Expand Up @@ -2161,17 +2161,18 @@ def test_specific_cases(self): # 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 Expand All @@ -2188,6 +2189,11 @@ def f(x): 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 Expand Down Expand Up @@ -2233,6 +2239,11 @@ def f(x): 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. Expand All @@ -2242,6 +2253,11 @@ def f(x): 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 Expand Down