++)
//{
// input[i] = (double) arr[i].Re;
//}
double error;
// int t;
// do
// {
error = _learning.Run(arr, types);
// t = TestData(data, size).Select(a => a).Where(a => a.Value >= 1 && a.Key.Equals(context)).Count();
// } while (t < 1 && confirmGood);
Debug.WriteLine("Data Error: " + error);
return(error);
}
public MusicAnalyzer(string loadLocation)
{
using (Stream loader = new FileStream(loadLocation, FileMode.Open))
{
var binaryFormatter = new BinaryFormatter();
// todo deseserialize standarizer
Version v = (Version)binaryFormatter.Deserialize(loader);
if (v.Major == Ver.Major && v.Minor == Ver.Minor)
{
_allContexts = (List <string>)binaryFormatter.Deserialize(loader);
_standard = (Standardizer)binaryFormatter.Deserialize(loader);
_network = (ActivationNetwork)Network.Load(loader);
}
else
{
throw new Exception(string.Format("{0}.{1}.{2} version does not match current {3}.{4}.{5}", v.Major, v.Minor, v.SubMinor, Ver.Major, Ver.Minor, Ver.SubMinor));
}
}
}
private double[] TypesValues(string context)
{
double[] types = new double[_allContexts.Count];
for (int i = 0; i < types.Length; i++)
{
if (_allContexts[i] == context)
{
types[i] = 1.0;
}
else
{
types[i] = -1.0;
}
}
Standardizer.ProcessCalculate(types);
// types = NormalizeDouble(types);
return(types);
}
public unsafe Dictionary <string, double> TestData(char *data, int size)
{
// var model = _teacher.Model;
// var arr = ByteArrToComplexArr(data, size);
// Fourier.FFT(arr, FourierDirection.Forward);
//double[] input = new double[arr.Length];
//for (int i = 0; i < arr.Length; i++)
//{
// input[i] = (double)arr[i].Re;
//}
var input = ByteArrToDoubleArr(data, size);
Standardizer.ProcessCalculate(input);
double[] probabilities = _network.Compute(input);
double total = 0;
foreach (double probability in probabilities)
{
total += probability;
}
for (int i = 0; i < probabilities.Length; i++)
{
probabilities[i] = probabilities[i] / total;
}
return(_allContexts.Zip(probabilities, (k, v) => new { s = k, d = v })
.OrderBy((k) => k.d).Reverse().ToDictionary(key => key.s, value => value.d));
}