151 lines
7.1 KiB
Diff
151 lines
7.1 KiB
Diff
diff --git a/benchmarks/benchmarks/stats.py b/benchmarks/benchmarks/stats.py
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index 0f105b39..ed399f4d 100644
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--- a/benchmarks/benchmarks/stats.py
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+++ b/benchmarks/benchmarks/stats.py
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@@ -162,6 +162,33 @@ class InferentialStats(Benchmark):
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stats.mstats.kruskal(self.a, self.b)
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+# Benchmark data for the truncnorm stats() method.
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+# The data in each row is:
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+# a, b, mean, variance, skewness, excess kurtosis. Generated using
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+# https://gist.github.com/WarrenWeckesser/636b537ee889679227d53543d333a720
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+truncnorm_cases = [[-20, -19, -19.052343945976656, 0.002725073018195613,
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+ -1.9838693623377885, 5.871801893091683],
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+ [-30, -29, -29.034401237736176, 0.0011806604886186853,
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+ -1.9929615171469608, 5.943905539773037],
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+ [-40, -39, -39.02560741993011, 0.0006548827702932775,
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+ -1.9960847672775606, 5.968744357649675],
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+ [39, 40, 39.02560741993011, 0.0006548827702932775,
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+ 1.9960847672775606, 5.968744357649675]]
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+truncnorm_cases = np.array(truncnorm_cases)
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+
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+
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+class TruncnormStats(Benchmark):
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+ param_names = ['case', 'moment']
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+ params = [list(range(len(truncnorm_cases))), ['m', 'v', 's', 'k']]
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+
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+ def track_truncnorm_stats_error(self, case, moment):
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+ result_indices = dict(zip(['m', 'v', 's', 'k'], range(2, 6)))
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+ ref = truncnorm_cases[case, result_indices[moment]]
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+ a, b = truncnorm_cases[case, 0:2]
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+ res = stats.truncnorm(a, b).stats(moments=moment)
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+ return np.abs((res - ref)/ref)
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+
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+
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class DistributionsAll(Benchmark):
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# all distributions are in this list. A conversion to a set is used to
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# remove duplicates that appear more than once in either `distcont` or
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diff --git a/scipy/stats/tests/test_distributions.py b/scipy/stats/tests/test_distributions.py
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index d18ad6f3..a15a9301 100644
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--- a/scipy/stats/tests/test_distributions.py
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+++ b/scipy/stats/tests/test_distributions.py
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@@ -910,61 +910,52 @@ class TestTruncnorm:
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assert_almost_equal(s, s0, decimal=decimal_s)
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assert_almost_equal(k, k0)
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- @pytest.mark.xfail_on_32bit("reduced accuracy with 32bit platforms.")
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- def test_moments(self):
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- # Values validated by changing TRUNCNORM_TAIL_X so as to evaluate
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- # using both the _norm_XXX() and _norm_logXXX() functions, and by
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- # removing the _stats and _munp methods in truncnorm tp force
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- # numerical quadrature.
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- # For m,v,s,k expect k to have the largest error as it is
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- # constructed from powers of lower moments
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-
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- self._test_moments_one_range(-30, 30, [0, 1, 0.0, 0.0])
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- self._test_moments_one_range(-10, 10, [0, 1, 0.0, 0.0])
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- self._test_moments_one_range(-3, 3, [0.0, 0.9733369246625415,
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- 0.0, -0.1711144363977444])
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- self._test_moments_one_range(-2, 2, [0.0, 0.7737413035499232,
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- 0.0, -0.6344632828703505])
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-
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- self._test_moments_one_range(0, np.inf, [0.7978845608028654,
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- 0.3633802276324186,
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- 0.9952717464311565,
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- 0.8691773036059725])
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- self._test_moments_one_range(-np.inf, 0, [-0.7978845608028654,
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- 0.3633802276324186,
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- -0.9952717464311565,
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- 0.8691773036059725])
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-
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- self._test_moments_one_range(-1, 3, [0.2827861107271540,
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- 0.6161417353578292,
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- 0.5393018494027878,
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- -0.2058206513527461])
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- self._test_moments_one_range(-3, 1, [-0.2827861107271540,
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- 0.6161417353578292,
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- -0.5393018494027878,
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- -0.2058206513527461])
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-
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- self._test_moments_one_range(-10, -9, [-9.1084562880124764,
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- 0.0114488058210104,
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- -1.8985607337519652,
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- 5.0733457094223553])
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- self._test_moments_one_range(-20, -19, [-19.0523439459766628,
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- 0.0027250730180314,
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- -1.9838694022629291,
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- 5.8717850028287586])
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- self._test_moments_one_range(-30, -29, [-29.0344012377394698,
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- 0.0011806603928891,
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- -1.9930304534611458,
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- 5.8854062968996566],
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- decimal_s=6)
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- self._test_moments_one_range(-40, -39, [-39.0256074199326264,
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- 0.0006548826719649,
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- -1.9963146354109957,
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- 5.6167758371700494])
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- self._test_moments_one_range(39, 40, [39.0256074199326264,
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- 0.0006548826719649,
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- 1.9963146354109957,
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- 5.6167758371700494])
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+ # Test data for the truncnorm stats() method.
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+ # The data in each row is:
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+ # a, b, mean, variance, skewness, excess kurtosis. Generated using
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+ # https://gist.github.com/WarrenWeckesser/636b537ee889679227d53543d333a720
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+ _truncnorm_stats_data = [
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+ [-30, 30,
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+ 0.0, 1.0, 0.0, 0.0],
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+ [-10, 10,
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+ 0.0, 1.0, 0.0, -1.4927521335810455e-19],
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+ [-3, 3,
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+ 0.0, 0.9733369246625415, 0.0, -0.17111443639774404],
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+ [-2, 2,
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+ 0.0, 0.7737413035499232, 0.0, -0.6344632828703505],
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+ [0, np.inf,
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+ 0.7978845608028654,
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+ 0.3633802276324187,
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+ 0.995271746431156,
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+ 0.8691773036059741],
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+ [-np.inf, 0,
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+ -0.7978845608028654,
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+ 0.3633802276324187,
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+ -0.995271746431156,
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+ 0.8691773036059741],
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+ [-1, 3,
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+ 0.282786110727154,
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+ 0.6161417353578293,
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+ 0.5393018494027877,
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+ -0.20582065135274694],
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+ [-3, 1,
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+ -0.282786110727154,
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+ 0.6161417353578293,
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+ -0.5393018494027877,
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+ -0.20582065135274694],
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+ [-10, -9,
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+ -9.108456288012409,
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+ 0.011448805821636248,
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+ -1.8985607290949496,
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+ 5.0733461105025075],
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+ ]
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+ _truncnorm_stats_data = np.array(_truncnorm_stats_data)
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+
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+ @pytest.mark.parametrize("case", _truncnorm_stats_data)
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+ def test_moments(self, case):
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+ a, b, m0, v0, s0, k0 = case
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+ m, v, s, k = stats.truncnorm.stats(a, b, moments='mvsk')
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+ assert_allclose([m, v, s, k], [m0, v0, s0, k0], atol=1e-17)
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def test_9902_moments(self):
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m, v = stats.truncnorm.stats(0, np.inf, moments='mv')
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