private void loadAllModels(string modelDirectoryName)
{
if (Directory.Exists(modelDirectoryName))
{
// Get all files of type .rec in the directory
string[] files = Directory.GetFiles(modelDirectoryName, "*.dat");
this.positionModels = new List<HiddenMarkovModel<MultivariateNormalDistribution>>();
this.jointModels = new List<HiddenMarkovModel<MultivariateNormalDistribution>>();
this.positionClasses = new List<string>();
this.jointClasses = new List<string>();
foreach (string s in files)
{
string actName = Path.GetFileNameWithoutExtension(s);
// strip off the hmm_ from the beginning
actName = actName.Substring(4);
bool isJointAction = Util.shouldUseJVals(actName);
// Deserialize the model and add it to the correct classifier
SerializableHmm serMod = new SerializableHmm(actName, modelDirectoryName);
HiddenMarkovModel<MultivariateNormalDistribution> hmm = serMod.LoadFromDisk();
if (isJointAction)
{
jointModels.Add(hmm);
jointClasses.Add(actName);
}
else
{
positionModels.Add(hmm);
positionClasses.Add(actName);
}
}
jointActionClassifier = new SequenceClassifier<MultivariateNormalDistribution>(jointModels.ToArray());
positionActionClassifier = new SequenceClassifier<MultivariateNormalDistribution>(positionModels.ToArray());
// TODO(pbrook) if there is time we should add a threshold model to these SequenceClassifiers
}
}
private void train(List<string> datafiles)
{
double[][][] sequences = new double[datafiles.Count][][];
// For each file
string actName = "";
string dirName = "";
for (int i = 0; i < datafiles.Count; i++)
{
string fname = datafiles[i];
actName = System.IO.Path.GetFileNameWithoutExtension(fname);
dirName = System.IO.Path.GetDirectoryName(fname);
// Read the file
List<HumanSkeleton> seq = new List<HumanSkeleton>();
using (StreamReader s = new StreamReader(fname))
{
while (!s.EndOfStream)
{
seq.Add(new HumanSkeleton(s.ReadLine()));
}
}
// convert it into an actionSequence of humanskeletons
ActionSequence<HumanSkeleton> actSeq = new ActionSequence<HumanSkeleton>(seq);
// Convert that actionSequence in to a double[][]
bool useJointVals = Util.shouldUseJVals(actName);
double[][] trainSeq = actSeq.toArray(useJointVals);
// add that to the sequences array
sequences[i] = trainSeq;
}
bool doTrain = true;
if (doTrain)
{
if (datafiles.Count > 0)
{
//PrincipalComponentAnalysis pca = computePCA(sequences);
// train a HMM
//double[][][] projSeqs = getProjectedSequences(sequences, pca);
HiddenMarkovModel<MultivariateNormalDistribution> hmm = trainHMM(sequences);
// save it to filename.hmm
//SerializableHmm s = new SerializableHmm(actName, hmm, pca);
SerializableHmm s = new SerializableHmm(actName, hmm);
s.SaveToDisk();
}
}
else
{
SerializableHmm ser = new SerializableHmm("wave right", MODEL_LIB_PATH);
HiddenMarkovModel<MultivariateNormalDistribution> hmm = ser.LoadFromDisk();
PrincipalComponentAnalysis pca = ser.getPCA();
foreach (double[][] seq in sequences)
{
double[,] data = jaggedToMulti(seq);
string fn = System.IO.Path.GetRandomFileName();
using (StreamWriter sr = new StreamWriter("Z:/WindowsFolders/Desktop/" + fn))
{
for (int i = 0; i < data.GetLength(0); i++)
{
for (int j = 0; j < data.GetLength(1); j++)
{
sr.Write(data[i, j] + " ");
}
sr.WriteLine();
}
}
//double[][] projSeq = getProjectedSequence(seq, pca);
double l = hmm.Evaluate(seq, false);
Console.WriteLine("Likelihood: " + l);
}
}
}
