/// <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>
///
/// <example>
/// For an example on how to fit an independent joint distribution, please
/// take a look at the examples section for <see cref="Independent{TDistribution}"/>.
/// </example>
///
public void Fit(double[][] observations, double[] weights, IndependentOptions options)
{
observations = observations.Transpose();
if (options != null)
{
if (options.InnerOptions != null)
{
for (int i = 0; i < components.Length; i++)
{
components[i].Fit(observations[i], weights, options.InnerOptions[i]);
}
}
else
{
for (int i = 0; i < components.Length; i++)
{
components[i].Fit(observations[i], weights, options.InnerOption);
}
}
}
else
{
for (int i = 0; i < components.Length; i++)
{
components[i].Fit(observations[i], weights, null);
}
}
mean = null;
variance = null;
covariance = null;
}
/// <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>
///
/// <example>
/// For an example on how to fit an independent joint distribution, please
/// take a look at the examples section for <see cref="Independent{TDistribution}"/>.
/// </example>
///
public void Fit(TObservation[][] observations, double[] weights, IndependentOptions options)
{
if (options != null)
{
if (!options.Transposed)
{
observations = observations.Transpose();
}
if (options.InnerOptions != null)
{
for (int i = 0; i < Components.Length; i++)
{
((IFittableDistribution <TObservation>)Components[i]).Fit(observations[i], weights, options.InnerOptions[i]);
}
}
else
{
for (int i = 0; i < Components.Length; i++)
{
((IFittableDistribution <TObservation>)Components[i]).Fit(observations[i], weights, options.InnerOption);
}
}
}
else
{
observations = observations.Transpose();
for (int i = 0; i < Components.Length; i++)
{
((IFittableDistribution <TObservation>)Components[i]).Fit(observations[i], weights);
}
}
Reset();
}
/// <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>
///
/// <example>
/// For an example on how to fit an independent joint distribution, please
/// take a look at the examples section for <see cref="Independent{TDistribution}"/>.
/// </example>
///
public override void Fit(double[][] observations, double[] weights, IFittingOptions options)
{
IndependentOptions independentOptions = options as IndependentOptions;
if (options != null && independentOptions == null)
{
throw new ArgumentException("The specified options' type is invalid.", "options");
}
Fit(observations, weights, independentOptions);
}
/// <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>
///
/// <example>
/// For an example on how to fit an independent joint distribution, please
/// take a look at the examples section for <see cref="Independent{TDistribution}"/>.
/// </example>
///
public void Fit(TObservation[][] observations, double[] weights = null,
IndependentOptions <TOptions> options = null)
{
base.Fit(observations, weights, options);
}
