/// <summary>
/// Divides the input data into K clusters modeling each
/// cluster as a multivariate Gaussian distribution.
/// </summary>
///
public int[] Compute(double[][] data, double[] weights)
{
if (clusters.Model == null)
{
Initialize(data, weights);
}
// Create the mixture options
var mixtureOptions = new MixtureOptions()
{
Threshold = this.Tolerance,
InnerOptions = this.Options,
Iterations = this.Iterations,
Logarithm = this.UseLogarithm
};
MultivariateMixture <MultivariateNormalDistribution> model = clusters.Model;
// Fit a multivariate Gaussian distribution
model.Fit(data, weights, mixtureOptions);
for (int i = 0; i < clusters.Model.Components.Length; i++)
{
clusters.Centroids[i] = new MixtureComponent <MultivariateNormalDistribution>(model, i);
}
if (ComputeLogLikelihood)
{
LogLikelihood = model.LogLikelihood(data);
}
if (ComputeLabels)
{
return(clusters.Nearest(data));
}
return(null);
}
int[] IClusteringAlgorithm <double[]> .Compute(double[][] data, double threshold)
{
Compute(data, threshold);
return(clusters.Nearest(data));
}