def test_error_equals_mean_pxz(self):
a = ctcpp_alpha(np.log(self.Y), self.T)
b = ctcpp_beta(np.log(self.Y), self.T)
pzx = np.logaddexp.reduce(a + b, axis=2)
error_expected = -pzx.mean()
error_calc, d = CTC(self.Y, LabelingTargets([self.T], binarize_to=3))
self.assertEqual(error_calc, error_expected)
def test_beta_values(self):
b = ctcpp_beta(np.log(self.Y), self.T)
b_expected = np.array(
[[.096, .06, 0, 0],
[.441, .48, .2, 0],
[0, .42, .2, 0],
[0, .57, .9, 1],
[0, 0, .7, 1]]).T
self.assertTrue(np.allclose(np.exp(b), b_expected.reshape(4, 1, 5)))
def test_beta_values_duplicate_label(self):
T = [0, 0]
b = ctcpp_beta(np.log(self.Y), T)
b_expected = np.array(
[[.003, 0, 0, 0],
[.219, .03, 0, 0],
[0, .27, .1, 0],
[0, .45, .8, 1],
[0, 0, .7, 1]]).T
self.assertTrue(np.allclose(np.exp(b), b_expected.reshape(4, 1, 5)))
def test_pxz_equal_for_all_t(self):
a = ctcpp_alpha(np.log(self.Y), self.T)
b = ctcpp_beta(np.log(self.Y), self.T)
pxz = np.exp(a + b).T.sum(0)
self.assertTrue(np.allclose(pxz, pxz.mean()))
