/// <summary>
/// Creates a Support Vector Machine and teaches it to recognize
/// the previously loaded dataset using the current UI settings.
/// </summary>
///
private void btnCreate_Click(object sender, EventArgs e)
{
if (dgvLearningSource.DataSource == null)
{
MessageBox.Show("Please load some data first.");
return;
}
// Finishes and save any pending changes to the given data
dgvLearningSource.EndEdit();
// Creates a matrix from the entire source data table
double[,] table = (dgvLearningSource.DataSource as DataTable).ToMatrix(out columnNames);
// Get only the input vector values (first two columns)
double[][] inputs = table.GetColumns(0, 1).ToJagged();
try
{
// Create and run the specified algorithm
this.clustering = createClustering(inputs);
lbStatus.Text = "Training complete!";
}
catch (ConvergenceException)
{
lbStatus.Text = "Convergence could not be attained. " +
"The learned clustering might still be usable.";
}
createSurface(table);
}
/// <summary>
/// Creates a Support Vector Machine and teaches it to recognize
/// the previously loaded dataset using the current UI settings.
/// </summary>
///
private void btnCreate_Click(object sender, EventArgs e)
{
if (dgvLearningSource.DataSource == null)
{
MessageBox.Show("Please load some data first.");
return;
}
// Finishes and save any pending changes to the given data
dgvLearningSource.EndEdit();
// Creates a matrix from the entire source data table
double[,] table = (dgvLearningSource.DataSource as DataTable).ToMatrix(out columnNames);
// Get only the input vector values (first two columns)
double[][] inputs = table.GetColumns(0, 1).ToJagged();
try
{
// Create and run the specified algorithm
this.clustering = createClustering(inputs);
lbStatus.Text = "Training complete!";
}
catch (ConvergenceException)
{
lbStatus.Text = "Convergence could not be attained. "+
"The learned clustering might still be usable.";
}
createSurface(table);
}
