public CovarianceMatrix(IGMN owner, int inputLength, int outputLength)
{
Vector scaledMaxMin = new Vector(owner.MaxMin.Elements.Length);
double prod = 1;
double prod2 = 1;
Vector invMaxMin = new Vector(owner.MaxMin.Elements.Length);
for (int i = 0; i < owner.MaxMin.Elements.Length; ++i)
{
double temp = Math.Pow(DELTA * owner.MaxMin.Elements[i], 1);
scaledMaxMin.Elements[i] = temp;
prod *= temp;
if (i < inputLength) prod2 *= temp;
invMaxMin.Elements[i] = 1 / temp;
}
determinant = prod;
covariance = DenseMatrix.Diag(scaledMaxMin);
inverseCovariance = DenseMatrix.Diag(invMaxMin);
inputDeterminant = prod2;
inputInverseCovariance = inverseCovariance.Part(0, 0, inputLength, inputLength);
}
public InvertedPendulumESIGMNQStore()
{
igmn = new IGMN(3, 1, new Vector(new double[] {12, 6, 0.4, 100} ));
}
public IGMNGaussian(IGMN owner, Vector mean, int inputLength, int outputLength)
{
this.owner = owner;
this.inputLength = inputLength;
this.outputLength = outputLength;
Age = 1;
Accumlator = 1;
Mean = new Vector(mean);
Covariance = new CovarianceMatrix(owner, inputLength, outputLength);
}
