def test_bayes(self):
mu = Dirichlet(concentration=np.ones(3))
model = Categorical(prob=mu)
self.assertEqual(
repr(model),
"Categorical(prob=\nDirichlet(concentration=[ 1. 1. 1.])\n)")
model.bayes(np.array([[1., 0., 0.], [1., 0., 0.], [0., 1., 0.]]))
self.assertEqual(
repr(model),
"Categorical(prob=\nDirichlet(concentration=[ 3. 2. 1.])\n)")
def test_init(self):
c = Categorical()
self.assertTrue(c.prob is None)
c = Categorical(np.ones(3) / 3)
self.assertEqual(c.n_classes, 3)
self.assertTrue(np.allclose(c.prob, np.ones(3) / 3))
def test_draw(self):
c = Categorical(np.ones(4) / 4)
self.assertTrue(
np.allclose(np.mean(c.draw(1000), axis=0), [0.25] * 4, 1e-1, 1e-1))
def test_pdf(self):
c = Categorical(prob=np.ones(4) / 4)
self.assertTrue((c.pdf(np.eye(4)) == 0.25).all())
def test_ml(self):
c = Categorical()
c.ml(np.array([[0, 1], [1, 0], [1, 0], [1, 0]]))
self.assertEqual(c.n_classes, 2)
self.assertTrue((c.prob == np.array([0.75, 0.25])).all())
def test_mean(self):
c = Categorical(prob=np.ones(4) / 4)
self.assertTrue((c.mean == c.prob).all())
def test_repr(self):
c = Categorical(prob=np.ones(2) / 2)
self.assertEqual(repr(c), "Categorical(prob=[ 0.5 0.5])")