diff options
| -rw-r--r-- | Lib/test/test_statistics.py | 68 |
1 files changed, 44 insertions, 24 deletions
diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py index 02cbebd..485ffe2 100644 --- a/Lib/test/test_statistics.py +++ b/Lib/test/test_statistics.py @@ -2040,6 +2040,13 @@ class TestStdev(VarianceStdevMixin, NumericTestCase): class TestNormalDist(unittest.TestCase): + # General note on precision: The pdf(), cdf(), and overlap() methods + # depend on functions in the math libraries that do not make + # explicit accuracy guarantees. Accordingly, some of the accuracy + # tests below may fail if the underlying math functions are + # inaccurate. There isn't much we can do about this short of + # implementing our own implementations from scratch. + def test_slots(self): nd = statistics.NormalDist(300, 23) with self.assertRaises(TypeError): @@ -2062,6 +2069,12 @@ class TestNormalDist(unittest.TestCase): with self.assertRaises(statistics.StatisticsError): statistics.NormalDist(500, -10) + # verify that subclass type is honored + class NewNormalDist(statistics.NormalDist): + pass + nnd = NewNormalDist(200, 5) + self.assertEqual(type(nnd), NewNormalDist) + def test_alternative_constructor(self): NormalDist = statistics.NormalDist data = [96, 107, 90, 92, 110] @@ -2077,6 +2090,12 @@ class TestNormalDist(unittest.TestCase): with self.assertRaises(statistics.StatisticsError): NormalDist.from_samples([10]) # only one input + # verify that subclass type is honored + class NewNormalDist(NormalDist): + pass + nnd = NewNormalDist.from_samples(data) + self.assertEqual(type(nnd), NewNormalDist) + def test_sample_generation(self): NormalDist = statistics.NormalDist mu, sigma = 10_000, 3.0 @@ -2099,12 +2118,6 @@ class TestNormalDist(unittest.TestCase): self.assertEqual(data2, data4) self.assertNotEqual(data1, data2) - # verify that subclass type is honored - class NewNormalDist(NormalDist): - pass - nnd = NewNormalDist(200, 5) - self.assertEqual(type(nnd), NewNormalDist) - def test_pdf(self): NormalDist = statistics.NormalDist X = NormalDist(100, 15) @@ -2151,8 +2164,8 @@ class TestNormalDist(unittest.TestCase): self.assertEqual(set(map(type, cdfs)), {float}) # Verify montonic self.assertEqual(cdfs, sorted(cdfs)) - # Verify center - self.assertAlmostEqual(X.cdf(100), 0.50) + # Verify center (should be exact) + self.assertEqual(X.cdf(100), 0.50) # Check against a table of known values # https://en.wikipedia.org/wiki/Standard_normal_table#Cumulative Z = NormalDist() @@ -2216,10 +2229,11 @@ class TestNormalDist(unittest.TestCase): p = 1.0 - p self.assertAlmostEqual(iq.cdf(iq.inv_cdf(p)), p) - # Now apply cdf() first. At six sigmas, the round-trip - # loses a lot of precision, so only check to 6 places. - for x in range(10, 190): - self.assertAlmostEqual(iq.inv_cdf(iq.cdf(x)), x, places=6) + # Now apply cdf() first. Near the tails, the round-trip loses + # precision and is ill-conditioned (small changes in the inputs + # give large changes in the output), so only check to 5 places. + for x in range(200): + self.assertAlmostEqual(iq.inv_cdf(iq.cdf(x)), x, places=5) # Error cases: with self.assertRaises(statistics.StatisticsError): @@ -2237,6 +2251,9 @@ class TestNormalDist(unittest.TestCase): iq.sigma = -0.1 # sigma under zero iq.inv_cdf(0.5) + # Special values + self.assertTrue(math.isnan(Z.inv_cdf(float('NaN')))) + def test_overlap(self): NormalDist = statistics.NormalDist @@ -2275,6 +2292,7 @@ class TestNormalDist(unittest.TestCase): (NormalDist(-100, 15), NormalDist(110, 15)), (NormalDist(-100, 15), NormalDist(-110, 15)), # Misc cases with unequal standard deviations + (NormalDist(100, 12), NormalDist(100, 15)), (NormalDist(100, 12), NormalDist(110, 15)), (NormalDist(100, 12), NormalDist(150, 15)), (NormalDist(100, 12), NormalDist(150, 35)), @@ -2305,18 +2323,6 @@ class TestNormalDist(unittest.TestCase): self.assertEqual(X.stdev, 15) self.assertEqual(X.variance, 225) - def test_unary_operations(self): - NormalDist = statistics.NormalDist - X = NormalDist(100, 12) - Y = +X - self.assertIsNot(X, Y) - self.assertEqual(X.mu, Y.mu) - self.assertEqual(X.sigma, Y.sigma) - Y = -X - self.assertIsNot(X, Y) - self.assertEqual(X.mu, -Y.mu) - self.assertEqual(X.sigma, Y.sigma) - def test_same_type_addition_and_subtraction(self): NormalDist = statistics.NormalDist X = NormalDist(100, 12) @@ -2340,13 +2346,27 @@ class TestNormalDist(unittest.TestCase): with self.assertRaises(TypeError): # __rtruediv__ y / X + def test_unary_operations(self): + NormalDist = statistics.NormalDist + X = NormalDist(100, 12) + Y = +X + self.assertIsNot(X, Y) + self.assertEqual(X.mu, Y.mu) + self.assertEqual(X.sigma, Y.sigma) + Y = -X + self.assertIsNot(X, Y) + self.assertEqual(X.mu, -Y.mu) + self.assertEqual(X.sigma, Y.sigma) + def test_equality(self): NormalDist = statistics.NormalDist nd1 = NormalDist() nd2 = NormalDist(2, 4) nd3 = NormalDist() + nd4 = NormalDist(2, 4) self.assertNotEqual(nd1, nd2) self.assertEqual(nd1, nd3) + self.assertEqual(nd2, nd4) # Test NotImplemented when types are different class A: |