/// <summary>
/// Sets this instance to the ratio of two sparse GPs.
/// </summary>
/// <param name="numerator">Sparse GP</param>
/// <param name="denominator">Sparse GP</param>
/// <param name="forceProper"></param>
public void SetToRatio(SparseGP numerator, SparseGP denominator, bool forceProper = false)
{
if (numerator.FixedParameters != denominator.FixedParameters)
{
throw new ArgumentException("SparseGPs do not have the same FixedParameters. numerator.FixedParameters = " + numerator.FixedParameters +
", denominator.FixedParameters = " + denominator.FixedParameters);
}
FixedParameters = numerator.FixedParameters;
if (numerator.IncludePrior)
{
IncludePrior = !denominator.IncludePrior;
}
else if (denominator.IncludePrior)
{
throw new ArgumentException("Only the denominator includes the prior. Cannot divide.");
}
else
{
IncludePrior = false; // neither include the prior
}
if (numerator.IsPointMass)
{
if (denominator.IsPointMass)
{
if (numerator.Point.Equals(denominator.Point))
{
SetToUniform();
}
else
{
throw new DivideByZeroException();
}
}
else
{
Point = numerator.Point;
}
}
else if (denominator.IsPointMass)
{
throw new DivideByZeroException();
}
else
{
// neither is point mass
InducingDist.SetToRatio(numerator.InducingDist, denominator.InducingDist, forceProper);
pointFunc = null;
ClearCachedValues();
}
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="SumVectorGaussianOp"]/message_doc[@name="ArrayAverageLogarithm1{TVectorGaussianList}(VectorGaussian, IList{VectorGaussian}, TVectorGaussianList)"]/*'/>
/// <typeparam name="TVectorGaussianList">A list of <see cref="VectorGaussian"/> distributions.</typeparam>
public static TVectorGaussianList ArrayAverageLogarithm1 <TVectorGaussianList>(
[SkipIfUniform] VectorGaussian sum,
[Stochastic, Proper] IList <VectorGaussian> array,
TVectorGaussianList to_array)
where TVectorGaussianList : IList <VectorGaussian>
{
// Check inputs for consistency
int dimension = CheckArgumentConsistency(sum, sum, array, to_array);
TVectorGaussianList result = to_array;
var sumMean = Vector.Zero(dimension);
var sumVariance = PositiveDefiniteMatrix.Identity(dimension);
sum.GetMeanAndVariance(sumMean, sumVariance);
// This version does one update of q(array[i]) for each array element in turn.
Vector arraySumOfMean = Vector.Zero(dimension);
foreach (VectorGaussian element in array)
{
arraySumOfMean.SetToSum(arraySumOfMean, element.GetMean());
}
for (int i = 0; i < result.Count; i++)
{
arraySumOfMean.SetToDifference(arraySumOfMean, array[i].GetMean());
VectorGaussian oldResult = result[i];
result[i] = new VectorGaussian(sumMean - arraySumOfMean, sumVariance);
oldResult.SetToRatio(result[i], oldResult);
oldResult.SetToProduct(array[i], oldResult);
arraySumOfMean.SetToSum(arraySumOfMean, oldResult.GetMean());
}
return(result);
}