/// <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, VonMisesOptions options)
{
double m, k;
if (weights != null)
{
m = Circular.WeightedMean(observations, weights);
k = Circular.WeightedConcentration(observations, weights, m);
}
else
{
m = Circular.Mean(observations);
k = Circular.Concentration(observations, m);
}
if (options != null)
{
// Parse optional estimation options
if (options.UseBiasCorrection)
{
double N = observations.Length;
if (k < 2)
{
k = System.Math.Max(k - 1.0 / (2.0 * (N * k)), 0);
}
else
{
double Nm1 = N - 1;
k = (Nm1 * Nm1 * Nm1 * k) / (N * N * N + N);
}
}
}
initialize(m, k);
}
public void KappaTest1()
{
double expected = 3.721646;
double actual = Circular.Concentration(angles);
Assert.AreEqual(expected, actual, 1e-6);
}