def gradient(self, x): if self.anal_grad: grad = self._g_select(x, self.select) if self.decomp: return np.concatenate((grad[0], grad[1])) else: return self.grad[0] else: return pygmo.estimate_gradient(callable=self.fitness, x=x, dx=self.dx)
def gradient(self, d_vec): """ Return the target function gradient value """ return pygmo.estimate_gradient(self.fitness, d_vec)
def gradient(self, x): # we here use the low precision gradient return pg.estimate_gradient(lambda x: self.fitness(x), x, 1e-8)
def gradient(self, z): return pg.estimate_gradient(self.fitness, z)
def gradient(self, x): return pg.estimate_gradient(lambda x: self.fitness(x), x)