413 lines
18 KiB
Python
413 lines
18 KiB
Python
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import itertools
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import sys
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import pytest
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import numpy as np
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from numpy.testing import assert_
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from scipy.special._testutils import FuncData
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from scipy.special import kolmogorov, kolmogi, smirnov, smirnovi
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from scipy.special._ufuncs import (_kolmogc, _kolmogci, _kolmogp,
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_smirnovc, _smirnovci, _smirnovp)
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_rtol = 1e-10
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class TestSmirnov:
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def test_nan(self):
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assert_(np.isnan(smirnov(1, np.nan)))
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def test_basic(self):
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dataset = [(1, 0.1, 0.9),
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(1, 0.875, 0.125),
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(2, 0.875, 0.125 * 0.125),
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(3, 0.875, 0.125 * 0.125 * 0.125)]
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dataset = np.asarray(dataset)
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FuncData(smirnov, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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dataset[:, -1] = 1 - dataset[:, -1]
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FuncData(_smirnovc, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_x_equals_0(self):
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dataset = [(n, 0, 1) for n in itertools.chain(range(2, 20), range(1010, 1020))]
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dataset = np.asarray(dataset)
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FuncData(smirnov, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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dataset[:, -1] = 1 - dataset[:, -1]
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FuncData(_smirnovc, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_x_equals_1(self):
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dataset = [(n, 1, 0) for n in itertools.chain(range(2, 20), range(1010, 1020))]
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dataset = np.asarray(dataset)
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FuncData(smirnov, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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dataset[:, -1] = 1 - dataset[:, -1]
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FuncData(_smirnovc, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_x_equals_0point5(self):
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dataset = [(1, 0.5, 0.5),
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(2, 0.5, 0.25),
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(3, 0.5, 0.166666666667),
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(4, 0.5, 0.09375),
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(5, 0.5, 0.056),
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(6, 0.5, 0.0327932098765),
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(7, 0.5, 0.0191958707681),
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(8, 0.5, 0.0112953186035),
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(9, 0.5, 0.00661933257355),
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(10, 0.5, 0.003888705)]
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dataset = np.asarray(dataset)
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FuncData(smirnov, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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dataset[:, -1] = 1 - dataset[:, -1]
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FuncData(_smirnovc, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_n_equals_1(self):
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x = np.linspace(0, 1, 101, endpoint=True)
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dataset = np.column_stack([[1]*len(x), x, 1-x])
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FuncData(smirnov, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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dataset[:, -1] = 1 - dataset[:, -1]
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FuncData(_smirnovc, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_n_equals_2(self):
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x = np.linspace(0.5, 1, 101, endpoint=True)
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p = np.power(1-x, 2)
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n = np.array([2] * len(x))
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dataset = np.column_stack([n, x, p])
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FuncData(smirnov, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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dataset[:, -1] = 1 - dataset[:, -1]
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FuncData(_smirnovc, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_n_equals_3(self):
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x = np.linspace(0.7, 1, 31, endpoint=True)
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p = np.power(1-x, 3)
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n = np.array([3] * len(x))
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dataset = np.column_stack([n, x, p])
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FuncData(smirnov, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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dataset[:, -1] = 1 - dataset[:, -1]
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FuncData(_smirnovc, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_n_large(self):
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# test for large values of n
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# Probabilities should go down as n goes up
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x = 0.4
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pvals = np.array([smirnov(n, x) for n in range(400, 1100, 20)])
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dfs = np.diff(pvals)
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assert_(np.all(dfs <= 0), msg='Not all diffs negative %s' % dfs)
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class TestSmirnovi:
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def test_nan(self):
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assert_(np.isnan(smirnovi(1, np.nan)))
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def test_basic(self):
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dataset = [(1, 0.4, 0.6),
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(1, 0.6, 0.4),
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(1, 0.99, 0.01),
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(1, 0.01, 0.99),
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(2, 0.125 * 0.125, 0.875),
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(3, 0.125 * 0.125 * 0.125, 0.875),
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(10, 1.0 / 16 ** 10, 1 - 1.0 / 16)]
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dataset = np.asarray(dataset)
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FuncData(smirnovi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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dataset[:, 1] = 1 - dataset[:, 1]
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FuncData(_smirnovci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_x_equals_0(self):
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dataset = [(n, 0, 1) for n in itertools.chain(range(2, 20), range(1010, 1020))]
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dataset = np.asarray(dataset)
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FuncData(smirnovi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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dataset[:, 1] = 1 - dataset[:, 1]
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FuncData(_smirnovci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_x_equals_1(self):
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dataset = [(n, 1, 0) for n in itertools.chain(range(2, 20), range(1010, 1020))]
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dataset = np.asarray(dataset)
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FuncData(smirnovi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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dataset[:, 1] = 1 - dataset[:, 1]
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FuncData(_smirnovci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_n_equals_1(self):
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pp = np.linspace(0, 1, 101, endpoint=True)
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# dataset = np.array([(1, p, 1-p) for p in pp])
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dataset = np.column_stack([[1]*len(pp), pp, 1-pp])
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FuncData(smirnovi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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dataset[:, 1] = 1 - dataset[:, 1]
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FuncData(_smirnovci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_n_equals_2(self):
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x = np.linspace(0.5, 1, 101, endpoint=True)
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p = np.power(1-x, 2)
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n = np.array([2] * len(x))
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dataset = np.column_stack([n, p, x])
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FuncData(smirnovi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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dataset[:, 1] = 1 - dataset[:, 1]
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FuncData(_smirnovci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_n_equals_3(self):
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x = np.linspace(0.7, 1, 31, endpoint=True)
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p = np.power(1-x, 3)
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n = np.array([3] * len(x))
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dataset = np.column_stack([n, p, x])
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FuncData(smirnovi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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dataset[:, 1] = 1 - dataset[:, 1]
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FuncData(_smirnovci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_round_trip(self):
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def _sm_smi(n, p):
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return smirnov(n, smirnovi(n, p))
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def _smc_smci(n, p):
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return _smirnovc(n, _smirnovci(n, p))
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dataset = [(1, 0.4, 0.4),
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(1, 0.6, 0.6),
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(2, 0.875, 0.875),
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(3, 0.875, 0.875),
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(3, 0.125, 0.125),
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(10, 0.999, 0.999),
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(10, 0.0001, 0.0001)]
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dataset = np.asarray(dataset)
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FuncData(_sm_smi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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FuncData(_smc_smci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_x_equals_0point5(self):
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dataset = [(1, 0.5, 0.5),
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(2, 0.5, 0.366025403784),
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(2, 0.25, 0.5),
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(3, 0.5, 0.297156508177),
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(4, 0.5, 0.255520481121),
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(5, 0.5, 0.234559536069),
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(6, 0.5, 0.21715965898),
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(7, 0.5, 0.202722580034),
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(8, 0.5, 0.190621765256),
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(9, 0.5, 0.180363501362),
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(10, 0.5, 0.17157867006)]
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dataset = np.asarray(dataset)
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FuncData(smirnovi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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dataset[:, 1] = 1 - dataset[:, 1]
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FuncData(_smirnovci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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class TestSmirnovp:
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def test_nan(self):
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assert_(np.isnan(_smirnovp(1, np.nan)))
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def test_basic(self):
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# Check derivative at endpoints
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n1_10 = np.arange(1, 10)
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dataset0 = np.column_stack([n1_10, np.full_like(n1_10, 0), np.full_like(n1_10, -1)])
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FuncData(_smirnovp, dataset0, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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n2_10 = np.arange(2, 10)
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dataset1 = np.column_stack([n2_10, np.full_like(n2_10, 1.0), np.full_like(n2_10, 0)])
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FuncData(_smirnovp, dataset1, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_oneminusoneovern(self):
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# Check derivative at x=1-1/n
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n = np.arange(1, 20)
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x = 1.0/n
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xm1 = 1-1.0/n
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pp1 = -n * x**(n-1)
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pp1 -= (1-np.sign(n-2)**2) * 0.5 # n=2, x=0.5, 1-1/n = 0.5, need to adjust
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dataset1 = np.column_stack([n, xm1, pp1])
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FuncData(_smirnovp, dataset1, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_oneovertwon(self):
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# Check derivative at x=1/2n (Discontinuous at x=1/n, so check at x=1/2n)
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n = np.arange(1, 20)
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x = 1.0/2/n
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pp = -(n*x+1) * (1+x)**(n-2)
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dataset0 = np.column_stack([n, x, pp])
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FuncData(_smirnovp, dataset0, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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def test_oneovern(self):
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# Check derivative at x=1/n (Discontinuous at x=1/n, hard to tell if x==1/n, only use n=power of 2)
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n = 2**np.arange(1, 10)
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x = 1.0/n
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pp = -(n*x+1) * (1+x)**(n-2) + 0.5
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dataset0 = np.column_stack([n, x, pp])
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FuncData(_smirnovp, dataset0, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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@pytest.mark.xfail(sys.maxsize <= 2**32,
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reason="requires 64-bit platform")
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def test_oneovernclose(self):
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# Check derivative at x=1/n (Discontinuous at x=1/n, test on either side: x=1/n +/- 2epsilon)
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n = np.arange(3, 20)
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x = 1.0/n - 2*np.finfo(float).eps
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pp = -(n*x+1) * (1+x)**(n-2)
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dataset0 = np.column_stack([n, x, pp])
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FuncData(_smirnovp, dataset0, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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x = 1.0/n + 2*np.finfo(float).eps
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pp = -(n*x+1) * (1+x)**(n-2) + 1
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dataset1 = np.column_stack([n, x, pp])
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FuncData(_smirnovp, dataset1, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
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class TestKolmogorov:
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def test_nan(self):
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assert_(np.isnan(kolmogorov(np.nan)))
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def test_basic(self):
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dataset = [(0, 1.0),
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(0.5, 0.96394524366487511),
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(0.8275735551899077, 0.5000000000000000),
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(1, 0.26999967167735456),
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(2, 0.00067092525577969533)]
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dataset = np.asarray(dataset)
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FuncData(kolmogorov, dataset, (0,), 1, rtol=_rtol).check()
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def test_linspace(self):
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x = np.linspace(0, 2.0, 21)
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dataset = [1.0000000000000000, 1.0000000000000000, 0.9999999999994950,
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0.9999906941986655, 0.9971923267772983, 0.9639452436648751,
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0.8642827790506042, 0.7112351950296890, 0.5441424115741981,
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0.3927307079406543, 0.2699996716773546, 0.1777181926064012,
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0.1122496666707249, 0.0680922218447664, 0.0396818795381144,
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0.0222179626165251, 0.0119520432391966, 0.0061774306344441,
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0.0030676213475797, 0.0014636048371873, 0.0006709252557797]
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dataset_c = [0.0000000000000000, 6.609305242245699e-53, 5.050407338670114e-13,
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9.305801334566668e-06, 0.0028076732227017, 0.0360547563351249,
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0.1357172209493958, 0.2887648049703110, 0.4558575884258019,
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0.6072692920593457, 0.7300003283226455, 0.8222818073935988,
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0.8877503333292751, 0.9319077781552336, 0.9603181204618857,
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0.9777820373834749, 0.9880479567608034, 0.9938225693655559,
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0.9969323786524203, 0.9985363951628127, 0.9993290747442203]
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dataset = np.column_stack([x, dataset])
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FuncData(kolmogorov, dataset, (0,), 1, rtol=_rtol).check()
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dataset_c = np.column_stack([x, dataset_c])
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FuncData(_kolmogc, dataset_c, (0,), 1, rtol=_rtol).check()
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|
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def test_linspacei(self):
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p = np.linspace(0, 1.0, 21, endpoint=True)
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dataset = [np.inf, 1.3580986393225507, 1.2238478702170823,
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||
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|
1.1379465424937751, 1.0727491749396481, 1.0191847202536859,
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||
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|
0.9730633753323726, 0.9320695842357622, 0.8947644549851197,
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||
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|
0.8601710725555463, 0.8275735551899077, 0.7964065373291559,
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||
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|
0.7661855555617682, 0.7364542888171910, 0.7067326523068980,
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||
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|
0.6764476915028201, 0.6448126061663567, 0.6105590999244391,
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|
0.5711732651063401, 0.5196103791686224, 0.0000000000000000]
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|
|
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||
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dataset_c = [0.0000000000000000, 0.5196103791686225, 0.5711732651063401,
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0.6105590999244391, 0.6448126061663567, 0.6764476915028201,
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||
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|
0.7067326523068980, 0.7364542888171910, 0.7661855555617682,
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||
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|
0.7964065373291559, 0.8275735551899077, 0.8601710725555463,
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||
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|
0.8947644549851196, 0.9320695842357622, 0.9730633753323727,
|
||
|
|
1.0191847202536859, 1.0727491749396481, 1.1379465424937754,
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||
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|
1.2238478702170825, 1.3580986393225509, np.inf]
|
||
|
|
|
||
|
|
dataset = np.column_stack([p[1:], dataset[1:]])
|
||
|
|
FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
|
||
|
|
dataset_c = np.column_stack([p[:-1], dataset_c[:-1]])
|
||
|
|
FuncData(_kolmogci, dataset_c, (0,), 1, rtol=_rtol).check()
|
||
|
|
|
||
|
|
def test_smallx(self):
|
||
|
|
epsilon = 0.1 ** np.arange(1, 14)
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||
|
|
x = np.array([0.571173265106, 0.441027698518, 0.374219690278, 0.331392659217,
|
||
|
|
0.300820537459, 0.277539353999, 0.259023494805, 0.243829561254,
|
||
|
|
0.231063086389, 0.220135543236, 0.210641372041, 0.202290283658,
|
||
|
|
0.19487060742])
|
||
|
|
|
||
|
|
dataset = np.column_stack([x, 1-epsilon])
|
||
|
|
FuncData(kolmogorov, dataset, (0,), 1, rtol=_rtol).check()
|
||
|
|
|
||
|
|
def test_round_trip(self):
|
||
|
|
def _ki_k(_x):
|
||
|
|
return kolmogi(kolmogorov(_x))
|
||
|
|
|
||
|
|
def _kci_kc(_x):
|
||
|
|
return _kolmogci(_kolmogc(_x))
|
||
|
|
|
||
|
|
x = np.linspace(0.0, 2.0, 21, endpoint=True)
|
||
|
|
x02 = x[(x == 0) | (x > 0.21)] # Exclude 0.1, 0.2. 0.2 almost makes succeeds, but 0.1 has no chance.
|
||
|
|
dataset02 = np.column_stack([x02, x02])
|
||
|
|
FuncData(_ki_k, dataset02, (0,), 1, rtol=_rtol).check()
|
||
|
|
|
||
|
|
dataset = np.column_stack([x, x])
|
||
|
|
FuncData(_kci_kc, dataset, (0,), 1, rtol=_rtol).check()
|
||
|
|
|
||
|
|
|
||
|
|
class TestKolmogi:
|
||
|
|
def test_nan(self):
|
||
|
|
assert_(np.isnan(kolmogi(np.nan)))
|
||
|
|
|
||
|
|
def test_basic(self):
|
||
|
|
dataset = [(1.0, 0),
|
||
|
|
(0.96394524366487511, 0.5),
|
||
|
|
(0.9, 0.571173265106),
|
||
|
|
(0.5000000000000000, 0.8275735551899077),
|
||
|
|
(0.26999967167735456, 1),
|
||
|
|
(0.00067092525577969533, 2)]
|
||
|
|
|
||
|
|
dataset = np.asarray(dataset)
|
||
|
|
FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
|
||
|
|
|
||
|
|
def test_smallpcdf(self):
|
||
|
|
epsilon = 0.5 ** np.arange(1, 55, 3)
|
||
|
|
# kolmogi(1-p) == _kolmogci(p) if 1-(1-p) == p, but not necessarily otherwise
|
||
|
|
# Use epsilon s.t. 1-(1-epsilon)) == epsilon, so can use same x-array for both results
|
||
|
|
|
||
|
|
x = np.array([0.8275735551899077, 0.5345255069097583, 0.4320114038786941,
|
||
|
|
0.3736868442620478, 0.3345161714909591, 0.3057833329315859,
|
||
|
|
0.2835052890528936, 0.2655578150208676, 0.2506869966107999,
|
||
|
|
0.2380971058736669, 0.2272549289962079, 0.2177876361600040,
|
||
|
|
0.2094254686862041, 0.2019676748836232, 0.1952612948137504,
|
||
|
|
0.1891874239646641, 0.1836520225050326, 0.1785795904846466])
|
||
|
|
|
||
|
|
dataset = np.column_stack([1-epsilon, x])
|
||
|
|
FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
|
||
|
|
|
||
|
|
dataset = np.column_stack([epsilon, x])
|
||
|
|
FuncData(_kolmogci, dataset, (0,), 1, rtol=_rtol).check()
|
||
|
|
|
||
|
|
def test_smallpsf(self):
|
||
|
|
epsilon = 0.5 ** np.arange(1, 55, 3)
|
||
|
|
# kolmogi(p) == _kolmogci(1-p) if 1-(1-p) == p, but not necessarily otherwise
|
||
|
|
# Use epsilon s.t. 1-(1-epsilon)) == epsilon, so can use same x-array for both results
|
||
|
|
|
||
|
|
x = np.array([0.8275735551899077, 1.3163786275161036, 1.6651092133663343,
|
||
|
|
1.9525136345289607, 2.2027324540033235, 2.4272929437460848,
|
||
|
|
2.6327688477341593, 2.8233300509220260, 3.0018183401530627,
|
||
|
|
3.1702735084088891, 3.3302184446307912, 3.4828258153113318,
|
||
|
|
3.6290214150152051, 3.7695513262825959, 3.9050272690877326,
|
||
|
|
4.0359582187082550, 4.1627730557884890, 4.2858371743264527])
|
||
|
|
|
||
|
|
dataset = np.column_stack([epsilon, x])
|
||
|
|
FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
|
||
|
|
|
||
|
|
dataset = np.column_stack([1-epsilon, x])
|
||
|
|
FuncData(_kolmogci, dataset, (0,), 1, rtol=_rtol).check()
|
||
|
|
|
||
|
|
def test_round_trip(self):
|
||
|
|
def _k_ki(_p):
|
||
|
|
return kolmogorov(kolmogi(_p))
|
||
|
|
|
||
|
|
p = np.linspace(0.1, 1.0, 10, endpoint=True)
|
||
|
|
dataset = np.column_stack([p, p])
|
||
|
|
FuncData(_k_ki, dataset, (0,), 1, rtol=_rtol).check()
|
||
|
|
|
||
|
|
|
||
|
|
class TestKolmogp:
|
||
|
|
def test_nan(self):
|
||
|
|
assert_(np.isnan(_kolmogp(np.nan)))
|
||
|
|
|
||
|
|
def test_basic(self):
|
||
|
|
dataset = [(0.000000, -0.0),
|
||
|
|
(0.200000, -1.532420541338916e-10),
|
||
|
|
(0.400000, -0.1012254419260496),
|
||
|
|
(0.600000, -1.324123244249925),
|
||
|
|
(0.800000, -1.627024345636592),
|
||
|
|
(1.000000, -1.071948558356941),
|
||
|
|
(1.200000, -0.538512430720529),
|
||
|
|
(1.400000, -0.2222133182429472),
|
||
|
|
(1.600000, -0.07649302775520538),
|
||
|
|
(1.800000, -0.02208687346347873),
|
||
|
|
(2.000000, -0.005367402045629683)]
|
||
|
|
|
||
|
|
dataset = np.asarray(dataset)
|
||
|
|
FuncData(_kolmogp, dataset, (0,), 1, rtol=_rtol).check()
|