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)
def gradient(self, x):
# we here use the low precision gradient
return pg.estimate_gradient(lambda x: self.fitness(x), x, 1e-8)
