public void TestToString()
{
double[] ps = { 5, 0, 0, 0 };
var funct = new GaussianFunction(3, ps, 0);
double[] x = { -1, 0, 1 };
funct.Evaluate(x);
Assert.AreEqual("[GaussianFunction:width=5.00,center=0.00,0.00,0.00]", funct.ToString());
}
public void TestEvaluate()
{
double[] ps = { 5, 0, 0, 0 };
var funct = new GaussianFunction(3, ps, 0);
double[] x = { -1, 0, 1 };
double y = funct.Evaluate(x);
Assert.AreEqual(0.9607894391523232, y, AIFH.DefaultPrecision);
}
public void TestOther()
{
double[] ps = { 5, 0, 0, 0 };
var funct = new GaussianFunction(3, ps, 0);
Assert.AreEqual(3, funct.Dimensions);
funct.SetCenter(0, 100);
Assert.AreEqual(100, funct.GetCenter(0), AIFH.DefaultPrecision);
funct.Width = 5;
Assert.AreEqual(5, funct.Width, AIFH.DefaultPrecision);
}
/// <summary>
/// Construct the RBF network.
/// </summary>
/// <param name="theInputCount">The input count.</param>
/// <param name="rbfCount">The number of RBF functions.</param>
/// <param name="theOutputCount">The output count.</param>
public RBFNetwork(int theInputCount, int rbfCount, int theOutputCount)
{
_inputCount = theInputCount;
_outputCount = theOutputCount;
// calculate input and output weight counts
// add 1 to output to account for an extra bias node
int inputWeightCount = _inputCount*rbfCount;
int outputWeightCount = (rbfCount + 1)*_outputCount;
int rbfParams = (_inputCount + 1)*rbfCount;
_longTermMemory = new double[
inputWeightCount + outputWeightCount + rbfParams];
_indexInputWeights = 0;
_indexOutputWeights = inputWeightCount + rbfParams;
_rbf = new IFnRBF[rbfCount];
for (int i = 0; i < rbfCount; i++)
{
int rbfIndex = inputWeightCount + ((_inputCount + 1)*i);
_rbf[i] = new GaussianFunction(_inputCount, _longTermMemory, rbfIndex);
}
}