/// <summary>
/// Construct a discrete HMM with the specified number of states.
/// </summary>
/// <param name="states">The number of states.</param>
public HiddenMarkovModel(int states)
{
_items = null;
pi = new double[states];
_transitionProbability = EngineArray.AllocateDouble2D(states, states);
_stateDistributions = new IStateDistribution[states];
for (int i = 0; i < states; i++)
{
pi[i] = 1.0 / states;
_stateDistributions[i] = new ContinousDistribution(
StateCount);
for (int j = 0; j < states; j++)
{
_transitionProbability[i][j] = 1.0 / states;
}
}
}
/// <inheritdoc/>
public void Save(Stream os, Object obj)
{
EncogWriteHelper writer = new EncogWriteHelper(os);
HiddenMarkovModel net = (HiddenMarkovModel)obj;
writer.AddSection("HMM");
writer.AddSubSection("PARAMS");
writer.AddProperties(net.Properties);
writer.AddSubSection("CONFIG");
writer.WriteProperty(HiddenMarkovModel.TAG_STATES, net.StateCount);
if (net.Items != null)
{
writer.WriteProperty(HiddenMarkovModel.TAG_ITEMS, net.Items);
}
writer.WriteProperty(HiddenMarkovModel.TAG_PI, net.Pi);
writer.WriteProperty(HiddenMarkovModel.TAG_TRANSITION, new Matrix(net.TransitionProbability));
for (int i = 0; i < net.StateCount; i++)
{
writer.AddSubSection("DISTRIBUTION-" + i);
IStateDistribution sd = net.StateDistributions[i];
writer.WriteProperty(HiddenMarkovModel.TAG_DIST_TYPE, sd.GetType().Name);
if (sd is ContinousDistribution)
{
ContinousDistribution cDist = (ContinousDistribution)sd;
writer.WriteProperty(HiddenMarkovModel.TAG_MEAN, cDist.Mean);
writer.WriteProperty(HiddenMarkovModel.TAG_COVARIANCE, cDist.Covariance);
}
else if (sd is DiscreteDistribution)
{
DiscreteDistribution dDist = (DiscreteDistribution)sd;
writer.WriteProperty(HiddenMarkovModel.TAG_PROBABILITIES, new Matrix(dDist.Probabilities));
}
}
writer.Flush();
}
/// <inheritdoc/>
public Object Read(Stream istream)
{
int states = 0;
int[] items;
double[] pi = null;
Matrix transitionProbability = null;
IDictionary <String, String> properties = null;
IList <IStateDistribution> distributions = new List <IStateDistribution>();
EncogReadHelper reader = new EncogReadHelper(istream);
EncogFileSection section;
while ((section = reader.ReadNextSection()) != null)
{
if (section.SectionName.Equals("HMM") &&
section.SubSectionName.Equals("PARAMS"))
{
properties = section.ParseParams();
}
if (section.SectionName.Equals("HMM") &&
section.SubSectionName.Equals("CONFIG"))
{
IDictionary <String, String> p = section.ParseParams();
states = EncogFileSection.ParseInt(p, HiddenMarkovModel.TAG_STATES);
if (p.ContainsKey(HiddenMarkovModel.TAG_ITEMS))
{
items = EncogFileSection.ParseIntArray(p, HiddenMarkovModel.TAG_ITEMS);
}
pi = section.ParseDoubleArray(p, HiddenMarkovModel.TAG_PI);
transitionProbability = EncogFileSection.ParseMatrix(p, HiddenMarkovModel.TAG_TRANSITION);
}
else if (section.SectionName.Equals("HMM") &&
section.SubSectionName.StartsWith("DISTRIBUTION-"))
{
IDictionary <String, String> p = section.ParseParams();
String t = p[HiddenMarkovModel.TAG_DIST_TYPE];
if ("ContinousDistribution".Equals(t))
{
double[] mean = section.ParseDoubleArray(p, HiddenMarkovModel.TAG_MEAN);
Matrix cova = EncogFileSection.ParseMatrix(p, HiddenMarkovModel.TAG_COVARIANCE);
ContinousDistribution dist = new ContinousDistribution(mean, cova.Data);
distributions.Add(dist);
}
else if ("DiscreteDistribution".Equals(t))
{
Matrix prob = EncogFileSection.ParseMatrix(p, HiddenMarkovModel.TAG_PROBABILITIES);
DiscreteDistribution dist = new DiscreteDistribution(prob.Data);
distributions.Add(dist);
}
}
}
HiddenMarkovModel result = new HiddenMarkovModel(states);
EngineArray.PutAll(properties, result.Properties);
result.TransitionProbability = transitionProbability.Data;
result.Pi = pi;
int index = 0;
foreach (IStateDistribution dist in distributions)
{
result.StateDistributions[index++] = dist;
}
return(result);
}
