def test_sracos_strategy_lm(self):
s0 = Solution(x=[1, 1, 1], value=0)
s1 = Solution(x=[2.2, 2.2, 2.2], value=1)
s2 = Solution(x=[3, 3, 3], value=2)
iset = [s0, s1, s2]
sracos = SRacos()
test_s1 = Solution(x=[2.1, 2.1, 2.1], value=2.1)
sracos.strategy_lm(iset, s0, test_s1)
assert iset[2].get_value() == 2.1
def test_sracos_strategy_rr(self):
s0 = Solution(value=0)
s1 = Solution(value=1)
s2 = Solution(value=2)
iset = [s0, s1, s2]
sracos = SRacos()
test_s1 = Solution(value=2.1)
sracos.strategy_rr(iset, test_s1)
assert len(iset) == 3 and (iset[0].get_value() == 2.1
or iset[1].get_value() == 2.1
or iset[2].get_value() == 2.1)
def opt(self, objective, parameter, strategy='WR'):
"""
This function will choose optimization algorithm and use it to optimize.
:param objective: a Objective object
:param parameter: a Parameter object
:param strategy: replace strategy, used by SRacos and SSRacos
:return: the best solution
"""
self.clear()
ub = parameter.get_uncertain_bits()
if ub is None:
ub = self.choose_ub(objective)
if parameter.get_parallel() is True:
self.__best_solution = ASRacos().opt(objective, parameter,
strategy, ub)
else:
if parameter.get_sequential():
if parameter.get_noise_handling(
) is True and parameter.get_suppression() is True:
self.__algorithm = SSRacos()
else:
self.__algorithm = SRacos()
self.__best_solution = self.__algorithm.opt(
objective, parameter, strategy, ub)
else:
self.__algorithm = Racos()
self.__best_solution = self.__algorithm.opt(
objective, parameter, ub)
return self.__best_solution
def test_sracos_binary_search(self):
s0 = Solution(value=0)
s1 = Solution(value=1)
s2 = Solution(value=2)
s3 = Solution(value=3)
s4 = Solution(value=4)
# 1 3 0 2 4
test_s1 = Solution(value=2.1)
test_s2 = Solution(value=4.5)
test_s3 = Solution(value=-1)
test_s4 = Solution(value=2)
set = [s0, s1, s2, s3, s4]
sracos = SRacos()
assert sracos.binary_search(set, test_s1, 0, 4) == 3
assert sracos.binary_search(set, test_s1, 0, 2) == 3
assert sracos.binary_search(set, test_s2, 0, 4) == 5
assert sracos.binary_search(set, test_s3, 0, 4) == 0
assert sracos.binary_search(set, test_s4, 0, 4) == 3
def test_sracos_replace(self):
s0 = Solution(x=[0, 0, 0], value=0.5)
s1 = Solution(x=[1, 1, 1], value=1)
s2 = Solution(x=[2, 2, 2], value=2)
s3 = Solution(x=[3, 3, 3], value=3)
s4 = Solution(x=[4, 4, 4], value=4)
pos_set = [s0, s1, s2, s3, s4]
neg_set = [s2, s3, s1, s4, s0]
x = Solution(x=[2.1, 2.1, 2.1], value=0.1)
sracos = SRacos()
sracos.replace(pos_set, x, 'pos', 'WR')
assert pos_set[4].get_value() == 3 and pos_set[0].get_value() == 0.1
sracos.replace(neg_set, x, 'neg', 'LM')
assert neg_set[3].get_value() == 0.1
def test_sracos_strategy_wr(self):
s0 = Solution(value=0)
s1 = Solution(value=1)
s2 = Solution(value=2)
s3 = Solution(value=3)
s4 = Solution(value=4)
iset = [s0, s1, s2, s3, s4]
sracos = SRacos()
test_s1 = Solution(value=2.1)
sracos.strategy_wr(iset, test_s1, 'pos')
assert len(iset) == 5 and iset[0].get_value() == 0 and iset[1].get_value() == 1 and iset[2].get_value() == 2 \
and iset[3].get_value() == 2.1 and iset[4].get_value() == 3
iset2 = [s1, s3, s0, s2, s4]
sracos.strategy_wr(iset2, test_s1, 'neg')
assert len(iset2) == 5 and iset2[4].get_value() == 2.1
def __init__(self):
"""
Initialization.
"""
SRacos.__init__(self)
return
def test_sracos_distance(self):
a = [2, 4]
b = [5, 8]
assert SRacos.distance(a, b) == 5
def __init__(self):
SRacos.__init__(self)
return
