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); }