Gaussian random vectors in WOLF: We've been wrong for a long time

Eigen method Random and setRandom produce unnifirm distributions in the range [-1,1].

In WOLF we are using random numbers to generate Gaussian noise. We have been using Eigen::Random, which is wrong.

To generate random vectors and matrices with arbitrary random distributions, Eigen's guru Gael Guennebaud recommends the following:

#include <Eigen/Sparse>
#include <iostream>
#include <random>

using namespace Eigen;

int main() {
  std::default_random_engine generator;
  std::poisson_distribution<int> distribution(4.1);
  auto poisson = [&] (int) {return distribution(generator);};

  RowVectorXi v = RowVectorXi::NullaryExpr(10, poisson );
  std::cout << v << "\n";
}

We should provide such mechanism in WOLF so that we can make tests with true Gaussian noises.

We can read more here:

• https://stackoverflow.com/questions/11024820/set-coefficients-of-an-eigenmatrix-according-an-arbitrary-distribution
• https://eigen.tuxfamily.org/bz/show_bug.cgi?id=720
• https://gitlab.com/libeigen/eigen/-/issues/720