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_kl(self): nchannels = 3 distr = np.array([0.1, 0.3, 0.05, 0.05, 0.2, 0.1, 0.1, 0.1]) distr2 = np.array([0.4, 0.2, 0.01, 0.09, 0.05, 0.05, 0.03, 0.17]) real_kl = (distr * np.log2(distr/distr2)).sum() kl1 = kl_tools.kl(distr, distr2) assert_almost_equal(kl1, real_kl, 6) kl2 = kl_tools.mean_KL_estimate(distr*100000, distr2*100000) assert_almost_equal(kl2, real_kl, 3) # sample states states = np.random.multinomial(1, distr, size=100000).argmax(1) states2 = np.random.multinomial(1, distr2, size=100000).argmax(1) distr = kl_tools.states2dict(states[:,None], nchannels) distr2 = kl_tools.states2dict(states2[:,None], nchannels) kl3, _ = kl_tools.kl_estimation(distr, distr2, 100000) assert_almost_equal(kl3, real_kl, 2)