public static new CondFMFiniteOut FromMatlabStruct(MatlabStruct s)
{
// s.className=class(this);
// s.featureMap=this.featureMap.toStruct();
// s.regParam=this.regParam;
// s.mapMatrix=this.mapMatrix;
string className = s.GetString("className");
if (!className.Equals(MATLAB_CLASS))
{
throw new ArgumentException("The input does not represent a " + typeof(CondFMFiniteOut));
}
MatlabStruct mapStruct = s.GetStruct("featureMap");
VectorMapper featureMap = VectorMapper.FromMatlabStruct(mapStruct);
// dz x numFeatures where dz is the dimension of output sufficient
// statistic
Matrix mapMatrix = s.GetMatrix("mapMatrix");
Console.WriteLine("{0}.mapMatrix size: ({1}, {2})", MATLAB_CLASS,
mapMatrix.Rows, mapMatrix.Cols);
return(new CondFMFiniteOut(featureMap, mapMatrix));
}
public new static GenericMapper <T> FromMatlabStruct(MatlabStruct s)
{
string className = s.GetString("className");
if (!(className.Equals(MATLAB_CLASS) ||
className.Equals(UAwareGenericMapper <T> .MATLAB_CLASS)))
{
throw new ArgumentException("The input does not represent a " +
typeof(GenericMapper <T>));
}
// nv = number of variables.
// int inVars = s.GetInt("nv");
MatlabStruct rawOperator = s.GetStruct("operator");
VectorMapper instancesMapper = VectorMapper.FromMatlabStruct(rawOperator);
MatlabStruct rawBuilder = s.GetStruct("distBuilder");
DistBuilder <T> distBuilder = DistBuilderBase.FromMatlabStruct(rawBuilder)
as DistBuilder <T>;
return(new GenericMapper <T>(instancesMapper, distBuilder));
}
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 static new BayesLinRegFM FromMatlabStruct(MatlabStruct s)
{
// s.className=class(this);
// s.featureMap=this.featureMap.toStruct();
// %s.regParam=this.regParam;
// s.mapMatrix=this.mapMatrix;
// s.posteriorCov = this.posteriorCov;
// s.noise_var = this.noise_var;
string className = s.GetString("className");
if (!className.Equals(MATLAB_CLASS))
{
throw new ArgumentException("The input does not represent a " + MATLAB_CLASS);
}
MatlabStruct fmStruct = s.GetStruct("featureMap");
RandomFeatureMap featureMap = RandomFeatureMap.FromMatlabStruct(fmStruct);
// This is the same as a posterior mean
Vector mapMatrix = s.Get1DVector("mapMatrix");
if (mapMatrix.Count != featureMap.GetOutputDimension())
{
throw new ArgumentException("mapMatrix and featureMap's dimenions are incompatible.");
}
Matrix postCov = s.GetMatrix("posteriorCov");
if (postCov.Cols != featureMap.GetOutputDimension())
{
throw new ArgumentException("posterior covariance and featureMap's dimenions are incompatible.");
}
double noise_var = s.GetDouble("noise_var");
Vector crossCorr = s.Get1DVector("crossCorrelation");
var bayes = new BayesLinRegFM();
bayes.featureMap = featureMap;
bayes.posteriorMean = mapMatrix;
bayes.posteriorCov = postCov;
bayes.noiseVar = noise_var;
bayes.crossCorr = crossCorr;
// No need to do the initial batch train because we loaded the result
// from .mat.
bayes.WillNeedInitialTrain = false;
return(bayes);
}
// construct a RFGMVMap from MatlabStruct.
// Matlab objects of class RadnFourierGaussMVMap.
// See RandFourierGaussMVMap.toStruct()
public new static RFGMVMap FromMatlabStruct(MatlabStruct s)
{
// s.className=class(this);
// s.mwidth2s=this.mwidth2s;
// s.vwidth2s=this.vwidth2s;
// s.numFeatures=this.numFeatures;
// s.rfgMap=this.rfgMap.toStruct();
string className = s.GetString("className");
if (!className.Equals("RandFourierGaussMVMap"))
{
throw new ArgumentException("The input does not represent a " + typeof(RFGMVMap));
}
// Gaussian width for mean of each input.
Vector mwidth2s = s.Get1DVector("mwidth2s");
Vector vwidth2s = s.Get1DVector("vwidth2s");
// int numFeatures = s.GetInt("numFeatures");
RFGMap rfgMap = RFGMap.FromMatlabStruct(s.GetStruct("rfgMap"));
// construct object
return(new RFGMVMap(mwidth2s, vwidth2s, rfgMap));
}