Eigen::VectorXd sample(const Uniform &d, RNG &rng)
{
std::uniform_real_distribution<double> dist(0, 1);
Eigen::ArrayXd sample(length(d));
for (Eigen::ArrayXd::Index i = 0; i < sample.rows(); i++)
{
sample(i) = dist(rng);
}
// Transform the output to fit within the support of the distribution
return d.min().array() + sample * (d.max() - d.min()).array();
}
Eigen::VectorXd var(const Uniform &d)
{
return 1.0 / 12.0 * (d.max() - d.min());
}
bool insupport(const Uniform &d, const Eigen::VectorXd &x)
{
// Check that all dimensions are within the bounds of the distribution.
return ((d.min().array() < x.array()) && (x.array() < d.max().array())).all();
}
Eigen::VectorXd mean(const Uniform &d)
{
return 0.5 * (d.min() + d.max());
}
