public void learn_test()
{
#region doc_learn
Accord.Math.Random.Generator.Seed = 0;
// Example regression problem. Suppose we are trying
// to model the following equation: f(x, y) = 2x + y
double[][] inputs = // (x, y)
{
new double[] { 0, 1 }, // 2*0 + 1 = 1
new double[] { 4, 3 }, // 2*4 + 3 = 11
new double[] { 8, -8 }, // 2*8 - 8 = 8
new double[] { 2, 2 }, // 2*2 + 2 = 6
new double[] { 6, 1 }, // 2*6 + 1 = 13
new double[] { 5, 4 }, // 2*5 + 4 = 14
new double[] { 9, 1 }, // 2*9 + 1 = 19
new double[] { 1, 6 }, // 2*1 + 6 = 8
};
double[] outputs = // f(x, y)
{
1, 11, 8, 6, 13, 14, 19, 8
};
// Create the sequential minimal optimization teacher
var learn = new SequentialMinimalOptimizationRegression<Polynomial>()
{
Kernel = new Polynomial(2), // Polynomial Kernel of 2nd degree
Complexity = 100
};
// Run the learning algorithm
SupportVectorMachine<Polynomial> svm = learn.Learn(inputs, outputs);
// Compute the predicted scores
double[] predicted = svm.Score(inputs);
// Compute the error between the expected and predicted
double error = new SquareLoss(outputs).Loss(predicted);
// Compute the answer for one particular example
double fxy = svm.Score(inputs[0]); // 1.0003849827673186
#endregion
Assert.AreEqual(1.0, fxy, 1e-2);
for (int i = 0; i < outputs.Length; i++)
Assert.AreEqual(outputs[i], predicted[i], 1e-2);
}
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).ToArray();
// Get only the outputs (last column)
double[] outputs = table.GetColumn(1);
// Create the specified Kernel
IKernel kernel = createKernel();
// Creates a new SMO for regression learning algorithm
var smo = new SequentialMinimalOptimizationRegression()
{
// Set learning parameters
Complexity = (double)numC.Value,
Tolerance = (double)numT.Value,
Epsilon = (double)numEpsilon.Value
};
try
{
// Run
svm = smo.Learn(inputs, outputs);
lbStatus.Text = "Training complete!";
}
catch (ConvergenceException)
{
lbStatus.Text = "Convergence could not be attained. " +
"The learned machine might still be usable.";
}
// Check if we got support vectors
if (svm.SupportVectors.Length == 0)
{
dgvSupportVectors.DataSource = null;
graphSupportVectors.GraphPane.CurveList.Clear();
return;
}
// Show support vectors on the Support Vectors tab page
double[][] supportVectorsWeights = svm.SupportVectors.InsertColumn(svm.Weights);
string[] supportVectorNames = columnNames.RemoveAt(columnNames.Length - 1).Concatenate("Weight");
dgvSupportVectors.DataSource = new ArrayDataView(supportVectorsWeights, supportVectorNames);
// Show the support vector labels on the scatter plot
double[] supportVectorLabels = new double[svm.SupportVectors.Length];
for (int i = 0; i < supportVectorLabels.Length; i++)
{
int j = inputs.Find(sv => sv == svm.SupportVectors[i])[0];
supportVectorLabels[i] = outputs[j];
}
double[][] graph = svm.SupportVectors.InsertColumn(supportVectorLabels);
CreateScatterplot(graphSupportVectors, graph.ToMatrix());
// Get the ranges for each variable (X and Y)
DoubleRange range = table.GetColumn(0).GetRange();
double[][] map = Vector.Interval(range, 0.05).ToArray();
// Classify each point in the Cartesian coordinate system
double[] result = map.Apply(svm.Compute);
double[,] surface = map.ToMatrix().InsertColumn(result);
CreateScatterplot(zedGraphControl2, surface);
}