public static void CreateModelFromFrames(string readPath, string writePath)
{
SequenceList seq = Utils.FramesToSequenceList(Utils.LoadListListFrame(readPath));
HiddenMarkovModel <MultivariateNormalDistribution> hmm;
MultivariateNormalDistribution mnd = new MultivariateNormalDistribution(seq.GetArray()[0][0].Length);
hmm = new HiddenMarkovModel <MultivariateNormalDistribution>(new Forward(5), mnd);
var teacher = new BaumWelchLearning <MultivariateNormalDistribution>(hmm);
teacher.Tolerance = 0.0001;
teacher.Iterations = 0;
teacher.FittingOptions = new NormalOptions()
{
Diagonal = true, // only diagonal covariance matrices
Regularization = 1e-5 // avoid non-positive definite errors
};
double logLikelihood = teacher.Run(seq.GetArray());
Debug.Log(readPath + " - " + seq.sequences.Count + " - " + logLikelihood);
hmm.Save(writePath);
}
/// <summary>
/// Classify our data using hidden markov model classifer and save the model.
/// </summary>
/// <param name="Data_Path">Path of the data on the disk.</param>
/// <param name="Classifier_Path">Path where we want to save the classifer on the disk.</param>
/// <param name="Classifier_Name">Name of the classifer we wnat to save.</param>
/// <returns></returns>
public void HMM(String Data_Path, String Classifier_Path, String Classifier_Name)
{
double[][] input = Serialize.DeSerializeObject <double[][]>(Data_Path);
int[][] sequences = new int[input.Length][];
for (int i = 0; i < input.Length; i++)
{
int[] temp = new int[2];
temp[0] = (int)input[i][0];
temp[1] = (int)input[i][1];
sequences[i] = temp;
}
// Create the learning algorithm
var teacher = new BaumWelchLearning()
{
Topology = new Ergodic(3), // Create a new Hidden Markov Model with 3 states for
NumberOfSymbols = 2, // an output alphabet of two characters (zero and one)
Tolerance = 0.0001, // train until log-likelihood changes less than 0.0001
Iterations = 0 // and use as many iterations as needed
};
// Estimate the model
HiddenMarkovModel hmm = teacher.Learn(sequences);
hmm.Save(Path.Combine(Classifier_Path, Classifier_Name));
//for (int i = 0; i < sequences.Length; i++)
//{
// double fl1 = hmm.LogLikelihood(sequences[i]);
// Console.WriteLine(fl1);
//}
}
/*public static List<List<Frame>> JoinListListFrame(List<List<Frame>> listF1, List<List<Frame>> listF2)
* {
* List<List<Frame>> returnVal = new List<List<Frame>>();
* returnVal.AddRange(listF1);
* returnVal.AddRange(listF2);
* return returnVal;
* }*/
public static void SaveHMM(HiddenMarkovModel <MultivariateNormalDistribution> model, string path)
{
model.Save(path);
}