purecma.py 文件源码

def ask(self):
        """sample lambda candidate solutions

        distributed according to::

            m + sigma * Normal(0,C) = m + sigma * B * D * Normal(0,I)
                                    = m + B * D * sigma * Normal(0,I)

        and return a `list` of the sampled "vectors".
        """
        self.C.update_eigensystem(self.counteval,
                                  self.params.lazy_gap_evals)
        candidate_solutions = []
        for k in range(self.params.lam):  # repeat lam times
            z = [self.sigma * eigenval**0.5 * self.randn(0, 1)
                 for eigenval in self.C.eigenvalues]
            y = dot(self.C.eigenbasis, z)
            candidate_solutions.append(plus(self.xmean, y))
        return candidate_solutions