public new static RFGJointKGG FromMatlabStruct(MatlabStruct s)
{
// s.className=class(this);
// s.embed_width2s_cell = this.embed_width2s_cell;
// s.outer_width2 = this.outer_width2;
// s.numFeatures=this.numFeatures;
// s.innerNumFeatures = this.innerNumFeatures;
// s.eprodMap=this.eprodMap.toStruct();
// s.Wout = this.Wout;
// s.Bout = this.Bout;
string className = s.GetString("className");
if (!className.Equals(MATLAB_CLASS))
{
throw new ArgumentException("The input does not represent a " + MATLAB_CLASS);
}
double outer_width2 = s.GetDouble("outer_width2");
int numFeatures = s.GetInt("numFeatures");
int innerNumFeatures = s.GetInt("innerNumFeatures");
MatlabStruct mapStruct = s.GetStruct("eprodMap");
RFGEProdMap eprodMap = RFGEProdMap.FromMatlabStruct(mapStruct);
Matrix Wout = s.GetMatrix("Wout");
if (innerNumFeatures != Wout.Rows)
{
throw new ArgumentException("inner #features must be = #rows of Wout");
}
if (numFeatures != Wout.Cols)
{
throw new ArgumentException("numFeatures must be = #cols of Wout");
}
Vector Bout = s.Get1DVector("Bout");
if (Bout.Count != numFeatures)
{
throw new ArgumentException("Bout must have length = numFeatures");
}
RFGJointKGG jointMap = new RFGJointKGG();
jointMap.outer_width2 = outer_width2;
jointMap.numFeatures = numFeatures;
jointMap.innerNumFeatures = innerNumFeatures;
jointMap.eprodMap = eprodMap;
jointMap.Wout = Wout;
jointMap.Bout = Bout;
// construct object
return(jointMap);
}
public void InitMap(DVectorNormal joint)
{
// dynamically initialize the random feature map
if (Wout == null)
{
Debug.Assert(Bout == null);
int totalDim = joint.GetDimension();
if (totalDim != flattenEmbedWidth2s.Length)
{
throw new SystemException("Expect total dim. of the joint to be = length of params.");
}
eprodMap = new RFGEProdMap(flattenEmbedWidth2s, innerNumFeatures);
Wout = MatrixUtils.Randn(innerNumFeatures, numFeatures) * (1.0 / Math.Sqrt(outer_width2));
double[] Bvec = MatrixUtils.UniformVector(0, 2.0 * Math.PI, numFeatures);
Bout = Vector.FromArray(Bvec);
// VectorGaussian.SampleFromMeanAndVariance();
}
}
public new static RFGEProdMap FromMatlabStruct(MatlabStruct s)
{
// s.className=class(this);
// s.gwidth2=this.gwidth2;
// s.numFeatures=this.numFeatures;
// s.W=this.W;
// s.B=this.B;
string className = s.GetString("className");
if (!className.Equals(MATLAB_CLASS))
{
throw new ArgumentException("The input does not represent a " + MATLAB_CLASS);
}
// Vector of Gaussian width^2 for the mebedding kernel, one for
// each dimension of the input.
// Can be a scalar i.e., same param for each dimension.
// double[] gwidth2 = s.Get1DDoubleArray("gwidth2");
int numFeatures = s.GetInt("numFeatures");
// weight matrix. dim x numFeatures.
Matrix W = s.GetMatrix("W");
if (W.Cols != numFeatures)
{
throw new ArgumentException("numFeatures should be = #cols of W");
}
// coefficients b. a vector of length numFeatures.
// Drawn form U[0, 2*pi]
Vector B = s.Get1DVector("B");
// int numFeatures = s.GetInt("numFeatures");
RFGEProdMap map = new RFGEProdMap();
// map.gwidth2 = gwidth2;
map.numFeatures = numFeatures;
map.W = W;
map.B = B;
return(map);
}
/** - Generate a cell array of FeatureMap candidates from medf,
* a list of factors to be multiplied with the
* diagonal of the average covariance matrices.
*
* - subsamples can be used to limit the samples used to compute
* median distance.
* See RFGJointKGG in Matlab
*/
public override List <RandomFeatureMap> GenCandidates(
List <IKEPDist[]> msgs, int[] numFeatures, double[] medianFactors, Random rng)
{
if (numFeatures.Length != 2)
{
throw new ArgumentException("numFeatures must have length = 2");
}
List <DVectorNormal> joints = msgs.Select(m => ToJointGaussian(m)).ToList();
int n = joints.Count;
int subsamples = Math.Min(1500, n);
List <IKEPDist> jointsBase = joints.Cast <IKEPDist>().ToList();
Vector avgCov = RFGEProdMap.GetAverageDiagCovariance(jointsBase, subsamples, rng);
double[] embedWidth2 = avgCov.ToArray();
// generate candidates
double meanEmbedMedian2 = KGGaussian <DVectorNormal> .MedianPairwise(
joints, embedWidth2);
var mapCandidates = new List <RandomFeatureMap>(medianFactors.Length);
for (int i = 0; i < medianFactors.Length; i++)
{
double medf = medianFactors[i];
if (medf <= 0)
{
string text = String.Format("medf must be strictly positive. Found i={0}, medf[i]={1}",
i, medf);
throw new ArgumentException(text);
}
double width2 = meanEmbedMedian2 * medf;
mapCandidates.Add(new RFGJointKGG(embedWidth2, width2,
numFeatures[0], numFeatures[1]));
}
return(mapCandidates);
}
