public FedKF(KF[] filters, DenseMatrix dcm, DenseMatrix covariances)
{
_filters = filters;
_filteredSignals = new DenseMatrix(_filters.Length, 1);
_dcm = dcm;
_dcmt = dcm.Transpose();
_cInv = covariances.Inverse();
}
public static FedKF ToFedKf(this Specimen spec)
{
int totalLength = SensorsCount * (NumOrder + DenOrder);
if (totalLength > spec.Genes.Length)
{
throw new ArgumentException("Genome of the specimen is too short;");
}
var filters = new KF[SensorsCount];
var numDenLength = NumOrder + DenOrder;
var partitioner = Partitioner.Create(0, totalLength, numDenLength);
Parallel.ForEach(
partitioner,
(range, loopstate) =>
{
int sensorIdx = range.Item1 / numDenLength;
var numParams = new double[NumOrder];
var denParams = new double[DenOrder];
for (int i = range.Item1, j = 0; i < range.Item2; i++, j++)
{
if (j < NumOrder)
{
numParams[j] = spec.Genes[i];
}
else
{
denParams[j - NumOrder] = spec.Genes[i];
}
}
filters[sensorIdx] = KFBuilder.BuildKf(numParams, denParams, new DenseMatrix(1, 1, SensorsOutputCovariances[sensorIdx, sensorIdx]), ProcessCovariances);
});
var fkf = new FedKF(filters, DirectCosinsMatrix, SensorsOutputCovariances);
return fkf;
}