/// <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>
/// 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 <see cref="MonteCarloIntegration">Monte Carlo integration method</see>.
/// </summary>
///
/// <param name="parameters">The number of parameters expected by the integrand.</param>
///
public MonteCarloIntegration(int parameters)
{
if (parameters <= 0)
{
throw new ArgumentOutOfRangeException("parameters",
"Number of parameters must be higher than zero.");
}
this.NumberOfParameters = parameters;
this.Range = new DoubleRange[parameters];
for (int i = 0; i < Range.Length; i++)
{
Range[i].Max = 1;
}
this.convergence = new RelativeConvergence();
}
/// <summary>
/// Initializes a new instance of the <see cref="HiddenGradientDescentLearning<T>"/> class.
/// </summary>
///
public HiddenGradientDescentLearning()
{
convergence = new RelativeConvergence();
}
/// <summary>
/// Constructs a new <see cref="MonteCarloIntegration">Monte Carlo integration method</see>.
/// </summary>
///
/// <param name="parameters">The number of parameters expected by the integrand.</param>
///
public MonteCarloIntegration(int parameters)
{
if (parameters <= 0)
{
throw new ArgumentOutOfRangeException("parameters",
"Number of parameters must be higher than zero.");
}
this.NumberOfParameters = parameters;
this.Range = new DoubleRange[parameters];
for (int i = 0; i < Range.Length; i++)
Range[i].Max = 1;
this.convergence = new RelativeConvergence();
}
/// <summary>
/// Initializes a new instance of the <see cref="HiddenResilientGradientLearning{T}"/> class.
/// </summary>
///
public HiddenResilientGradientLearning()
{
convergence = new RelativeConvergence(iterations: 100, tolerance: 0, checks: 3);
}
