def test_crossover(self):
"""
Should perform binary crossover on a subset of the population
"""
ind0 = np.random.rand(4)
fit0 = np.arange(len(ind0))
pop = Population(individuals=ind0, fitness=fit0)
# should change some values
pop.crossover(p=1.)
count = 0
for f0, f1 in zip(ind0, pop.individuals):
if f0 != f1:
count += 1
self.assertGreater(count, 0)
# should set fitness for changed individuals to nan
self.assertGreater(len(pop.inew), 0)
# should handle > 1d
ind0 = np.random.rand(50, 10)
pop = Population(individuals=ind0, fitness=range(50))
pop.crossover(p=1.)
self.assertGreater(len(pop.inew), 0)
# should not change shape of individuals
self.assertEqual(pop.individuals.shape, ind0.shape)
# FIXME?? fails on np.log10(0) in power calc. in float2bin()
## should not produce NaNs
ind0 = np.round(np.random.rand(100))
pop = Population(individuals=ind0)
for p in [0.1, 0.5, 1.]:
pop.crossover(p=p)
self.assertEqual(len(np.nonzero(np.isnan(pop.individuals))[0]), 0)
