diff options
Diffstat (limited to 'Lib/test/test_statistics.py')
| -rw-r--r-- | Lib/test/test_statistics.py | 38 |
1 files changed, 27 insertions, 11 deletions
diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py index 1922de5..946c742 100644 --- a/Lib/test/test_statistics.py +++ b/Lib/test/test_statistics.py @@ -2161,17 +2161,18 @@ class TestQuantiles(unittest.TestCase): # Quantiles should be idempotent if len(expected) >= 2: self.assertEqual(quantiles(expected, n=n), expected) - # Cross-check against other methods - if len(data) >= n: - # After end caps are added, method='inclusive' should - # give the same result as method='exclusive' whenever - # there are more data points than desired cut points. - padded_data = [min(data) - 1000] + data + [max(data) + 1000] - self.assertEqual( - quantiles(data, n=n), - quantiles(padded_data, n=n, method='inclusive'), - (n, data), - ) + # Cross-check against method='inclusive' which should give + # the same result after adding in minimum and maximum values + # extrapolated from the two lowest and two highest points. + sdata = sorted(data) + lo = 2 * sdata[0] - sdata[1] + hi = 2 * sdata[-1] - sdata[-2] + padded_data = data + [lo, hi] + self.assertEqual( + quantiles(data, n=n), + quantiles(padded_data, n=n, method='inclusive'), + (n, data), + ) # Invariant under tranlation and scaling def f(x): return 3.5 * x - 1234.675 @@ -2188,6 +2189,11 @@ class TestQuantiles(unittest.TestCase): actual = quantiles(statistics.NormalDist(), n=n) self.assertTrue(all(math.isclose(e, a, abs_tol=0.0001) for e, a in zip(expected, actual))) + # Q2 agrees with median() + for k in range(2, 60): + data = random.choices(range(100), k=k) + q1, q2, q3 = quantiles(data) + self.assertEqual(q2, statistics.median(data)) def test_specific_cases_inclusive(self): # Match results computed by hand and cross-checked @@ -2233,6 +2239,11 @@ class TestQuantiles(unittest.TestCase): actual = quantiles(statistics.NormalDist(), n=n, method="inclusive") self.assertTrue(all(math.isclose(e, a, abs_tol=0.0001) for e, a in zip(expected, actual))) + # Natural deciles + self.assertEqual(quantiles([0, 100], n=10, method='inclusive'), + [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0]) + self.assertEqual(quantiles(range(0, 101), n=10, method='inclusive'), + [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0]) # Whenever n is smaller than the number of data points, running # method='inclusive' should give the same result as method='exclusive' # after the two included extreme points are removed. @@ -2242,6 +2253,11 @@ class TestQuantiles(unittest.TestCase): data.remove(max(data)) expected = quantiles(data, n=32) self.assertEqual(expected, actual) + # Q2 agrees with median() + for k in range(2, 60): + data = random.choices(range(100), k=k) + q1, q2, q3 = quantiles(data, method='inclusive') + self.assertEqual(q2, statistics.median(data)) def test_equal_inputs(self): quantiles = statistics.quantiles |