public override void train(double[,] sourceData, int[] labels)
{
pca = new PrincipalComponentAnalysis(sourceData, Accord.Statistics.Analysis.AnalysisMethod.Standardize);
pca.Compute();
}
/// <summary>
/// Creates a principal component representation.
/// </summary>
/// <param name="analysis">The analysis to which this component belongs.</param>
/// <param name="index">The component index.</param>
internal PrincipalComponent(PrincipalComponentAnalysis analysis, int index)
{
this.index = index;
this.principalComponentAnalysis = analysis;
}
/// <summary>
/// Deserializes the object.
/// </summary>
/// <param name="info">the data necessary to deserialize this object</param>
/// <param name="ctxt">specifies the serialization stream</param>
public SerializableHmm(SerializationInfo info, StreamingContext ctxt)
{
name = (string)info.GetValue("name", typeof(string));
transitions = (double[,])info.GetValue("transitions", typeof(double[,]));
mean = (double[,])info.GetValue("mean", typeof(double[,]));
covariance = (double[,,])info.GetValue("covariance", typeof(double[,,]));
probabilities = (double[])info.GetValue("probabilities", typeof(double[]));
try
{
pca = (PrincipalComponentAnalysis)(info.GetValue("pca", typeof(PrincipalComponentAnalysis)));
}
catch (System.Runtime.Serialization.SerializationException ex)
{
pca = null;
}
}
/// <summary>
/// Loads and returns a HMM that was previously serialized to disk.
/// </summary>
/// <returns></returns>
public HiddenMarkovModel<MultivariateNormalDistribution> LoadFromDisk()
{
Stream stream = File.Open(this.directory + SERIALIZATION_PREFIX + name + SERIALIZATION_SUFFIX, FileMode.Open);
BinaryFormatter bf = new BinaryFormatter();
// Deserialize the SerializableHmm into a new instance.
SerializableHmm otherSHmm = (SerializableHmm)bf.Deserialize(stream);
pca = otherSHmm.pca;
stream.Close();
MultivariateNormalDistribution[] emissions = new MultivariateNormalDistribution[otherSHmm.mean.GetLength(0)];
// Populate the emissions matrix.
for (int distribution = 0; distribution < otherSHmm.mean.GetLength(0); distribution++)
{
double[] mean = new double[otherSHmm.mean.GetLength(1)];
double[,] covariance = new double[otherSHmm.covariance.GetLength(1), otherSHmm.covariance.GetLength(2)];
// Copy the mean vector for this distribution.
for (int i = 0; i < otherSHmm.mean.GetLength(1); i++)
mean[i] = otherSHmm.mean[distribution, i];
// Copy the covariance matrix for this distribution.
for (int row = 0; row < otherSHmm.covariance.GetLength(1); row++)
{
for (int col = 0; col < otherSHmm.covariance.GetLength(2); col++)
{
covariance[row, col] = otherSHmm.covariance[distribution, row, col];
}
}
emissions[distribution] = new MultivariateNormalDistribution(mean, covariance);
}
return new HiddenMarkovModel<MultivariateNormalDistribution>(otherSHmm.transitions, emissions, otherSHmm.probabilities);
}
public SerializableHmm(string actName, HiddenMarkovModel<MultivariateNormalDistribution> hmm, Accord.Statistics.Analysis.PrincipalComponentAnalysis pca)
{
this.pca = pca;
this.name = actName;
construct(hmm);
}
public override void train(double[,] sourceData, int[] labels)
{
pca = new PrincipalComponentAnalysis(sourceData, Accord.Statistics.Analysis.AnalysisMethod.Standardize);
pca.Compute();
}
