Steven D'Aprano added the comment:
> The newly-added statistics.geometric_mean function appears to have no
> tests at all
That's weird and unfortunate. I certainly wrote tests, and I have a
backup of them. I have no idea what happened.
Attached is a patch that adds the tests, but obviously I haven't been
able to run this until I resolve my gcc issues and can build 3.6 again.
(I did run this some weeks ago, and they passed *then*, but I cannot be
sure they still pass now.) If somebody would like to test this for me,
it would be appreciated.
----------
keywords: +patch
Added file: http://bugs.python.org/file44920/geometric_mean_tests.patch
_______________________________________
Python tracker <rep...@bugs.python.org>
<http://bugs.python.org/issue28328>
_______________________________________
diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py
--- a/Lib/test/test_statistics.py
+++ b/Lib/test/test_statistics.py
@@ -1694,6 +1694,124 @@
self.assertEqual(statistics.mean([tiny]*n), tiny)
+class TestGeometricMean(NumericTestCase, AverageMixin, UnivariateTypeMixin):
+ def setUp(self):
+ self.func = statistics.geometric_mean
+
+ def prepare_data(self):
+ # Override mixin method.
+ values = super().prepare_data()
+ values.remove(0)
+ return values
+
+ def prepare_types_for_conservation_test(self):
+ # Override mixin method.
+ return (float, Decimal)
+
+ def prepare_values_for_repeated_single_test(self):
+ # Override mixin method.
+ return (3.5, 17, 2.5e9, Decimal('4.9712'), Decimal('9236.40781'))
+
+ def test_repeated_fractions(self):
+ # This test has been split out from the test_repeated_single_value
mixin.
+ for x in (Fraction(61, 67), Fraction(935, 821)):
+ expected = float(x)
+ for n in (2, 14, 93):
+ with self.subTest(x=x, n=n):
+ self.assertEqual(self.func([x]*n), expected)
+
+ def test_repeated_huge_single_value(self):
+ # Test geometric mean with a single really big float repeated many
times.
+ x = 2.5e15
+ for count in (2, 3, 10):
+ self.assertEqual(self.func([x]*count), x)
+ count = 20
+ self.assertApproxEqual(self.func([x]*count), x, rel=1e-15)
+
+ def test_zero(self):
+ # Test that geometric mean returns zero if zero is an element.
+ values = [1, 2, 3, 0, 5]
+ self.assertEqual(self.func(values), 0.0)
+
+ def test_negative_error(self):
+ # Test that geometric mean raises when given a negative value.
+ exc = statistics.StatisticsError
+ for values in ([-1], [1, -2, 3]):
+ with self.subTest(values=values):
+ self.assertRaises(exc, self.func, values)
+
+ def test_ints(self):
+ # Test geometric mean with ints.
+ data = [12, 21, 294]
+ random.shuffle(data)
+ self.assertEqual(self.func(data), 42.0)
+ data = [90, 135, 270, 320]
+ random.shuffle(data)
+ self.assertEqual(self.func(data), 180.0)
+
+ def test_floats_exact(self):
+ # Test geometric mean with some carefully chosen floats.
+ data = [1.0, 2.0, 6.125, 12.25]
+ random.shuffle(data)
+ self.assertEqual(self.func(data), 3.5)
+
+ def test_singleton_lists(self):
+ # Test that geometric mean([x]) returns x.
+ for i in range(100):
+ x = random.uniform(0.0, 10000.0)
+ self.assertEqual(self.func([x]), x)
+
+ def test_decimals_exact(self):
+ # Test geometric mean with some carefully chosen Decimals.
+ D = Decimal
+ data = [D("0.972"), D("8.748"), D("23.328")]
+ random.shuffle(data)
+ self.assertEqual(self.func(data), D("5.832"))
+
+ def test_fractions(self):
+ # Test geometric mean with Fractions.
+ F = Fraction
+ data = [F(1, 2), F(2, 3), F(3, 4), F(4, 5), F(5, 6), F(6, 7), F(7, 8)]
+ random.shuffle(data)
+ expected = 1/(8**(1/7))
+ self.assertApproxEqual(self.func(data), expected, rel=1e-13)
+
+ def test_inf(self):
+ # Test geometric mean with infinity.
+ INF = float('inf')
+ values = [2.0, INF, 1.0]
+ self.assertEqual(self.func(values), INF)
+
+ def test_nan(self):
+ # Test geometric mean with NANs.
+ values = [2.0, float('nan'), 1.0]
+ self.assertTrue(math.isnan(self.func(values)))
+
+ def test_multiply_data_points(self):
+ # Test multiplying every data point by a constant.
+ c = 111
+ data = [3.4, 4.5, 4.9, 6.7, 6.8, 7.2, 8.0, 8.1, 9.4]
+ expected = self.func(data)*c
+ result = self.func([x*c for x in data])
+ self.assertApproxEqual(self.func(data), expected, rel=1e-13)
+
+ def test_doubled_data(self):
+ # Test doubling data from [a,b...z] to [a,a,b,b...z,z].
+ data = [random.uniform(1, 500) for _ in range(1000)]
+ expected = self.func(data)
+ actual = self.func(data*2)
+ self.assertApproxEqual(actual, expected, rel=1e-13)
+
+ def test_float_overflow(self):
+ # Test with a set of data which will overflow when multiplied.
+ data = [2.0**900, 2.0**920, 2.0**960, 2.0**980, 2.0**990]
+ expected = 2.0**950
+ self.assertApproxEqual(self.func(data), expected, rel=1e-13)
+ data = [2e300, 4e300, 8e300, 16e300, 32e300]
+ expected = 8e300
+ self.assertApproxEqual(self.func(data), expected, rel=1e-13)
+
+
class TestHarmonicMean(NumericTestCase, AverageMixin, UnivariateTypeMixin):
def setUp(self):
self.func = statistics.harmonic_mean
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