def test_kl_zero(self):
nchannels = 3
distr = np.array([0.1, 0.3, 0.05, 0.05, 0.2, 0.1, 0.1, 0.1])
# same states, KL divergence should converge to zero
states = np.random.multinomial(1, distr, size=100000).argmax(1)
states2 = np.random.permutation(states)
distr = kl_tools.states2distr(states, nchannels)
distr2 = kl_tools.states2distr(states2, nchannels)
kl1 = kl_tools.mean_KL_estimate(distr, distr2)
assert_almost_equal(kl1, 0., 3)
distr = kl_tools.states2dict(states[:,None], nchannels)
distr2 = kl_tools.states2dict(states2[:,None], nchannels)
kl2, _ = kl_tools.kl_estimation(distr, distr2, 100000)
assert_almost_equal(kl2, 0., 3)
def test_states2distr(self):
nchannels = 4
patterns = np.array([[1, 0, 1, 0],
[1, 0, 1, 0],
[1, 0, 1, 0],
[1, 1, 1, 1],
[0, 0, 0, 0]])
states = kl_tools.spikes2states(patterns)
# histogram over all possible states
desired = np.zeros(16)
desired[10] = 3
desired[-1] = 1
desired[0] = 1
distr = kl_tools.states2distr(states, nchannels, normed=False)
self.assertEqual(len(distr), 2**nchannels)
assert_array_equal(desired, distr)
distr = kl_tools.states2distr(states, nchannels, normed=True)
self.assertEqual(len(distr), 2**nchannels)
assert_array_equal(desired/5., distr)
