private void DoFitGMM()
{
// Create a new Gaussian Mixture Model
var gmm = new GaussianMixtureModel(this.k);
// If available, initialize with k-means
if (this.kmeans != null)
{
gmm.Initialize(this.kmeans);
}
// Compute the model
gmm.Compute(this.mixture);
// Classify all instances in mixture data
var classifications = gmm.Gaussians.Nearest(this.mixture);
// Draw the classifications
this.UpdateGraph(classifications);
}
private void btnCompute_Click(object sender, EventArgs e)
{
// Create a new Gaussian Mixture Model
GaussianMixtureModel gmm = new GaussianMixtureModel(k);
// If available, initialize with k-means
if (kmeans != null)
{
gmm.Initialize(kmeans);
}
// Compute the model
gmm.Compute(mixture);
// Classify all instances in mixture data
int[] classifications = gmm.Classify(mixture);
// Draw the classifications
updateGraph(classifications);
}
/// <summary>
/// Estimates Gaussian distributions from the data.
/// </summary>
///
private void btnCompute_Click(object sender, EventArgs e)
{
// Create a new Gaussian Mixture Model
var gmm = new GaussianMixtureModel(k);
// If available, initialize with k-means
if (kmeans != null)
{
gmm.Initialize(kmeans);
}
// Compute the model
GaussianClusterCollection clustering = gmm.Learn(observations);
// Classify all instances in mixture data
int[] classifications = clustering.Decide(observations);
// Draw the classifications
updateGraph(classifications);
}
/// <summary>
/// Estimates Gaussian distributions from the data.
/// </summary>
///
private void btnCompute_Click(object sender, EventArgs e)
{
// Create a new Gaussian Mixture Model
GaussianMixtureModel gmm = new GaussianMixtureModel(k);
// If available, initialize with k-means
if (kmeans != null) gmm.Initialize(kmeans);
// Compute the model
gmm.Compute(mixture);
// Classify all instances in mixture data
int[] classifications = gmm.Gaussians.Nearest(mixture);
// Draw the classifications
updateGraph(classifications);
}
/// <summary>
/// Estimates Gaussian distributions from the data.
/// </summary>
///
private void btnCompute_Click(object sender, EventArgs e)
{
// Create a new Gaussian Mixture Model
var gmm = new GaussianMixtureModel(k);
// If available, initialize with k-means
if (kmeans != null)
gmm.Initialize(kmeans);
// Compute the model
GaussianClusterCollection clustering = gmm.Learn(observations);
// Classify all instances in mixture data
int[] classifications = clustering.Decide(observations);
// Draw the classifications
updateGraph(classifications);
}