public void GenerateTest()
{
// Example taken from http://en.wikipedia.org/wiki/Viterbi_algorithm
double[,] transition =
{
{ 0.7, 0.3 },
{ 0.4, 0.6 }
};
double[,] emission =
{
{ 0.1, 0.4, 0.5 },
{ 0.6, 0.3, 0.1 }
};
double[] initial =
{
0.6, 0.4
};
HiddenMarkovModel hmm = new HiddenMarkovModel(transition, emission, initial);
double logLikelihood;
int[] path;
int[] samples = hmm.Generate(10, out path, out logLikelihood);
double expected = hmm.Evaluate(samples, path);
Assert.AreEqual(expected, logLikelihood);
}
public static void Generate()
{
MathHelper.SetupGenerator(42);
// Consider some phrases:
//
string[][] phrases =
{
new[] { "those", "are", "sample", "words", "from", "a", "dictionary" },
new[] { "those", "are", "sample", "words" },
new[] { "sample", "words", "are", "words" },
new[] { "those", "words" },
new[] { "those", "are", "words" },
new[] { "words", "from", "a", "dictionary" },
new[] { "those", "are", "words", "from", "a", "dictionary" }
};
// Let's begin by transforming them to sequence of
// integer labels using a codification codebook:
var codebook = new Codification(phrases);
// Now we can create the training data for the models:
int[][] sequence = codebook.Translate(phrases);
// To create the models, we will specify a forward topology,
// as the sequences have definite start and ending points.
//
var topology = new Forward(state_count: 4);
int symbols = codebook.SymbolCount; // We have 7 different words
Console.WriteLine("Symbol Count: {0}", symbols);
// Create the hidden Markov model
HiddenMarkovModel hmm = new HiddenMarkovModel(topology, symbols);
// Create the learning algorithm
BaumWelchLearning teacher = new BaumWelchLearning(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:
//
int[] sample = hmm.Generate(3);
// And the result will be: "those", "are", "words".
string[] result = codebook.Translate(sample);
foreach (string result_word in result)
{
Console.WriteLine(result_word);
}
}
public void GenerateTest2()
{
#region doc_generate
Accord.Math.Random.Generator.Seed = 42;
// Let's say we have the following set of sequences
string[][] phrases =
{
new[] { "those", "are", "sample", "words", "from", "a", "dictionary" },
new[] { "those", "are", "sample", "words" },
new[] { "sample", "words", "are", "words" },
new[] { "those", "words" },
new[] { "those", "are", "words" },
new[] { "words", "from", "a", "dictionary" },
new[] { "those", "are", "words", "from", "a", "dictionary" }
};
// 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: 4);
int symbols = codebook["Words"].Symbols; // We have 7 different words
// Create the hidden Markov model
var hmm = new HiddenMarkovModel(topology, symbols);
// Create the learning algorithm
var teacher = new BaumWelchLearning(hmm);
// Teach the model
teacher.Learn(sequence);
// Now, we can ask the model to generate new samples
// from the word distributions it has just learned:
//
int[] sample = hmm.Generate(3);
// And the result will be: "those", "are", "words".
string[] result = codebook.Translate("Words", sample);
#endregion
Assert.AreEqual("those", result[0]);
Assert.AreEqual("are", result[1]);
Assert.AreEqual("words", result[2]);
}
public override List <Note> Generate()
{
if (model == null)
{
return(new List <Note>());
}
int[] sample = model.Generate(64);
List <Note> result = new List <Note>();
foreach (int note in sample)
{
int noteLength = (int)Math.Pow(2.0, note % 5);
int noteId = (note / 5) - addToNote;
result.Add(new Note(noteId, (NoteLength)noteLength));
}
return(result);
}