/// <summary>
/// Constructs a new potential function modeling Hidden Markov Models.
/// </summary>
///
/// <param name="classifier">The classifier model.</param>
///
public HiddenMarkovClassifierFunction(HiddenMarkovClassifier classifier)
{
this.Symbols = classifier.Symbols;
this.Outputs = classifier.Classes;
var l = new List <double>();
var f = new List <IFeature <int> >();
this.Factors = new FactorPotential <int> [Outputs];
int index = 0;
// Create features for initial class probabilities
for (int c = 0; c < classifier.Classes; c++)
{
int parameters = 0;
var model = classifier[c];
// l.Add(Math.Log(classifier.Priors[c]));
// f.Add(new OutputFeature<int>(this, c, c));
// parameters++;
// Create features for initial state probabilities
for (int i = 0; i < model.States; i++)
{
l.Add(model.Probabilities[i]);
f.Add(new InitialFeature <int>(this, c, i));
parameters++;
}
// Create features for state transition probabilities
for (int i = 0; i < model.States; i++)
{
for (int j = 0; j < model.States; j++)
{
l.Add(model.Transitions[i, j]);
f.Add(new TransitionFeature <int>(this, c, i, j));
parameters++;
}
}
// Create features for symbol emission probabilities
for (int i = 0; i < model.States; i++)
{
for (int j = 0; j < model.Symbols; j++)
{
l.Add(model.LogEmissions[i, j]);
f.Add(new EmissionFeature(this, c, i, j));
parameters++;
}
}
Factors[c] = new FactorPotential <int>(this, index, model.States, parameters);
index += parameters;
}
this.Weights = l.ToArray();
this.Features = f.ToArray();
}
internal void Deoptimize()
{
for (int i = 0; i < Factors.Length; i++)
{
var factor = Factors[i];
Factors[i] = new FactorPotential <T>(factor.Owner, factor.States, factor.Index,
factor.EdgeParameters.Offset, factor.EdgeParameters.Count,
factor.StateParameters.Offset, factor.StateParameters.Count,
factor.OutputParameters.Offset, factor.OutputParameters.Count);
}
}
/// <summary>
/// Constructs a new potential function modeling Hidden Markov Models.
/// </summary>
///
/// <param name="classifier">A hidden Markov sequence classifier.</param>
///
public MultivariateNormalHiddenMarkovClassifierFunction(
HiddenMarkovClassifier <MultivariateNormalDistribution> classifier)
{
this.Outputs = classifier.Classes;
var l = new List <double>();
var f = new List <IFeature <double[]> >();
this.Factors = new FactorPotential <double[]> [Outputs];
int index = 0;
// Create features for initial class probabilities
for (int c = 0; c < classifier.Classes; c++)
{
int parameters = 0;
var model = classifier[c];
//l.Add(Math.Log(classifier.Priors[c]));
//f.Add(new OutputFeature<double[]>(this, c, c));
//parameters++;
// Create features for initial state probabilities
for (int i = 0; i < model.States; i++)
{
l.Add(model.Probabilities[i]);
f.Add(new InitialFeature <double[]>(this, c, i));
parameters++;
}
// Create features for state transition probabilities
for (int i = 0; i < model.States; i++)
{
for (int j = 0; j < model.States; j++)
{
l.Add(model.Transitions[i, j]);
f.Add(new TransitionFeature <double[]>(this, c, i, j));
parameters++;
}
}
// Create features emission probabilities
for (int i = 0; i < model.States; i++)
{
for (int j = 0; j < model.Emissions[i].Mean.Length; j++)
{
double mean = model.Emissions[i].Mean[j];
double var = model.Emissions[i].Variance[j];
// Occupancy
l.Add(-0.5 * (Math.Log(2.0 * Math.PI * var) + (mean * mean) / var));
f.Add(new OccupancyFeature <double[]>(this, c, i));
parameters++;
// 1st Moment (x)
l.Add(mean / var);
f.Add(new MultivariateFirstMomentFeature(this, c, i, j));
parameters++;
// 2nd Moment (x²)
l.Add(-1.0 / (2.0 * var));
f.Add(new MultivariateSecondMomentFeature(this, c, i, j));
parameters++;
}
}
Factors[c] = new FactorPotential <double[]>(this, index, model.States, parameters);
index += parameters;
}
this.Weights = l.ToArray();
this.Features = f.ToArray();
}
/// <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 HiddenMarkovClassifierFunction(int states, int symbols, int outputClasses)
{
this.Symbols = symbols;
this.Outputs = outputClasses;
var l = new List <double>();
var f = new List <IFeature <int> >();
this.Factors = new FactorPotential <int> [Outputs];
int index = 0;
// Create features for initial class probabilities
for (int c = 0; c < outputClasses; c++)
{
int parameters = 0;
l.Add(Math.Log(1.0 / outputClasses));
f.Add(new OutputFeature <int>(this, c, c));
parameters++;
// Create features for initial state probabilities
for (int i = 0; i < states; i++)
{
if (i == 0)
{
l.Add(Math.Log(1.0));
}
else
{
l.Add(Math.Log(0.0));
}
f.Add(new InitialFeature <int>(this, c, i));
parameters++;
}
// Create features for state transition probabilities
for (int i = 0; i < states; i++)
{
for (int j = 0; j < states; j++)
{
l.Add(Math.Log(1.0 / states));
f.Add(new TransitionFeature <int>(this, c, i, j));
parameters++;
}
}
// Create features for symbol emission probabilities
for (int i = 0; i < states; i++)
{
for (int j = 0; j < symbols; j++)
{
l.Add(Math.Log(1.0 / symbols));
f.Add(new EmissionFeature(this, c, i, j));
parameters++;
}
}
Factors[c] = new FactorPotential <int>(this, index, states, parameters);
index += parameters;
}
this.Features = f.ToArray();
this.Weights = l.ToArray();
}
