def test_prob_item_given_tag(self):
self.__init_test(test.SMALL_DEL_FILE)
lambda_ = 0.3
smooth_func = 'Bayes'
p = SmoothEstimator(smooth_func, lambda_, self.annots, 1)
for tag in [0, 1, 2, 3, 4, 5]:
pis = []
pits = []
for item in [0, 1, 2, 3, 4]:
pi = p.prob_item(item)
pti = p.prob_tag_given_item(item, tag)
pis.append(pi)
pits.append(pti * pi)
#Assert
pis = np.array(pis)
pis /= pis.sum()
pits = np.array(pits)
pits /= pits.sum()
gamma_items = np.array([0, 1, 2, 3, 4])
assert_array_almost_equal(pis, p.prob_items(gamma_items))
assert_array_almost_equal(pits, p.prob_items_given_tag(tag,
gamma_items))
self.assertAlmostEqual(1, sum(p.prob_items(gamma_items)))
self.assertAlmostEqual(1,
sum(p.prob_items_given_tag(tag, gamma_items)))
def test_prob_item_given_tag(self):
self.__init_test(test.SMALL_DEL_FILE)
lambda_ = 0.3
smooth_func = 'Bayes'
p = SmoothEstimator(smooth_func, lambda_, self.annots, 1)
for tag in [0, 1, 2, 3, 4, 5]:
pis = []
pits = []
for item in [0, 1, 2, 3, 4]:
pi = p.prob_item(item)
pti = p.prob_tag_given_item(item, tag)
pis.append(pi)
pits.append(pti * pi)
#Assert
pis = np.array(pis)
pis /= pis.sum()
pits = np.array(pits)
pits /= pits.sum()
gamma_items = np.array([0, 1, 2, 3, 4])
assert_array_almost_equal(pis, p.prob_items(gamma_items))
assert_array_almost_equal(pits,
p.prob_items_given_tag(tag, gamma_items))
self.assertAlmostEqual(1, sum(p.prob_items(gamma_items)))
self.assertAlmostEqual(
1, sum(p.prob_items_given_tag(tag, gamma_items)))
