/// <summary>
/// Initializes a new instance of a Sequential Minimal Optimization (SMO) algorithm.
/// </summary>
///
/// <param name="machine">A Support Vector Machine.</param>
/// <param name="inputs">The input data points as row vectors.</param>
/// <param name="outputs">The classification label for each data point in the range [-1;+1].</param>
///
public SequentialMinimalOptimization(SupportVectorMachine machine,
double[][] inputs, int[] outputs)
{
// Initial argument checking
if (machine == null)
throw new ArgumentNullException("machine");
if (inputs == null)
throw new ArgumentNullException("inputs");
if (outputs == null)
throw new ArgumentNullException("outputs");
if (inputs.Length != outputs.Length)
throw new ArgumentException("The number of inputs and outputs does not match.", "outputs");
for (int i = 0; i < outputs.Length; i++)
{
if (outputs[i] != 1 && outputs[i] != -1)
throw new ArgumentOutOfRangeException("outputs", "One of the labels in the output vector is neither +1 or -1.");
}
if (machine.Inputs > 0)
{
// This machine has a fixed input vector size
for (int i = 0; i < inputs.Length; i++)
if (inputs[i].Length != machine.Inputs)
throw new ArgumentException(
"The size of the input vectors does not match the expected number of inputs of the machine");
}
// Machine
this.machine = machine;
// Kernel (if applicable)
KernelSupportVectorMachine ksvm = machine as KernelSupportVectorMachine;
this.kernel = (ksvm != null) ? ksvm.Kernel : new Linear();
// Learning data
this.inputs = inputs;
this.outputs = outputs;
}
public void CreateKernelplot(ZedGraphControl zgc, double[,] graph, SupportVectorMachine svm)
{
GraphPane myPane = zgc.GraphPane;
myPane.CurveList.Clear();
// Set the titles
myPane.Title.IsVisible = false;
myPane.XAxis.Title.Text = _sourceColumns[0];
myPane.YAxis.Title.Text = _sourceColumns[1];
// Classification problem
PointPairList list1 = new PointPairList(); // Z = -1
PointPairList list2 = new PointPairList(); // Z = +1
for (int i = 0; i < graph.GetLength(0); i++)
{
if (graph[i, 2] == -1)
list1.Add(graph[i, 0], graph[i, 1]);
if (graph[i, 2] == 1)
list2.Add(graph[i, 0], graph[i, 1]);
}
var list3 = new PointPairList();
var list_up = new PointPairList();
var list_down = new PointPairList();
double x, y = 0.0, y_down = 0.0, y_up = 0.0;
for (var i = -2; i < 12; i++)
{
var a = -svm.Weights[0] / svm.Weights[1];
y = a * i - svm.Threshold / svm.Weights[1];
var b = svm.SupportVectorsTwoSides[0];
y_up = a * i + (b[1] - a * b[0]);
list3.Add(i, y);
list_up.Add(i, y_up);
b = svm.SupportVectorsTwoSides[1];
y_down = a * i + (b[1] - a * b[0]);
list_down.Add(i, y_down);
}
// Add the curve
LineItem myCurve = myPane.AddCurve("G1", list1, Color.Blue, SymbolType.Diamond);
myCurve.Line.IsVisible = false;
myCurve.Symbol.Border.IsVisible = false;
myCurve.Symbol.Fill = new Fill(Color.Blue);
myCurve = myPane.AddCurve("G2", list2, Color.Green, SymbolType.Diamond);
myCurve.Line.IsVisible = false;
myCurve.Symbol.Border.IsVisible = false;
myCurve.Symbol.Fill = new Fill(Color.Green);
myCurve = myPane.AddCurve("L", list3, Color.Black, SymbolType.Circle);
myCurve.Line.IsVisible = true;
myCurve = myPane.AddCurve("L", list_up, Color.Black, SymbolType.Circle);
myCurve.Line.IsVisible = true;
myCurve = myPane.AddCurve("L", list_down, Color.Black, SymbolType.Circle);
myCurve.Line.IsVisible = true;
// Fill the background of the chart rect and pane
//myPane.Chart.Fill = new Fill(Color.White, Color.LightGoldenrodYellow, 45.0f);
//myPane.Fill = new Fill(Color.White, Color.SlateGray, 45.0f);
myPane.Fill = new Fill(Color.WhiteSmoke);
zgc.AxisChange();
zgc.Invalidate();
}
private void btnSimpleSMO_Click(object sender, EventArgs e)
{
double[,] sourceMatrix;
double[,] inputs;
int[] labels;
GetData(out sourceMatrix, out inputs, out labels);
//_svm.SimpleSMO(inputs, labels);
// Perform classification
SequentialMinimalOptimization smo;
// Creates the Support Vector Machine using the selected kernel
//svm = new KernelSupportVectorMachine(kernel, 2);
SupportVectorMachine svm = new SupportVectorMachine(2);
// Creates a new instance of the SMO Learning Algorithm
smo = new SequentialMinimalOptimization(svm, inputs.ToArray(), labels);
// Set learning parameters
smo.Complexity =1.0;
smo.Tolerance = 0.001;
bool converged = true;
try
{
// Run
double error = smo.Run();
}
catch
{
converged = false;
}
var a = sourceMatrix.Submatrix(0, sourceMatrix.GetLength(0) - 1, 0, 2);
CreateScatterplot(graphInput, a, svm);
}