def q1_lognormal():
"""
About the simplest problem you could ask for:
optimize a one-variable quadratic function.
"""
return {
'loss': scope.max(-(hp_lognormal('x', 0, 2) - 3)**2, -100),
'status': base.STATUS_OK
}
def many_dists():
a = hp_choice('a', [0, 1, 2])
b = hp_randint('b', 10)
c = hp_uniform('c', 4, 7)
d = hp_loguniform('d', -2, 0)
e = hp_quniform('e', 0, 10, 3)
f = hp_qloguniform('f', 0, 3, 2)
g = hp_normal('g', 4, 7)
h = hp_lognormal('h', -2, 2)
i = hp_qnormal('i', 0, 10, 2)
j = hp_qlognormal('j', 0, 2, 1)
k = hp_pchoice('k', [(.1, 0), (.9, 1)])
z = a + b + c + d + e + f + g + h + i + j + k
return {
'loss': scope.float(scope.log(1e-12 + z**2)),
'status': base.STATUS_OK
}
def q1_lognormal():
"""
About the simplest problem you could ask for:
optimize a one-variable quadratic function.
"""
return {'loss': scope.max(-(hp_lognormal('x', 0, 2) - 3) ** 2, -100)}
