def test_theoretical_output(self):
"""
proper_value is calculated using R function infotheo::entropy(method="emp")
"""
input_1 = [9, 8, 7, 6, 5, 4, 3, 2, 9]
proper_value_1 = 2.043192
self.assertAlmostEqual(entropy(input_1), proper_value_1, places=3)
input_2 = [0, 0, 0, 0, 1, 0, 0, 0, 0]
proper_value_2 = 0.3488321
self.assertAlmostEqual(entropy(input_2), proper_value_2, places=3)
# Entropy must be higher in more diverse vectors
self.assertGreater(entropy(input_1), entropy(input_2))
def test_empty_input(self):
input = []
with self.assertRaises(AssertionError):
entropy(input)
def test_nparray_input(self):
np_input = np.array([1, 2, 3, 5, 432, 42, 31234, 342, 34])
self.assertIsInstance(entropy(np_input), float)
def test_list_input(self):
list_input = [1, 2, 3, 5, 432, 42, 31234, 342, 34]
self.assertIsInstance(entropy(list_input), float)
def test_the_same_vectors(self):
input_1 = [9, 8, 7, 6, 5, 4, 3, 2, 9]
input_2 = [9, 8, 7, 6, 5, 4, 3, 2, 9]
self.assertEqual(mutual_information(input_1, input_2),
entropy(input_1))
def test_one_number_input(self):
self.assertEqual(entropy([1]), 0.0)