Ejemplo n.º 1
0
    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)
Ejemplo n.º 2
0
    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)