evolutionary_algorithm.py 文件源码

def _eval_models(self, models):
        n = models.shape[0]
        if self._mpi:
            fit = np.zeros(n)
            fit_mpi = np.zeros_like(fit)
            self._mpi_comm.Barrier()
            self._mpi_comm.Bcast([ models, MPI.DOUBLE ], root = 0)
            for i in np.arange(self._mpi_rank, n, self._mpi_size):
                fit_mpi[i] = self._func(self._unstandardize(models[i,:]))
            self._mpi_comm.Barrier()
            self._mpi_comm.Allreduce([ fit_mpi, MPI.DOUBLE ], [ fit, MPI.DOUBLE ],
                                     op = MPI.SUM)
        else:
            fit = np.array([ self._func(self._unstandardize(models[i,:])) for i in range(n) ])
        return fit