// This returns the patch names from the PNAMES lump
// A directory resource does not support this lump, but the wads in the directory may contain this lump
public override PatchNames LoadPatchNames()
{
PatchNames pnames;
// Error when suspended
if (issuspended)
{
throw new Exception("Data reader is suspended");
}
// Load from wad files
// Note the backward order, because the last wad's images have priority
for (int i = wads.Count - 1; i >= 0; i--)
{
pnames = wads[i].LoadPatchNames();
if (pnames != null)
{
return(pnames);
}
}
// If none of the wads provides patch names, let's see if we can
string pnamesfile = FindFirstFile("PNAMES", false);
if ((pnamesfile != null) && FileExists(pnamesfile))
{
MemoryStream pnamesdata = LoadFile(pnamesfile);
pnames = new PatchNames(pnamesdata);
pnamesdata.Dispose();
return(pnames);
}
return(null);
}
public SamplingWithReplacement(CompositionCategory cat, PatchNames instrument)
{
this.MaxIterations = 200;
this.MaxLength = 100;
this.instrument = instrument;
this.category = cat;
random = new SimpleRNG();
random.SetSeedFromSystemTime();
model = new MarkovChain<Note>(1);
foreach(var comp in cat.Compositions)
{
foreach(var track in comp.Tracks)
{
if (!IsValidTrack(track))
continue;
var mel = (track.GetMainSequence() as MelodySequence).Clone() as MelodySequence;
mel.StandardizeDuration();
model.Add(mel.ToArray());
break;//only first track
}
}
CalculateStats();
}
public HMMGenerator(PatchNames instrument)
{
this.book = new Codebook<Note>();
this.instrument = instrument;
DotNetLearn.Data.SampleSet asdasd;
Accord.Math.Tools.SetupGenerator(10);
// Consider some phrases:
//
string[][] phrases =
{
"The Big Brown Fox Jumps Over the Ugly Dog".Split(new char[]{' '}, StringSplitOptions.RemoveEmptyEntries),
"This is too hot to handle".Split(new char[]{' '}, StringSplitOptions.RemoveEmptyEntries),
"I am flying away like a gold eagle".Split(new char[]{' '}, StringSplitOptions.RemoveEmptyEntries),
"Onamae wa nan desu ka".Split(new char[]{' '}, StringSplitOptions.RemoveEmptyEntries),
"And then she asked, why is it so small?".Split(new char[]{' '}, StringSplitOptions.RemoveEmptyEntries),
"Great stuff John! Now you will surely be promoted".Split(new char[]{' '}, StringSplitOptions.RemoveEmptyEntries),
"Jayne was taken aback when she found out her son was gay".Split(new char[]{' '}, StringSplitOptions.RemoveEmptyEntries),
};
// Let's begin by transforming them to sequence of
// integer labels using a codification codebook:
var codebook = new Codification("Words", phrases);
// Now we can create the training data for the models:
int[][] sequence = codebook.Translate("Words", phrases);
// To create the models, we will specify a forward topology,
// as the sequences have definite start and ending points.
//
var topology = new Forward(states: codebook["Words"].Symbols);
int symbols = codebook["Words"].Symbols; // We have 7 different words
// Create the hidden Markov model
HiddenMarkovModel hmm = new HiddenMarkovModel(topology, symbols);
// Create the learning algorithm
var teacher = new ViterbiLearning(hmm);
// Teach the model about the phrases
double error = teacher.Run(sequence);
// Now, we can ask the model to generate new samples
// from the word distributions it has just learned:
//
List<int> sample = new List<int>();
int count = 10;
sample.Add(hmm.Generate(1)[0]);
while(sample.Count < count)
{
var k = hmm.Predict(sample.ToArray(), 1);
sample.AddRange(k);
}
// And the result will be: "those", "are", "words".
string[] result = codebook.Translate("Words", sample.ToArray());
}
public void Play(MelodySequence seq, PatchNames instrument = PatchNames.Acoustic_Grand)
{
if (playingThread != null)
playingThread.Abort();
player.SetPatch((int)instrument, 1);
playingThread = new System.Threading.Thread(() => player.Play(seq));
playingThread.Start();
}
public PlaybackMessage(PatchNames patch, byte channel_)
{
message = PlaybackMessageType.Patch;
velocity = (byte)patch;
channel = channel_;
pitch = 0;
duration = 0;
tag = null;
}
public ReflectingBrownNoteGenerator(
NoteRangeRestrictor nrr, Random randGen,
int lowestPitchChange, int highestPitchChange,
int lowestLengthStep, int highestLengthStep, PatchNames instrument = PatchNames.Acoustic_Grand)
: base(nrr, lowestPitchChange, highestPitchChange,
lowestLengthStep, highestLengthStep)
{
this.instrument = instrument;
this.MaxNotes = 200;
gen = randGen;
}
public GeneticGenerator(IFitnessFunction fitnessFunction, PatchNames instrument = PatchNames.Acoustic_Grand, CompositionCategory cat=null)
{
this.fitnessFunction = fitnessFunction;
this.cat = cat;
this.MaxGenerations = 1000;
this.PrintProgress = true;
this.instrument = instrument;
if (cat != null)
{
// Markov generator
var mark = new MarkovChainGenerator(instrument, 2);
// Allowed notes
HashSet<Durations> durs = new HashSet<Durations>();
HashSet<int> fullPitches = new HashSet<int>();
foreach(var c in cat.Compositions)
{
if (c.Tracks.Count < 1)
continue;
var cloneMel = (c.Tracks[0].GetMainSequence() as MelodySequence).Clone() as MelodySequence;
cloneMel.StandardizeDuration();
mark.AddMelody(cloneMel);
foreach (var n in cloneMel.Notes)
{
durs.Add(NoteGene.GetClosestDuration(n.Duration));
fullPitches.Add(n.Pitch);
}
avgLength += cloneMel.Length;
}
avgLength /= cat.Compositions.Length;
GPCustomTree.generator = mark;
NoteGene.AllowedDurations = durs.ToArray();
NoteGene.AllowedFullPitches = fullPitches.ToArray();
}
}
public MarkovGenerator(MelodySequence[] seqs, PatchNames instrument = PatchNames.Acoustic_Grand)
{
this.instrument = instrument;
int count = seqs.Length;
note_map = new Dictionary<Note, int>();
int pitch = 0;
int j = 0;
pitches = new int[seqs.Length][];
foreach (MelodySequence m in seqs)
{
Note[] song = m.ToArray();
pitches[j] = new int[m.Length];
for (int i = 0; i < song.Length; i++)
{
if (note_map.ContainsKey(song[i]))
{
}
else
{
note_map[song[i]] = pitch++;
}
pitches[j][i] = note_map[song[i]];
}
j++;
if (m.Length > max_length)
max_length = m.Length;
}
hmm = new HiddenMarkovModel(3, pitch+1);
var teacher = new BaumWelchLearning(hmm);
teacher.Iterations = 10000;
teacher.Run(pitches);
Console.WriteLine("Done training");
}
static void Play(IEnumerable<Note> notes, PatchNames instrument = PatchNames.Vibraphone)
{
MusicPlayer player = new MusicPlayer();
player.SetPatch((int)instrument, 1);
player.PlayNotes(notes);
player.Close();
}
public Track(PatchNames instrument, byte channel)
{
this.Instrument = instrument;
this.Channel = channel;
sequences = new List<ISequence>();
}
public AccompanimentGeneratorANNFF(CompositionCategory category, PatchNames instrument)
{
this.Epochs = 50000;
this.category = category;
this.instrument = instrument;
}
public AccompanimentGenerator(CompositionCategory category, PatchNames instrument)
{
this.category = category;
this.instrument = instrument;
}
public ExistingSequenceGenerator(MelodySequence sequence, PatchNames instrument)
{
this.sequence = sequence;
this.instrument = instrument;
}
public MelodySequence GenerateInstrument(PatchNames patch)
{
Random r = new Random();
int randomSeed = r.Next();
return GenerateInstrument(patch, randomSeed);
}
public TrackGenerator(CompositionCategory category, Composition comp)
{
this.category = category;
InitializeComponent();
// Load data for accompany instruments
string path = "save/" + category.CategoryName;
if(System.IO.Directory.Exists(path))
{
var files = System.IO.Directory.GetFiles(path);
foreach(var file in files)
{
try
{
string filename = System.IO.Path.GetFileNameWithoutExtension(file);
if (!filename.ToLower().Contains("accomp_ann_ff_"))
continue;
var sub = filename.Substring(14);
PatchNames instrument = (PatchNames)(int.Parse(sub));
if(!accompInstruBox.Items.Contains(instrument))
accompInstruBox.Items.Add(instrument);
}
catch(Exception E)
{
Console.WriteLine(E);
}
}
}
// Load data for accompany tracks
if(comp != null)
for(int i = 0; i < comp.Tracks.Count; i++)
{
var track = comp.Tracks[i];
ComboBoxItem item = new ComboBoxItem();
item.Content = string.Format("Track {0} - {1}", i.ToString(), track.Instrument.ToString());
item.Tag = track;
accompTrackBox.Items.Add(item);
}
randomScale.Items.Clear();
foreach(var s in Scales.ScaleTypes)
{
//ListBoxItem item = new ListBoxItem();
randomScale.Items.Add(s);
}
randomInstrument.Items.Clear();
var popularInstruments = new PatchNames[]{PatchNames.Acoustic_Grand,PatchNames.String_Ensemble_1,
PatchNames.Acoustic_Bass,PatchNames.Trumpet,PatchNames.Violin,PatchNames.Electric_Grand,
PatchNames.French_Horn,PatchNames.Flute,PatchNames.Trombone,PatchNames.Acoustic_Guitarnylon, PatchNames.Orchestral_Strings};
foreach (var d in popularInstruments)
{
loadInstrument.Items.Add(d);
geneticInstrumentBox.Items.Add(d);
randomInstrument.Items.Add(d);
}
StopSpinner();
LoadInstrumentalData();
}
public MelodySequence GenerateInstrument(PatchNames patch, int seed)
{
lastInstrument = patch;
var chain = instruments[patch];
return new MelodySequence(chain.Chain(MaxNotes,seed));
}
public void SetInstrument(PatchNames instrument)
{
this.lastInstrument = instrument;
}
// This loads the textures
public override ICollection <ImageData> LoadTextures(PatchNames pnames)
{
Dictionary <long, ImageData> images = new Dictionary <long, ImageData>();
ICollection <ImageData> collection;
List <ImageData> imgset = new List <ImageData>();
// Error when suspended
if (issuspended)
{
throw new Exception("Data reader is suspended");
}
// Load from wad files (NOTE: backward order, because the last wad's images have priority)
for (int i = wads.Count - 1; i >= 0; i--)
{
collection = wads[i].LoadTextures(pnames);
AddImagesToList(images, collection);
}
// Should we load the images in this directory as textures?
if (roottextures)
{
collection = LoadDirectoryImages("", ImageDataFormat.DOOMPICTURE, false);
AddImagesToList(images, collection);
}
// Add images from texture directory
collection = LoadDirectoryImages(TEXTURES_DIR, ImageDataFormat.DOOMPICTURE, true);
AddImagesToList(images, collection);
// Load TEXTURE1 lump file
imgset.Clear();
string texture1file = FindFirstFile("TEXTURE1", false);
if ((texture1file != null) && FileExists(texture1file))
{
MemoryStream filedata = LoadFile(texture1file);
WADReader.LoadTextureSet("TEXTURE1", filedata, ref imgset, pnames);
filedata.Dispose();
}
// Load TEXTURE2 lump file
string texture2file = FindFirstFile("TEXTURE2", false);
if ((texture2file != null) && FileExists(texture2file))
{
MemoryStream filedata = LoadFile(texture2file);
WADReader.LoadTextureSet("TEXTURE2", filedata, ref imgset, pnames);
filedata.Dispose();
}
// Add images from TEXTURE1 and TEXTURE2 lump files
AddImagesToList(images, imgset);
// Load TEXTURES lump file
imgset.Clear();
string[] alltexturefiles = GetAllFilesWithTitle("", "TEXTURES", false);
foreach (string texturesfile in alltexturefiles)
{
MemoryStream filedata = LoadFile(texturesfile);
WADReader.LoadHighresTextures(filedata, texturesfile, ref imgset, images, null);
filedata.Dispose();
}
// Add images from TEXTURES lump file
AddImagesToList(images, imgset);
// Add images to the container-specific texture set
foreach (ImageData img in images.Values)
{
textureset.AddImage(img);
}
return(new List <ImageData>(images.Values));
}
public AccompanyGeneratorMarkov(CompositionCategory cat, PatchNames instrument)
{
this.instrument = instrument;
Add(cat);
}
MelodySequence GenerateInstrument(PatchNames patch, int seed)
{
var chain = instruments[patch];
return new MelodySequence(chain.Chain(100,seed));
}
public MarkovChainGenerator(PatchNames instrument, int order = 3, int length = 150)
{
this.instrument = instrument;
this.chain = new MarkovChain<Note>(order);
this.MaxLength = length;
}
static void TrainANNFFAccomp()
{
Console.WriteLine("This application trains accompaniment neural networks");
var cats = Databank.GetCategories();
Databank db = new Databank("lib");
var popularInstruments = new PatchNames[]{PatchNames.Acoustic_Grand,PatchNames.String_Ensemble_1,
PatchNames.Acoustic_Bass,PatchNames.Trumpet,PatchNames.Vibraphone,PatchNames.Electric_Grand,
PatchNames.French_Horn,PatchNames.Flute,PatchNames.Trombone,PatchNames.Music_Box};
foreach (var catName in cats)
{
Console.WriteLine("Category {0}", catName);
var cat = db.Load(catName);
if (cat.Compositions.Length > 3000)
Console.WriteLine("Skipping category {0} due to length", cat.CategoryName);
foreach (var instr in popularInstruments)
{
Console.WriteLine("Training ANN for {0} - {1}, {2} epochs", cat.ToString(), instr.ToString(), 6000);
try
{
AccompanimentGeneratorANNFF ann = new AccompanimentGeneratorANNFF(cat, instr);
ann.Epochs = 5000;
ann.Train();
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
}
}
Console.ReadLine();
}
static void Play(MelodySequence notes, PatchNames instrument = PatchNames.Vibraphone)
{
MusicPlayer player = new MusicPlayer();
player.SetPatch((int)instrument, 1);
player.Play(notes);
player.Close();
}
private void Button_Click_3(object sender, RoutedEventArgs e)
{
Composition comp;
Microsoft.Win32.OpenFileDialog dlg = new Microsoft.Win32.OpenFileDialog();
dlg.DefaultExt = ".mid";
dlg.Filter = "MIDI Files (*.mid)|*.mid";
dlg.Title = "Load MIDI file";
Nullable<bool> result = dlg.ShowDialog();
if (result == true)
{
comp = Composition.LoadFromMIDI(dlg.FileName);
TrackSelector trackSel = new TrackSelector(comp);
if(trackSel.ShowDialog() == true)
{
this.GeneratedSequence = trackSel.SelectedSequence;
this.Instrument = trackSel.SelectedInstrument;
this.Generator = new ExistingSequenceGenerator(this.GeneratedSequence, this.Instrument);
this.DialogResult = true;
this.Close();
}
}
}
private void Generate()
{
int index = optionsTab.SelectedIndex;
progressGenSlider.Value = 0;
if (index == 0)
{
if (geneticInstrumentBox.SelectedItem == null)
return;
fitnessPlot.ResetAllAxes();
SetupLinePlot(fitnessPlot, "Average Fitness");
(fitnessPlot.Model.Series[0] as LineSeries).Points.Clear();
fitnessPlot.Model.Series[0].Unselect();
fitnessPlot.InvalidatePlot();
//GA
IFitnessFunction fitness = null;
//Options
List<IMetric> activeMetrics = new List<IMetric>();
// if (metricChromaticTone.IsChecked == true)
// activeMetrics.Add(new ChromaticTone());
if (metricChromaticToneDistance.IsChecked == true)
activeMetrics.Add(new ChromaticToneDistance());
if (metricChromaticToneDuration.IsChecked == true)
activeMetrics.Add(new ChromaticToneDuration());
if (metricMelodicBigram.IsChecked == true)
activeMetrics.Add(new MelodicBigram());
if (metricMelodicInterval.IsChecked == true)
activeMetrics.Add(new MelodicInterval());
// if (metricPitch.IsChecked == true)
// activeMetrics.Add(new Pitch());
// if (metricPitchDistance.IsChecked == true)
// activeMetrics.Add(new PitchDistance());
// if (metricRhythm.IsChecked == true)
// activeMetrics.Add(new Rhythm());
if (metricRhythmicBigram.IsChecked == true)
activeMetrics.Add(new RhythmicBigram());
if (metricRhythmicInterval.IsChecked == true)
activeMetrics.Add(new RhythmicInterval());
fitness = GeneticMIDI.FitnessFunctions.MetricSimilarity.GenerateMetricSimilarityMulti(category.Compositions, activeMetrics.ToArray(), GeneticMIDI.FitnessFunctions.SimilarityType.Cosine);
/*
if (fitnessFuncCombo.SelectedIndex == 0)
fitness = new GeneticMIDI.FitnessFunctions.MetricSimilarity(seq, activeMetrics.ToArray(), GeneticMIDI.FitnessFunctions.SimilarityType.Cosine);
if (fitnessFuncCombo.SelectedIndex == 1)
fitness = new GeneticMIDI.FitnessFunctions.MetricSimilarity(seq, activeMetrics.ToArray(), GeneticMIDI.FitnessFunctions.SimilarityType.Euclidian);
if (fitnessFuncCombo.SelectedIndex == 2)
fitness = new GeneticMIDI.FitnessFunctions.MetricSimilarity(seq, activeMetrics.ToArray(), GeneticMIDI.FitnessFunctions.SimilarityType.Pearson);
if (fitnessFuncCombo.SelectedIndex == 3)
fitness = new GeneticMIDI.FitnessFunctions.CrossCorrelation(seq);
if (fitnessFuncCombo.SelectedIndex == 4)
fitness = GeneticMIDI.FitnessFunctions.NCD.FromMelodies(category);*/
Instrument = (PatchNames)geneticInstrumentBox.SelectedItem;
var gen = new GeneticGenerator(fitness, Instrument, category);
gen.OnPercentage += gen_OnPercentage;
gen.MaxGenerations = (int)maxGenerationSlider.Value;
Generator = gen;
new Thread(() =>
{
var notes = gen.Generate();
var mel = notes;
GeneratedSequence = mel;
progressGenSlider.Dispatcher.Invoke(() =>
{
progressGenSlider.Value = 100;
});
}).Start();
}
if (index == 1)
{
if (instrBox.SelectedItem as ListBoxItem == null || (instrBox.SelectedItem as ListBoxItem).Tag == null)
return;
if ((int)((instrBox.SelectedItem as ListBoxItem).Tag) == -1)
{
// Drum Generator
DrumGenerator gen = new DrumGenerator();
Generator = gen;
Instrument = PatchNames.Helicopter;
new Thread(() =>
{
StartSpinner();
gen.Initialize(new Databank(GeneticMIDI.Constants.LOCAL_LIBRARY_PATH));
GeneratedSequence = gen.Generate();
StopSpinner();
progressGenSlider.Dispatcher.Invoke(() =>
{
progressGenSlider.Value = 100;
});
}).Start();
}
else
{
PatchNames instrument = (PatchNames)((instrBox.SelectedItem as ListBoxItem).Tag);
Instrument = instrument;
InstrumentalGenerator gen = Generator as InstrumentalGenerator;
if (gen == null)
return;
gen.SetInstrument(Instrument);
new Thread(() =>
{
if (gen.IsInitialized)
{
StartSpinner();
GeneratedSequence = gen.GenerateInstrument(instrument);
StopSpinner();
}
progressGenSlider.Dispatcher.Invoke(() =>
{
progressGenSlider.Value = 100;
});
}).Start();
}
}
if(index == 2)
{
if (accompInstruBox.Items.Count == 0 || accompTrackBox.Items.Count == 0)
return;
Instrument = (PatchNames)(accompInstruBox.SelectedItem);
Track track = (accompTrackBox.SelectedItem as ListBoxItem).Tag as Track;
var melSeq = track.GetMainSequence() as MelodySequence;
Random rnd = new Random();
if(accompMethoBox.SelectedIndex == 0)
{
AccompanyGeneratorMarkov gen = new AccompanyGeneratorMarkov(category, Instrument);
Generator = gen;
new Thread(() =>
{
StartSpinner();
GeneratedSequence = gen.Generate(melSeq,rnd.Next());
StopSpinner();
progressGenSlider.Dispatcher.Invoke(() =>
{
progressGenSlider.Value = 100;
});
}).Start();
}
else if (accompMethoBox.SelectedIndex == 1)
{
AccompanimentGeneratorANNFF gen = new AccompanimentGeneratorANNFF(category, Instrument);
Generator = gen;
gen.SetSequence(melSeq);
new Thread(() =>
{
StartSpinner();
gen.Load();
GeneratedSequence = gen.Generate();
StopSpinner();
progressGenSlider.Dispatcher.Invoke(() =>
{
progressGenSlider.Value = 100;
});
}).Start();
}
}
if(index == 3)
{
//stochasticLogPlot.Model.Series.Clear();
stochasticLogPlot.ResetAllAxes();
SetupLinePlot(stochasticLogPlot, "Log Likelihood");
(stochasticLogPlot.Model.Series[0] as LineSeries).Points.Clear();
stochasticLogPlot.Model.Series[0].Unselect();
stochasticLogPlot.InvalidatePlot();
if (loadInstrument.SelectedItem == null)
return;
Instrument = (PatchNames)(loadInstrument.SelectedItem);
SamplingWithReplacement swr = new SamplingWithReplacement(category, Instrument);
swr.OnProgressChange += swr_OnProgressChange;
swr.MaxIterations = (int)loadGenerationsSlider.Value;
Generator = swr;
new Thread(() =>
{
StartSpinner();
GeneratedSequence = swr.Generate();
StopSpinner();
progressGenSlider.Dispatcher.Invoke(() =>
{
progressGenSlider.Maximum = 100;
progressGenSlider.Value = 100;
});
}).Start();
}
if(index == 4)
{
if (randomInstrument.SelectedItem == null)
return;
Instrument = (PatchNames)randomInstrument.SelectedItem;
int centralNotePitch = (int)randomOctave.Value * 12;
int noteShift = (int)randomPitchVar.Value;
int minNote = centralNotePitch - noteShift;
int maxNote = centralNotePitch + noteShift;
if (minNote <= 0)
minNote = 1;
if (maxNote >= 127)
maxNote = 126;
int durMin = (int)Math.Pow(randomDurationRange.LowerValue, 2);
int durMax = (int)Math.Pow(randomDurationRange.UpperValue, 2);
int length = (int)randomLength.Value;
ScaleType scale = randomScale.SelectedItem as ScaleType;
var gen = new ReflectingBrownNoteGenerator(new NoteRangeRestrictor(minNote, maxNote, durMin, durMax, scale), new Random(), -2, 2, -1, 1, Instrument);
Generator = gen;
gen.MaxNotes = length;
new Thread(() =>
{
StartSpinner();
GeneratedSequence = gen.Generate();
StopSpinner();
progressGenSlider.Dispatcher.Invoke(() =>
{
progressGenSlider.Value = 100;
});
}).Start();
Console.ReadLine();
}
}
void listBoxItem_MouseDoubleClick(object sender, MouseButtonEventArgs e)
{
Track track = (sender as ListBoxItem).Tag as Track;
this.SelectedSequence = track.GetMainSequence() as MelodySequence;
this.SelectedInstrument = track.Instrument;
this.DialogResult = true;
this.Close();
}
