def test_clone(self):
"""
Should copy the population with a bias towards fitter individuals
"""
# should select towards all ones
f0 = np.arange(-3, 5)
for i in range(5):
fit0 = 1. / np.abs(f0 - 1.)
rank = tools.linear_rank(fit0, sp=2)
nchild = np.round(rank)
f0 = tools.clone(f0, nchild)
self.assertEqual(np.sum(f0), len(f0))
def test_linear_rank(self):
# should handle dimensions correctly
for sp in np.arange(1, 2, 0.1):
fitness = np.random.rand(5)
rank = tools.linear_rank(fitness, sp=sp)
# should return an array with the same length as fitness
self.assertEqual(len(fitness), len(rank))
self.assertEqual(rank.shape, (len(fitness),))
# should always sum to the length of fitness
self.assertAlmostEqual(sum(rank), len(fitness), 6)
# should rank according to values if sp > 1.
if sp > 1.:
self.assertEqual(np.argmax(rank), np.argmax(fitness))