def test_noob():
assert istat(mknoob()) == (0.0, 1.0)
p = Population([mknoob(), mknoob()])
assert strat_mean(pstat(p).mean_proposer_strategy) == 0.0
assert strat_mean(pstat(p).mean_responder_strategy) == 1.0
p2 = Population([mknoob(), Individ(mkuniform(), mkuniform())])
assert strat_mean(pstat(p2).mean_proposer_strategy) > 0.0
def test_evolve():
for i in range(100):
pop = Population([mknoob(), Individ(mkuniform(), mkuniform())])
comp = Competition(pop, 10)
compete(comp, pop)
assert scores(comp, 0) == 0
e = Evolution(0.5)
pop2 = evolve(e, comp, pop)
for i in pop2.individs:
assert not isnoob(i)
def test_compete():
for i in range(100):
pop = Population([mknoob(), Individ(mkuniform(), mkuniform())])
assert isnoob(pop.individs[0])
comp = Competition(pop, 10)
compete(comp, pop)
# print('--->', comp.pscores, comp.rscores)
assert comp.pscores[0] == 0
assert comp.rscores[0] == 0, f"{comp.log}"
assert scores(comp, 0) <= scores(
comp, 1), f"{scores(comp,0)} should be < {scores(comp,1)}"
def test_propose():
p = Individ(
normalized([(0 if (x < 0.1 or x > 0.4) else 20) for x in OFFERS]),
mkuniform())
for x in range(1000):
offer = propose(p)
assert_valid_offer(offer)
assert (offer >= 0.1 and offer <= 0.4)
def test_mutate():
e = Evolution()
st = mkuniform()
s = set(range(len(st)))
for i in range(1000):
st2, nbin = mutate_(e, st)
s -= set([nbin])
assert s == set([])
def test_response():
r = Individ(
mkuniform(),
normalized([(0.0 if (x < 0.1 or x > 0.4) else 20.0) for x in OFFERS]))
r1s = set()
r2s = set()
r3s = set()
for x in range(1000):
r1s.add(respond(r, 1.0 - 0.05))
r2s.add(respond(r, 1.0 - 0.2))
r3s.add(respond(r, 1.0 - 0.9))
assert r1s == set([False]), f"{r1s}"
assert r2s == set([False, True]), f"{r2s}"
assert r3s == set([True]), f"{r3s}"
def pop1(n=30) -> Population:
return Population([Individ(mkuniform(), mkuniform()) for x in range(n)])
def test_mean_strategy():
ms = mean_strategy([mkuniform(), mknorm()])
s = np.sum(ms)
assert isclose(s, 1.0), f"{s}"