/// <summary>
/// Uses a collection of HMMs to recognize which action among a corpus of actions was performed
/// </summary>
/// <param name="action">The motion sequence representing the action</param>
/// <returns></returns>
public Tuple<string, double> recognizeAction(ActionSequence<HumanSkeleton> action)
{
// Compute most likely position action
double[] likelihoods = new double[positionActionClassifier.Classes];
int cls = positionActionClassifier.Compute(action.toArray(), out likelihoods);
double maxPositionLikelihood = likelihoods.Max();
//maxPositionLikelihood = 0;
// Compute most likely joint action
likelihoods = new double[jointActionClassifier.Classes];
int jointCls = jointActionClassifier.Compute(action.toArray(true), out likelihoods);
double maxJointLikelihood = likelihoods.Max();
double maxLikelihood = Math.Max(maxJointLikelihood, maxPositionLikelihood);
if (maxLikelihood < 1e-300 || (jointCls == -1 && cls == -1))
{
return Tuple.Create("", maxLikelihood);
}
// And return the one which is more likely among the two
if (maxJointLikelihood > maxPositionLikelihood)
{
return Tuple.Create(jointClasses[jointCls], maxJointLikelihood);
}
else
{
return Tuple.Create(positionClasses[cls], maxPositionLikelihood);
}
}
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);
}
}
}
