/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="MatrixVectorProductOp"]/message_doc[@name="BAverageConditional(VectorGaussianMoments, DistributionArray2D{Gaussian,double}, VectorGaussian)"]/*'/>
public static VectorGaussian BAverageConditional([SkipIfUniform] VectorGaussianMoments product, DistributionArray2D <Gaussian, double> A, VectorGaussian result)
{
if (!A.IsPointMass)
{
throw new ArgumentException("A is not a point mass");
}
return(BAverageConditional(product, new Matrix(A.Point), result));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="MatrixVectorProductOp"]/message_doc[@name="BAverageConditional(VectorGaussianMoments, Matrix, VectorGaussian)"]/*'/>
public static VectorGaussian BAverageConditional([SkipIfUniform] VectorGaussianMoments product, Matrix A, VectorGaussian result)
{
if (product.IsPointMass)
{
return(BAverageConditional(product.Point, A, result));
}
VectorGaussian productNatural = new VectorGaussian(product.Mean, product.Variance);
return(BAverageConditional(productNatural, A, result));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="ArrayFromVectorOp"]/message_doc[@name="LogEvidenceRatio(IList{Gaussian}, VectorGaussian, VectorGaussian, IList{Gaussian})"]/*'/>
public static double LogEvidenceRatio(IList <Gaussian> array, VectorGaussianMoments vector, [Fresh] VectorGaussianMoments to_vector, [Fresh] IList <Gaussian> to_array)
{
double result = LogAverageFactor(array, vector, to_vector);
int length = array.Count;
for (int i = 0; i < length; i++)
{
result -= to_array[i].GetLogAverageOf(array[i]);
}
return(result);
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="ArrayFromVectorOp"]/message_doc[@name="LogEvidenceRatio(double[], VectorGaussian)"]/*'/>
public static double LogEvidenceRatio(double[] array, VectorGaussianMoments vector)
{
return(LogAverageFactor(array, vector));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="ArrayFromVectorOp"]/message_doc[@name="LogAverageFactor(double[], VectorGaussian)"]/*'/>
public static double LogAverageFactor(double[] array, VectorGaussianMoments vector)
{
return(vector.GetLogProb(Vector.FromArray(array)));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="ArrayFromVectorOp"]/message_doc[@name="ArrayAverageConditional{GaussianList}(IList{Gaussian}, VectorGaussianMoments, GaussianList)"]/*'/>
/// <typeparam name="GaussianList">The type of the resulting array.</typeparam>
public static GaussianList ArrayAverageConditional <GaussianList>(
[NoInit] IList <Gaussian> array, [SkipIfUniform] VectorGaussianMoments 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;
if (array.All(g => g.IsUniform()) || vector.IsUniform() || vector.IsPointMass)
{
for (int i = 0; i < length; i++)
{
result[i] = Gaussian.FromMeanAndVariance(vector.Mean[i], vector.Variance[i, i]);
}
}
else
{
// 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
VectorGaussianMoments product = new VectorGaussianMoments(length);
Vector mean = product.Mean;
PositiveDefiniteMatrix variance = product.Variance;
vector.GetMeanAndVariance(mean, variance);
for (int i = 0; i < length; i++)
{
double m, v;
array[i].GetMeanAndVariance(out m, out v);
variance[i, i] += v;
mean[i] -= m;
}
double offset = 1e-10;
for (int i = 0; i < length; i++)
{
variance[i, i] += offset;
}
PositiveDefiniteMatrix precision = variance.Inverse();
Vector meanTimesPrecision = precision * mean;
double precisionThreshold = 1;
if (array.Any(g => g.Precision <= precisionThreshold))
{
// compute the posterior mean and variance
product.SetToProduct(vector, array);
mean = product.Mean;
variance = product.Variance;
}
for (int i = 0; i < length; i++)
{
if (array[i].Precision <= precisionThreshold)
{
result[i] = Gaussian.FromMeanAndVariance(mean[i], variance[i, i]) / array[i];
}
else
{
double alpha = meanTimesPrecision[i];
double beta = precision[i, i];
if (double.IsNaN(alpha))
{
throw new Exception("alpha is NaN");
}
if (double.IsNaN(beta))
{
throw new Exception("beta is NaN");
}
result[i] = GaussianOp.GaussianFromAlphaBeta(array[i], alpha, beta, false);
if (double.IsNaN(result[i].MeanTimesPrecision))
{
throw new Exception("result is NaN");
}
if (result[i].Precision < 0)
{
throw new Exception("result is improper");
}
}
}
}
return(result);
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="ArrayFromVectorOp"]/message_doc[@name="VectorAverageLogarithm(double[], VectorGaussian)"]/*'/>
public static VectorGaussianMoments VectorAverageLogarithm(double[] array, VectorGaussianMoments result)
{
result.Point = result.Point;
result.Point.SetTo(array);
return(result);
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="ArrayFromVectorOp"]/message_doc[@name="VectorAverageConditional(double[], VectorGaussian)"]/*'/>
public static VectorGaussianMoments VectorAverageConditional(double[] array, VectorGaussianMoments result)
{
return(VectorAverageLogarithm(array, result));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="ArrayFromVectorOp"]/message_doc[@name="VectorAverageLogarithm(IList{Gaussian}, VectorGaussian)"]/*'/>
public static VectorGaussianMoments VectorAverageLogarithm([SkipIfAllUniform] IList <Gaussian> array, VectorGaussianMoments result)
{
if (result.Dimension != array.Count)
{
throw new ArgumentException("array.Count (" + array.Count + ") != result.Dimension (" + result.Dimension + ")");
}
result.Variance.SetAllElementsTo(0.0);
int length = array.Count;
for (int i = 0; i < length; i++)
{
Gaussian item = array[i];
double m, v;
item.GetMeanAndVariance(out m, out v);
result.Variance[i, i] = v;
result.Mean[i] = m;
}
return(result);
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="MatrixVectorProductOp"]/message_doc[@name="ProductAverageConditional(double[,], Vector, PositiveDefiniteMatrix, VectorGaussianMoments)"]/*'/>
public static VectorGaussianMoments ProductAverageConditional(DistributionArray2D <Gaussian, double> A, Vector BMean, PositiveDefiniteMatrix BVariance, VectorGaussianMoments result)
{
if (!A.IsPointMass)
{
throw new ArgumentException("A is not a point mass");
}
return(ProductAverageConditional(new Matrix(A.Point), BMean, BVariance, result));
}
public static double AverageLogFactor(IList <Gaussian> array, VectorGaussianMoments vector)
{
return(0.0);
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="MatrixVectorProductOp"]/message_doc[@name="BAverageLogarithm(VectorGaussianMoments, Matrix, VectorGaussian)"]/*'/>
public static VectorGaussian BAverageLogarithm([SkipIfUniform] VectorGaussianMoments product, Matrix A, VectorGaussian result)
{
return(BAverageConditional(product, A, result));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="MatrixVectorProductOp"]/message_doc[@name="BAverageConditional(VectorGaussianMoments, double[,], VectorGaussian)"]/*'/>
public static VectorGaussian BAverageConditional([SkipIfUniform] VectorGaussianMoments product, double[,] A, VectorGaussian result)
{
return(BAverageConditional(product, new Matrix(A), result));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="MatrixVectorProductOp"]/message_doc[@name="ProductAverageLogarithm(double[,], Vector, PositiveDefiniteMatrix, VectorGaussianMoments)"]/*'/>
public static VectorGaussianMoments ProductAverageLogarithm(double[,] A, Vector BMean, PositiveDefiniteMatrix BVariance, VectorGaussianMoments result)
{
return(ProductAverageConditional(A, BMean, BVariance, result));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="ArrayFromVectorOp"]/message_doc[@name="LogAverageFactor(IList{Gaussian}, VectorGaussian, VectorGaussian)"]/*'/>
public static double LogAverageFactor(IList <Gaussian> array, VectorGaussianMoments vector, [Fresh] VectorGaussianMoments to_vector)
{
return(vector.GetLogAverageOf(to_vector));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="ArrayFromVectorOp"]/message_doc[@name="VectorAverageConditional(IList{Gaussian}, VectorGaussian)"]/*'/>
public static VectorGaussianMoments VectorAverageConditional([SkipIfAllUniform] IList <Gaussian> array, VectorGaussianMoments result)
{
return(VectorAverageLogarithm(array, result));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="MatrixVectorProductOp"]/message_doc[@name="ProductAverageConditional(Matrix, Vector, PositiveDefiniteMatrix, VectorGaussianMoments)"]/*'/>
public static VectorGaussianMoments ProductAverageConditional(Matrix A, Vector BMean, PositiveDefiniteMatrix BVariance, VectorGaussianMoments result)
{
GetProductMoments(A, BMean, BVariance, result.Mean, result.Variance);
return(result);
}
