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import hypothesis.extra.numpy as npst
import unittest
from hypothesis import given, settings, strategies as st
from cmaes import CMA, SepCMA
class TestFuzzing(unittest.TestCase):
@settings(deadline=1800000)
@given(
data=st.data(),
)
def test_cma_tell(self, data):
dim = data.draw(st.integers(min_value=2, max_value=100))
mean = data.draw(npst.arrays(dtype=float, shape=dim))
sigma = data.draw(st.floats(min_value=1e-16))
n_iterations = data.draw(st.integers(min_value=1))
try:
optimizer = CMA(mean, sigma)
except AssertionError:
return
popsize = optimizer.population_size
for _ in range(n_iterations):
tell_solutions = data.draw(
st.lists(
st.tuples(npst.arrays(dtype=float, shape=dim), st.floats()),
min_size=popsize,
max_size=popsize,
)
)
optimizer.ask()
try:
optimizer.tell(tell_solutions)
except AssertionError:
return
optimizer.ask()
@settings(deadline=1800000)
@given(
data=st.data(),
)
def test_sepcma_tell(self, data):
dim = data.draw(st.integers(min_value=2, max_value=100))
mean = data.draw(npst.arrays(dtype=float, shape=dim))
sigma = data.draw(st.floats(min_value=1e-16))
n_iterations = data.draw(st.integers(min_value=1))
try:
optimizer = SepCMA(mean, sigma)
except AssertionError:
return
popsize = optimizer.population_size
for _ in range(n_iterations):
tell_solutions = data.draw(
st.lists(
st.tuples(npst.arrays(dtype=float, shape=dim), st.floats()),
min_size=popsize,
max_size=popsize,
)
)
optimizer.ask()
try:
optimizer.tell(tell_solutions)
except AssertionError:
return
optimizer.ask()
|
