/// <summary>
/// Constructs a new potential function modeling Hidden Markov Models.
/// </summary>
///
/// <param name="states">The number of states.</param>
/// <param name="symbols">The number of symbols.</param>
/// <param name="outputClasses">The number of output classes.</param>
///
public MarkovDiscreteFunction(int states, int symbols, int outputClasses)
{
this.Outputs = outputClasses;
this.Symbols = symbols;
int factorIndex = 0;
var factorParams = new List <double>();
var factorFeatures = new List <IFeature <int> >();
this.Factors = new FactorPotential <int> [Outputs];
int[] classOffset = new int[outputClasses];
int[] edgeOffset = new int[outputClasses];
int[] stateOffset = new int[outputClasses];
int[] classCount = new int[outputClasses];
int[] edgeCount = new int[outputClasses];
int[] stateCount = new int[outputClasses];
// Create features for initial class probabilities
for (int c = 0; c < outputClasses; c++)
{
var stateParams = new List <double>();
var stateFeatures = new List <IFeature <int> >();
var edgeParams = new List <double>();
var edgeFeatures = new List <IFeature <int> >();
var classParams = new List <double>();
var classFeatures = new List <IFeature <int> >();
// Create features for class labels
classParams.Add(Math.Log(1.0 / outputClasses));
classFeatures.Add(new OutputFeature <int>(this, c, c));
// Create features for initial state probabilities
for (int i = 0; i < states; i++)
{
edgeParams.Add((i == 0) ? Math.Log(1.0) : Math.Log(0.0));
edgeFeatures.Add(new InitialFeature <int>(this, c, i));
}
// Create features for state transition probabilities
for (int i = 0; i < states; i++)
{
for (int j = 0; j < states; j++)
{
edgeParams.Add(Math.Log(1.0 / states));
edgeFeatures.Add(new TransitionFeature <int>(this, c, i, j));
}
}
// Create features for symbol emission probabilities
for (int i = 0; i < states; i++)
{
for (int k = 0; k < symbols; k++)
{
stateParams.Add(Math.Log(1.0 / symbols));
stateFeatures.Add(new EmissionFeature(this, c, i, k));
}
}
classOffset[c] = factorIndex;
edgeOffset[c] = factorIndex + classParams.Count;
stateOffset[c] = factorIndex + classParams.Count + edgeParams.Count;
classCount[c] = classParams.Count;
edgeCount[c] = edgeParams.Count;
stateCount[c] = stateParams.Count;
// 1. classes
factorFeatures.AddRange(classFeatures);
factorParams.AddRange(classParams);
// 2. edges
factorFeatures.AddRange(edgeFeatures);
factorParams.AddRange(edgeParams);
// 3. states
factorFeatures.AddRange(stateFeatures);
factorParams.AddRange(stateParams);
factorIndex += classParams.Count + stateParams.Count + edgeParams.Count;
}
Accord.Diagnostics.Debug.Assert(factorIndex == factorParams.Count);
Accord.Diagnostics.Debug.Assert(factorIndex == factorFeatures.Count);
this.Weights = factorParams.ToArray();
this.Features = factorFeatures.ToArray();
for (int c = 0; c < outputClasses; c++)
{
Factors[c] = new MarkovDiscreteFactor(this, states, c, symbols,
classIndex: classOffset[c], classCount: classCount[c], // 1. classes
edgeIndex: edgeOffset[c], edgeCount: edgeCount[c], // 2. edges
stateIndex: stateOffset[c], stateCount: stateCount[c]); // 3. states
}
}
/// <summary>
/// Constructs a new potential function modeling Hidden Markov Models.
/// </summary>
///
/// <param name="classifier">The classifier model.</param>
/// <param name="includePriors">True to include class features (priors), false otherwise.</param>
///
public MarkovDiscreteFunction(HiddenMarkovClassifier <GeneralDiscreteDistribution, int> classifier, bool includePriors = true)
{
this.Symbols = classifier.Models[0].Emissions[0].Length;
this.Outputs = classifier.Classes;
int factorIndex = 0;
var factorParams = new List <double>();
var factorFeatures = new List <IFeature <int> >();
this.Factors = new FactorPotential <int> [Outputs];
int[] classOffset = new int[classifier.Classes];
int[] edgeOffset = new int[classifier.Classes];
int[] stateOffset = new int[classifier.Classes];
int[] classCount = new int[classifier.Classes];
int[] edgeCount = new int[classifier.Classes];
int[] stateCount = new int[classifier.Classes];
// Create features for initial class probabilities
for (int c = 0; c < classifier.Classes; c++)
{
var stateParams = new List <double>();
var stateFeatures = new List <IFeature <int> >();
var edgeParams = new List <double>();
var edgeFeatures = new List <IFeature <int> >();
var classParams = new List <double>();
var classFeatures = new List <IFeature <int> >();
var model = classifier[c];
if (includePriors)
{
// Create features for class labels
classParams.Add(Math.Log(classifier.Priors[c]));
classFeatures.Add(new OutputFeature <int>(this, c, c));
}
// Create features for initial state probabilities
for (int i = 0; i < model.States; i++)
{
edgeParams.Add(model.LogInitial[i]);
edgeFeatures.Add(new InitialFeature <int>(this, c, i));
}
// Create features for state transition probabilities
for (int i = 0; i < model.States; i++)
{
for (int j = 0; j < model.States; j++)
{
edgeParams.Add(model.LogTransitions[i][j]);
edgeFeatures.Add(new TransitionFeature <int>(this, c, i, j));
}
}
// Create features for symbol emission probabilities
for (int i = 0; i < model.States; i++)
{
for (int k = 0; k < model.Emissions[i].Length; k++)
{
stateParams.Add(model.Emissions[i][k]);
stateFeatures.Add(new EmissionFeature(this, c, i, k));
}
}
classOffset[c] = factorIndex;
edgeOffset[c] = factorIndex + classParams.Count;
stateOffset[c] = factorIndex + classParams.Count + edgeParams.Count;
classCount[c] = classParams.Count;
edgeCount[c] = edgeParams.Count;
stateCount[c] = stateParams.Count;
// 1. classes
factorFeatures.AddRange(classFeatures);
factorParams.AddRange(classParams);
// 2. edges
factorFeatures.AddRange(edgeFeatures);
factorParams.AddRange(edgeParams);
// 3. states
factorFeatures.AddRange(stateFeatures);
factorParams.AddRange(stateParams);
factorIndex += classParams.Count + stateParams.Count + edgeParams.Count;
}
Accord.Diagnostics.Debug.Assert(factorIndex == factorParams.Count);
Accord.Diagnostics.Debug.Assert(factorIndex == factorFeatures.Count);
this.Weights = factorParams.ToArray();
this.Features = factorFeatures.ToArray();
for (int c = 0; c < classifier.Models.Length; c++)
{
Factors[c] = new MarkovDiscreteFactor(this, classifier.Models[c].States, c, Symbols,
classIndex: classOffset[c], classCount: classCount[c], // 1. classes
edgeIndex: edgeOffset[c], edgeCount: edgeCount[c], // 2. edges
stateIndex: stateOffset[c], stateCount: stateCount[c]); // 3. states
}
}