def test_correct_1D_indexed(self):
np.random.seed(100)
for _ in range(20):
sample = np.random.dirichlet(np.ones(np.random.randint(10, 15)))
for index0 in range(len(sample)):
inverse = npext.invert_softmax(sample,
enforce_unique=int(index0))
self.assertTrue(np.allclose(sample, softmax(inverse)))
def test_index_0(self):
np.random.seed(100)
for _ in range(20):
sample = np.random.dirichlet(np.ones(np.random.randint(10, 15)))
for index0 in range(len(sample)):
inverse = npext.invert_softmax(sample,
enforce_unique=int(index0))
self.assertAlmostEqual(inverse[int(index0)], 0)
def test_correct_2D(self):
np.random.seed(100)
for _ in range(50):
sample = np.random.dirichlet(
np.ones(15),
np.random.choice(np.random.randint(2, 5)) + 1)
inverse = npext.invert_softmax(sample)
self.assertTrue(np.allclose(sample, softmax(inverse, axis=-1)))
def test_correct_KD_indexed(self):
np.random.seed(100)
for _ in range(50):
sample = np.random.dirichlet(
np.ones(np.random.randint(1, 15)),
np.random.randint(1, 6, size=np.random.randint(1, 3)))
for index0 in range(sample.shape[-1]):
inverse = npext.invert_softmax(sample,
enforce_unique=int(index0))
self.assertTrue(np.allclose(sample, softmax(inverse, axis=-1)))
self.assertTrue(np.allclose(inverse[..., int(index0)], 0))
def test_correct_2D_indexed(self):
np.random.seed(100)
for _ in range(20):
sample = np.random.dirichlet(
np.ones(15),
np.random.choice(np.random.randint(2, 5)) + 1)
for index0 in range(15):
inverse = npext.invert_softmax(sample,
enforce_unique=int(index0))
self.assertTrue(np.allclose(sample, softmax(inverse, axis=-1)))
self.assertTrue(np.allclose(inverse[:, int(index0)], 0))
def test_sum_to_0(self):
np.random.seed(100)
for _ in range(50):
sample = np.random.dirichlet(np.ones(np.random.randint(10, 20)))
inverse = npext.invert_softmax(sample)
self.assertAlmostEqual(inverse.sum(), 0)
def test_correct_1D(self):
np.random.seed(100)
for _ in range(50):
sample = np.random.dirichlet(np.ones(np.random.randint(10, 20)))
inverse = npext.invert_softmax(sample)
self.assertTrue(np.allclose(sample, softmax(inverse)))