/// <summary>
/// Fits the underlying distribution to a given set of observations.
/// </summary>
///
/// <param name="observations">The array of observations to fit the model against. The array
/// elements can be either of type double (for univariate data) or
/// type double[] (for multivariate data).</param>
/// <param name="weights">The weight vector containing the weight for each of the samples.</param>
/// <param name="options">Optional arguments which may be used during fitting, such
/// as regularization constants and additional parameters.</param>
///
/// <remarks>
/// Although both double[] and double[][] arrays are supported,
/// providing a double[] for a multivariate distribution or a
/// double[][] for a univariate distribution may have a negative
/// impact in performance.
/// </remarks>
///
public void Fit(double[][] observations, double[] weights, HiddenMarkovOptions options)
{
if (options == null)
{
throw new ArgumentNullException("options");
}
options.Learning(model, observations, weights);
}
/// <summary>
/// Fits the underlying distribution to a given set of observations.
/// </summary>
///
/// <param name="observations">The array of observations to fit the model against. The array
/// elements can be either of type double (for univariate data) or
/// type double[] (for multivariate data).</param>
/// <param name="weights">The weight vector containing the weight for each of the samples.</param>
/// <param name="options">Optional arguments which may be used during fitting, such
/// as regularization constants and additional parameters.</param>
///
/// <remarks>
/// Although both double[] and double[][] arrays are supported,
/// providing a double[] for a multivariate distribution or a
/// double[][] for a univariate distribution may have a negative
/// impact in performance.
/// </remarks>
///
public override void Fit(double[][] observations, double[] weights, Fitting.IFittingOptions options)
{
HiddenMarkovOptions normalOptions = options as HiddenMarkovOptions;
if (options != null && normalOptions == null)
{
throw new ArgumentException("The specified options' type is invalid.", "options");
}
Fit(observations, weights, normalOptions);
}
