def test_on_independant(self):
p = np.random.dirichlet([3.] * 10)
p = np.vstack([.2 * p, .8 * p])
self.assertAlmostEqual(conditional_entropy(p, axis=0),
entropy(p.sum(axis=0)))
self.assertAlmostEqual(conditional_entropy(p, axis=1),
entropy(p.sum(axis=1)))
def test_on_independant(self):
p = np.random.dirichlet([3.] * 10)
p = np.vstack([.2 * p, .8 * p])
self.assertAlmostEqual(conditional_entropy(p, axis=0),
entropy(p.sum(axis=0)))
self.assertAlmostEqual(conditional_entropy(p, axis=1),
entropy(p.sum(axis=1)))
def test_is_symetric(self):
p = np.random.dirichlet([3.] * 10)
e1 = entropy(p)
np.random.shuffle(p)
e2 = entropy(p)
self.assertAlmostEqual(e1, e2)
def test_is_positive(self):
p = np.random.dirichlet([3.] * 10)
self.assertTrue(entropy(p) >= 0)
def test_is_less_than_entropy(self):
p = np.random.dirichlet([3.] * 10).reshape((2, 5))
self.assertTrue(conditional_entropy(p, axis=0)
<= entropy(p.sum(axis=0)))
self.assertTrue(conditional_entropy(p, axis=1)
<= entropy(p.sum(axis=1)))
def test_fits_relation_to_entropy(self):
p = np.random.dirichlet([3.] * 10).reshape((2, 5))
info1 = mutual_information(p)
info2 = entropy(p.sum(axis=0)) + entropy(p.sum(axis=1)) - entropy(p)
self.assertAlmostEqual(info1, info2)
def test_is_0_on_deterministic(self):
self.assertEqual(entropy(np.array([0, 1])), 0)
def test_is_log_n_on_equi(self):
p = np.ones((10,)) / 10.
self.assertAlmostEqual(entropy(p), np.log(10.))
def test_is_log_n_on_equi(self):
p = np.ones((10, )) / 10.
self.assertAlmostEqual(entropy(p), np.log(10.))
def test_is_symetric(self):
p = np.random.dirichlet([3.] * 10)
e1 = entropy(p)
np.random.shuffle(p)
e2 = entropy(p)
self.assertAlmostEqual(e1, e2)
def test_is_positive(self):
p = np.random.dirichlet([3.] * 10)
self.assertTrue(entropy(p) >= 0)
def test_is_less_than_entropy(self):
p = np.random.dirichlet([3.] * 10).reshape((2, 5))
self.assertTrue(
conditional_entropy(p, axis=0) <= entropy(p.sum(axis=0)))
self.assertTrue(
conditional_entropy(p, axis=1) <= entropy(p.sum(axis=1)))
def test_fits_relation_to_entropy(self):
p = np.random.dirichlet([3.] * 10).reshape((2, 5))
info1 = mutual_information(p)
info2 = entropy(p.sum(axis=0)) + entropy(p.sum(axis=1)) - entropy(p)
self.assertAlmostEqual(info1, info2)
def test_is_0_on_deterministic(self):
self.assertEqual(entropy(np.array([0, 1])), 0)