public override Vector MapToVector(params IKEPDist[] msgs)
{
if (msgs.Length != 1)
{
string err = string.Format("{0} only works on one distribution", MATLAB_CLASS);
throw new ArgumentException(err);
}
IKEPDist dist = msgs[0];
InitMap(dist);
Vector mean = dist.GetMeanVector();
Matrix cov = dist.GetCovarianceMatrix();
Vector wtm = mean * W;
Matrix VW = cov * W;
Matrix WVW = new Matrix(W.Rows, W.Cols);
WVW.SetToElementwiseProduct(VW, W);
int dim = W.Rows;
Vector wvwVec = (Vector.Zero(dim) + 1) * WVW;
double[] cosExp = Enumerable.Range(0, numFeatures).Select(
i => Math.Cos(wtm[i] + B[i]) * Math.Exp(-0.5 * wvwVec[i])
).ToArray();
Vector feature = Math.Sqrt(2.0 / numFeatures) * Vector.FromArray(cosExp);
return(feature);
}
// public override int NumInputMessages(){
// Debug.Assert(mwidth2s.Count == vwidth2s.Count);
// return mwidth2s.Count;
// }
private Vector ToMVStack(IKEPDist[] msgs)
{
// stack all means and variances. Divide each by its corresponding
// sqrt(gauss_width2).
// *** Implementation must match RandFourierGaussMVMap.toMVStack()
// in Matlab ***
// empty vector
Vector meanStack = Vector.Zero(0);
Vector varStack = Vector.Zero(0);
for (int i = 0; i < msgs.Length; i++)
{
IKEPDist di = msgs[i];
Vector smean = di.GetMeanVector() * (1 / Math.Sqrt(mwidth2s[i]));
Matrix svar = di.GetCovarianceMatrix() * (1 / Math.Sqrt(vwidth2s[i]));
// reshape column-wise as in Matlab.
// Matrix in Infer.NET is stored row-wise
Vector svarVec = Vector.FromArray(svar.Transpose().SourceArray);
meanStack = Vector.Concat(meanStack, smean);
varStack = Vector.Concat(varStack, svarVec);
}
Vector mv = Vector.Concat(meanStack, varStack);
return(mv);
}
public void InitMap(IKEPDist dist)
{
// dynamically initialize the random feature map
if (W == null)
{
Debug.Assert(B == null);
int dim = dist.GetMeanVector().Count;
double[] reci = MatrixUtils.Reciprocal(gwidth2);
double[] zero = Vector.Zero(dim).ToArray();
W = MatrixUtils.SampleDiagonalVectorGaussian(zero, reci, numFeatures);
double[] Bvec = MatrixUtils.UniformVector(0, 2.0 * Math.PI, numFeatures);
B = Vector.FromArray(Bvec);
// VectorGaussian.SampleFromMeanAndVariance();
}
}