public void RunTest1()
{
double[][] inputs =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
int[] outputs =
{
-1,
1,
1,
-1
};
KernelSupportVectorMachine svm = new KernelSupportVectorMachine(new Gaussian(3.6), 2);
var smo = new SequentialMinimalOptimization(svm, inputs, outputs);
double error1 = smo.Run();
Assert.AreEqual(0, error1);
double[] distances = new double[outputs.Length];
for (int i = 0; i < outputs.Length; i++)
{
int y = svm.Compute(inputs[i], out distances[i]);
Assert.AreEqual(outputs[i], y);
}
var target = new ProbabilisticOutputCalibration(svm, inputs, outputs);
double ll0 = target.LogLikelihood(inputs, outputs);
double ll1 = target.Run();
double ll2 = target.LogLikelihood(inputs, outputs);
Assert.AreEqual(5.5451735748694571, ll1);
Assert.AreEqual(ll1, ll2);
Assert.IsTrue(ll1 > ll0);
double[] newdistances = new double[outputs.Length];
for (int i = 0; i < outputs.Length; i++)
{
int y = svm.Compute(inputs[i], out newdistances[i]);
Assert.AreEqual(outputs[i], y);
}
double[] probs = new double[outputs.Length];
for (int i = 0; i < outputs.Length; i++)
{
int y;
probs[i] = svm.ToMulticlass().Probability(inputs[i], out y);
Assert.AreEqual(outputs[i], y == 1 ? 1 : -1);
}
Assert.AreEqual(0.25, probs[0], 1e-5);
Assert.AreEqual(0.75, probs[1], 1e-5);
Assert.AreEqual(0.75, probs[2], 1e-5);
Assert.AreEqual(0.25, probs[3], 1e-5);
foreach (var p in probs)
{
Assert.IsFalse(Double.IsNaN(p));
}
}