/**
* Sample this random vector and write the result over the mean x_;
*/
void sample(){
VecType x_sample, indep_noise;
if(!isValid_Sx_L_){
::Eigen::LLT<MatType> cholesky( Sx_ );
Sx_L_ = cholesky.matrixL();
isValid_Sx_L_ = true;
}
if(gen_ == NULL){
gen_ = new ::boost::variate_generator< ::boost::mt19937,
::boost::normal_distribution<double> >
(::boost::mt19937(rand()), ::boost::normal_distribution<double>());
}
int n = Sx_L_.cols();
for(int i = 0; i < n; i++){
indep_noise(i) = (*gen_)();
}
x_ += Sx_L_ * indep_noise;
}
/**
* Sample this random vector
* \param[out] s_sample The sampled vector, with time and covariance copied from this random vector
*/
void sample( RandomVec<VecType, MatType> &s_sample ){
VecType x_sample, indep_noise;
if(!isValid_Sx_L_){
Eigen::LLT<MatType> cholesky( Sx_ );
Sx_L_ = cholesky.matrixL();
isValid_Sx_L_ = true;
}
if(gen_ == NULL){
gen_ = new boost::variate_generator< boost::mt19937,
boost::normal_distribution<double> >
(boost::mt19937(rand()), boost::normal_distribution<double>());
}
int n = Sx_L_.cols();
for(int i = 0; i < n; i++){
indep_noise(i) = (*gen_)();
}
x_sample = x_ + Sx_L_ * indep_noise;
s_sample.set( x_sample, Sx_, t_ );
}
