def test_theoretical_output(self):
"""
proper_value is calculated using R function infotheo::condentropy(method="emp")
"""
input_1 = [9, 8, 7, 6, 5, 4, 3, 2, 9]
cond_1 = [1, 1, 1, 1, 0, 0, 0, 0, 0]
proper_value_1 = 1.510263
self.assertAlmostEqual(conditional_entropy(input_1, cond_1),
proper_value_1,
places=3)
input_2 = [0, 0, 0, 0, 1, 0, 0, 0, 0]
cond_2 = [1, 1, 1, 1, 0, 0, 0, 0, 0]
proper_value_2 = 0.2780013
self.assertAlmostEqual(conditional_entropy(input_2, cond_2),
proper_value_2,
places=3)
# Entropy must be higher in more diverse vectors
self.assertGreater(conditional_entropy(input_1, cond_1),
conditional_entropy(input_2, cond_2))
def test_nparray_input(self):
np_input = np.array([1, 2, 3, 5, 432, 42, 31234, 342, 34])
condition = np.array([1, 1, 1, 1, 0, 0, 0, 0, 0])
self.assertIsInstance(conditional_entropy(np_input, condition), float)
def test_list_input(self):
list_input = [1, 2, 3, 5, 432, 42, 31234, 342, 34]
condition = [1, 1, 1, 1, 0, 0, 0, 0, 0]
self.assertIsInstance(conditional_entropy(list_input, condition),
float)
def test_different_input_sizes(self):
input = [0, 1]
condition = [1, 2, 3]
with self.assertRaises(AssertionError):
conditional_entropy(input, condition)
def test_one_number_input(self):
self.assertEqual(conditional_entropy([1], [0]), 0.0)
def test_empty_input(self):
input = []
condition = []
with self.assertRaises(AssertionError):
conditional_entropy(input, condition)