Expand Up @@ -2038,6 +2038,94 @@ def test_compare_to_variance(self): expected = math.sqrt(statistics.variance(data)) self.assertEqual(self.func(data), expected)
class TestGeometricMean(unittest.TestCase):
def test_basics(self): geometric_mean = statistics.geometric_mean self.assertAlmostEqual(geometric_mean([54, 24, 36]), 36.0) self.assertAlmostEqual(geometric_mean([4.0, 9.0]), 6.0) self.assertAlmostEqual(geometric_mean([17.625]), 17.625)
random.seed(86753095551212) for rng in [ range(1, 100), range(1, 1_000), range(1, 10_000), range(500, 10_000, 3), range(10_000, 500, -3), [12, 17, 13, 5, 120, 7], [random.expovariate(50.0) for i in range(1_000)], [random.lognormvariate(20.0, 3.0) for i in range(2_000)], [random.triangular(2000, 3000, 2200) for i in range(3_000)], ]: gm_decimal = math.prod(map(Decimal, rng)) ** (Decimal(1) / len(rng)) gm_float = geometric_mean(rng) self.assertTrue(math.isclose(gm_float, float(gm_decimal)))
def test_various_input_types(self): geometric_mean = statistics.geometric_mean D = Decimal F = Fraction # https://www.wolframalpha.com/input/?i=geometric+mean+3.5,+4.0,+5.25 expected_mean = 4.18886 for data, kind in [ ([3.5, 4.0, 5.25], 'floats'), ([D('3.5'), D('4.0'), D('5.25')], 'decimals'), ([F(7, 2), F(4, 1), F(21, 4)], 'fractions'), ([3.5, 4, F(21, 4)], 'mixed types'), ((3.5, 4.0, 5.25), 'tuple'), (iter([3.5, 4.0, 5.25]), 'iterator'), ]: actual_mean = geometric_mean(data) self.assertIs(type(actual_mean), float, kind) self.assertAlmostEqual(actual_mean, expected_mean, places=5)
def test_big_and_small(self): geometric_mean = statistics.geometric_mean
# Avoid overflow to infinity large = 2.0 ** 1000 big_gm = geometric_mean([54.0 * large, 24.0 * large, 36.0 * large]) self.assertTrue(math.isclose(big_gm, 36.0 * large)) self.assertFalse(math.isinf(big_gm))
# Avoid underflow to zero small = 2.0 ** -1000 small_gm = geometric_mean([54.0 * small, 24.0 * small, 36.0 * small]) self.assertTrue(math.isclose(small_gm, 36.0 * small)) self.assertNotEqual(small_gm, 0.0)
def test_error_cases(self): geometric_mean = statistics.geometric_mean StatisticsError = statistics.StatisticsError with self.assertRaises(StatisticsError): geometric_mean([]) # empty input with self.assertRaises(StatisticsError): geometric_mean([3.5, 0.0, 5.25]) # zero input with self.assertRaises(StatisticsError): geometric_mean([3.5, -4.0, 5.25]) # negative input with self.assertRaises(StatisticsError): geometric_mean(iter([])) # empty iterator with self.assertRaises(TypeError): geometric_mean(None) # non-iterable input with self.assertRaises(TypeError): geometric_mean([10, None, 20]) # non-numeric input with self.assertRaises(TypeError): geometric_mean() # missing data argument with self.assertRaises(TypeError): geometric_mean([10, 20, 60], 70) # too many arguments
def test_special_values(self): # Rules for special values are inherited from math.fsum() geometric_mean = statistics.geometric_mean NaN = float('Nan') Inf = float('Inf') self.assertTrue(math.isnan(geometric_mean([10, NaN])), 'nan') self.assertTrue(math.isnan(geometric_mean([NaN, Inf])), 'nan and infinity') self.assertTrue(math.isinf(geometric_mean([10, Inf])), 'infinity') with self.assertRaises(ValueError): geometric_mean([Inf, -Inf])
class TestNormalDist(unittest.TestCase):
# General note on precision: The pdf(), cdf(), and overlap() methods Expand Down