/// <summary>
/// Initializes a new instance of the <see cref="HiddenGradientDescentLearning<T>"/> class.
/// </summary>
///
/// <param name="model">The model to be trained.</param>
///
public HiddenGradientDescentLearning(HiddenConditionalRandomField<T> model)
{
Model = model;
convergence = new RelativeConvergence();
calculator = new ForwardBackwardGradient<T>(model);
gradient = new double[Model.Function.Weights.Length];
}
/// <summary>
/// Constructs a new Conjugate Gradient learning algorithm.
/// </summary>
///
public HiddenConjugateGradientLearning(HiddenConditionalRandomField <T> model)
{
Model = model;
calculator = new ForwardBackwardGradient <T>(model);
optimizer = new ConjugateGradient(model.Function.Weights.Length);
optimizer.Progress += new EventHandler <OptimizationProgressEventArgs>(progressChanged);
optimizer.Function = calculator.Objective;
optimizer.Gradient = calculator.Gradient;
}
/// <summary>
/// Constructs a new L-BFGS learning algorithm.
/// </summary>
///
public HiddenQuasiNewtonLearning(HiddenConditionalRandomField <T> model)
{
Model = model;
calculator = new ForwardBackwardGradient <T>(model);
lbfgs = new BroydenFletcherGoldfarbShanno(model.Function.Weights.Length);
lbfgs.Tolerance = 1e-3;
lbfgs.Function = calculator.Objective;
lbfgs.Gradient = calculator.Gradient;
}
/// <summary>
/// Releases unmanaged and - optionally - managed resources
/// </summary>
///
/// <param name="disposing"><c>true</c> to release both managed
/// and unmanaged resources; <c>false</c> to release only unmanaged
/// resources.</param>
///
protected virtual void Dispose(bool disposing)
{
if (disposing)
{
// free managed resources
if (calculator != null)
{
calculator.Dispose();
calculator = null;
}
}
}
/// <summary>
/// Initializes a new instance of the <see cref="HiddenResilientGradientLearning{T}"/> class.
/// </summary>
///
/// <param name="model">Model to teach.</param>
///
public HiddenResilientGradientLearning(HiddenConditionalRandomField<T> model)
{
Model = model;
calculator = new ForwardBackwardGradient<T>(model);
convergence = new RelativeConvergence(iterations: 100, tolerance: 0, checks: 3);
int parameters = Model.Function.Weights.Length;
gradient = new double[parameters];
previousGradient = new double[parameters];
weightsUpdates = new double[parameters];
// Initialize steps
Reset(initialStep);
}
/// <summary>
/// Constructs a new L-BFGS learning algorithm.
/// </summary>
///
public HiddenQuasiNewtonLearning(HiddenConditionalRandomField <T> model)
{
Model = model;
calculator = new ForwardBackwardGradient <T>(model);
lbfgs = new BoundedBroydenFletcherGoldfarbShanno(model.Function.Weights.Length);
lbfgs.FunctionTolerance = 1e-3;
lbfgs.Function = calculator.Objective;
lbfgs.Gradient = calculator.Gradient;
for (int i = 0; i < lbfgs.UpperBounds.Length; i++)
{
lbfgs.UpperBounds[i] = 1e10;
lbfgs.LowerBounds[i] = -1e100;
}
}
