/// <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).ToArray();
// Get only the output labels (last column)
int[] outputs = table.GetColumn(2).ToInt32();
// Create a sparse logistic learning algorithm
var pcd = new ProbabilisticCoordinateDescent()
{
// Set learning parameters
Complexity = (double)numC.Value,
Tolerance = (double)numT.Value,
PositiveWeight = (double)numPositiveWeight.Value,
NegativeWeight = (double)numNegativeWeight.Value,
};
try
{
// Run
svm = pcd.Learn(inputs, outputs);
lbStatus.Text = "Training complete!";
}
catch (ConvergenceException)
{
lbStatus.Text = "Convergence could not be attained. " +
"The learned machine might still be usable.";
}
svm.Compress(); // reduce support vectors to a single weight vector
Trace.Assert(svm.SupportVectors.Length == 1);
Trace.Assert(svm.Weights.Length == 1);
createSurface(table);
// Show feature weight importance
double[] weights = svm.SupportVectors[0].Abs();
string[] featureNames = columnNames.RemoveAt(columnNames.Length - 1);
dgvSupportVectors.DataSource = new ArrayDataView(weights, featureNames);
CreateBarGraph(weights, featureNames);
}
