def branin():
"""
The Branin, or Branin-Hoo, function has three global minima,
and is roughly an angular trough across a 2D input space.
f(x, y) = a (y - b x ** 2 + c x - r ) ** 2 + s (1 - t) cos(x) + s
The recommended values of a, b, c, r, s and t are:
a = 1
b = 5.1 / (4 pi ** 2)
c = 5 / pi
r = 6
s = 10
t = 1 / (8 * pi)
Global Minima:
[(-pi, 12.275),
(pi, 2.275),
(9.42478, 2.475)]
Source: http://www.sfu.ca/~ssurjano/branin.html
"""
x = hp.uniform("x", -5.0, 10.0)
y = hp.uniform("y", 0.0, 15.0)
pi = float(np.pi)
loss = (
(y - (old_div(5.1, (4 * pi ** 2))) * x ** 2 + 5 * x / pi - 6) ** 2
+ 10 * (1 - old_div(1, (8 * pi))) * scope.cos(x)
+ 10
)
return {"loss": loss, "loss_variance": 0, "status": base.STATUS_OK}
def branin():
"""
The Branin, or Branin-Hoo, function has three global minima,
and is roughly an angular trough across a 2D input space.
f(x, y) = a (y - b x ** 2 + c x - r ) ** 2 + s (1 - t) cos(x) + s
The recommended values of a, b, c, r, s and t are:
a = 1
b = 5.1 / (4 pi ** 2)
c = 5 / pi
r = 6
s = 10
t = 1 / (8 * pi)
Global Minima:
[(-pi, 12.275),
(pi, 2.275),
(9.42478, 2.475)]
Source: http://www.sfu.ca/~ssurjano/branin.html
"""
x = hp.uniform('x', -5., 10.)
y = hp.uniform('y', 0., 15.)
pi = float(np.pi)
loss = ((y - (old_div(5.1, (4 * pi ** 2))) * x ** 2 + 5 * x / pi - 6) ** 2 +
10 * (1 - old_div(1, (8 * pi))) * scope.cos(x) + 10)
return {'loss': loss,
'loss_variance': 0,
'status': base.STATUS_OK}