public Task <double> GetVarianceForCombinationAsync(IPs temporalWeights, ICellRequest cell, double baseNodeVariance)
{
var capturedValues = Tuple.Create(temporalWeights, cell, baseNodeVariance);
return(Task.Factory.StartNew(obj =>
{
Tuple <IPs, ICellRequest, double> typedObj = (Tuple <IPs, ICellRequest, double>)obj;
var name = typedObj.Item2.VariableName;
var processDescription = dict.GetOrAdd(name, new Lazy <IGaussianProcessDescription>(() =>
{
var v = variogramFactory.Create(name);
if (v == null)
{
ts.TraceEvent(TraceEventType.Warning, 1, string.Format("Could not find temporal variogram for the \"{0}\" variable. Skipping uncertainty propagation analysis", name));
}
else
{
ts.TraceEvent(TraceEventType.Information, 2, string.Format("Loaded temporal variogram for \"{0}\" variable", name));
}
return v;
}, System.Threading.LazyThreadSafetyMode.ExecutionAndPublication)).Value;
if (processDescription == null) //Variogram not found.
{
return double.MaxValue;
}
var variogram = processDescription.Variogram;
var tempPrecomputedNugget = variogram.Nugget;
var tempPrecomputedSill = variogram.Sill;
double tempSpatIndependentVariance = double.IsNaN(typedObj.Item3) ? (tempPrecomputedNugget) : (typedObj.Item3);
var tempVarProps = new TemporalVarianceProperties(tempPrecomputedSill - tempPrecomputedNugget + tempSpatIndependentVariance, new Func <double, double, double>((coord1, coord2) => (tempPrecomputedSill - variogram.GetGamma(processDescription.Dist(coord1, coord2)))));
double temporalVariance = GetTemporalVariance(typedObj.Item1, timeAxis, axisLocator.getAproximationGrid(cell.Time), tempVarProps);
Debug.Assert(temporalVariance >= 0.0);
return temporalVariance;
}, capturedValues));
}
private static double GetTemporalVariance(IPs temporalWeights, double[] nodeLocations, double[] targetLocations, TemporalVarianceProperties varianceProps)
{
//Var(x) = Cov(0) + sum(sum(v_i*v_j*Cov(X_i,X_j)) - 2.0 * sum(v_i*Cov(X_i,X))) for semivariograms.
double tempCov0 = varianceProps.Cov0;
var tempCovariogram = varianceProps.Covariance;
double acc = tempCov0; //Cov(0)
var weights = temporalWeights.Weights;
var indices = temporalWeights.Indices;
int N = temporalWeights.Weights.Length;
int M = targetLocations.Length;
double sum2 = 0.0;
for (int i = 0; i < N; i++)
{
// sum(sum(lambda.[i]*lambda.[j]*Cov(X_i,X_j))
var weight_i = weights[i];
acc += weight_i * weight_i * tempCov0;
for (int j = i + 1; j < N; j++)
{
acc += 2.0 * weight_i * weights[j] * tempCovariogram.Invoke(nodeLocations[indices[i]], nodeLocations[indices[j]]);
}
//cov(x,x_i))
double cov_x_xi = 0.0;
for (int j = 0; j < M; j++) //average cov(x,x_i) as in block kriging for region requsts
{
cov_x_xi += tempCovariogram.Invoke(nodeLocations[indices[i]], targetLocations[j]);
}
sum2 += weight_i * cov_x_xi / M;
}
return(0.5 * acc - sum2); //as dealing with full variograms instead of semivariogrmas acc - 2.0* sum2
}
