public void XORTest()
{
var range = Enumerable.Range(-10,16);
var log2gammas = range.Select(i => Math.Pow(2, i));
var log2Cs = range.Select(i => Math.Pow(2, i + 1));
var log2Rs = range.Select(i => Math.Pow(2, i + 1));
var prob = ProblemHelper.ReadProblem(XOR_TRAINING_FILE);
//Assert.IsTrue(prob.l == 4);
Tuple<double, double, double, int> best = Tuple.Create(0.0, 0.0 ,0.0, prob.l);
foreach (var g in log2gammas)
{
foreach (var c in log2Cs)
{
foreach (var r in log2Rs)
{
var svm = new C_SVC(prob, KernelHelper.SigmoidKernel(g,r), c);
var errorCout = 0;
for (int i = 0; i < prob.l; i++)
{
//var x = (prob.x[i].FirstOrDefault(xi => xi.index == 1) == null) ? 0.0 : prob.x[i].FirstOrDefault(xi => xi.index == 1).value;
//var y = (prob.x[i].FirstOrDefault(xi => xi.index == 2) == null) ? 0.0 : prob.x[i].FirstOrDefault(xi => xi.index == 2).value;
var z = svm.Predict(prob.x[i]);
var probabilities = svm.PredictProbabilities(prob.x[i]);
if (z != prob.y[i])
errorCout++;
//Debug.WriteLine(String.Format("x={0} & y={1} => z={2} -- {3}", x, y, z, z == prob.y[i]));
}
if (errorCout < best.Item4)
best = Tuple.Create(g, c, r, errorCout);
//Debug.WriteLine(String.Format("g={0} && C={1} && C={2} => Error rate = {3}%", g, c, r, (double)errorCout / prob.l * 100));
}
}
}
Debug.WriteLine(String.Format("BEST :: g={0} && C={1} && R={2} => Error rate = {3}%", best.Item1, best.Item2, best.Item3, (double)best.Item4 / (double)prob.l * 100));
}
public static void SVMPredict()
{
var svm = new C_SVC(prob, KernelHelper.RadialBasisFunctionKernel(gamma), C);
double accuracy = svm.GetCrossValidationAccuracy(nr_fold);
for (int i = 0; i < test.l; i++)
{
svm_node[] x = test.x[i];
double y = test.y[i];
double predict = svm.Predict(x); // returns the predicted value 'y'
Dictionary<int, double> probabilities = svm.PredictProbabilities(x);
// returns the probabilities for each 'y' value
Console.WriteLine(predict + " :" + probabilities[1]);
}
Console.ReadKey();
}
/// <summary>
///Show how to predict probabilities for classification problems
///Verify that the prediction is always the most probable class
///</summary>
//[TestMethod()]
public void PredictTest()
{
var svm = new C_SVC(_prob, KernelHelper.RadialBasisFunctionKernel(gamma), C);
var nb_class = _prob.y.Distinct().Count();
for (int i = 0; i < _prob.l; i++)
{
var x = _prob.x[i];
var y = _prob.y[i];
var probabilities = svm.PredictProbabilities(x);
var predict = svm.Predict(x);
Assert.IsTrue(predict == probabilities.OrderByDescending(p => p.Value).First().Key);
Assert.IsNotNull(probabilities);
Assert.IsTrue(probabilities.Count == nb_class);
var sum = probabilities.Sum(e => e.Value) ;
}
}