public IReadOnlyCollection <NGram <Chord>[]> Themes(INGramWeightAssigner <Chord> assigner)
{
NGramGraphChainRetriever <Chord> mostProbable = new NGramGraphChainRetriever <Chord>(this.MarkovGraph);
var chains = mostProbable.FromEachNodeRandom(assigner, this.WalkerDepth, 3);
//KMeans<NGram<Chord>> cluster = new KMeans<NGram<Chord>>(chains.ToArray(), new LevenshteinDistance<NGram<Chord>>(), 500);
//cluster.RunFrames(2);
DiscreteNeuralNetworkByChord teacher = DiscreteNeuralNetworkByChord.Load(@"C:\Users\armen_000\Documents\Visual Studio 2013\Projects\Improvisation\Improvisation\bin\Debug\Learning\eminemNN.txt");
ChordChainGeneticFunction function = new ChordChainGeneticFunction(
teacher,
this.MarkovGraph,
assigner,
ChordChainGeneticFunction.ChordRandomFunctionType.AllowRandomSelection,
ChordChainGeneticFunction.ChordCrossFunctionType.DiscreteChoice)
{
RandomSelectionCoefficient = 0.3D
};
GeneticAlgorithm <NGram <Chord>[]> genetic = new GeneticAlgorithm <NGram <Chord>[]>(
function,
new GeneticSettings(0.1F, 0.05f, 5000, GeneticSettings.OrderOfEvolution.MutateCrossover),
chains.Take(500));
for (int i = 0; i < 3; i++)
{
try
{
var l = genetic.SingleEvolutionaryCycle();
}
catch (Exception e)
{
continue;
}
// KMeans<NGram<Chord>> chords = new KMeans<NGram<Chord>>(
// genetic.CurrentPopulation.Select(x => x.Value).RandomValues(1000),
// new GaussianNoiseDistance<NGram<Chord>>(new ChordHammingDistance(), 010F), 1000);
// chords.RunFrames(3);
// genetic.SubstitutePopulation(chords.Centers);
}
return(genetic.ToList().AsReadOnly());
}
private void loadNeuralNetworkButton_Click(object sender, EventArgs e)
{
OpenFileDialog openFileDialog1 = new OpenFileDialog();
DialogResult result = openFileDialog1.ShowDialog(); // Show the dialog.
if (result == DialogResult.OK) // Test result.
{
string file = openFileDialog1.FileName;
try
{
this.neuralNetwork = DiscreteNeuralNetworkByChord.Load(file);
this.loadNeuralNetworkTextbox.Text = FinalUIHelperMethods.FileFriendlyString(openFileDialog1.FileName);
}
catch (Exception ex)
{
MessageBox.Show("Could Not Load NN", ex.Message);
}
}
}
private void loadNeuralNetworkButton_Click(object sender, EventArgs e)
{
OpenFileDialog openFileDialog1 = new OpenFileDialog();
// Test result.
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
string file = openFileDialog1.FileName;
try
{
this.neuralNetwork = DiscreteNeuralNetworkByChord.Load(file);
this.loadNeuralNetworkTextbox.Text = Path.GetFileName(openFileDialog1.FileName);
}
catch (Exception ex)
{
MessageBox.Show("Could Not Load Neural Network", ex.Message);
}
}
}
private void trainingButton_Click(object sender, EventArgs e)
{
var retriever = new PianoNoteRetriever();
var midiEvents = new InstrumentMidiEventProducer(this.files.Select(x => new Sequence(x)));
IReadOnlyList <MidiEvent> midi = midiEvents.GetOrderedMessages(GeneralMidiInstrument.AcousticGrandPiano);
Chord.AllowForComplexSimplification = this.checkBox.Checked;
var accords = Chord.RetrieveChords(midi, retriever);
DiscreteDataRetriever data = new DiscreteDataRetriever(accords.ToList());
DiscreteNeuralNetworkByChord.OkayWeight = (double)this.okayWeight.Value;
DiscreteNeuralNetworkByChord.HiddenLayerSize = (int)hiddelLayerSize.Value;
if (this.okayFiles != null)
{
var midiEvents1 = new InstrumentMidiEventProducer(this.okayFiles.Select(x => new Sequence(x)));
var midi1 = midiEvents1.GetOrderedMessages(GeneralMidiInstrument.AcousticGrandPiano);
Chord.AllowForComplexSimplification = this.checkBox.Checked;
var accords1 = Chord.RetrieveChords(midi1, retriever);
DiscreteDataRetriever data1 = new DiscreteDataRetriever(accords1.ToList());
this.nnByChord = new DiscreteNeuralNetworkByChord(data.Good, data1.Good, data.Bad.Union(data1.Bad).Take(data.Bad.Count).ToList(),
new AForge.Neuro.BipolarSigmoidFunction());
}
else
{
this.nnByChord = new DiscreteNeuralNetworkByChord(data.Good, data.Okay, data.Bad, new AForge.Neuro.SigmoidFunction());
}
foreach (var item in this.Controls.OfType <Control>())
{
item.Enabled = false;
}
threadTrain = new Thread(Start);
threadTrain.Start();
}
