private List <NGram <Chord>[]> RetrieveOkay()
{
NGramGraphMarkovChain <Chord> graph = new NGramGraphMarkovChain <Chord>(this.HomogenousWindowingData);
NGramGraphChainRetriever <Chord> mostProbable = new NGramGraphChainRetriever <Chord>(graph);
return(mostProbable
.FromEachNodeRandom(
(this.Assigner == null) ? new NGramIDWeightAssigner <Chord>(graph) : this.Assigner,
this.WindowSize - 1,
10));
}
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 GeneticSearch(object a)
{
var assigner = (INGramWeightAssigner <Chord>)(a);
NGramGraphChainRetriever <Chord> mostProbable = new NGramGraphChainRetriever <Chord>(this.model)
{
Max = 200
};
var chains = mostProbable.FromEachNodeRandom(assigner, (int)this.walkerDepth.Value, (int)this.fromEachNode.Value);
ChordChainGeneticFunction function = new ChordChainGeneticFunction(this.neuralNetwork, this.model, assigner,
(this.allowRandomChordSelection.Checked) ?
ChordChainGeneticFunction.ChordRandomFunctionType.AllowRandomSelection :
ChordChainGeneticFunction.ChordRandomFunctionType.NoRandomSelection,
(this.mergeChordsCheckBox.Checked) ?
ChordChainGeneticFunction.ChordCrossFunctionType.Merge :
ChordChainGeneticFunction.ChordCrossFunctionType.DiscreteChoice)
{
RandomSelectionCoefficient = 0.3D
};
GeneticAlgorithm <NGram <Chord>[]> genetic = new GeneticAlgorithm <NGram <Chord>[]>(function, new GeneticSettings(0.1F, 0.05f, (int)this.geneticInitialPopulation.Value,
(this.mutateCrossCheckBox.Checked) ?
GeneticSettings.OrderOfEvolution.MutateCrossover :
GeneticSettings.OrderOfEvolution.CrossoverMutate),
chains.Take(500));
int iMax = (int)this.geneticEpochs.Value;
this.progressBar1.Invoke((Action)(() => this.progressBar1.Maximum = iMax));
for (int i = 0; i < iMax; i++)
{
this.progressBar1.Invoke((Action)(() => this.progressBar1.Value = i));
var text = genetic.SingleEvolutionaryCycle().ToString();
this.geneticErrorTextBox.Invoke((Action)(() => this.geneticErrorTextBox.Text = text));
}
this.progressBar1.Invoke((Action)(() => this.progressBar1.Value = this.progressBar1.Maximum));
this.songListBox.Invoke((Action)(() => this.songListBox.Enabled = true));
this.AddItemsToListBoxFromGenetic(genetic.CurrentPopulation);
}
private List<NGram<Chord>[]> RetrieveOkay()
{
NGramGraphMarkovChain<Chord> graph = new NGramGraphMarkovChain<Chord>(this.HomogenousWindowingData);
NGramGraphChainRetriever<Chord> mostProbable = new NGramGraphChainRetriever<Chord>(graph);
return mostProbable
.FromEachNodeRandom(
(this.Assigner == null) ? new NGramIDWeightAssigner<Chord>(graph) : this.Assigner,
this.WindowSize - 1,
10);
}