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);
}
/**
* Initialize an empty Bayesian linear regressor suitable for online
* learning from scratch.
*/
public BayesLinRegFM(RandomFeatureMap featureMap)
{
// if(noiseVar < 0){
// throw new ArgumentException("Require noise variance >= 0");
// }
// this.noiseVar = noiseVar;
// if(uThreshold < 0){
// throw new ArgumentException("Require uncertainty threshold >= 0");
// }
// this.uThreshold = uThreshold;
int D = featureMap.GetOutputDimension();
this.featureMap = featureMap;
// this.noiseVar = noiseVar;
// this.uThreshold = uThreshold;
this.posteriorMean = Vector.Zero(D);
// assume that the prior for W is N(0, 1)
this.posteriorCov = Matrix.IdentityScaledBy(D, 1.0);
this.crossCorr = Vector.Zero(D);
}