private NGramGraphMarkovChain(bool het)
{
this.graph = new Dictionary <NGram <T>, List <NonRecursiveNGramProbabilisticEdge <T> > >();
this.IsHeterogenous = het;
this.Distrubution = new NGramDistribution <T>(new NGram <T> [0]);
this.Grams = HomogenousNGrams <T> .DirectBuiltUnsafe(new NGram <T> [0], 1);
}
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 void BuildFromHomogenousNGrams(INGrams <T> grams, NGram <T>[] array, NGramDistribution <T> distrubution)
{
var blockedGrams = NGramHomogenousMatrixMarkovChainHelper <T> .BuildBlockedNGrams(grams.ToArray());
var transitionMatrix = Matrix <float> .Build.Sparse(
array.Length,
array.Length,
new Func <int, int, float>(
(x, y) => NGramHomogenousMatrixMarkovChainHelper <T> .CalculateProbability(blockedGrams, distrubution, array, x, y)));
int rowIndex = 0;
foreach (var row in transitionMatrix.EnumerateRows())
{
this.graph.Add(array[rowIndex], new List <NonRecursiveNGramProbabilisticEdge <T> >());
for (int i = 0; i < row.Count; i++)
{
if (row[i] != 0)
{
this.graph[array[rowIndex]].Add(new NonRecursiveNGramProbabilisticEdge <T>(row[i], array[i]));
}
}
rowIndex++;
}
}
public DiscreteDataRetriever(List <Chord> chordsInOrder, int clusterSize = 2, int windowSize = 20)
{
chordsInOrder.NullCheck();
chordsInOrder.Any().AssertTrue();
this.Chords = chordsInOrder.AsReadOnly();
this.WindowSize = windowSize;
this.NGramSize = clusterSize;
this.HomogenousWindowingData = HomogenousNGrams <Chord> .BuildNGrams(this.NGramSize, this.Chords.ToList());
this.badOkayGoodChords = Tuple.Create <List <NGram <Chord>[]>, List <NGram <Chord>[]>, List <NGram <Chord>[]> >(this.RetrieveBad(), this.RetrieveOkay(), this.RetrieveGood());
}
public DiscreteDataRetriever(List<Chord> chordsInOrder, int clusterSize = 2, int windowSize = 20)
{
chordsInOrder.NullCheck();
chordsInOrder.Any().AssertTrue();
this.Chords = chordsInOrder.AsReadOnly();
this.WindowSize = windowSize;
this.NGramSize = clusterSize;
this.HomogenousWindowingData = HomogenousNGrams<Chord>.BuildNGrams(this.NGramSize, this.Chords.ToList());
this.badOkayGoodChords = Tuple.Create<List<NGram<Chord>[]>, List<NGram<Chord>[]>, List<NGram<Chord>[]>>(this.RetrieveBad(), this.RetrieveOkay(), this.RetrieveGood());
}
public NGramGraphMarkovChain(INGrams <T> grams)
{
grams.NullCheck();
var array = new HashSet <NGram <T> >(grams).ToArray();
this.Distrubution = new NGramDistribution <T>(grams);
var typeOfNGram = (grams as IHeterogenousNGrams <T>);
this.IsHeterogenous = typeOfNGram != null;
this.graph = new Dictionary <NGram <T>, List <NonRecursiveNGramProbabilisticEdge <T> > >();
this.Grams = grams;
if (!this.IsHeterogenous)
{
this.BuildFromHomogenousNGrams(grams, array, this.Distrubution);
}
else
{
this.BuildFromHeterogenousNGrams(typeOfNGram, array, this.Distrubution);
}
}
public NGramDistribution(INGrams <T> ngrams)
: this(ngrams.AsEnumerable())
{
}
