public static VectorGaussianWishart Combine(VectorGaussian position, Wishart orientation, VectorGaussianWishart result)
{
if (orientation.IsUniform())
{
result.SetToUniform();
}
else if (position.IsUniform())
{
result.SetTo(orientation.Shape, orientation.Rate, Vector.Zero(2), 0);
}
else
{
PositiveDefiniteMatrix rateTimesPrecision = new PositiveDefiniteMatrix(2, 2);
rateTimesPrecision.SetToProduct(orientation.Rate, position.Precision);
double trace = MathHelpers.Invert(rateTimesPrecision).Trace();
Vector positionMean = position.MeanTimesPrecision * MathHelpers.Invert(position.Precision);
result.SetTo(orientation.Shape, orientation.Rate, positionMean, orientation.Dimension / (orientation.Shape * trace));
}
return result;
}
/// <summary>
/// Asks the distribution whether it is uniform
/// </summary>
/// <returns>True or false</returns>
public bool IsUniform()
{
return(!IncludePrior && InducingDist.IsUniform());
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="ArrayFromVectorOp"]/message_doc[@name="ArrayAverageConditional{GaussianList}(IList{Gaussian}, VectorGaussian, GaussianList)"]/*'/>
/// <typeparam name="GaussianList">The type of the resulting array.</typeparam>
public static GaussianList ArrayAverageConditional <GaussianList>(
[NoInit] IList <Gaussian> array, [SkipIfUniform] VectorGaussian vector, GaussianList result)
where GaussianList : IList <Gaussian>
{
if (result.Count != vector.Dimension)
{
throw new ArgumentException("vector.Dimension (" + vector.Dimension + ") != result.Count (" + result.Count + ")");
}
int length = result.Count;
bool allPointMass = array.All(g => g.IsPointMass);
if (allPointMass)
{
// efficient special case
for (int i = 0; i < length; i++)
{
double x = array[i].Point;
// -prec*(x-m) = -prec*x + prec*m
double dlogp = vector.MeanTimesPrecision[i];
for (int j = 0; j < length; j++)
{
dlogp -= vector.Precision[i, j] * array[j].Point;
}
double ddlogp = -vector.Precision[i, i];
result[i] = Gaussian.FromDerivatives(x, dlogp, ddlogp, false);
}
}
else if (vector.IsPointMass)
{
// efficient special case
Vector mean = vector.Point;
for (int i = 0; i < length; i++)
{
result[i] = Gaussian.PointMass(mean[i]);
}
}
else if (vector.IsUniform())
{
for (int i = 0; i < length; i++)
{
result[i] = Gaussian.Uniform();
}
}
else if (array.Any(g => g.IsPointMass))
{
// Z = N(m1; m2, V1+V2)
// logZ = -0.5 (m1-m2)'inv(V1+V2)(m1-m2)
// dlogZ = (m1-m2)'inv(V1+V2) dm2
// ddlogZ = -dm2'inv(V1+V2) dm2
Vector mean = Vector.Zero(length);
PositiveDefiniteMatrix variance = new PositiveDefiniteMatrix(length, length);
vector.GetMeanAndVariance(mean, variance);
for (int i = 0; i < length; i++)
{
if (array[i].IsUniform())
{
continue;
}
double m, v;
array[i].GetMeanAndVariance(out m, out v);
variance[i, i] += v;
mean[i] -= m;
}
PositiveDefiniteMatrix precision = variance.Inverse();
Vector meanTimesPrecision = precision * mean;
for (int i = 0; i < length; i++)
{
if (array[i].IsUniform())
{
result[i] = Gaussian.FromMeanAndVariance(mean[i], variance[i, i]);
}
else
{
double alpha = meanTimesPrecision[i];
double beta = precision[i, i];
result[i] = GaussianOp.GaussianFromAlphaBeta(array[i], alpha, beta, false);
}
}
}
else
{
// Compute inv(V1+V2)*(m1-m2) as inv(V2)*inv(inv(V1) + inv(V2))*(inv(V1)*m1 + inv(V2)*m2) - inv(V2)*m2 = inv(V2)*(m - m2)
// Compute inv(V1+V2) as inv(V2)*inv(inv(V1) + inv(V2))*inv(V2) - inv(V2)
PositiveDefiniteMatrix precision = (PositiveDefiniteMatrix)vector.Precision.Clone();
Vector meanTimesPrecision = vector.MeanTimesPrecision.Clone();
for (int i = 0; i < length; i++)
{
Gaussian g = array[i];
precision[i, i] += g.Precision;
meanTimesPrecision[i] += g.MeanTimesPrecision;
}
bool fastMethod = true;
if (fastMethod)
{
bool isPosDef;
// this destroys precision
LowerTriangularMatrix precisionChol = precision.CholeskyInPlace(out isPosDef);
if (!isPosDef)
{
throw new PositiveDefiniteMatrixException();
}
// variance = inv(precisionChol*precisionChol') = inv(precisionChol)'*inv(precisionChol) = varianceChol*varianceChol'
// this destroys meanTimesPrecision
var mean = meanTimesPrecision.PredivideBy(precisionChol);
mean = mean.PredivideByTranspose(precisionChol);
var varianceCholTranspose = precisionChol;
// this destroys precisionChol
varianceCholTranspose.SetToInverse(precisionChol);
for (int i = 0; i < length; i++)
{
Gaussian g = array[i];
double variance_ii = GetSquaredLengthOfColumn(varianceCholTranspose, i);
// works when g is uniform, but not when g is point mass
result[i] = Gaussian.FromMeanAndVariance(mean[i], variance_ii) / g;
}
}
else
{
// equivalent to above, but slower
PositiveDefiniteMatrix variance = precision.Inverse();
var mean = variance * meanTimesPrecision;
for (int i = 0; i < length; i++)
{
Gaussian g = array[i];
// works when g is uniform, but not when g is point mass
result[i] = Gaussian.FromMeanAndVariance(mean[i], variance[i, i]) / g;
}
}
}
return(result);
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="MatrixVectorProductOp"]/message_doc[@name="AAverageConditional(VectorGaussian, DistributionArray2D{Gaussian, double}, Vector, PositiveDefiniteMatrix, DistributionStructArray2D{Gaussian, double})"]/*'/>
public static DistributionStructArray2D <Gaussian, double> AAverageConditional([SkipIfUniform] VectorGaussian product, DistributionArray2D <Gaussian, double> A, Vector BMean, PositiveDefiniteMatrix BVariance, DistributionStructArray2D <Gaussian, double> result)
{
if (product.IsUniform())
{
result.SetToUniform();
return(result);
}
if (!A.IsPointMass)
{
throw new ArgumentException("A is not a point mass");
}
// logZ = log N(mProduct; A*BMean, vProduct + A*BVariance*A')
// = -0.5 (mProduct - A*BMean)' inv(vProduct + A*BVariance*A') (mProduct - A*BMean) - 0.5 logdet(vProduct + A*BVariance*A')
// = -0.5 (mProduct - A*BMean)' pPrec inv(pProduct + pProduct*A*BVariance*A'*pProduct) pProduct (mProduct - A*BMean)
// - 0.5 logdet(pProduct + pProduct*A*BVariance*A'*pProduct) + logdet(pProduct)
// dlogZ = 0.5 (dA*BMean)' pProduct inv(pProduct + pProduct*A*BVariance*A'*pProduct) pProduct (mProduct - A*BMean)
// +0.5 (mProduct - A*BMean)' pProduct inv(pProduct + pProduct*A*BVariance*A'*pProduct) pProduct (dA*BMean)
// +0.5 (mProduct - A*BMean)' pProduct inv(pProduct + pProduct*A*BVariance*A'*pProduct) (pProduct*dA*BVariance*A'*pProduct + pProduct*A*BVariance*dA'*pProduct) inv(pProduct + pProduct*A*BVariance*A'*pProduct) pProduct (mProduct - A*BMean)
// - 0.5 tr(inv(pProduct + pProduct*A*BVariance*A'*pProduct) (pProduct*dA*BVariance*A'*pProduct + pProduct*A*BVariance*dA'*pProduct))
// dlogZ/dA = pProduct inv(pProduct + pProduct*A*BVariance*A'*pProduct) pProduct (mProduct - A*BMean) BMean'
// + pProduct inv(pProduct + pProduct*A*BVariance*A'*pProduct) pProduct (mProduct - A*BMean) (mProduct - A*BMean)' pProduct inv(pProduct + pProduct*A*BVariance*A'*pProduct) pProduct*A*BVariance
// - pProduct inv(pProduct + pProduct*A*BVariance*A'*pProduct) pProduct A*BVariance
var Amatrix = new Matrix(A.Point);
var pProductA = product.Precision * Amatrix;
var pProductABV = pProductA * BVariance;
PositiveDefiniteMatrix prec = new PositiveDefiniteMatrix(product.Dimension, product.Dimension);
prec.SetToSum(product.Precision, pProductABV * pProductA.Transpose());
// pProductA is now free
for (int i = 0; i < prec.Rows; i++)
{
if (prec[i, i] == 0)
{
prec[i, i] = 1;
}
}
var v = prec.Inverse();
var ABM = Amatrix * BMean;
var pProductABM = product.Precision * ABM;
var diff = pProductABM;
diff.SetToDifference(product.MeanTimesPrecision, pProductABM);
// ABM is now free
var pProductV = product.Precision * v;
var pProductVdiff = ABM;
pProductVdiff.SetToProduct(pProductV, diff);
var Vdiff = v * diff;
pProductV.Scale(-1);
pProductV.SetToSumWithOuter(pProductV, 1, pProductVdiff, Vdiff);
Matrix dlogZ = pProductA;
dlogZ.SetToProduct(pProductV, pProductABV);
dlogZ.SetToSumWithOuter(dlogZ, 1, pProductVdiff, BMean);
int rows = A.GetLength(0);
int cols = A.GetLength(1);
for (int i = 0; i < rows; i++)
{
for (int j = 0; j < cols; j++)
{
double dlogp = dlogZ[i, j];
// for now, we don't compute the second derivative.
double ddlogp = -1;
result[i, j] = Gaussian.FromDerivatives(A[i, j].Point, dlogp, ddlogp, false);
}
}
return(result);
}