public void LinearComputeTest1()
{
double[][] inputs =
{
new double[] { 1, 4, 2, 0, 1 },
new double[] { 1, 3, 2, 0, 1 },
new double[] { 3, 0, 1, 1, 1 },
new double[] { 3, 0, 1, 0, 1 },
new double[] { 0, 5, 5, 5, 5 },
new double[] { 1, 5, 5, 5, 5 },
new double[] { 1, 0, 0, 0, 0 },
new double[] { 1, 0, 0, 0, 0 },
};
int[] outputs =
{
0, 0,
1, 1,
2, 2,
3, 3,
};
var msvm = new MulticlassSupportVectorMachine(5, 4);
var smo = new MulticlassSupportVectorLearning(msvm, inputs, outputs);
smo.Algorithm = (svm, classInputs, classOutputs, i, j) =>
new LinearCoordinateDescent(svm, classInputs, classOutputs)
{
Complexity = 1
};
msvm.ParallelOptions.MaxDegreeOfParallelism = 1;
smo.ParallelOptions.MaxDegreeOfParallelism = 1;
Assert.AreEqual(0, msvm.GetLastKernelEvaluations());
double error = smo.Run();
// Linear machines in compact form do not require kernel evaluations
Assert.AreEqual(0, msvm.GetLastKernelEvaluations());
for (int i = 0; i < inputs.Length; i++)
{
double expected = outputs[i];
double actual = msvm.Compute(inputs[i], MulticlassComputeMethod.Elimination);
Assert.AreEqual(expected, actual);
Assert.AreEqual(0, msvm.GetLastKernelEvaluations());
}
for (int i = 0; i < inputs.Length; i++)
{
double expected = outputs[i];
double actual = msvm.Compute(inputs[i], MulticlassComputeMethod.Voting);
Assert.AreEqual(expected, actual);
Assert.AreEqual(0, msvm.GetLastKernelEvaluations());
}
}