private void beginGenerationButton_Click(object sender, EventArgs e)
{
try
{
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.checkBox1.Checked;
var accords = Chord.RetrieveChords(midi, retriever);
INGrams <Chord> grams = null;
if (this.homogeneous)
{
grams = HomogenousNGrams <Chord> .BuildNGrams((int)this.leftRangeNumericUpDown.Value, accords);
}
else
{
grams = HeterogenousNGrams <Chord> .BuildNGrams((int)this.leftRangeNumericUpDown.Value, (int)this.rightRangeNumericUpDown.Value, accords);
}
NGramGraphMarkovChain <Chord> graph = new NGramGraphMarkovChain <Chord>(grams);
this.Save(graph);
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
private async void startMusicGeneration_Click(object sender, EventArgs e)
{
this.model = TemperaryVariables.Graph;
if (null == this.model || null == this.neuralNetwork)
{
MessageBox.Show("Load neccessary files");
}
INGramWeightAssigner <Chord> assi = null;
if (this.useWeightAssignerCheckBox.Checked)
{
NGramLinearSizeVsTimeWeightAssigner <Chord> sizetime = new NGramLinearSizeVsTimeWeightAssigner <Chord>(1F);
NGramLinearSizeDistrubutionAssigner <Chord> size = new NGramLinearSizeDistrubutionAssigner <Chord>(this.model);
Dictionary <INGramWeightAssigner <Chord>, float> dic = new Dictionary <INGramWeightAssigner <Chord>, float>()
{
{ sizetime, this.sizeVsWeightAssigner.Value / 100F },
{ size, this.countDistAssigner.Value / 100F }
};
assi = new NGramMetaWeightAssigner <Chord>(dic);
}
else
{
assi = new NGramIDWeightAssigner <Chord>(this.model);
}
this.trainingThread = new Thread(this.GeneticSearch);
this.trainingThread.Start(assi);
}
private float DistanceCompleteWithRespectToLeft(NGramGraphMarkovChain <T> left, NGramGraphMarkovChain <T> right)
{
float sum = 0;
foreach (var item in left)
{
var node = item.Key;
if (!right.ValidNode(node))
{
sum += this.ToleranceTransform(this.TransformFunction(this.EmptyNodeWeight));
continue;
}
var rightConnections = right.Edges(node).Select(y => this.TransformFunction(y.Probability)).ToArray();
var total = this.TransformFunction((float)this.ProbabilityDistace.Distance(item.Value.Select(x => this.TransformFunction(x.Probability)).ToArray(), rightConnections));
if (total / (float)left.Count() < this.TolerancePerNode)
{
sum += 0;
}
else
{
sum += total;
}
}
return(sum / (this.TransformFunction(1F) * (float)right.Grams.Count()));
}
private void beginGenerationButton_Click(object sender, EventArgs e)
{
try
{
PianoNoteRetriever retriever = new PianoNoteRetriever();
var midiEvents = new InstrumentMidiEventProducer(this.files.Select(x => new Sequence(x)));
var midi = midiEvents.GetOrderedMessages(GeneralMidiInstrument.AcousticGrandPiano);
Chord.AllowForComplexSimplification = this.checkBox1.Checked;
var accords = Chord.RetrieveChords(midi, retriever);
INGrams<Chord> grams = null;
if (this.homogeneous)
{
grams = HomogenousNGrams<Chord>.BuildNGrams((int)this.leftRangeNumericUpDown.Value, accords);
}
else
{
grams = HeterogenousNGrams<Chord>.BuildNGrams((int)this.leftRangeNumericUpDown.Value, (int)this.rightRangeNumericUpDown.Value, accords);
}
NGramGraphMarkovChain<Chord> graph = new NGramGraphMarkovChain<Chord>(grams);
this.Save(graph);
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
public ChordChainGeneticFunction(
DiscreteNeuralNetworkByChord dnn,
NGramGraphMarkovChain <Chord> chord,
INGramWeightAssigner <Chord> assigner,
ChordRandomFunctionType type = ChordRandomFunctionType.NoRandomSelection,
ChordCrossFunctionType cross = ChordCrossFunctionType.DiscreteChoice)
{
dnn.NullCheck();
chord.NullCheck();
assigner.NullCheck();
this.NeuralNetwork = dnn;
this.NGramGraph = chord;
this.RandomFunctionType = type;
this.CrossFunctionType = cross;
this.ChordDepth = this.NeuralNetwork.InputsCount * 2;
this.NGramDepth = 10;
this.MutateCoefficient = 0.05D;
this.Assigner = assigner;
this.random = new Random();
switch (type)
{
case ChordRandomFunctionType.AllowRandomSelection:
this.RandomSelectionCoefficient = .05D;
return;
}
}
public NGramLinearSizeDistrubutionAssigner(NGramGraphMarkovChain <T> fullGraph, int windowSize = 20)
{
(windowSize > 5).AssertTrue();
fullGraph.Any().AssertTrue();
this.WindowSize = windowSize;
this.Distrubution = RetrieveDistrubution(fullGraph.Grams);
}
private void UpdateGraph(NGramGraphMarkovChain <T> chain)
{
this.wpfContainer.Child = GenerateWpfVisuals(GraphUIHelper.GenerateGraphUI <T>(chain));
GraphArea.GenerateGraph(true);
GraphArea.SetVerticesDrag(true, true);
ZoomControl.ZoomToFill();
}
public NGramGraphChainRetriever(NGramGraphMarkovChain <T> a)
{
a.NullCheck();
this.graph = a;
this.NodeDepthBeforeGreedy = 100;
this.Max = int.MaxValue;
}
private void ShowMelodyDifference(NGram <Chord>[] nGram)
{
NGramGraphMarkovChain <Chord> melodyGraph = new NGramGraphMarkovChain <Chord>(HomogenousNGrams <Chord> .DirectBuiltUnsafe(nGram, 1));
NGramSemanticGraphDistance <Chord> a = new Library.GraphOperations.NGramSemanticGraphDistance <Chord>();
string text = a.Distance(melodyGraph, TemporaryVariables.Graph, (this.useSubGraphCheckBox.Checked) ?
NGramGraphDistanceType.SubGraph : NGramGraphDistanceType.CompleteGraph).ToString();
this.graphDifferenceTextBox.Invoke((Action)(() => this.graphDifferenceTextBox.Text = text));
}
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));
}
private void Save(NGramGraphMarkovChain <Chord> a)
{
TemperaryVariables.Graph = a;
this.Dispose();
return;
SaveFileDialog saveFileDialog1 = new SaveFileDialog();
saveFileDialog1.Filter = "model files (*.smf)|*.smf";
saveFileDialog1.FilterIndex = 2;
saveFileDialog1.RestoreDirectory = true;
saveFileDialog1.CreatePrompt = true;
saveFileDialog1.ShowDialog();
//a.Save(saveFileDialog1.FileName);
}
public float Distance(NGramGraphMarkovChain <T> left, NGramGraphMarkovChain <T> right, NGramGraphDistanceType type = NGramGraphDistanceType.CompleteGraph)
{
left.NullCheck();
right.NullCheck();
switch (type)
{
case NGramGraphDistanceType.CompleteGraph:
return((this.DistanceCompleteWithRespectToLeft(left, right) + this.DistanceCompleteWithRespectToLeft(right, left)) / 2F);
case NGramGraphDistanceType.SubGraph:
if (left.Count() > right.Count())
{
return(this.DistanceCompleteWithRespectToLeft(right, left));
}
return(this.DistanceCompleteWithRespectToLeft(left, right));
}
throw new NotImplementedException();
}
private Dictionary <NGram <T>, float> NextPossibleStateAssignment(NGramGraphMarkovChain <T> subGraph)
{
int timeMax = subGraph.Max(x => x.Value.Any() ? x.Value.Count(y => y.Edge.N) : 0);
int sizeMax = subGraph.Max(y => y.Key.N);
Func <int, int, float, float> func = new Func <int, int, float, float>((max, value, weight) => ((float)value / (float)max) * weight);
var k = subGraph
.Skip(1)
.ToDictionary(
x => x.Key,
y => func(sizeMax, y.Key.N, this.SizeWeight)
+ func(timeMax, y.Value.Any() ? y.Value.Max(z => z.Edge.N) : 0, this.TimeWeight));
var lastmax = k.Max(x => x.Value);
return(k.ToDictionary(x => x.Key,
y => y.Value / lastmax));
}
public override void FormLoad(object sender, EventArgs e)
{
this.DisableButtons();
this.serachTextBox.ReadOnly = true;
this.Size = this.Size;
// List<string> corp = new List<string>() { "a", "b", "c", "a", "e", "f", "g", "h", "e", "f", "h", "b", "b", "c", "h", "g" };
List <string> corp = TextCorpus.RetrieveAnyCompleteWordCorpus();
HeterogenousNGrams <string> grams = HeterogenousNGrams <string> .BuildNGrams(1, 3, corp);
//HomogenousNGrams<string> grams = HomogenousNGrams<string>.BuildNGrams(1, corp);
//NGramGraphMarkovChain<string> graphHetero = new NGramGraphMarkovChain<string>(heterograms);
NGramGraphMarkovChain <string> graph = new NGramGraphMarkovChain <string>(grams);
this.MarkovGraph = graph;
this.wpfContainer.Child = GenerateWpfVisuals(GraphUIHelper.GenerateGraphUI <string>(NGramGraphMarkovChain <string> .Empty(true)));
ZoomControl.ZoomToFill();
this.serachTextBox.ReadOnly = false;
}
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);
}
private void Save(NGramGraphMarkovChain<Chord> a)
{
TemperaryVariables.Graph = a;
this.Dispose();
return;
SaveFileDialog saveFileDialog1 = new SaveFileDialog();
saveFileDialog1.Filter = "model files (*.smf)|*.smf";
saveFileDialog1.FilterIndex = 2;
saveFileDialog1.RestoreDirectory = true;
saveFileDialog1.CreatePrompt = true;
saveFileDialog1.ShowDialog();
//a.Save(saveFileDialog1.FileName);
}
public Dictionary <NGram <T>, float> NextPossibleStateAssignment(NGram <T>[] past, NGramGraphMarkovChain <T> subGraph)
{
var start = past.Last();
return(graph.Edges(start).ToDictionary(item => item.Edge, item => item.Probability));
}
public Dictionary <NGram <T>, float> NextPossibleStateAssignment(NGram <T>[] past, NGramGraphMarkovChain <T> subGraph)
{
Dictionary <INGramWeightAssigner <T>, Dictionary <NGram <T>, float> > list = this.functionsWithWeight
.ToDictionary(
item => item.Key,
item => item.Key.NextPossibleStateAssignment(past, subGraph));
Dictionary <NGram <T>, float> built = new Dictionary <NGram <T>, float>();
foreach (var assigner in list)
{
foreach (var item in assigner.Value)
{
if (built.ContainsKey(item.Key))
{
built[item.Key] = built[item.Key] + (this.functionsWithWeight[assigner.Key] * item.Value);
continue;
}
built.Add(item.Key, (this.functionsWithWeight[assigner.Key] * item.Value));
}
}
return(built);
}
public Dictionary <NGram <T>, float> NextPossibleStateAssignment(NGram <T>[] past, NGramGraphMarkovChain <T> subGraph)
{
return(this.NextPossibleStateAssignment(subGraph));
}
private void Save(NGramGraphMarkovChain <Chord> a)
{
TemporaryVariables.Graph = a;
this.Dispose();
}
public Dictionary <NGram <T>, float> NextPossibleStateAssignment(NGram <T>[] past, NGramGraphMarkovChain <T> subGraph)
{
var possibleChoices = subGraph.Select(x => x.Key).ToList();
var num = Math.Min(possibleChoices.Count, past.Length);
var tempDictionary = NGramLinearSizeDistrubutionAssigner <T> .RetrieveDistrubution(possibleChoices.GetRange(possibleChoices.Count - num, num));
var subtracted = NGramLinearSizeDistrubutionAssigner <T> .SubtractedDistrubutions(this.Distrubution, tempDictionary);
var normalizationFactor = subtracted.Sum(x => Math.Abs(x.Value));
var shiftFactor = subtracted.Where(x => x.Value < 0).Select(x => x.Value).Sum();
return(possibleChoices.ToDictionary(
item => item,
item => ((float)this.Distrubution[item.N] + (float)shiftFactor) / normalizationFactor));
}
public void LoadGraph(NGramGraphMarkovChain <T> graph)
{
graph.NullCheck();
this.Graph = graph;
}
public NGramIDWeightAssigner(NGramGraphMarkovChain <T> graph)
{
this.graph = graph;
}