/// <summary>
/// Divides the input data into K clusters modeling each
/// cluster as a multivariate Gaussian distribution.
/// </summary>
///
public double Compute(double[][] data, GaussianMixtureModelOptions options)
{
if (data == null)
{
throw new ArgumentNullException("data");
}
if (model == null)
{
// TODO: Perform K-Means multiple times to avoid
// a poor Gaussian Mixture model initialization.
Initialize(data, options.Threshold);
}
// Create the mixture options
var mixtureOptions = new MixtureOptions()
{
Threshold = options.Threshold,
InnerOptions = options.NormalOptions,
Iterations = options.Iterations,
Logarithm = options.Logarithm
};
// Check if we have weighted samples
double[] weights = options.Weights;
// Fit a multivariate Gaussian distribution
model.Fit(data, weights, mixtureOptions);
// Return the log-likelihood as a measure of goodness-of-fit
return(model.LogLikelihood(data));
}
/// <summary>
/// Divides the input data into K clusters modeling each
/// cluster as a multivariate Gaussian distribution.
/// </summary>
///
public double Compute(double[][] data, double threshold)
{
var options = new GaussianMixtureModelOptions
{
Threshold = threshold
};
return(Compute(data, options));
}
/// <summary>
/// Divides the input data into K clusters modeling each
/// cluster as a multivariate Gaussian distribution.
/// </summary>
///
public double Compute(double[][] data, double threshold, double regularization)
{
var options = new GaussianMixtureModelOptions()
{
Threshold = threshold,
NormalOptions = new NormalOptions()
{
Regularization = regularization
}
};
return(Compute(data, options));
}
/// <summary>
/// Divides the input data into K clusters modeling each
/// cluster as a multivariate Gaussian distribution.
/// </summary>
///
public double Compute(double[][] data)
{
var options = new GaussianMixtureModelOptions();
return(Compute(data, options));
}