public void TestComputeClassification()
{
var network = new RBFNetwork(2, 1, 2);
double[] ltm = {
2.0, // input 1 to RBF 1
2.0, // input 2 to RBF 1
5.0, // RBF width
2.0, // RBF, center-0
4.0, // RBF, center-1
3.0, // RBF1 to Output 1
4.0, // Bias to Output 1
5.0, // RBF1 to Output 2
6.0}; // Bias to Output 2
Array.Copy(ltm, 0, network.LongTermMemory, 0, ltm.Length);
double[] x = { 1, 2 };
double[] y = network.ComputeRegression(x);
// Inputs: (2*1) + (2*2) = 6
// RBF: Gaussian(6) = 1
// Outputs: (1*3) + (1*4) = 7
Assert.AreEqual(7, y[0], AIFH.DefaultPrecision);
// Inputs: (2*1) + (2*2) = 6
// RBF: Gaussian(6) = 1
// Outputs: (1*5) + (1*6) = 11
Assert.AreEqual(11, y[1], AIFH.DefaultPrecision);
int cls = network.ComputeClassification(x);
// class 1 is higher than class 0
Assert.AreEqual(1, cls);
}
public void TestComputeRegression()
{
var network = new RBFNetwork(2, 1, 1);
double[] ltm = {
2.0, // input 1 to RBF 1
2.0, // input 2 to RBF 1
5.0, // RBF width
2.0, // RBF, center-0
4.0, // RBF, center-1
3.0, // RBF1 to Output 1
4.0}; // Bias to Output 1
Array.Copy(ltm, 0, network.LongTermMemory, 0, ltm.Length);
double[] x = { 1, 2 };
double y = network.ComputeRegression(x)[0];
// Inputs: (2*1) + (2*2) = 6
// RBF: Gaussian(6) = 1
// Outputs: (1*3) + (1*4) = 7
Assert.AreEqual(7, y, AIFH.DefaultPrecision);
}
