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speech2derive.old/lib/python3.8/site-packages/scipy/stats/tests/test_distributions.py

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"""
Test functions for stats module
"""
import warnings
import re
import sys
import pickle
import os
from numpy.testing import (assert_equal, assert_array_equal,
assert_almost_equal, assert_array_almost_equal,
assert_allclose, assert_, assert_warns,
assert_array_less, suppress_warnings)
import pytest
from pytest import raises as assert_raises
import numpy
import numpy as np
from numpy import typecodes, array
from numpy.lib.recfunctions import rec_append_fields
from scipy import special
from scipy._lib._util import check_random_state
from scipy.integrate import IntegrationWarning, quad
import scipy.stats as stats
from scipy.stats._distn_infrastructure import argsreduce
import scipy.stats.distributions
from scipy.special import xlogy
from .test_continuous_basic import distcont
from scipy.stats._continuous_distns import FitDataError
from scipy.optimize import root
# python -OO strips docstrings
DOCSTRINGS_STRIPPED = sys.flags.optimize > 1
def _assert_hasattr(a, b, msg=None):
if msg is None:
msg = '%s does not have attribute %s' % (a, b)
assert_(hasattr(a, b), msg=msg)
def test_api_regression():
# https://github.com/scipy/scipy/issues/3802
_assert_hasattr(scipy.stats.distributions, 'f_gen')
def check_vonmises_pdf_periodic(k, L, s, x):
vm = stats.vonmises(k, loc=L, scale=s)
assert_almost_equal(vm.pdf(x), vm.pdf(x % (2*numpy.pi*s)))
def check_vonmises_cdf_periodic(k, L, s, x):
vm = stats.vonmises(k, loc=L, scale=s)
assert_almost_equal(vm.cdf(x) % 1, vm.cdf(x % (2*numpy.pi*s)) % 1)
def test_vonmises_pdf_periodic():
for k in [0.1, 1, 101]:
for x in [0, 1, numpy.pi, 10, 100]:
check_vonmises_pdf_periodic(k, 0, 1, x)
check_vonmises_pdf_periodic(k, 1, 1, x)
check_vonmises_pdf_periodic(k, 0, 10, x)
check_vonmises_cdf_periodic(k, 0, 1, x)
check_vonmises_cdf_periodic(k, 1, 1, x)
check_vonmises_cdf_periodic(k, 0, 10, x)
def test_vonmises_line_support():
assert_equal(stats.vonmises_line.a, -np.pi)
assert_equal(stats.vonmises_line.b, np.pi)
def test_vonmises_numerical():
vm = stats.vonmises(800)
assert_almost_equal(vm.cdf(0), 0.5)
# Expected values of the vonmises PDF were computed using
# mpmath with 50 digits of precision:
#
# def vmpdf_mp(x, kappa):
# x = mpmath.mpf(x)
# kappa = mpmath.mpf(kappa)
# num = mpmath.exp(kappa*mpmath.cos(x))
# den = 2 * mpmath.pi * mpmath.besseli(0, kappa)
# return num/den
#
@pytest.mark.parametrize('x, kappa, expected_pdf',
[(0.1, 0.01, 0.16074242744907072),
(0.1, 25.0, 1.7515464099118245),
(0.1, 800, 0.2073272544458798),
(2.0, 0.01, 0.15849003875385817),
(2.0, 25.0, 8.356882934278192e-16),
(2.0, 800, 0.0)])
def test_vonmises_pdf(x, kappa, expected_pdf):
pdf = stats.vonmises.pdf(x, kappa)
assert_allclose(pdf, expected_pdf, rtol=1e-15)
def _assert_less_or_close_loglike(dist, data, func, **kwds):
"""
This utility function checks that the log-likelihood (computed by
func) of the result computed using dist.fit() is less than or equal
to the result computed using the generic fit method. Because of
normal numerical imprecision, the "equality" check is made using
`np.allclose` with a relative tolerance of 1e-15.
"""
mle_analytical = dist.fit(data, **kwds)
numerical_opt = super(type(dist), dist).fit(data, **kwds)
ll_mle_analytical = func(mle_analytical, data)
ll_numerical_opt = func(numerical_opt, data)
assert (ll_mle_analytical <= ll_numerical_opt or
np.allclose(ll_mle_analytical, ll_numerical_opt, rtol=1e-15))
def assert_fit_warnings(dist):
param = ['floc', 'fscale']
if dist.shapes:
nshapes = len(dist.shapes.split(","))
param += ['f0', 'f1', 'f2'][:nshapes]
all_fixed = dict(zip(param, np.arange(len(param))))
data = [1, 2, 3]
with pytest.raises(RuntimeError,
match="All parameters fixed. There is nothing "
"to optimize."):
dist.fit(data, **all_fixed)
with pytest.raises(RuntimeError,
match="The data contains non-finite values"):
dist.fit([np.nan])
with pytest.raises(RuntimeError,
match="The data contains non-finite values"):
dist.fit([np.inf])
with pytest.raises(TypeError, match="Unknown keyword arguments:"):
dist.fit(data, extra_keyword=2)
with pytest.raises(TypeError, match="Too many positional arguments."):
dist.fit(data, *[1]*(len(param) - 1))
@pytest.mark.parametrize('dist',
['alpha', 'betaprime',
'fatiguelife', 'invgamma', 'invgauss', 'invweibull',
'johnsonsb', 'levy', 'levy_l', 'lognorm', 'gilbrat',
'powerlognorm', 'rayleigh', 'wald'])
def test_support(dist):
"""gh-6235"""
dct = dict(distcont)
args = dct[dist]
dist = getattr(stats, dist)
assert_almost_equal(dist.pdf(dist.a, *args), 0)
assert_equal(dist.logpdf(dist.a, *args), -np.inf)
assert_almost_equal(dist.pdf(dist.b, *args), 0)
assert_equal(dist.logpdf(dist.b, *args), -np.inf)
class TestRandInt(object):
def setup_method(self):
np.random.seed(1234)
def test_rvs(self):
vals = stats.randint.rvs(5, 30, size=100)
assert_(numpy.all(vals < 30) & numpy.all(vals >= 5))
assert_(len(vals) == 100)
vals = stats.randint.rvs(5, 30, size=(2, 50))
assert_(numpy.shape(vals) == (2, 50))
assert_(vals.dtype.char in typecodes['AllInteger'])
val = stats.randint.rvs(15, 46)
assert_((val >= 15) & (val < 46))
assert_(isinstance(val, numpy.ScalarType), msg=repr(type(val)))
val = stats.randint(15, 46).rvs(3)
assert_(val.dtype.char in typecodes['AllInteger'])
def test_pdf(self):
k = numpy.r_[0:36]
out = numpy.where((k >= 5) & (k < 30), 1.0/(30-5), 0)
vals = stats.randint.pmf(k, 5, 30)
assert_array_almost_equal(vals, out)
def test_cdf(self):
x = np.linspace(0, 36, 100)
k = numpy.floor(x)
out = numpy.select([k >= 30, k >= 5], [1.0, (k-5.0+1)/(30-5.0)], 0)
vals = stats.randint.cdf(x, 5, 30)
assert_array_almost_equal(vals, out, decimal=12)
class TestBinom(object):
def setup_method(self):
np.random.seed(1234)
def test_rvs(self):
vals = stats.binom.rvs(10, 0.75, size=(2, 50))
assert_(numpy.all(vals >= 0) & numpy.all(vals <= 10))
assert_(numpy.shape(vals) == (2, 50))
assert_(vals.dtype.char in typecodes['AllInteger'])
val = stats.binom.rvs(10, 0.75)
assert_(isinstance(val, int))
val = stats.binom(10, 0.75).rvs(3)
assert_(isinstance(val, numpy.ndarray))
assert_(val.dtype.char in typecodes['AllInteger'])
def test_pmf(self):
# regression test for Ticket #1842
vals1 = stats.binom.pmf(100, 100, 1)
vals2 = stats.binom.pmf(0, 100, 0)
assert_allclose(vals1, 1.0, rtol=1e-15, atol=0)
assert_allclose(vals2, 1.0, rtol=1e-15, atol=0)
def test_entropy(self):
# Basic entropy tests.
b = stats.binom(2, 0.5)
expected_p = np.array([0.25, 0.5, 0.25])
expected_h = -sum(xlogy(expected_p, expected_p))
h = b.entropy()
assert_allclose(h, expected_h)
b = stats.binom(2, 0.0)
h = b.entropy()
assert_equal(h, 0.0)
b = stats.binom(2, 1.0)
h = b.entropy()
assert_equal(h, 0.0)
def test_warns_p0(self):
# no spurious warnigns are generated for p=0; gh-3817
with warnings.catch_warnings():
warnings.simplefilter("error", RuntimeWarning)
assert_equal(stats.binom(n=2, p=0).mean(), 0)
assert_equal(stats.binom(n=2, p=0).std(), 0)
class TestBernoulli(object):
def setup_method(self):
np.random.seed(1234)
def test_rvs(self):
vals = stats.bernoulli.rvs(0.75, size=(2, 50))
assert_(numpy.all(vals >= 0) & numpy.all(vals <= 1))
assert_(numpy.shape(vals) == (2, 50))
assert_(vals.dtype.char in typecodes['AllInteger'])
val = stats.bernoulli.rvs(0.75)
assert_(isinstance(val, int))
val = stats.bernoulli(0.75).rvs(3)
assert_(isinstance(val, numpy.ndarray))
assert_(val.dtype.char in typecodes['AllInteger'])
def test_entropy(self):
# Simple tests of entropy.
b = stats.bernoulli(0.25)
expected_h = -0.25*np.log(0.25) - 0.75*np.log(0.75)
h = b.entropy()
assert_allclose(h, expected_h)
b = stats.bernoulli(0.0)
h = b.entropy()
assert_equal(h, 0.0)
b = stats.bernoulli(1.0)
h = b.entropy()
assert_equal(h, 0.0)
class TestBradford(object):
# gh-6216
def test_cdf_ppf(self):
c = 0.1
x = np.logspace(-20, -4)
q = stats.bradford.cdf(x, c)
xx = stats.bradford.ppf(q, c)
assert_allclose(x, xx)
class TestNBinom(object):
def setup_method(self):
np.random.seed(1234)
def test_rvs(self):
vals = stats.nbinom.rvs(10, 0.75, size=(2, 50))
assert_(numpy.all(vals >= 0))
assert_(numpy.shape(vals) == (2, 50))
assert_(vals.dtype.char in typecodes['AllInteger'])
val = stats.nbinom.rvs(10, 0.75)
assert_(isinstance(val, int))
val = stats.nbinom(10, 0.75).rvs(3)
assert_(isinstance(val, numpy.ndarray))
assert_(val.dtype.char in typecodes['AllInteger'])
def test_pmf(self):
# regression test for ticket 1779
assert_allclose(np.exp(stats.nbinom.logpmf(700, 721, 0.52)),
stats.nbinom.pmf(700, 721, 0.52))
# logpmf(0,1,1) shouldn't return nan (regression test for gh-4029)
val = scipy.stats.nbinom.logpmf(0, 1, 1)
assert_equal(val, 0)
class TestGenInvGauss(object):
def setup_method(self):
np.random.seed(1234)
@pytest.mark.slow
def test_rvs_with_mode_shift(self):
# ratio_unif w/ mode shift
gig = stats.geninvgauss(2.3, 1.5)
_, p = stats.kstest(gig.rvs(size=1500, random_state=1234), gig.cdf)
assert_equal(p > 0.05, True)
@pytest.mark.slow
def test_rvs_without_mode_shift(self):
# ratio_unif w/o mode shift
gig = stats.geninvgauss(0.9, 0.75)
_, p = stats.kstest(gig.rvs(size=1500, random_state=1234), gig.cdf)
assert_equal(p > 0.05, True)
@pytest.mark.slow
def test_rvs_new_method(self):
# new algorithm of Hoermann / Leydold
gig = stats.geninvgauss(0.1, 0.2)
_, p = stats.kstest(gig.rvs(size=1500, random_state=1234), gig.cdf)
assert_equal(p > 0.05, True)
@pytest.mark.slow
def test_rvs_p_zero(self):
def my_ks_check(p, b):
gig = stats.geninvgauss(p, b)
rvs = gig.rvs(size=1500, random_state=1234)
return stats.kstest(rvs, gig.cdf)[1] > 0.05
# boundary cases when p = 0
assert_equal(my_ks_check(0, 0.2), True) # new algo
assert_equal(my_ks_check(0, 0.9), True) # ratio_unif w/o shift
assert_equal(my_ks_check(0, 1.5), True) # ratio_unif with shift
def test_rvs_negative_p(self):
# if p negative, return inverse
assert_equal(
stats.geninvgauss(-1.5, 2).rvs(size=10, random_state=1234),
1 / stats.geninvgauss(1.5, 2).rvs(size=10, random_state=1234))
def test_invgauss(self):
# test that invgauss is special case
ig = stats.geninvgauss.rvs(size=1500, p=-0.5, b=1, random_state=1234)
assert_equal(stats.kstest(ig, 'invgauss', args=[1])[1] > 0.15, True)
# test pdf and cdf
mu, x = 100, np.linspace(0.01, 1, 10)
pdf_ig = stats.geninvgauss.pdf(x, p=-0.5, b=1 / mu, scale=mu)
assert_allclose(pdf_ig, stats.invgauss(mu).pdf(x))
cdf_ig = stats.geninvgauss.cdf(x, p=-0.5, b=1 / mu, scale=mu)
assert_allclose(cdf_ig, stats.invgauss(mu).cdf(x))
def test_pdf_R(self):
# test against R package GIGrvg
# x <- seq(0.01, 5, length.out = 10)
# GIGrvg::dgig(x, 0.5, 1, 1)
vals_R = np.array([2.081176820e-21, 4.488660034e-01, 3.747774338e-01,
2.693297528e-01, 1.905637275e-01, 1.351476913e-01,
9.636538981e-02, 6.909040154e-02, 4.978006801e-02,
3.602084467e-02])
x = np.linspace(0.01, 5, 10)
assert_allclose(vals_R, stats.geninvgauss.pdf(x, 0.5, 1))
def test_pdf_zero(self):
# pdf at 0 is 0, needs special treatment to avoid 1/x in pdf
assert_equal(stats.geninvgauss.pdf(0, 0.5, 0.5), 0)
# if x is large and p is moderate, make sure that pdf does not
# overflow because of x**(p-1); exp(-b*x) forces pdf to zero
assert_equal(stats.geninvgauss.pdf(2e6, 50, 2), 0)
class TestNormInvGauss(object):
def setup_method(self):
np.random.seed(1234)
def test_cdf_R(self):
# test pdf and cdf vals against R
# require("GeneralizedHyperbolic")
# x_test <- c(-7, -5, 0, 8, 15)
# r_cdf <- GeneralizedHyperbolic::pnig(x_test, mu = 0, a = 1, b = 0.5)
# r_pdf <- GeneralizedHyperbolic::dnig(x_test, mu = 0, a = 1, b = 0.5)
r_cdf = np.array([8.034920282e-07, 2.512671945e-05, 3.186661051e-01,
9.988650664e-01, 9.999848769e-01])
x_test = np.array([-7, -5, 0, 8, 15])
vals_cdf = stats.norminvgauss.cdf(x_test, a=1, b=0.5)
assert_allclose(vals_cdf, r_cdf, atol=1e-9)
def test_pdf_R(self):
# values from R as defined in test_cdf_R
r_pdf = np.array([1.359600783e-06, 4.413878805e-05, 4.555014266e-01,
7.450485342e-04, 8.917889931e-06])
x_test = np.array([-7, -5, 0, 8, 15])
vals_pdf = stats.norminvgauss.pdf(x_test, a=1, b=0.5)
assert_allclose(vals_pdf, r_pdf, atol=1e-9)
def test_stats(self):
a, b = 1, 0.5
gamma = np.sqrt(a**2 - b**2)
v_stats = (b / gamma, a**2 / gamma**3, 3.0 * b / (a * np.sqrt(gamma)),
3.0 * (1 + 4 * b**2 / a**2) / gamma)
assert_equal(v_stats, stats.norminvgauss.stats(a, b, moments='mvsk'))
def test_ppf(self):
a, b = 1, 0.5
x_test = np.array([0.001, 0.5, 0.999])
vals = stats.norminvgauss.ppf(x_test, a, b)
assert_allclose(x_test, stats.norminvgauss.cdf(vals, a, b))
class TestGeom(object):
def setup_method(self):
np.random.seed(1234)
def test_rvs(self):
vals = stats.geom.rvs(0.75, size=(2, 50))
assert_(numpy.all(vals >= 0))
assert_(numpy.shape(vals) == (2, 50))
assert_(vals.dtype.char in typecodes['AllInteger'])
val = stats.geom.rvs(0.75)
assert_(isinstance(val, int))
val = stats.geom(0.75).rvs(3)
assert_(isinstance(val, numpy.ndarray))
assert_(val.dtype.char in typecodes['AllInteger'])
def test_pmf(self):
vals = stats.geom.pmf([1, 2, 3], 0.5)
assert_array_almost_equal(vals, [0.5, 0.25, 0.125])
def test_logpmf(self):
# regression test for ticket 1793
vals1 = np.log(stats.geom.pmf([1, 2, 3], 0.5))
vals2 = stats.geom.logpmf([1, 2, 3], 0.5)
assert_allclose(vals1, vals2, rtol=1e-15, atol=0)
# regression test for gh-4028
val = stats.geom.logpmf(1, 1)
assert_equal(val, 0.0)
def test_cdf_sf(self):
vals = stats.geom.cdf([1, 2, 3], 0.5)
vals_sf = stats.geom.sf([1, 2, 3], 0.5)
expected = array([0.5, 0.75, 0.875])
assert_array_almost_equal(vals, expected)
assert_array_almost_equal(vals_sf, 1-expected)
def test_logcdf_logsf(self):
vals = stats.geom.logcdf([1, 2, 3], 0.5)
vals_sf = stats.geom.logsf([1, 2, 3], 0.5)
expected = array([0.5, 0.75, 0.875])
assert_array_almost_equal(vals, np.log(expected))
assert_array_almost_equal(vals_sf, np.log1p(-expected))
def test_ppf(self):
vals = stats.geom.ppf([0.5, 0.75, 0.875], 0.5)
expected = array([1.0, 2.0, 3.0])
assert_array_almost_equal(vals, expected)
def test_ppf_underflow(self):
# this should not underflow
assert_allclose(stats.geom.ppf(1e-20, 1e-20), 1.0, atol=1e-14)
class TestPlanck(object):
def setup_method(self):
np.random.seed(1234)
def test_sf(self):
vals = stats.planck.sf([1, 2, 3], 5.)
expected = array([4.5399929762484854e-05,
3.0590232050182579e-07,
2.0611536224385579e-09])
assert_array_almost_equal(vals, expected)
def test_logsf(self):
vals = stats.planck.logsf([1000., 2000., 3000.], 1000.)
expected = array([-1001000., -2001000., -3001000.])
assert_array_almost_equal(vals, expected)
class TestGennorm(object):
def test_laplace(self):
# test against Laplace (special case for beta=1)
points = [1, 2, 3]
pdf1 = stats.gennorm.pdf(points, 1)
pdf2 = stats.laplace.pdf(points)
assert_almost_equal(pdf1, pdf2)
def test_norm(self):
# test against normal (special case for beta=2)
points = [1, 2, 3]
pdf1 = stats.gennorm.pdf(points, 2)
pdf2 = stats.norm.pdf(points, scale=2**-.5)
assert_almost_equal(pdf1, pdf2)
class TestHalfgennorm(object):
def test_expon(self):
# test against exponential (special case for beta=1)
points = [1, 2, 3]
pdf1 = stats.halfgennorm.pdf(points, 1)
pdf2 = stats.expon.pdf(points)
assert_almost_equal(pdf1, pdf2)
def test_halfnorm(self):
# test against half normal (special case for beta=2)
points = [1, 2, 3]
pdf1 = stats.halfgennorm.pdf(points, 2)
pdf2 = stats.halfnorm.pdf(points, scale=2**-.5)
assert_almost_equal(pdf1, pdf2)
def test_gennorm(self):
# test against generalized normal
points = [1, 2, 3]
pdf1 = stats.halfgennorm.pdf(points, .497324)
pdf2 = stats.gennorm.pdf(points, .497324)
assert_almost_equal(pdf1, 2*pdf2)
class TestLaplaceasymmetric(object):
def test_laplace(self):
# test against Laplace (special case for kappa=1)
points = np.array([1, 2, 3])
pdf1 = stats.laplace_asymmetric.pdf(points, 1)
pdf2 = stats.laplace.pdf(points)
assert_allclose(pdf1, pdf2)
def test_asymmetric_laplace_pdf(self):
# test assymetric Laplace
points = np.array([1, 2, 3])
kappa = 2
kapinv = 1/kappa
pdf1 = stats.laplace_asymmetric.pdf(points, kappa)
pdf2 = stats.laplace_asymmetric.pdf(points*(kappa**2), kapinv)
assert_allclose(pdf1, pdf2)
def test_asymmetric_laplace_log_10_16(self):
# test assymetric Laplace
points = np.array([-np.log(16), np.log(10)])
kappa = 2
pdf1 = stats.laplace_asymmetric.pdf(points, kappa)
cdf1 = stats.laplace_asymmetric.cdf(points, kappa)
sf1 = stats.laplace_asymmetric.sf(points, kappa)
pdf2 = np.array([1/10, 1/250])
cdf2 = np.array([1/5, 1 - 1/500])
sf2 = np.array([4/5, 1/500])
ppf1 = stats.laplace_asymmetric.ppf(cdf2, kappa)
ppf2 = points
isf1 = stats.laplace_asymmetric.isf(sf2, kappa)
isf2 = points
assert_allclose(np.concatenate((pdf1, cdf1, sf1, ppf1, isf1)),
np.concatenate((pdf2, cdf2, sf2, ppf2, isf2)))
class TestTruncnorm(object):
def setup_method(self):
np.random.seed(1234)
def test_ppf_ticket1131(self):
vals = stats.truncnorm.ppf([-0.5, 0, 1e-4, 0.5, 1-1e-4, 1, 2], -1., 1.,
loc=[3]*7, scale=2)
expected = np.array([np.nan, 1, 1.00056419, 3, 4.99943581, 5, np.nan])
assert_array_almost_equal(vals, expected)
def test_isf_ticket1131(self):
vals = stats.truncnorm.isf([-0.5, 0, 1e-4, 0.5, 1-1e-4, 1, 2], -1., 1.,
loc=[3]*7, scale=2)
expected = np.array([np.nan, 5, 4.99943581, 3, 1.00056419, 1, np.nan])
assert_array_almost_equal(vals, expected)
def test_gh_2477_small_values(self):
# Check a case that worked in the original issue.
low, high = -11, -10
x = stats.truncnorm.rvs(low, high, 0, 1, size=10)
assert_(low < x.min() < x.max() < high)
# Check a case that failed in the original issue.
low, high = 10, 11
x = stats.truncnorm.rvs(low, high, 0, 1, size=10)
assert_(low < x.min() < x.max() < high)
def test_gh_2477_large_values(self):
# Check a case that used to fail because of extreme tailness.
low, high = 100, 101
x = stats.truncnorm.rvs(low, high, 0, 1, size=10)
assert_(low <= x.min() <= x.max() <= high), str([low, high, x])
# Check some additional extreme tails
low, high = 1000, 1001
x = stats.truncnorm.rvs(low, high, 0, 1, size=10)
assert_(low < x.min() < x.max() < high)
low, high = 10000, 10001
x = stats.truncnorm.rvs(low, high, 0, 1, size=10)
assert_(low < x.min() < x.max() < high)
low, high = -10001, -10000
x = stats.truncnorm.rvs(low, high, 0, 1, size=10)
assert_(low < x.min() < x.max() < high)
def test_gh_9403_nontail_values(self):
for low, high in [[3, 4], [-4, -3]]:
xvals = np.array([-np.inf, low, high, np.inf])
xmid = (high+low)/2.0
cdfs = stats.truncnorm.cdf(xvals, low, high)
sfs = stats.truncnorm.sf(xvals, low, high)
pdfs = stats.truncnorm.pdf(xvals, low, high)
expected_cdfs = np.array([0, 0, 1, 1])
expected_sfs = np.array([1.0, 1.0, 0.0, 0.0])
expected_pdfs = np.array([0, 3.3619772, 0.1015229, 0])
if low < 0:
expected_pdfs = np.array([0, 0.1015229, 3.3619772, 0])
assert_almost_equal(cdfs, expected_cdfs)
assert_almost_equal(sfs, expected_sfs)
assert_almost_equal(pdfs, expected_pdfs)
assert_almost_equal(np.log(expected_pdfs[1]/expected_pdfs[2]),
low + 0.5)
pvals = np.array([0, 0.5, 1.0])
ppfs = stats.truncnorm.ppf(pvals, low, high)
expected_ppfs = np.array([low, np.sign(low)*3.1984741, high])
assert_almost_equal(ppfs, expected_ppfs)
if low < 0:
assert_almost_equal(stats.truncnorm.sf(xmid, low, high),
0.8475544278436675)
assert_almost_equal(stats.truncnorm.cdf(xmid, low, high),
0.1524455721563326)
else:
assert_almost_equal(stats.truncnorm.cdf(xmid, low, high),
0.8475544278436675)
assert_almost_equal(stats.truncnorm.sf(xmid, low, high),
0.1524455721563326)
pdf = stats.truncnorm.pdf(xmid, low, high)
assert_almost_equal(np.log(pdf/expected_pdfs[2]), (xmid+0.25)/2)
def test_gh_9403_medium_tail_values(self):
for low, high in [[39, 40], [-40, -39]]:
xvals = np.array([-np.inf, low, high, np.inf])
xmid = (high+low)/2.0
cdfs = stats.truncnorm.cdf(xvals, low, high)
sfs = stats.truncnorm.sf(xvals, low, high)
pdfs = stats.truncnorm.pdf(xvals, low, high)
expected_cdfs = np.array([0, 0, 1, 1])
expected_sfs = np.array([1.0, 1.0, 0.0, 0.0])
expected_pdfs = np.array([0, 3.90256074e+01, 2.73349092e-16, 0])
if low < 0:
expected_pdfs = np.array([0, 2.73349092e-16,
3.90256074e+01, 0])
assert_almost_equal(cdfs, expected_cdfs)
assert_almost_equal(sfs, expected_sfs)
assert_almost_equal(pdfs, expected_pdfs)
assert_almost_equal(np.log(expected_pdfs[1]/expected_pdfs[2]),
low + 0.5)
pvals = np.array([0, 0.5, 1.0])
ppfs = stats.truncnorm.ppf(pvals, low, high)
expected_ppfs = np.array([low, np.sign(low)*39.01775731, high])
assert_almost_equal(ppfs, expected_ppfs)
cdfs = stats.truncnorm.cdf(ppfs, low, high)
assert_almost_equal(cdfs, pvals)
if low < 0:
assert_almost_equal(stats.truncnorm.sf(xmid, low, high),
0.9999999970389126)
assert_almost_equal(stats.truncnorm.cdf(xmid, low, high),
2.961048103554866e-09)
else:
assert_almost_equal(stats.truncnorm.cdf(xmid, low, high),
0.9999999970389126)
assert_almost_equal(stats.truncnorm.sf(xmid, low, high),
2.961048103554866e-09)
pdf = stats.truncnorm.pdf(xmid, low, high)
assert_almost_equal(np.log(pdf/expected_pdfs[2]), (xmid+0.25)/2)
xvals = np.linspace(low, high, 11)
xvals2 = -xvals[::-1]
assert_almost_equal(stats.truncnorm.cdf(xvals, low, high),
stats.truncnorm.sf(xvals2, -high, -low)[::-1])
assert_almost_equal(stats.truncnorm.sf(xvals, low, high),
stats.truncnorm.cdf(xvals2, -high, -low)[::-1])
assert_almost_equal(stats.truncnorm.pdf(xvals, low, high),
stats.truncnorm.pdf(xvals2, -high, -low)[::-1])
def _test_moments_one_range(self, a, b, expected, decimal_s=7):
m0, v0, s0, k0 = expected[:4]
m, v, s, k = stats.truncnorm.stats(a, b, moments='mvsk')
assert_almost_equal(m, m0)
assert_almost_equal(v, v0)
assert_almost_equal(s, s0, decimal=decimal_s)
assert_almost_equal(k, k0)
@pytest.mark.xfail_on_32bit("reduced accuracy with 32bit platforms.")
def test_moments(self):
# Values validated by changing TRUNCNORM_TAIL_X so as to evaluate
# using both the _norm_XXX() and _norm_logXXX() functions, and by
# removing the _stats and _munp methods in truncnorm tp force
# numerical quadrature.
# For m,v,s,k expect k to have the largest error as it is
# constructed from powers of lower moments
self._test_moments_one_range(-30, 30, [0, 1, 0.0, 0.0])
self._test_moments_one_range(-10, 10, [0, 1, 0.0, 0.0])
self._test_moments_one_range(-3, 3, [0.0, 0.9733369246625415,
0.0, -0.1711144363977444])
self._test_moments_one_range(-2, 2, [0.0, 0.7737413035499232,
0.0, -0.6344632828703505])
self._test_moments_one_range(0, np.inf, [0.7978845608028654,
0.3633802276324186,
0.9952717464311565,
0.8691773036059725])
self._test_moments_one_range(-np.inf, 0, [-0.7978845608028654,
0.3633802276324186,
-0.9952717464311565,
0.8691773036059725])
self._test_moments_one_range(-1, 3, [0.2827861107271540,
0.6161417353578292,
0.5393018494027878,
-0.2058206513527461])
self._test_moments_one_range(-3, 1, [-0.2827861107271540,
0.6161417353578292,
-0.5393018494027878,
-0.2058206513527461])
self._test_moments_one_range(-10, -9, [-9.1084562880124764,
0.0114488058210104,
-1.8985607337519652,
5.0733457094223553])
self._test_moments_one_range(-20, -19, [-19.0523439459766628,
0.0027250730180314,
-1.9838694022629291,
5.8717850028287586])
self._test_moments_one_range(-30, -29, [-29.0344012377394698,
0.0011806603928891,
-1.9930304534611458,
5.8854062968996566],
decimal_s=6)
self._test_moments_one_range(-40, -39, [-39.0256074199326264,
0.0006548826719649,
-1.9963146354109957,
5.6167758371700494])
self._test_moments_one_range(39, 40, [39.0256074199326264,
0.0006548826719649,
1.9963146354109957,
5.6167758371700494])
def test_9902_moments(self):
m, v = stats.truncnorm.stats(0, np.inf, moments='mv')
assert_almost_equal(m, 0.79788456)
assert_almost_equal(v, 0.36338023)
def test_gh_1489_trac_962_rvs(self):
# Check the original example.
low, high = 10, 15
x = stats.truncnorm.rvs(low, high, 0, 1, size=10)
assert_(low < x.min() < x.max() < high)
def test_gh_11299_rvs(self):
# Arose from investigating gh-11299
# Test multiple shape parameters simultaneously.
low = [-10, 10, -np.inf, -5, -np.inf, -np.inf, -45, -45, 40, -10, 40]
high = [-5, 11, 5, np.inf, 40, -40, 40, -40, 45, np.inf, np.inf]
x = stats.truncnorm.rvs(low, high, size=(5, len(low)))
assert np.shape(x) == (5, len(low))
assert_(np.all(low <= x.min(axis=0)))
assert_(np.all(x.max(axis=0) <= high))
def test_rvs_Generator(self):
# check that rvs can use a Generator
if hasattr(np.random, "default_rng"):
stats.truncnorm.rvs(-10, -5, size=5,
random_state=np.random.default_rng())
class TestGenLogistic:
# Expected values computed with mpmath with 50 digits of precision.
@pytest.mark.parametrize('x, expected', [(-1000, -1499.5945348918917),
(-125, -187.09453489189184),
(0, -1.3274028432916989),
(100, -99.59453489189184),
(1000, -999.5945348918918)])
def test_logpdf(self, x, expected):
c = 1.5
logp = stats.genlogistic.logpdf(x, c)
assert_allclose(logp, expected, rtol=1e-13)
class TestHypergeom(object):
def setup_method(self):
np.random.seed(1234)
def test_rvs(self):
vals = stats.hypergeom.rvs(20, 10, 3, size=(2, 50))
assert_(numpy.all(vals >= 0) &
numpy.all(vals <= 3))
assert_(numpy.shape(vals) == (2, 50))
assert_(vals.dtype.char in typecodes['AllInteger'])
val = stats.hypergeom.rvs(20, 3, 10)
assert_(isinstance(val, int))
val = stats.hypergeom(20, 3, 10).rvs(3)
assert_(isinstance(val, numpy.ndarray))
assert_(val.dtype.char in typecodes['AllInteger'])
def test_precision(self):
# comparison number from mpmath
M = 2500
n = 50
N = 500
tot = M
good = n
hgpmf = stats.hypergeom.pmf(2, tot, good, N)
assert_almost_equal(hgpmf, 0.0010114963068932233, 11)
def test_args(self):
# test correct output for corner cases of arguments
# see gh-2325
assert_almost_equal(stats.hypergeom.pmf(0, 2, 1, 0), 1.0, 11)
assert_almost_equal(stats.hypergeom.pmf(1, 2, 1, 0), 0.0, 11)
assert_almost_equal(stats.hypergeom.pmf(0, 2, 0, 2), 1.0, 11)
assert_almost_equal(stats.hypergeom.pmf(1, 2, 1, 0), 0.0, 11)
def test_cdf_above_one(self):
# for some values of parameters, hypergeom cdf was >1, see gh-2238
assert_(0 <= stats.hypergeom.cdf(30, 13397950, 4363, 12390) <= 1.0)
def test_precision2(self):
# Test hypergeom precision for large numbers. See #1218.
# Results compared with those from R.
oranges = 9.9e4
pears = 1.1e5
fruits_eaten = np.array([3, 3.8, 3.9, 4, 4.1, 4.2, 5]) * 1e4
quantile = 2e4
res = [stats.hypergeom.sf(quantile, oranges + pears, oranges, eaten)
for eaten in fruits_eaten]
expected = np.array([0, 1.904153e-114, 2.752693e-66, 4.931217e-32,
8.265601e-11, 0.1237904, 1])
assert_allclose(res, expected, atol=0, rtol=5e-7)
# Test with array_like first argument
quantiles = [1.9e4, 2e4, 2.1e4, 2.15e4]
res2 = stats.hypergeom.sf(quantiles, oranges + pears, oranges, 4.2e4)
expected2 = [1, 0.1237904, 6.511452e-34, 3.277667e-69]
assert_allclose(res2, expected2, atol=0, rtol=5e-7)
def test_entropy(self):
# Simple tests of entropy.
hg = stats.hypergeom(4, 1, 1)
h = hg.entropy()
expected_p = np.array([0.75, 0.25])
expected_h = -np.sum(xlogy(expected_p, expected_p))
assert_allclose(h, expected_h)
hg = stats.hypergeom(1, 1, 1)
h = hg.entropy()
assert_equal(h, 0.0)
def test_logsf(self):
# Test logsf for very large numbers. See issue #4982
# Results compare with those from R (v3.2.0):
# phyper(k, n, M-n, N, lower.tail=FALSE, log.p=TRUE)
# -2239.771
k = 1e4
M = 1e7
n = 1e6
N = 5e4
result = stats.hypergeom.logsf(k, M, n, N)
expected = -2239.771 # From R
assert_almost_equal(result, expected, decimal=3)
k = 1
M = 1600
n = 600
N = 300
result = stats.hypergeom.logsf(k, M, n, N)
expected = -2.566567e-68 # From R
assert_almost_equal(result, expected, decimal=15)
def test_logcdf(self):
# Test logcdf for very large numbers. See issue #8692
# Results compare with those from R (v3.3.2):
# phyper(k, n, M-n, N, lower.tail=TRUE, log.p=TRUE)
# -5273.335
k = 1
M = 1e7
n = 1e6
N = 5e4
result = stats.hypergeom.logcdf(k, M, n, N)
expected = -5273.335 # From R
assert_almost_equal(result, expected, decimal=3)
# Same example as in issue #8692
k = 40
M = 1600
n = 50
N = 300
result = stats.hypergeom.logcdf(k, M, n, N)
expected = -7.565148879229e-23 # From R
assert_almost_equal(result, expected, decimal=15)
k = 125
M = 1600
n = 250
N = 500
result = stats.hypergeom.logcdf(k, M, n, N)
expected = -4.242688e-12 # From R
assert_almost_equal(result, expected, decimal=15)
# test broadcasting robustness based on reviewer
# concerns in PR 9603; using an array version of
# the example from issue #8692
k = np.array([40, 40, 40])
M = 1600
n = 50
N = 300
result = stats.hypergeom.logcdf(k, M, n, N)
expected = np.full(3, -7.565148879229e-23) # filled from R result
assert_almost_equal(result, expected, decimal=15)
class TestLoggamma(object):
def test_stats(self):
# The following precomputed values are from the table in section 2.2
# of "A Statistical Study of Log-Gamma Distribution", by Ping Shing
# Chan (thesis, McMaster University, 1993).
table = np.array([
# c, mean, var, skew, exc. kurt.
0.5, -1.9635, 4.9348, -1.5351, 4.0000,
1.0, -0.5772, 1.6449, -1.1395, 2.4000,
12.0, 2.4427, 0.0869, -0.2946, 0.1735,
]).reshape(-1, 5)
for c, mean, var, skew, kurt in table:
computed = stats.loggamma.stats(c, moments='msvk')
assert_array_almost_equal(computed, [mean, var, skew, kurt],
decimal=4)
class TestLogistic(object):
# gh-6226
def test_cdf_ppf(self):
x = np.linspace(-20, 20)
y = stats.logistic.cdf(x)
xx = stats.logistic.ppf(y)
assert_allclose(x, xx)
def test_sf_isf(self):
x = np.linspace(-20, 20)
y = stats.logistic.sf(x)
xx = stats.logistic.isf(y)
assert_allclose(x, xx)
def test_extreme_values(self):
# p is chosen so that 1 - (1 - p) == p in double precision
p = 9.992007221626409e-16
desired = 34.53957599234088
assert_allclose(stats.logistic.ppf(1 - p), desired)
assert_allclose(stats.logistic.isf(p), desired)
def test_logpdf_basic(self):
logp = stats.logistic.logpdf([-15, 0, 10])
# Expected values computed with mpmath with 50 digits of precision.
expected = [-15.000000611804547,
-1.3862943611198906,
-10.000090797798434]
assert_allclose(logp, expected, rtol=1e-13)
def test_logpdf_extreme_values(self):
logp = stats.logistic.logpdf([800, -800])
# For such large arguments, logpdf(x) = -abs(x) when computed
# with 64 bit floating point.
assert_equal(logp, [-800, -800])
@pytest.mark.parametrize("loc_rvs,scale_rvs", [np.random.rand(2)])
def test_fit(self, loc_rvs, scale_rvs):
data = stats.logistic.rvs(size=100, loc=loc_rvs, scale=scale_rvs)
# test that result of fit method is the same as optimization
def func(input, data):
a, b = input
n = len(data)
x1 = np.sum(np.exp((data - a) / b) /
(1 + np.exp((data - a) / b))) - n / 2
x2 = np.sum(((data - a) / b) *
((np.exp((data - a) / b) - 1) /
(np.exp((data - a) / b) + 1))) - n
return x1, x2
expected_solution = root(func, stats.logistic._fitstart(data), args=(
data,)).x
fit_method = stats.logistic.fit(data)
# other than computational variances, the fit method and the solution
# to this system of equations are equal
assert_allclose(fit_method, expected_solution, atol=1e-30)
@pytest.mark.parametrize("loc_rvs,scale_rvs", [np.random.rand(2)])
def test_fit_comp_optimizer(self, loc_rvs, scale_rvs):
data = stats.logistic.rvs(size=100, loc=loc_rvs, scale=scale_rvs)
# obtain objective function to compare results of the fit methods
args = [data, (stats.logistic._fitstart(data),)]
func = stats.logistic._reduce_func(args, {})[1]
_assert_less_or_close_loglike(stats.logistic, data, func)
class TestLogser(object):
def setup_method(self):
np.random.seed(1234)
def test_rvs(self):
vals = stats.logser.rvs(0.75, size=(2, 50))
assert_(numpy.all(vals >= 1))
assert_(numpy.shape(vals) == (2, 50))
assert_(vals.dtype.char in typecodes['AllInteger'])
val = stats.logser.rvs(0.75)
assert_(isinstance(val, int))
val = stats.logser(0.75).rvs(3)
assert_(isinstance(val, numpy.ndarray))
assert_(val.dtype.char in typecodes['AllInteger'])
def test_pmf_small_p(self):
m = stats.logser.pmf(4, 1e-20)
# The expected value was computed using mpmath:
# >>> import mpmath
# >>> mpmath.mp.dps = 64
# >>> k = 4
# >>> p = mpmath.mpf('1e-20')
# >>> float(-(p**k)/k/mpmath.log(1-p))
# 2.5e-61
# It is also clear from noticing that for very small p,
# log(1-p) is approximately -p, and the formula becomes
# p**(k-1) / k
assert_allclose(m, 2.5e-61)
def test_mean_small_p(self):
m = stats.logser.mean(1e-8)
# The expected mean was computed using mpmath:
# >>> import mpmath
# >>> mpmath.dps = 60
# >>> p = mpmath.mpf('1e-8')
# >>> float(-p / ((1 - p)*mpmath.log(1 - p)))
# 1.000000005
assert_allclose(m, 1.000000005)
class TestGumbel_r_l:
def setup_method(self):
np.random.seed(1234)
@pytest.mark.parametrize("dist", [stats.gumbel_r, stats.gumbel_l])
@pytest.mark.parametrize("loc_rvs,scale_rvs", ([np.random.rand(2)]))
def test_fit_comp_optimizer(self, dist, loc_rvs, scale_rvs):
data = dist.rvs(size=100, loc=loc_rvs, scale=scale_rvs)
# obtain objective function to compare results of the fit methods
args = [data, (dist._fitstart(data),)]
func = dist._reduce_func(args, {})[1]
# test that the gumbel_* fit method is better than super method
_assert_less_or_close_loglike(dist, data, func)
@pytest.mark.parametrize("dist, sgn", [(stats.gumbel_r, 1),
(stats.gumbel_l, -1)])
def test_fit(self, dist, sgn):
z = sgn*np.array([3, 3, 3, 3, 3, 3, 3, 3.00000001])
loc, scale = dist.fit(z)
# The expected values were computed with mpmath with 60 digits
# of precision.
assert_allclose(loc, sgn*3.0000000001667906)
assert_allclose(scale, 1.2495222465145514e-09, rtol=1e-6)
class TestPareto(object):
def test_stats(self):
# Check the stats() method with some simple values. Also check
# that the calculations do not trigger RuntimeWarnings.
with warnings.catch_warnings():
warnings.simplefilter("error", RuntimeWarning)
m, v, s, k = stats.pareto.stats(0.5, moments='mvsk')
assert_equal(m, np.inf)
assert_equal(v, np.inf)
assert_equal(s, np.nan)
assert_equal(k, np.nan)
m, v, s, k = stats.pareto.stats(1.0, moments='mvsk')
assert_equal(m, np.inf)
assert_equal(v, np.inf)
assert_equal(s, np.nan)
assert_equal(k, np.nan)
m, v, s, k = stats.pareto.stats(1.5, moments='mvsk')
assert_equal(m, 3.0)
assert_equal(v, np.inf)
assert_equal(s, np.nan)
assert_equal(k, np.nan)
m, v, s, k = stats.pareto.stats(2.0, moments='mvsk')
assert_equal(m, 2.0)
assert_equal(v, np.inf)
assert_equal(s, np.nan)
assert_equal(k, np.nan)
m, v, s, k = stats.pareto.stats(2.5, moments='mvsk')
assert_allclose(m, 2.5 / 1.5)
assert_allclose(v, 2.5 / (1.5*1.5*0.5))
assert_equal(s, np.nan)
assert_equal(k, np.nan)
m, v, s, k = stats.pareto.stats(3.0, moments='mvsk')
assert_allclose(m, 1.5)
assert_allclose(v, 0.75)
assert_equal(s, np.nan)
assert_equal(k, np.nan)
m, v, s, k = stats.pareto.stats(3.5, moments='mvsk')
assert_allclose(m, 3.5 / 2.5)
assert_allclose(v, 3.5 / (2.5*2.5*1.5))
assert_allclose(s, (2*4.5/0.5)*np.sqrt(1.5/3.5))
assert_equal(k, np.nan)
m, v, s, k = stats.pareto.stats(4.0, moments='mvsk')
assert_allclose(m, 4.0 / 3.0)
assert_allclose(v, 4.0 / 18.0)
assert_allclose(s, 2*(1+4.0)/(4.0-3) * np.sqrt((4.0-2)/4.0))
assert_equal(k, np.nan)
m, v, s, k = stats.pareto.stats(4.5, moments='mvsk')
assert_allclose(m, 4.5 / 3.5)
assert_allclose(v, 4.5 / (3.5*3.5*2.5))
assert_allclose(s, (2*5.5/1.5) * np.sqrt(2.5/4.5))
assert_allclose(k, 6*(4.5**3 + 4.5**2 - 6*4.5 - 2)/(4.5*1.5*0.5))
def test_sf(self):
x = 1e9
b = 2
scale = 1.5
p = stats.pareto.sf(x, b, loc=0, scale=scale)
expected = (scale/x)**b # 2.25e-18
assert_allclose(p, expected)
@pytest.mark.filterwarnings("ignore:invalid value encountered in "
"double_scalars")
@pytest.mark.parametrize("rvs_shape", [1, 2])
@pytest.mark.parametrize("rvs_loc", [0, 2])
@pytest.mark.parametrize("rvs_scale", [1, 5])
def test_fit(self, rvs_shape, rvs_loc, rvs_scale):
data = stats.pareto.rvs(size=100, b=rvs_shape, scale=rvs_scale,
loc=rvs_loc)
# shape can still be fixed with multiple names
shape_mle_analytical1 = stats.pareto.fit(data, floc=0, f0=1.04)[0]
shape_mle_analytical2 = stats.pareto.fit(data, floc=0, fix_b=1.04)[0]
shape_mle_analytical3 = stats.pareto.fit(data, floc=0, fb=1.04)[0]
assert (shape_mle_analytical1 == shape_mle_analytical2 ==
shape_mle_analytical3 == 1.04)
# data can be shifted with changes to `loc`
data = stats.pareto.rvs(size=100, b=rvs_shape, scale=rvs_scale,
loc=(rvs_loc + 2))
shape_mle_a, loc_mle_a, scale_mle_a = stats.pareto.fit(data, floc=2)
assert_equal(scale_mle_a + 2, data.min())
assert_equal(shape_mle_a, 1/((1/len(data - 2)) *
np.sum(np.log((data
- 2)/(data.min() - 2)))))
assert_equal(loc_mle_a, 2)
@pytest.mark.filterwarnings("ignore:invalid value encountered in "
"double_scalars")
@pytest.mark.parametrize("rvs_shape", [1, 2])
@pytest.mark.parametrize("rvs_loc", [0, 2])
@pytest.mark.parametrize("rvs_scale", [1, 5])
def test_fit_MLE_comp_optimzer(self, rvs_shape, rvs_loc, rvs_scale):
data = stats.pareto.rvs(size=100, b=rvs_shape, scale=rvs_scale,
loc=rvs_loc)
args = [data, (stats.pareto._fitstart(data), )]
func = stats.pareto._reduce_func(args, {})[1]
# fixed `floc` to actual location provides a better fit than the
# super method
_assert_less_or_close_loglike(stats.pareto, data, func, floc=rvs_loc)
# fixing `floc` to an arbitrary number, 0, still provides a better
# fit than the super method
_assert_less_or_close_loglike(stats.pareto, data, func, floc=0)
# fixed shape still uses MLE formula and provides a better fit than
# the super method
_assert_less_or_close_loglike(stats.pareto, data, func, floc=0, f0=4)
# valid fixed fscale still uses MLE formulas and provides a better
# fit than the super method
_assert_less_or_close_loglike(stats.pareto, data, func, floc=0,
fscale=rvs_scale/2)
def test_fit_warnings(self):
assert_fit_warnings(stats.pareto)
# `floc` that causes invalid negative data
assert_raises(FitDataError, stats.pareto.fit, [1, 2, 3], floc=2)
# `floc` and `fscale` combination causes invalid data
assert_raises(FitDataError, stats.pareto.fit, [5, 2, 3], floc=1,
fscale=3)
class TestGenpareto(object):
def test_ab(self):
# c >= 0: a, b = [0, inf]
for c in [1., 0.]:
c = np.asarray(c)
a, b = stats.genpareto._get_support(c)
assert_equal(a, 0.)
assert_(np.isposinf(b))
# c < 0: a=0, b=1/|c|
c = np.asarray(-2.)
a, b = stats.genpareto._get_support(c)
assert_allclose([a, b], [0., 0.5])
def test_c0(self):
# with c=0, genpareto reduces to the exponential distribution
# rv = stats.genpareto(c=0.)
rv = stats.genpareto(c=0.)
x = np.linspace(0, 10., 30)
assert_allclose(rv.pdf(x), stats.expon.pdf(x))
assert_allclose(rv.cdf(x), stats.expon.cdf(x))
assert_allclose(rv.sf(x), stats.expon.sf(x))
q = np.linspace(0., 1., 10)
assert_allclose(rv.ppf(q), stats.expon.ppf(q))
def test_cm1(self):
# with c=-1, genpareto reduces to the uniform distr on [0, 1]
rv = stats.genpareto(c=-1.)
x = np.linspace(0, 10., 30)
assert_allclose(rv.pdf(x), stats.uniform.pdf(x))
assert_allclose(rv.cdf(x), stats.uniform.cdf(x))
assert_allclose(rv.sf(x), stats.uniform.sf(x))
q = np.linspace(0., 1., 10)
assert_allclose(rv.ppf(q), stats.uniform.ppf(q))
# logpdf(1., c=-1) should be zero
assert_allclose(rv.logpdf(1), 0)
def test_x_inf(self):
# make sure x=inf is handled gracefully
rv = stats.genpareto(c=0.1)
assert_allclose([rv.pdf(np.inf), rv.cdf(np.inf)], [0., 1.])
assert_(np.isneginf(rv.logpdf(np.inf)))
rv = stats.genpareto(c=0.)
assert_allclose([rv.pdf(np.inf), rv.cdf(np.inf)], [0., 1.])
assert_(np.isneginf(rv.logpdf(np.inf)))
rv = stats.genpareto(c=-1.)
assert_allclose([rv.pdf(np.inf), rv.cdf(np.inf)], [0., 1.])
assert_(np.isneginf(rv.logpdf(np.inf)))
def test_c_continuity(self):
# pdf is continuous at c=0, -1
x = np.linspace(0, 10, 30)
for c in [0, -1]:
pdf0 = stats.genpareto.pdf(x, c)
for dc in [1e-14, -1e-14]:
pdfc = stats.genpareto.pdf(x, c + dc)
assert_allclose(pdf0, pdfc, atol=1e-12)
cdf0 = stats.genpareto.cdf(x, c)
for dc in [1e-14, 1e-14]:
cdfc = stats.genpareto.cdf(x, c + dc)
assert_allclose(cdf0, cdfc, atol=1e-12)
def test_c_continuity_ppf(self):
q = np.r_[np.logspace(1e-12, 0.01, base=0.1),
np.linspace(0.01, 1, 30, endpoint=False),
1. - np.logspace(1e-12, 0.01, base=0.1)]
for c in [0., -1.]:
ppf0 = stats.genpareto.ppf(q, c)
for dc in [1e-14, -1e-14]:
ppfc = stats.genpareto.ppf(q, c + dc)
assert_allclose(ppf0, ppfc, atol=1e-12)
def test_c_continuity_isf(self):
q = np.r_[np.logspace(1e-12, 0.01, base=0.1),
np.linspace(0.01, 1, 30, endpoint=False),
1. - np.logspace(1e-12, 0.01, base=0.1)]
for c in [0., -1.]:
isf0 = stats.genpareto.isf(q, c)
for dc in [1e-14, -1e-14]:
isfc = stats.genpareto.isf(q, c + dc)
assert_allclose(isf0, isfc, atol=1e-12)
def test_cdf_ppf_roundtrip(self):
# this should pass with machine precision. hat tip @pbrod
q = np.r_[np.logspace(1e-12, 0.01, base=0.1),
np.linspace(0.01, 1, 30, endpoint=False),
1. - np.logspace(1e-12, 0.01, base=0.1)]
for c in [1e-8, -1e-18, 1e-15, -1e-15]:
assert_allclose(stats.genpareto.cdf(stats.genpareto.ppf(q, c), c),
q, atol=1e-15)
def test_logsf(self):
logp = stats.genpareto.logsf(1e10, .01, 0, 1)
assert_allclose(logp, -1842.0680753952365)
# Values in 'expected_stats' are
# [mean, variance, skewness, excess kurtosis].
@pytest.mark.parametrize(
'c, expected_stats',
[(0, [1, 1, 2, 6]),
(1/4, [4/3, 32/9, 10/np.sqrt(2), np.nan]),
(1/9, [9/8, (81/64)*(9/7), (10/9)*np.sqrt(7), 754/45]),
(-1, [1/2, 1/12, 0, -6/5])])
def test_stats(self, c, expected_stats):
result = stats.genpareto.stats(c, moments='mvsk')
assert_allclose(result, expected_stats, rtol=1e-13, atol=1e-15)
def test_var(self):
# Regression test for gh-11168.
v = stats.genpareto.var(1e-8)
assert_allclose(v, 1.000000040000001, rtol=1e-13)
class TestPearson3(object):
def setup_method(self):
np.random.seed(1234)
def test_rvs(self):
vals = stats.pearson3.rvs(0.1, size=(2, 50))
assert_(numpy.shape(vals) == (2, 50))
assert_(vals.dtype.char in typecodes['AllFloat'])
val = stats.pearson3.rvs(0.5)
assert_(isinstance(val, float))
val = stats.pearson3(0.5).rvs(3)
assert_(isinstance(val, numpy.ndarray))
assert_(val.dtype.char in typecodes['AllFloat'])
assert_(len(val) == 3)
def test_pdf(self):
vals = stats.pearson3.pdf(2, [0.0, 0.1, 0.2])
assert_allclose(vals, np.array([0.05399097, 0.05555481, 0.05670246]),
atol=1e-6)
vals = stats.pearson3.pdf(-3, 0.1)
assert_allclose(vals, np.array([0.00313791]), atol=1e-6)
vals = stats.pearson3.pdf([-3, -2, -1, 0, 1], 0.1)
assert_allclose(vals, np.array([0.00313791, 0.05192304, 0.25028092,
0.39885918, 0.23413173]), atol=1e-6)
def test_cdf(self):
vals = stats.pearson3.cdf(2, [0.0, 0.1, 0.2])
assert_allclose(vals, np.array([0.97724987, 0.97462004, 0.97213626]),
atol=1e-6)
vals = stats.pearson3.cdf(-3, 0.1)
assert_allclose(vals, [0.00082256], atol=1e-6)
vals = stats.pearson3.cdf([-3, -2, -1, 0, 1], 0.1)
assert_allclose(vals, [8.22563821e-04, 1.99860448e-02, 1.58550710e-01,
5.06649130e-01, 8.41442111e-01], atol=1e-6)
def test_negative_cdf_bug_11186(self):
# incorrect CDFs for negative skews in gh-11186; fixed in gh-12640
# Also check vectorization w/ negative, zero, and positive skews
skews = [-3, -1, 0, 0.5]
x_eval = 0.5
neg_inf = -30 # avoid RuntimeWarning caused by np.log(0)
cdfs = stats.pearson3.cdf(x_eval, skews)
int_pdfs = [quad(stats.pearson3(skew).pdf, neg_inf, x_eval)[0]
for skew in skews]
assert_allclose(cdfs, int_pdfs)
def test_return_array_bug_11746(self):
# pearson3.moment was returning size 0 or 1 array instead of float
# The first moment is equal to the loc, which defaults to zero
moment = stats.pearson3.moment(1, 2)
assert_equal(moment, 0)
assert_equal(type(moment), float)
moment = stats.pearson3.moment(1, 0.000001)
assert_equal(moment, 0)
assert_equal(type(moment), float)
class TestKappa4(object):
def test_cdf_genpareto(self):
# h = 1 and k != 0 is generalized Pareto
x = [0.0, 0.1, 0.2, 0.5]
h = 1.0
for k in [-1.9, -1.0, -0.5, -0.2, -0.1, 0.1, 0.2, 0.5, 1.0,
1.9]:
vals = stats.kappa4.cdf(x, h, k)
# shape parameter is opposite what is expected
vals_comp = stats.genpareto.cdf(x, -k)
assert_allclose(vals, vals_comp)
def test_cdf_genextreme(self):
# h = 0 and k != 0 is generalized extreme value
x = np.linspace(-5, 5, 10)
h = 0.0
k = np.linspace(-3, 3, 10)
vals = stats.kappa4.cdf(x, h, k)
vals_comp = stats.genextreme.cdf(x, k)
assert_allclose(vals, vals_comp)
def test_cdf_expon(self):
# h = 1 and k = 0 is exponential
x = np.linspace(0, 10, 10)
h = 1.0
k = 0.0
vals = stats.kappa4.cdf(x, h, k)
vals_comp = stats.expon.cdf(x)
assert_allclose(vals, vals_comp)
def test_cdf_gumbel_r(self):
# h = 0 and k = 0 is gumbel_r
x = np.linspace(-5, 5, 10)
h = 0.0
k = 0.0
vals = stats.kappa4.cdf(x, h, k)
vals_comp = stats.gumbel_r.cdf(x)
assert_allclose(vals, vals_comp)
def test_cdf_logistic(self):
# h = -1 and k = 0 is logistic
x = np.linspace(-5, 5, 10)
h = -1.0
k = 0.0
vals = stats.kappa4.cdf(x, h, k)
vals_comp = stats.logistic.cdf(x)
assert_allclose(vals, vals_comp)
def test_cdf_uniform(self):
# h = 1 and k = 1 is uniform
x = np.linspace(-5, 5, 10)
h = 1.0
k = 1.0
vals = stats.kappa4.cdf(x, h, k)
vals_comp = stats.uniform.cdf(x)
assert_allclose(vals, vals_comp)
def test_integers_ctor(self):
# regression test for gh-7416: _argcheck fails for integer h and k
# in numpy 1.12
stats.kappa4(1, 2)
class TestPoisson(object):
def setup_method(self):
np.random.seed(1234)
def test_pmf_basic(self):
# Basic case
ln2 = np.log(2)
vals = stats.poisson.pmf([0, 1, 2], ln2)
expected = [0.5, ln2/2, ln2**2/4]
assert_allclose(vals, expected)
def test_mu0(self):
# Edge case: mu=0
vals = stats.poisson.pmf([0, 1, 2], 0)
expected = [1, 0, 0]
assert_array_equal(vals, expected)
interval = stats.poisson.interval(0.95, 0)
assert_equal(interval, (0, 0))
def test_rvs(self):
vals = stats.poisson.rvs(0.5, size=(2, 50))
assert_(numpy.all(vals >= 0))
assert_(numpy.shape(vals) == (2, 50))
assert_(vals.dtype.char in typecodes['AllInteger'])
val = stats.poisson.rvs(0.5)
assert_(isinstance(val, int))
val = stats.poisson(0.5).rvs(3)
assert_(isinstance(val, numpy.ndarray))
assert_(val.dtype.char in typecodes['AllInteger'])
def test_stats(self):
mu = 16.0
result = stats.poisson.stats(mu, moments='mvsk')
assert_allclose(result, [mu, mu, np.sqrt(1.0/mu), 1.0/mu])
mu = np.array([0.0, 1.0, 2.0])
result = stats.poisson.stats(mu, moments='mvsk')
expected = (mu, mu, [np.inf, 1, 1/np.sqrt(2)], [np.inf, 1, 0.5])
assert_allclose(result, expected)
class TestKSTwo(object):
def setup_method(self):
np.random.seed(1234)
def test_cdf(self):
for n in [1, 2, 3, 10, 100, 1000]:
# Test x-values:
# 0, 1/2n, where the cdf should be 0
# 1/n, where the cdf should be n!/n^n
# 0.5, where the cdf should match ksone.cdf
# 1-1/n, where cdf = 1-2/n^n
# 1, where cdf == 1
# (E.g. Exact values given by Eqn 1 in Simard / L'Ecuyer)
x = np.array([0, 0.5/n, 1/n, 0.5, 1-1.0/n, 1])
v1 = (1.0/n)**n
lg = scipy.special.gammaln(n+1)
elg = (np.exp(lg) if v1 != 0 else 0)
expected = np.array([0, 0, v1 * elg,
1 - 2*stats.ksone.sf(0.5, n),
max(1 - 2*v1, 0.0),
1.0])
vals_cdf = stats.kstwo.cdf(x, n)
assert_allclose(vals_cdf, expected)
def test_sf(self):
x = np.linspace(0, 1, 11)
for n in [1, 2, 3, 10, 100, 1000]:
# Same x values as in test_cdf, and use sf = 1 - cdf
x = np.array([0, 0.5/n, 1/n, 0.5, 1-1.0/n, 1])
v1 = (1.0/n)**n
lg = scipy.special.gammaln(n+1)
elg = (np.exp(lg) if v1 != 0 else 0)
expected = np.array([1.0, 1.0,
1 - v1 * elg,
2*stats.ksone.sf(0.5, n),
min(2*v1, 1.0), 0])
vals_sf = stats.kstwo.sf(x, n)
assert_allclose(vals_sf, expected)
def test_cdf_sqrtn(self):
# For fixed a, cdf(a/sqrt(n), n) -> kstwobign(a) as n->infinity
# cdf(a/sqrt(n), n) is an increasing function of n (and a)
# Check that the function is indeed increasing (allowing for some
# small floating point and algorithm differences.)
x = np.linspace(0, 2, 11)[1:]
ns = [50, 100, 200, 400, 1000, 2000]
for _x in x:
xn = _x / np.sqrt(ns)
probs = stats.kstwo.cdf(xn, ns)
diffs = np.diff(probs)
assert_array_less(diffs, 1e-8)
def test_cdf_sf(self):
x = np.linspace(0, 1, 11)
for n in [1, 2, 3, 10, 100, 1000]:
vals_cdf = stats.kstwo.cdf(x, n)
vals_sf = stats.kstwo.sf(x, n)
assert_array_almost_equal(vals_cdf, 1 - vals_sf)
def test_cdf_sf_sqrtn(self):
x = np.linspace(0, 1, 11)
for n in [1, 2, 3, 10, 100, 1000]:
xn = x / np.sqrt(n)
vals_cdf = stats.kstwo.cdf(xn, n)
vals_sf = stats.kstwo.sf(xn, n)
assert_array_almost_equal(vals_cdf, 1 - vals_sf)
def test_ppf_of_cdf(self):
x = np.linspace(0, 1, 11)
for n in [1, 2, 3, 10, 100, 1000]:
xn = x[x > 0.5/n]
vals_cdf = stats.kstwo.cdf(xn, n)
# CDFs close to 1 are better dealt with using the SF
cond = (0 < vals_cdf) & (vals_cdf < 0.99)
vals = stats.kstwo.ppf(vals_cdf, n)
assert_allclose(vals[cond], xn[cond], rtol=1e-4)
def test_isf_of_sf(self):
x = np.linspace(0, 1, 11)
for n in [1, 2, 3, 10, 100, 1000]:
xn = x[x > 0.5/n]
vals_isf = stats.kstwo.isf(xn, n)
cond = (0 < vals_isf) & (vals_isf < 1.0)
vals = stats.kstwo.sf(vals_isf, n)
assert_allclose(vals[cond], xn[cond], rtol=1e-4)
def test_ppf_of_cdf_sqrtn(self):
x = np.linspace(0, 1, 11)
for n in [1, 2, 3, 10, 100, 1000]:
xn = (x / np.sqrt(n))[x > 0.5/n]
vals_cdf = stats.kstwo.cdf(xn, n)
cond = (0 < vals_cdf) & (vals_cdf < 1.0)
vals = stats.kstwo.ppf(vals_cdf, n)
assert_allclose(vals[cond], xn[cond])
def test_isf_of_sf_sqrtn(self):
x = np.linspace(0, 1, 11)
for n in [1, 2, 3, 10, 100, 1000]:
xn = (x / np.sqrt(n))[x > 0.5/n]
vals_sf = stats.kstwo.sf(xn, n)
# SFs close to 1 are better dealt with using the CDF
cond = (0 < vals_sf) & (vals_sf < 0.95)
vals = stats.kstwo.isf(vals_sf, n)
assert_allclose(vals[cond], xn[cond])
def test_ppf(self):
probs = np.linspace(0, 1, 11)[1:]
for n in [1, 2, 3, 10, 100, 1000]:
xn = stats.kstwo.ppf(probs, n)
vals_cdf = stats.kstwo.cdf(xn, n)
assert_allclose(vals_cdf, probs)
def test_simard_lecuyer_table1(self):
# Compute the cdf for values near the mean of the distribution.
# The mean u ~ log(2)*sqrt(pi/(2n))
# Compute for x in [u/4, u/3, u/2, u, 2u, 3u]
# This is the computation of Table 1 of Simard, R., L'Ecuyer, P. (2011)
# "Computing the Two-Sided Kolmogorov-Smirnov Distribution".
# Except that the values below are not from the published table, but
# were generated using an independent SageMath implementation of
# Durbin's algorithm (with the exponentiation and scaling of
# Marsaglia/Tsang/Wang's version) using 500 bit arithmetic.
# Some of the values in the published table have relative
# errors greater than 1e-4.
ns = [10, 50, 100, 200, 500, 1000]
ratios = np.array([1.0/4, 1.0/3, 1.0/2, 1, 2, 3])
expected = np.array([
[1.92155292e-08, 5.72933228e-05, 2.15233226e-02, 6.31566589e-01,
9.97685592e-01, 9.99999942e-01],
[2.28096224e-09, 1.99142563e-05, 1.42617934e-02, 5.95345542e-01,
9.96177701e-01, 9.99998662e-01],
[1.00201886e-09, 1.32673079e-05, 1.24608594e-02, 5.86163220e-01,
9.95866877e-01, 9.99998240e-01],
[4.93313022e-10, 9.52658029e-06, 1.12123138e-02, 5.79486872e-01,
9.95661824e-01, 9.99997964e-01],
[2.37049293e-10, 6.85002458e-06, 1.01309221e-02, 5.73427224e-01,
9.95491207e-01, 9.99997750e-01],
[1.56990874e-10, 5.71738276e-06, 9.59725430e-03, 5.70322692e-01,
9.95409545e-01, 9.99997657e-01]
])
for idx, n in enumerate(ns):
x = ratios * np.log(2) * np.sqrt(np.pi/2/n)
vals_cdf = stats.kstwo.cdf(x, n)
assert_allclose(vals_cdf, expected[idx], rtol=1e-5)
class TestZipf(object):
def setup_method(self):
np.random.seed(1234)
def test_rvs(self):
vals = stats.zipf.rvs(1.5, size=(2, 50))
assert_(numpy.all(vals >= 1))
assert_(numpy.shape(vals) == (2, 50))
assert_(vals.dtype.char in typecodes['AllInteger'])
val = stats.zipf.rvs(1.5)
assert_(isinstance(val, int))
val = stats.zipf(1.5).rvs(3)
assert_(isinstance(val, numpy.ndarray))
assert_(val.dtype.char in typecodes['AllInteger'])
def test_moments(self):
# n-th moment is finite iff a > n + 1
m, v = stats.zipf.stats(a=2.8)
assert_(np.isfinite(m))
assert_equal(v, np.inf)
s, k = stats.zipf.stats(a=4.8, moments='sk')
assert_(not np.isfinite([s, k]).all())
class TestDLaplace(object):
def setup_method(self):
np.random.seed(1234)
def test_rvs(self):
vals = stats.dlaplace.rvs(1.5, size=(2, 50))
assert_(numpy.shape(vals) == (2, 50))
assert_(vals.dtype.char in typecodes['AllInteger'])
val = stats.dlaplace.rvs(1.5)
assert_(isinstance(val, int))
val = stats.dlaplace(1.5).rvs(3)
assert_(isinstance(val, numpy.ndarray))
assert_(val.dtype.char in typecodes['AllInteger'])
assert_(stats.dlaplace.rvs(0.8) is not None)
def test_stats(self):
# compare the explicit formulas w/ direct summation using pmf
a = 1.
dl = stats.dlaplace(a)
m, v, s, k = dl.stats('mvsk')
N = 37
xx = np.arange(-N, N+1)
pp = dl.pmf(xx)
m2, m4 = np.sum(pp*xx**2), np.sum(pp*xx**4)
assert_equal((m, s), (0, 0))
assert_allclose((v, k), (m2, m4/m2**2 - 3.), atol=1e-14, rtol=1e-8)
def test_stats2(self):
a = np.log(2.)
dl = stats.dlaplace(a)
m, v, s, k = dl.stats('mvsk')
assert_equal((m, s), (0., 0.))
assert_allclose((v, k), (4., 3.25))
class TestInvgauss(object):
def setup_method(self):
np.random.seed(1234)
@pytest.mark.parametrize("rvs_mu,rvs_loc,rvs_scale",
[(2, 0, 1), (np.random.rand(3)*10)])
def test_fit(self, rvs_mu, rvs_loc, rvs_scale):
data = stats.invgauss.rvs(size=100, mu=rvs_mu,
loc=rvs_loc, scale=rvs_scale)
# Analytical MLEs are calculated with formula when `floc` is fixed
mu, loc, scale = stats.invgauss.fit(data, floc=rvs_loc)
data = data - rvs_loc
mu_temp = np.mean(data)
scale_mle = len(data) / (np.sum(data**(-1) - mu_temp**(-1)))
mu_mle = mu_temp/scale_mle
# `mu` and `scale` match analytical formula
assert_allclose(mu_mle, mu, atol=1e-15, rtol=1e-15)
assert_allclose(scale_mle, scale, atol=1e-15, rtol=1e-15)
assert_equal(loc, rvs_loc)
data = stats.invgauss.rvs(size=100, mu=rvs_mu,
loc=rvs_loc, scale=rvs_scale)
# fixed parameters are returned
mu, loc, scale = stats.invgauss.fit(data, floc=rvs_loc - 1,
fscale=rvs_scale + 1)
assert_equal(rvs_scale + 1, scale)
assert_equal(rvs_loc - 1, loc)
# shape can still be fixed with multiple names
shape_mle1 = stats.invgauss.fit(data, fmu=1.04)[0]
shape_mle2 = stats.invgauss.fit(data, fix_mu=1.04)[0]
shape_mle3 = stats.invgauss.fit(data, f0=1.04)[0]
assert shape_mle1 == shape_mle2 == shape_mle3 == 1.04
@pytest.mark.parametrize("rvs_mu,rvs_loc,rvs_scale",
[(2, 0, 1), (np.random.rand(3)*10)])
def test_fit_MLE_comp_optimzer(self, rvs_mu, rvs_loc, rvs_scale):
data = stats.invgauss.rvs(size=100, mu=rvs_mu,
loc=rvs_loc, scale=rvs_scale)
super_fit = super(type(stats.invgauss), stats.invgauss).fit
# fitting without `floc` uses superclass fit method
super_fitted = super_fit(data)
invgauss_fit = stats.invgauss.fit(data)
assert_equal(super_fitted, invgauss_fit)
# fitting with `fmu` is uses superclass fit method
super_fitted = super_fit(data, floc=0, fmu=2)
invgauss_fit = stats.invgauss.fit(data, floc=0, fmu=2)
assert_equal(super_fitted, invgauss_fit)
# obtain log-likelihood objective function to compare results
args = [data, (stats.invgauss._fitstart(data), )]
func = stats.invgauss._reduce_func(args, {})[1]
# fixed `floc` uses analytical formula and provides better fit than
# super method
_assert_less_or_close_loglike(stats.invgauss, data, func, floc=rvs_loc)
# fixed `floc` not resulting in invalid data < 0 uses analytical
# formulas and provides a better fit than the super method
assert np.all((data - (rvs_loc - 1)) > 0)
_assert_less_or_close_loglike(stats.invgauss, data, func,
floc=rvs_loc - 1)
# fixed `floc` to an arbitrary number, 0, still provides a better fit
# than the super method
_assert_less_or_close_loglike(stats.invgauss, data, func, floc=0)
# fixed `fscale` to an arbitrary number still provides a better fit
# than the super method
_assert_less_or_close_loglike(stats.invgauss, data, func, floc=rvs_loc,
fscale=np.random.rand(1)[0])
def test_fit_raise_errors(self):
assert_fit_warnings(stats.invgauss)
# FitDataError is raised when negative invalid data
with pytest.raises(FitDataError):
stats.invgauss.fit([1, 2, 3], floc=2)
class TestLaplace(object):
@pytest.mark.parametrize("rvs_loc", [-5, 0, 1, 2])
@pytest.mark.parametrize("rvs_scale", [1, 2, 3, 10])
def test_fit(self, rvs_loc, rvs_scale):
# tests that various inputs follow expected behavior
# for a variety of `loc` and `scale`.
data = stats.laplace.rvs(size=100, loc=rvs_loc, scale=rvs_scale)
# MLE estimates are given by
loc_mle = np.median(data)
scale_mle = np.sum(np.abs(data - loc_mle)) / len(data)
# standard outputs should match analytical MLE formulas
loc, scale = stats.laplace.fit(data)
assert_allclose(loc, loc_mle, atol=1e-15, rtol=1e-15)
assert_allclose(scale, scale_mle, atol=1e-15, rtol=1e-15)
# fixed parameter should use analytical formula for other
loc, scale = stats.laplace.fit(data, floc=loc_mle)
assert_allclose(scale, scale_mle, atol=1e-15, rtol=1e-15)
loc, scale = stats.laplace.fit(data, fscale=scale_mle)
assert_allclose(loc, loc_mle)
# test with non-mle fixed parameter
# create scale with non-median loc
loc = rvs_loc * 2
scale_mle = np.sum(np.abs(data - loc)) / len(data)
# fixed loc to non median, scale should match
# scale calculation with modified loc
loc, scale = stats.laplace.fit(data, floc=loc)
assert_equal(scale_mle, scale)
# fixed scale created with non median loc,
# loc output should still be the data median.
loc, scale = stats.laplace.fit(data, fscale=scale_mle)
assert_equal(loc_mle, loc)
# error raised when both `floc` and `fscale` are fixed
assert_raises(RuntimeError, stats.laplace.fit, data, floc=loc_mle,
fscale=scale_mle)
# error is raised with non-finite values
assert_raises(RuntimeError, stats.laplace.fit, [np.nan])
assert_raises(RuntimeError, stats.laplace.fit, [np.inf])
@pytest.mark.parametrize("rvs_scale,rvs_loc", [(10, -5),
(5, 10),
(.2, .5)])
def test_fit_MLE_comp_optimzer(self, rvs_loc, rvs_scale):
data = stats.laplace.rvs(size=1000, loc=rvs_loc, scale=rvs_scale)
# the log-likelihood function for laplace is given by
def ll(loc, scale, data):
return -1 * (- (len(data)) * np.log(2*scale) -
(1/scale)*np.sum(np.abs(data - loc)))
# test that the objective function result of the analytical MLEs is
# less than or equal to that of the numerically optimized estimate
loc, scale = stats.laplace.fit(data)
loc_opt, scale_opt = super(type(stats.laplace),
stats.laplace).fit(data)
ll_mle = ll(loc, scale, data)
ll_opt = ll(loc_opt, scale_opt, data)
assert ll_mle < ll_opt or np.allclose(ll_mle, ll_opt,
atol=1e-15, rtol=1e-15)
def test_fit_simple_non_random_data(self):
data = np.array([1.0, 1.0, 3.0, 5.0, 8.0, 14.0])
# with `floc` fixed to 6, scale should be 4.
loc, scale = stats.laplace.fit(data, floc=6)
assert_allclose(scale, 4, atol=1e-15, rtol=1e-15)
# with `fscale` fixed to 6, loc should be 4.
loc, scale = stats.laplace.fit(data, fscale=6)
assert_allclose(loc, 4, atol=1e-15, rtol=1e-15)
class TestInvGamma(object):
def test_invgamma_inf_gh_1866(self):
# invgamma's moments are only finite for a>n
# specific numbers checked w/ boost 1.54
with warnings.catch_warnings():
warnings.simplefilter('error', RuntimeWarning)
mvsk = stats.invgamma.stats(a=19.31, moments='mvsk')
expected = [0.05461496450, 0.0001723162534, 1.020362676,
2.055616582]
assert_allclose(mvsk, expected)
a = [1.1, 3.1, 5.6]
mvsk = stats.invgamma.stats(a=a, moments='mvsk')
expected = ([10., 0.476190476, 0.2173913043], # mmm
[np.inf, 0.2061430632, 0.01312749422], # vvv
[np.nan, 41.95235392, 2.919025532], # sss
[np.nan, np.nan, 24.51923076]) # kkk
for x, y in zip(mvsk, expected):
assert_almost_equal(x, y)
def test_cdf_ppf(self):
# gh-6245
x = np.logspace(-2.6, 0)
y = stats.invgamma.cdf(x, 1)
xx = stats.invgamma.ppf(y, 1)
assert_allclose(x, xx)
def test_sf_isf(self):
# gh-6245
if sys.maxsize > 2**32:
x = np.logspace(2, 100)
else:
# Invgamme roundtrip on 32-bit systems has relative accuracy
# ~1e-15 until x=1e+15, and becomes inf above x=1e+18
x = np.logspace(2, 18)
y = stats.invgamma.sf(x, 1)
xx = stats.invgamma.isf(y, 1)
assert_allclose(x, xx, rtol=1.0)
class TestF(object):
def test_endpoints(self):
# Compute the pdf at the left endpoint dst.a.
data = [[stats.f, (2, 1), 1.0]]
for _f, _args, _correct in data:
ans = _f.pdf(_f.a, *_args)
ans = [_f.pdf(_f.a, *_args) for _f, _args, _ in data]
correct = [_correct_ for _f, _args, _correct_ in data]
assert_array_almost_equal(ans, correct)
def test_f_moments(self):
# n-th moment of F distributions is only finite for n < dfd / 2
m, v, s, k = stats.f.stats(11, 6.5, moments='mvsk')
assert_(np.isfinite(m))
assert_(np.isfinite(v))
assert_(np.isfinite(s))
assert_(not np.isfinite(k))
def test_moments_warnings(self):
# no warnings should be generated for dfd = 2, 4, 6, 8 (div by zero)
with warnings.catch_warnings():
warnings.simplefilter('error', RuntimeWarning)
stats.f.stats(dfn=[11]*4, dfd=[2, 4, 6, 8], moments='mvsk')
def test_stats_broadcast(self):
dfn = np.array([[3], [11]])
dfd = np.array([11, 12])
m, v, s, k = stats.f.stats(dfn=dfn, dfd=dfd, moments='mvsk')
m2 = [dfd / (dfd - 2)]*2
assert_allclose(m, m2)
v2 = 2 * dfd**2 * (dfn + dfd - 2) / dfn / (dfd - 2)**2 / (dfd - 4)
assert_allclose(v, v2)
s2 = ((2*dfn + dfd - 2) * np.sqrt(8*(dfd - 4)) /
((dfd - 6) * np.sqrt(dfn*(dfn + dfd - 2))))
assert_allclose(s, s2)
k2num = 12 * (dfn * (5*dfd - 22) * (dfn + dfd - 2) +
(dfd - 4) * (dfd - 2)**2)
k2den = dfn * (dfd - 6) * (dfd - 8) * (dfn + dfd - 2)
k2 = k2num / k2den
assert_allclose(k, k2)
def test_rvgeneric_std():
# Regression test for #1191
assert_array_almost_equal(stats.t.std([5, 6]), [1.29099445, 1.22474487])
def test_moments_t():
# regression test for #8786
assert_equal(stats.t.stats(df=1, moments='mvsk'),
(np.inf, np.nan, np.nan, np.nan))
assert_equal(stats.t.stats(df=1.01, moments='mvsk'),
(0.0, np.inf, np.nan, np.nan))
assert_equal(stats.t.stats(df=2, moments='mvsk'),
(0.0, np.inf, np.nan, np.nan))
assert_equal(stats.t.stats(df=2.01, moments='mvsk'),
(0.0, 2.01/(2.01-2.0), np.nan, np.inf))
assert_equal(stats.t.stats(df=3, moments='sk'), (np.nan, np.inf))
assert_equal(stats.t.stats(df=3.01, moments='sk'), (0.0, np.inf))
assert_equal(stats.t.stats(df=4, moments='sk'), (0.0, np.inf))
assert_equal(stats.t.stats(df=4.01, moments='sk'), (0.0, 6.0/(4.01 - 4.0)))
class TestRvDiscrete(object):
def setup_method(self):
np.random.seed(1234)
def test_rvs(self):
states = [-1, 0, 1, 2, 3, 4]
probability = [0.0, 0.3, 0.4, 0.0, 0.3, 0.0]
samples = 1000
r = stats.rv_discrete(name='sample', values=(states, probability))
x = r.rvs(size=samples)
assert_(isinstance(x, numpy.ndarray))
for s, p in zip(states, probability):
assert_(abs(sum(x == s)/float(samples) - p) < 0.05)
x = r.rvs()
assert_(isinstance(x, int))
def test_entropy(self):
# Basic tests of entropy.
pvals = np.array([0.25, 0.45, 0.3])
p = stats.rv_discrete(values=([0, 1, 2], pvals))
expected_h = -sum(xlogy(pvals, pvals))
h = p.entropy()
assert_allclose(h, expected_h)
p = stats.rv_discrete(values=([0, 1, 2], [1.0, 0, 0]))
h = p.entropy()
assert_equal(h, 0.0)
def test_pmf(self):
xk = [1, 2, 4]
pk = [0.5, 0.3, 0.2]
rv = stats.rv_discrete(values=(xk, pk))
x = [[1., 4.],
[3., 2]]
assert_allclose(rv.pmf(x),
[[0.5, 0.2],
[0., 0.3]], atol=1e-14)
def test_cdf(self):
xk = [1, 2, 4]
pk = [0.5, 0.3, 0.2]
rv = stats.rv_discrete(values=(xk, pk))
x_values = [-2, 1., 1.1, 1.5, 2.0, 3.0, 4, 5]
expected = [0, 0.5, 0.5, 0.5, 0.8, 0.8, 1, 1]
assert_allclose(rv.cdf(x_values), expected, atol=1e-14)
# also check scalar arguments
assert_allclose([rv.cdf(xx) for xx in x_values],
expected, atol=1e-14)
def test_ppf(self):
xk = [1, 2, 4]
pk = [0.5, 0.3, 0.2]
rv = stats.rv_discrete(values=(xk, pk))
q_values = [0.1, 0.5, 0.6, 0.8, 0.9, 1.]
expected = [1, 1, 2, 2, 4, 4]
assert_allclose(rv.ppf(q_values), expected, atol=1e-14)
# also check scalar arguments
assert_allclose([rv.ppf(q) for q in q_values],
expected, atol=1e-14)
def test_cdf_ppf_next(self):
# copied and special cased from test_discrete_basic
vals = ([1, 2, 4, 7, 8], [0.1, 0.2, 0.3, 0.3, 0.1])
rv = stats.rv_discrete(values=vals)
assert_array_equal(rv.ppf(rv.cdf(rv.xk[:-1]) + 1e-8),
rv.xk[1:])
def test_multidimension(self):
xk = np.arange(12).reshape((3, 4))
pk = np.array([[0.1, 0.1, 0.15, 0.05],
[0.1, 0.1, 0.05, 0.05],
[0.1, 0.1, 0.05, 0.05]])
rv = stats.rv_discrete(values=(xk, pk))
assert_allclose(rv.expect(), np.sum(rv.xk * rv.pk), atol=1e-14)
def test_bad_input(self):
xk = [1, 2, 3]
pk = [0.5, 0.5]
assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))
pk = [1, 2, 3]
assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))
xk = [1, 2, 3]
pk = [0.5, 1.2, -0.7]
assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))
xk = [1, 2, 3, 4, 5]
pk = [0.3, 0.3, 0.3, 0.3, -0.2]
assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))
def test_shape_rv_sample(self):
# tests added for gh-9565
# mismatch of 2d inputs
xk, pk = np.arange(4).reshape((2, 2)), np.full((2, 3), 1/6)
assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))
# same number of elements, but shapes not compatible
xk, pk = np.arange(6).reshape((3, 2)), np.full((2, 3), 1/6)
assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))
# same shapes => no error
xk, pk = np.arange(6).reshape((3, 2)), np.full((3, 2), 1/6)
assert_equal(stats.rv_discrete(values=(xk, pk)).pmf(0), 1/6)
def test_expect1(self):
xk = [1, 2, 4, 6, 7, 11]
pk = [0.1, 0.2, 0.2, 0.2, 0.2, 0.1]
rv = stats.rv_discrete(values=(xk, pk))
assert_allclose(rv.expect(), np.sum(rv.xk * rv.pk), atol=1e-14)
def test_expect2(self):
# rv_sample should override _expect. Bug report from
# https://stackoverflow.com/questions/63199792
y = [200.0, 300.0, 400.0, 500.0, 600.0, 700.0, 800.0, 900.0, 1000.0,
1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1600.0, 1700.0, 1800.0,
1900.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0,
2700.0, 2800.0, 2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0,
3500.0, 3600.0, 3700.0, 3800.0, 3900.0, 4000.0, 4100.0, 4200.0,
4300.0, 4400.0, 4500.0, 4600.0, 4700.0, 4800.0]
py = [0.0004, 0.0, 0.0033, 0.006500000000000001, 0.0, 0.0,
0.004399999999999999, 0.6862, 0.0, 0.0, 0.0,
0.00019999999999997797, 0.0006000000000000449,
0.024499999999999966, 0.006400000000000072,
0.0043999999999999595, 0.019499999999999962,
0.03770000000000007, 0.01759999999999995, 0.015199999999999991,
0.018100000000000005, 0.04500000000000004, 0.0025999999999999357,
0.0, 0.0041000000000001036, 0.005999999999999894,
0.0042000000000000925, 0.0050000000000000044,
0.0041999999999999815, 0.0004999999999999449,
0.009199999999999986, 0.008200000000000096,
0.0, 0.0, 0.0046999999999999265, 0.0019000000000000128,
0.0006000000000000449, 0.02510000000000001, 0.0,
0.007199999999999984, 0.0, 0.012699999999999934, 0.0, 0.0,
0.008199999999999985, 0.005600000000000049, 0.0]
rv = stats.rv_discrete(values=(y, py))
# check the mean
assert_allclose(rv.expect(), rv.mean(), atol=1e-14)
assert_allclose(rv.expect(),
sum(v * w for v, w in zip(y, py)), atol=1e-14)
# also check the second moment
assert_allclose(rv.expect(lambda x: x**2),
sum(v**2 * w for v, w in zip(y, py)), atol=1e-14)
class TestSkewNorm(object):
def setup_method(self):
self.rng = check_random_state(1234)
def test_normal(self):
# When the skewness is 0 the distribution is normal
x = np.linspace(-5, 5, 100)
assert_array_almost_equal(stats.skewnorm.pdf(x, a=0),
stats.norm.pdf(x))
def test_rvs(self):
shape = (3, 4, 5)
x = stats.skewnorm.rvs(a=0.75, size=shape, random_state=self.rng)
assert_equal(shape, x.shape)
x = stats.skewnorm.rvs(a=-3, size=shape, random_state=self.rng)
assert_equal(shape, x.shape)
def test_moments(self):
X = stats.skewnorm.rvs(a=4, size=int(1e6), loc=5, scale=2,
random_state=self.rng)
expected = [np.mean(X), np.var(X), stats.skew(X), stats.kurtosis(X)]
computed = stats.skewnorm.stats(a=4, loc=5, scale=2, moments='mvsk')
assert_array_almost_equal(computed, expected, decimal=2)
X = stats.skewnorm.rvs(a=-4, size=int(1e6), loc=5, scale=2,
random_state=self.rng)
expected = [np.mean(X), np.var(X), stats.skew(X), stats.kurtosis(X)]
computed = stats.skewnorm.stats(a=-4, loc=5, scale=2, moments='mvsk')
assert_array_almost_equal(computed, expected, decimal=2)
def test_cdf_large_x(self):
# Regression test for gh-7746.
# The x values are large enough that the closest 64 bit floating
# point representation of the exact CDF is 1.0.
p = stats.skewnorm.cdf([10, 20, 30], -1)
assert_allclose(p, np.ones(3), rtol=1e-14)
p = stats.skewnorm.cdf(25, 2.5)
assert_allclose(p, 1.0, rtol=1e-14)
def test_cdf_sf_small_values(self):
# Triples are [x, a, cdf(x, a)]. These values were computed
# using CDF[SkewNormDistribution[0, 1, a], x] in Wolfram Alpha.
cdfvals = [
[-8, 1, 3.870035046664392611e-31],
[-4, 2, 8.1298399188811398e-21],
[-2, 5, 1.55326826787106273e-26],
[-9, -1, 2.257176811907681295e-19],
[-10, -4, 1.523970604832105213e-23],
]
for x, a, cdfval in cdfvals:
p = stats.skewnorm.cdf(x, a)
assert_allclose(p, cdfval, rtol=1e-8)
# For the skew normal distribution, sf(-x, -a) = cdf(x, a).
p = stats.skewnorm.sf(-x, -a)
assert_allclose(p, cdfval, rtol=1e-8)
class TestExpon(object):
def test_zero(self):
assert_equal(stats.expon.pdf(0), 1)
def test_tail(self): # Regression test for ticket 807
assert_equal(stats.expon.cdf(1e-18), 1e-18)
assert_equal(stats.expon.isf(stats.expon.sf(40)), 40)
def test_nan_raises_error(self):
# see gh-issue 10300
x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
assert_raises(RuntimeError, stats.expon.fit, x)
def test_inf_raises_error(self):
# see gh-issue 10300
x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
assert_raises(RuntimeError, stats.expon.fit, x)
class TestNorm(object):
def test_nan_raises_error(self):
# see gh-issue 10300
x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
assert_raises(RuntimeError, stats.norm.fit, x)
def test_inf_raises_error(self):
# see gh-issue 10300
x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
assert_raises(RuntimeError, stats.norm.fit, x)
def test_bad_keyword_arg(self):
x = [1, 2, 3]
assert_raises(TypeError, stats.norm.fit, x, plate="shrimp")
class TestUniform(object):
"""gh-10300"""
def test_nan_raises_error(self):
# see gh-issue 10300
x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
assert_raises(RuntimeError, stats.uniform.fit, x)
def test_inf_raises_error(self):
# see gh-issue 10300
x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
assert_raises(RuntimeError, stats.uniform.fit, x)
class TestExponNorm(object):
def test_moments(self):
# Some moment test cases based on non-loc/scaled formula
def get_moms(lam, sig, mu):
# See wikipedia for these formulae
# where it is listed as an exponentially modified gaussian
opK2 = 1.0 + 1 / (lam*sig)**2
exp_skew = 2 / (lam * sig)**3 * opK2**(-1.5)
exp_kurt = 6.0 * (1 + (lam * sig)**2)**(-2)
return [mu + 1/lam, sig*sig + 1.0/(lam*lam), exp_skew, exp_kurt]
mu, sig, lam = 0, 1, 1
K = 1.0 / (lam * sig)
sts = stats.exponnorm.stats(K, loc=mu, scale=sig, moments='mvsk')
assert_almost_equal(sts, get_moms(lam, sig, mu))
mu, sig, lam = -3, 2, 0.1
K = 1.0 / (lam * sig)
sts = stats.exponnorm.stats(K, loc=mu, scale=sig, moments='mvsk')
assert_almost_equal(sts, get_moms(lam, sig, mu))
mu, sig, lam = 0, 3, 1
K = 1.0 / (lam * sig)
sts = stats.exponnorm.stats(K, loc=mu, scale=sig, moments='mvsk')
assert_almost_equal(sts, get_moms(lam, sig, mu))
mu, sig, lam = -5, 11, 3.5
K = 1.0 / (lam * sig)
sts = stats.exponnorm.stats(K, loc=mu, scale=sig, moments='mvsk')
assert_almost_equal(sts, get_moms(lam, sig, mu))
def test_nan_raises_error(self):
# see gh-issue 10300
x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
assert_raises(RuntimeError, stats.exponnorm.fit, x, floc=0, fscale=1)
def test_inf_raises_error(self):
# see gh-issue 10300
x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
assert_raises(RuntimeError, stats.exponnorm.fit, x, floc=0, fscale=1)
def test_extremes_x(self):
# Test for extreme values against overflows
assert_almost_equal(stats.exponnorm.pdf(-900, 1), 0.0)
assert_almost_equal(stats.exponnorm.pdf(+900, 1), 0.0)
assert_almost_equal(stats.exponnorm.pdf(-900, 0.01), 0.0)
assert_almost_equal(stats.exponnorm.pdf(+900, 0.01), 0.0)
# Expected values for the PDF were computed with mpmath, with
# the following function, and with mpmath.mp.dps = 50.
#
# def exponnorm_stdpdf(x, K):
# x = mpmath.mpf(x)
# K = mpmath.mpf(K)
# t1 = mpmath.exp(1/(2*K**2) - x/K)
# erfcarg = -(x - 1/K)/mpmath.sqrt(2)
# t2 = mpmath.erfc(erfcarg)
# return t1 * t2 / (2*K)
#
@pytest.mark.parametrize('x, K, expected',
[(20, 0.01, 6.90010764753618e-88),
(1, 0.01, 0.24438994313247364),
(-1, 0.01, 0.23955149623472075),
(-20, 0.01, 4.6004708690125477e-88),
(10, 1, 7.48518298877006e-05),
(10, 10000, 9.990005048283775e-05)])
def test_std_pdf(self, x, K, expected):
assert_allclose(stats.exponnorm.pdf(x, K), expected, rtol=1e-12)
class TestGenExpon(object):
def test_pdf_unity_area(self):
from scipy.integrate import simps
# PDF should integrate to one
p = stats.genexpon.pdf(numpy.arange(0, 10, 0.01), 0.5, 0.5, 2.0)
assert_almost_equal(simps(p, dx=0.01), 1, 1)
def test_cdf_bounds(self):
# CDF should always be positive
cdf = stats.genexpon.cdf(numpy.arange(0, 10, 0.01), 0.5, 0.5, 2.0)
assert_(numpy.all((0 <= cdf) & (cdf <= 1)))
class TestExponpow(object):
def test_tail(self):
assert_almost_equal(stats.exponpow.cdf(1e-10, 2.), 1e-20)
assert_almost_equal(stats.exponpow.isf(stats.exponpow.sf(5, .8), .8),
5)
class TestSkellam(object):
def test_pmf(self):
# comparison to R
k = numpy.arange(-10, 15)
mu1, mu2 = 10, 5
skpmfR = numpy.array(
[4.2254582961926893e-005, 1.1404838449648488e-004,
2.8979625801752660e-004, 6.9177078182101231e-004,
1.5480716105844708e-003, 3.2412274963433889e-003,
6.3373707175123292e-003, 1.1552351566696643e-002,
1.9606152375042644e-002, 3.0947164083410337e-002,
4.5401737566767360e-002, 6.1894328166820688e-002,
7.8424609500170578e-002, 9.2418812533573133e-002,
1.0139793148019728e-001, 1.0371927988298846e-001,
9.9076583077406091e-002, 8.8546660073089561e-002,
7.4187842052486810e-002, 5.8392772862200251e-002,
4.3268692953013159e-002, 3.0248159818374226e-002,
1.9991434305603021e-002, 1.2516877303301180e-002,
7.4389876226229707e-003])
assert_almost_equal(stats.skellam.pmf(k, mu1, mu2), skpmfR, decimal=15)
def test_cdf(self):
# comparison to R, only 5 decimals
k = numpy.arange(-10, 15)
mu1, mu2 = 10, 5
skcdfR = numpy.array(
[6.4061475386192104e-005, 1.7810985988267694e-004,
4.6790611790020336e-004, 1.1596768997212152e-003,
2.7077485103056847e-003, 5.9489760066490718e-003,
1.2286346724161398e-002, 2.3838698290858034e-002,
4.3444850665900668e-002, 7.4392014749310995e-002,
1.1979375231607835e-001, 1.8168808048289900e-001,
2.6011268998306952e-001, 3.5253150251664261e-001,
4.5392943399683988e-001, 5.5764871387982828e-001,
6.5672529695723436e-001, 7.4527195703032389e-001,
8.1945979908281064e-001, 8.7785257194501087e-001,
9.2112126489802404e-001, 9.5136942471639818e-001,
9.7136085902200120e-001, 9.8387773632530240e-001,
9.9131672394792536e-001])
assert_almost_equal(stats.skellam.cdf(k, mu1, mu2), skcdfR, decimal=5)
class TestLognorm(object):
def test_pdf(self):
# Regression test for Ticket #1471: avoid nan with 0/0 situation
# Also make sure there are no warnings at x=0, cf gh-5202
with warnings.catch_warnings():
warnings.simplefilter('error', RuntimeWarning)
pdf = stats.lognorm.pdf([0, 0.5, 1], 1)
assert_array_almost_equal(pdf, [0.0, 0.62749608, 0.39894228])
def test_logcdf(self):
# Regression test for gh-5940: sf et al would underflow too early
x2, mu, sigma = 201.68, 195, 0.149
assert_allclose(stats.lognorm.sf(x2-mu, s=sigma),
stats.norm.sf(np.log(x2-mu)/sigma))
assert_allclose(stats.lognorm.logsf(x2-mu, s=sigma),
stats.norm.logsf(np.log(x2-mu)/sigma))
class TestBeta(object):
def test_logpdf(self):
# Regression test for Ticket #1326: avoid nan with 0*log(0) situation
logpdf = stats.beta.logpdf(0, 1, 0.5)
assert_almost_equal(logpdf, -0.69314718056)
logpdf = stats.beta.logpdf(0, 0.5, 1)
assert_almost_equal(logpdf, np.inf)
def test_logpdf_ticket_1866(self):
alpha, beta = 267, 1472
x = np.array([0.2, 0.5, 0.6])
b = stats.beta(alpha, beta)
assert_allclose(b.logpdf(x).sum(), -1201.699061824062)
assert_allclose(b.pdf(x), np.exp(b.logpdf(x)))
def test_fit_bad_keyword_args(self):
x = [0.1, 0.5, 0.6]
assert_raises(TypeError, stats.beta.fit, x, floc=0, fscale=1,
plate="shrimp")
def test_fit_duplicated_fixed_parameter(self):
# At most one of 'f0', 'fa' or 'fix_a' can be given to the fit method.
# More than one raises a ValueError.
x = [0.1, 0.5, 0.6]
assert_raises(ValueError, stats.beta.fit, x, fa=0.5, fix_a=0.5)
class TestBetaPrime(object):
def test_logpdf(self):
alpha, beta = 267, 1472
x = np.array([0.2, 0.5, 0.6])
b = stats.betaprime(alpha, beta)
assert_(np.isfinite(b.logpdf(x)).all())
assert_allclose(b.pdf(x), np.exp(b.logpdf(x)))
def test_cdf(self):
# regression test for gh-4030: Implementation of
# scipy.stats.betaprime.cdf()
x = stats.betaprime.cdf(0, 0.2, 0.3)
assert_equal(x, 0.0)
alpha, beta = 267, 1472
x = np.array([0.2, 0.5, 0.6])
cdfs = stats.betaprime.cdf(x, alpha, beta)
assert_(np.isfinite(cdfs).all())
# check the new cdf implementation vs generic one:
gen_cdf = stats.rv_continuous._cdf_single
cdfs_g = [gen_cdf(stats.betaprime, val, alpha, beta) for val in x]
assert_allclose(cdfs, cdfs_g, atol=0, rtol=2e-12)
class TestGamma(object):
def test_pdf(self):
# a few test cases to compare with R
pdf = stats.gamma.pdf(90, 394, scale=1./5)
assert_almost_equal(pdf, 0.002312341)
pdf = stats.gamma.pdf(3, 10, scale=1./5)
assert_almost_equal(pdf, 0.1620358)
def test_logpdf(self):
# Regression test for Ticket #1326: cornercase avoid nan with 0*log(0)
# situation
logpdf = stats.gamma.logpdf(0, 1)
assert_almost_equal(logpdf, 0)
def test_fit_bad_keyword_args(self):
x = [0.1, 0.5, 0.6]
assert_raises(TypeError, stats.gamma.fit, x, floc=0, plate="shrimp")
class TestChi2(object):
# regression tests after precision improvements, ticket:1041, not verified
def test_precision(self):
assert_almost_equal(stats.chi2.pdf(1000, 1000), 8.919133934753128e-003,
decimal=14)
assert_almost_equal(stats.chi2.pdf(100, 100), 0.028162503162596778,
decimal=14)
def test_ppf(self):
# Expected values computed with mpmath.
df = 4.8
x = stats.chi2.ppf(2e-47, df)
assert_allclose(x, 1.098472479575179840604902808e-19, rtol=1e-10)
x = stats.chi2.ppf(0.5, df)
assert_allclose(x, 4.15231407598589358660093156, rtol=1e-10)
df = 13
x = stats.chi2.ppf(2e-77, df)
assert_allclose(x, 1.0106330688195199050507943e-11, rtol=1e-10)
x = stats.chi2.ppf(0.1, df)
assert_allclose(x, 7.041504580095461859307179763, rtol=1e-10)
class TestGumbelL(object):
# gh-6228
def test_cdf_ppf(self):
x = np.linspace(-100, -4)
y = stats.gumbel_l.cdf(x)
xx = stats.gumbel_l.ppf(y)
assert_allclose(x, xx)
def test_logcdf_logsf(self):
x = np.linspace(-100, -4)
y = stats.gumbel_l.logcdf(x)
z = stats.gumbel_l.logsf(x)
u = np.exp(y)
v = -special.expm1(z)
assert_allclose(u, v)
def test_sf_isf(self):
x = np.linspace(-20, 5)
y = stats.gumbel_l.sf(x)
xx = stats.gumbel_l.isf(y)
assert_allclose(x, xx)
class TestGumbelR:
def test_sf(self):
# Expected value computed with mpmath:
# >>> import mpmath
# >>> mpmath.mp.dps = 40
# >>> float(mpmath.mp.one - mpmath.exp(-mpmath.exp(-50)))
# 1.9287498479639178e-22
assert_allclose(stats.gumbel_r.sf(50), 1.9287498479639178e-22,
rtol=1e-14)
def test_isf(self):
# Expected value computed with mpmath:
# >>> import mpmath
# >>> mpmath.mp.dps = 40
# >>> float(-mpmath.log(-mpmath.log(mpmath.mp.one - 1e-17)))
# 39.14394658089878
assert_allclose(stats.gumbel_r.isf(1e-17), 39.14394658089878,
rtol=1e-14)
class TestLevyStable(object):
def test_fit(self):
# construct data to have percentiles that match
# example in McCulloch 1986.
x = [-.05413, -.05413,
0., 0., 0., 0.,
.00533, .00533, .00533, .00533, .00533,
.03354, .03354, .03354, .03354, .03354,
.05309, .05309, .05309, .05309, .05309]
alpha1, beta1, loc1, scale1 = stats.levy_stable._fitstart(x)
assert_allclose(alpha1, 1.48, rtol=0, atol=0.01)
assert_almost_equal(beta1, -.22, 2)
assert_almost_equal(scale1, 0.01717, 4)
# to 2 dps due to rounding error in McCulloch86
assert_almost_equal(loc1, 0.00233, 2)
# cover alpha=2 scenario
x2 = x + [.05309, .05309, .05309, .05309, .05309]
alpha2, beta2, loc2, scale2 = stats.levy_stable._fitstart(x2)
assert_equal(alpha2, 2)
assert_equal(beta2, -1)
assert_almost_equal(scale2, .02503, 4)
assert_almost_equal(loc2, .03354, 4)
@pytest.mark.slow
def test_pdf_nolan_samples(self):
""" Test pdf values against Nolan's stablec.exe output
see - http://fs2.american.edu/jpnolan/www/stable/stable.html
There's a known limitation of Nolan's executable for alpha < 0.2.
Repeat following with beta = -1, -.5, 0, .5 and 1
stablec.exe <<
1 # pdf
1 # Nolan S equivalent to S0 in scipy
.25,2,.25 # alpha
-1,-1,0 # beta
-10,10,1 # x
1,0 # gamma, delta
2 # output file
"""
fn = os.path.abspath(os.path.join(os.path.dirname(__file__),
'data/stable-pdf-sample-data.npy'))
data = np.load(fn)
data = np.core.records.fromarrays(data.T, names='x,p,alpha,beta')
# support numpy 1.8.2 for travis
npisin = np.isin if hasattr(np, "isin") else np.in1d
tests = [
# best selects
['best', None, 8, None],
# quadrature is accurate for most alpha except 0.25; perhaps
# limitation of Nolan stablec?
# we reduce size of x to speed up computation as numerical
# integration slow.
['quadrature', None, 8,
lambda r: ((r['alpha'] > 0.25) &
(npisin(r['x'], [-10, -5, 0, 5, 10])))],
# zolatarev is accurate except at alpha==1, beta != 0
['zolotarev', None, 8, lambda r: r['alpha'] != 1],
['zolotarev', None, 8,
lambda r: (r['alpha'] == 1) & (r['beta'] == 0)],
['zolotarev', None, 1,
lambda r: (r['alpha'] == 1) & (r['beta'] != 0)],
# fft accuracy reduces as alpha decreases, fails at low values of
# alpha and x=0
['fft', 0, 4, lambda r: r['alpha'] > 1],
['fft', 0, 3, lambda r: (r['alpha'] < 1) & (r['alpha'] > 0.25)],
# not useful here
['fft', 0, 1, lambda r: (r['alpha'] == 0.25) & (r['x'] != 0)],
]
for ix, (default_method, fft_min_points,
decimal_places, filter_func) in enumerate(tests):
stats.levy_stable.pdf_default_method = default_method
stats.levy_stable.pdf_fft_min_points_threshold = fft_min_points
subdata = (data[filter_func(data)] if filter_func is not None else
data)
with suppress_warnings() as sup:
sup.record(RuntimeWarning,
"Density calculation unstable for alpha=1 "
"and beta!=0.*")
sup.record(RuntimeWarning,
"Density calculations experimental for FFT "
"method.*")
p = stats.levy_stable.pdf(subdata['x'], subdata['alpha'],
subdata['beta'], scale=1, loc=0)
subdata2 = rec_append_fields(subdata, 'calc', p)
padiff = np.abs(p-subdata['p'])
failures = subdata2[(padiff >= 1.5*10.**(-decimal_places)) |
np.isnan(p)]
assert_almost_equal(p, subdata['p'], decimal_places,
("pdf test %s failed with method '%s'\n%s"
% (ix, default_method, failures)),
verbose=False)
@pytest.mark.slow
def test_cdf_nolan_samples(self):
""" Test cdf values against Nolan's stablec.exe output
see - http://fs2.american.edu/jpnolan/www/stable/stable.html
There's a known limitation of Nolan's executable for alpha < 0.2.
Repeat following with beta = -1, -.5, 0, .5 and 1
stablec.exe <<
2 # cdf
1 # Nolan S equivalent to S0 in scipy
.25,2,.25 # alpha
-1,-1,0 # beta
-10,10,1 # x
1,0 # gamma, delta
2 # output file
"""
fn = os.path.abspath(os.path.join(os.path.dirname(__file__),
'data/stable-cdf-sample-data.npy'))
data = np.load(fn)
data = np.core.records.fromarrays(data.T, names='x,p,alpha,beta')
tests = [
# zolatarev is accurate for all values
['zolotarev', None, 8, None],
# fft accuracy poor, very poor alpha < 1
['fft', 0, 2, lambda r: r['alpha'] > 1],
]
for ix, (default_method, fft_min_points, decimal_places,
filter_func) in enumerate(tests):
stats.levy_stable.pdf_default_method = default_method
stats.levy_stable.pdf_fft_min_points_threshold = fft_min_points
subdata = (data[filter_func(data)] if filter_func is not None else
data)
with suppress_warnings() as sup:
sup.record(RuntimeWarning, 'FFT method is considered ' +
'experimental for cumulative distribution ' +
'function evaluations.*')
p = stats.levy_stable.cdf(subdata['x'], subdata['alpha'],
subdata['beta'], scale=1, loc=0)
subdata2 = rec_append_fields(subdata, 'calc', p)
padiff = np.abs(p - subdata['p'])
failures = subdata2[(padiff >= 1.5*10.**(-decimal_places)) |
np.isnan(p)]
assert_almost_equal(p, subdata['p'], decimal_places,
("cdf test %s failed with method '%s'\n%s"
% (ix, default_method, failures)),
verbose=False)
def test_pdf_alpha_equals_one_beta_non_zero(self):
"""
sample points extracted from Tables and Graphs of Stable Probability
Density Functions - Donald R Holt - 1973 - p 187.
"""
xs = np.array([0, 0, 0, 0,
1, 1, 1, 1,
2, 2, 2, 2,
3, 3, 3, 3,
4, 4, 4, 4])
density = np.array([.3183, .3096, .2925, .2622,
.1591, .1587, .1599, .1635,
.0637, .0729, .0812, .0955,
.0318, .0390, .0458, .0586,
.0187, .0236, .0285, .0384])
betas = np.array([0, .25, .5, 1,
0, .25, .5, 1,
0, .25, .5, 1,
0, .25, .5, 1,
0, .25, .5, 1])
tests = [
['quadrature', None, 4],
['zolotarev', None, 1],
]
with np.errstate(all='ignore'), suppress_warnings() as sup:
sup.filter(category=RuntimeWarning,
message="Density calculation unstable.*")
for default_method, fft_min_points, decimal_places in tests:
stats.levy_stable.pdf_default_method = default_method
stats.levy_stable.pdf_fft_min_points_threshold = fft_min_points
pdf = stats.levy_stable.pdf(xs, 1, betas, scale=1, loc=0)
assert_almost_equal(pdf, density, decimal_places,
default_method)
def test_stats(self):
param_sets = [
[(1.48, -.22, 0, 1), (0, np.inf, np.NaN, np.NaN)],
[(2, .9, 10, 1.5), (10, 4.5, 0, 0)]
]
for args, exp_stats in param_sets:
calc_stats = stats.levy_stable.stats(args[0], args[1],
loc=args[2], scale=args[3],
moments='mvsk')
assert_almost_equal(calc_stats, exp_stats)
class TestArrayArgument(object): # test for ticket:992
def setup_method(self):
np.random.seed(1234)
def test_noexception(self):
rvs = stats.norm.rvs(loc=(np.arange(5)), scale=np.ones(5),
size=(10, 5))
assert_equal(rvs.shape, (10, 5))
class TestDocstring(object):
def test_docstrings(self):
# See ticket #761
if stats.rayleigh.__doc__ is not None:
assert_("rayleigh" in stats.rayleigh.__doc__.lower())
if stats.bernoulli.__doc__ is not None:
assert_("bernoulli" in stats.bernoulli.__doc__.lower())
def test_no_name_arg(self):
# If name is not given, construction shouldn't fail. See #1508.
stats.rv_continuous()
stats.rv_discrete()
class TestEntropy(object):
def test_entropy_positive(self):
# See ticket #497
pk = [0.5, 0.2, 0.3]
qk = [0.1, 0.25, 0.65]
eself = stats.entropy(pk, pk)
edouble = stats.entropy(pk, qk)
assert_(0.0 == eself)
assert_(edouble >= 0.0)
def test_entropy_base(self):
pk = np.ones(16, float)
S = stats.entropy(pk, base=2.)
assert_(abs(S - 4.) < 1.e-5)
qk = np.ones(16, float)
qk[:8] = 2.
S = stats.entropy(pk, qk)
S2 = stats.entropy(pk, qk, base=2.)
assert_(abs(S/S2 - np.log(2.)) < 1.e-5)
def test_entropy_zero(self):
# Test for PR-479
assert_almost_equal(stats.entropy([0, 1, 2]), 0.63651416829481278,
decimal=12)
def test_entropy_2d(self):
pk = [[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]]
qk = [[0.2, 0.1], [0.3, 0.6], [0.5, 0.3]]
assert_array_almost_equal(stats.entropy(pk, qk),
[0.1933259, 0.18609809])
def test_entropy_2d_zero(self):
pk = [[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]]
qk = [[0.0, 0.1], [0.3, 0.6], [0.5, 0.3]]
assert_array_almost_equal(stats.entropy(pk, qk),
[np.inf, 0.18609809])
pk[0][0] = 0.0
assert_array_almost_equal(stats.entropy(pk, qk),
[0.17403988, 0.18609809])
def test_entropy_base_2d_nondefault_axis(self):
pk = [[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]]
assert_array_almost_equal(stats.entropy(pk, axis=1),
[0.63651417, 0.63651417, 0.66156324])
def test_entropy_2d_nondefault_axis(self):
pk = [[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]]
qk = [[0.2, 0.1], [0.3, 0.6], [0.5, 0.3]]
assert_array_almost_equal(stats.entropy(pk, qk, axis=1),
[0.231049, 0.231049, 0.127706])
def test_entropy_raises_value_error(self):
pk = [[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]]
qk = [[0.1, 0.2], [0.6, 0.3]]
assert_raises(ValueError, stats.entropy, pk, qk)
def test_base_entropy_with_axis_0_is_equal_to_default(self):
pk = [[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]]
assert_array_almost_equal(stats.entropy(pk, axis=0),
stats.entropy(pk))
def test_entropy_with_axis_0_is_equal_to_default(self):
pk = [[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]]
qk = [[0.2, 0.1], [0.3, 0.6], [0.5, 0.3]]
assert_array_almost_equal(stats.entropy(pk, qk, axis=0),
stats.entropy(pk, qk))
def test_base_entropy_transposed(self):
pk = np.array([[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]])
assert_array_almost_equal(stats.entropy(pk.T).T,
stats.entropy(pk, axis=1))
def test_entropy_transposed(self):
pk = np.array([[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]])
qk = np.array([[0.2, 0.1], [0.3, 0.6], [0.5, 0.3]])
assert_array_almost_equal(stats.entropy(pk.T, qk.T).T,
stats.entropy(pk, qk, axis=1))
def TestArgsreduce():
a = array([1, 3, 2, 1, 2, 3, 3])
b, c = argsreduce(a > 1, a, 2)
assert_array_equal(b, [3, 2, 2, 3, 3])
assert_array_equal(c, [2, 2, 2, 2, 2])
b, c = argsreduce(2 > 1, a, 2)
assert_array_equal(b, a[0])
assert_array_equal(c, [2])
b, c = argsreduce(a > 0, a, 2)
assert_array_equal(b, a)
assert_array_equal(c, [2] * numpy.size(a))
class TestFitMethod(object):
skip = ['ncf', 'ksone', 'kstwo']
def setup_method(self):
np.random.seed(1234)
# skip these b/c deprecated, or only loc and scale arguments
fitSkipNonFinite = ['expon', 'norm', 'uniform']
@pytest.mark.parametrize('dist,args', distcont)
def test_fit_w_non_finite_data_values(self, dist, args):
"""gh-10300"""
if dist in self.fitSkipNonFinite:
pytest.skip("%s fit known to fail or deprecated" % dist)
x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
y = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
distfunc = getattr(stats, dist)
assert_raises(RuntimeError, distfunc.fit, x, floc=0, fscale=1)
assert_raises(RuntimeError, distfunc.fit, y, floc=0, fscale=1)
def test_fix_fit_2args_lognorm(self):
# Regression test for #1551.
np.random.seed(12345)
with np.errstate(all='ignore'):
x = stats.lognorm.rvs(0.25, 0., 20.0, size=20)
expected_shape = np.sqrt(((np.log(x) - np.log(20))**2).mean())
assert_allclose(np.array(stats.lognorm.fit(x, floc=0, fscale=20)),
[expected_shape, 0, 20], atol=1e-8)
def test_fix_fit_norm(self):
x = np.arange(1, 6)
loc, scale = stats.norm.fit(x)
assert_almost_equal(loc, 3)
assert_almost_equal(scale, np.sqrt(2))
loc, scale = stats.norm.fit(x, floc=2)
assert_equal(loc, 2)
assert_equal(scale, np.sqrt(3))
loc, scale = stats.norm.fit(x, fscale=2)
assert_almost_equal(loc, 3)
assert_equal(scale, 2)
def test_fix_fit_gamma(self):
x = np.arange(1, 6)
meanlog = np.log(x).mean()
# A basic test of gamma.fit with floc=0.
floc = 0
a, loc, scale = stats.gamma.fit(x, floc=floc)
s = np.log(x.mean()) - meanlog
assert_almost_equal(np.log(a) - special.digamma(a), s, decimal=5)
assert_equal(loc, floc)
assert_almost_equal(scale, x.mean()/a, decimal=8)
# Regression tests for gh-2514.
# The problem was that if `floc=0` was given, any other fixed
# parameters were ignored.
f0 = 1
floc = 0
a, loc, scale = stats.gamma.fit(x, f0=f0, floc=floc)
assert_equal(a, f0)
assert_equal(loc, floc)
assert_almost_equal(scale, x.mean()/a, decimal=8)
f0 = 2
floc = 0
a, loc, scale = stats.gamma.fit(x, f0=f0, floc=floc)
assert_equal(a, f0)
assert_equal(loc, floc)
assert_almost_equal(scale, x.mean()/a, decimal=8)
# loc and scale fixed.
floc = 0
fscale = 2
a, loc, scale = stats.gamma.fit(x, floc=floc, fscale=fscale)
assert_equal(loc, floc)
assert_equal(scale, fscale)
c = meanlog - np.log(fscale)
assert_almost_equal(special.digamma(a), c)
def test_fix_fit_beta(self):
# Test beta.fit when both floc and fscale are given.
def mlefunc(a, b, x):
# Zeros of this function are critical points of
# the maximum likelihood function.
n = len(x)
s1 = np.log(x).sum()
s2 = np.log(1-x).sum()
psiab = special.psi(a + b)
func = [s1 - n * (-psiab + special.psi(a)),
s2 - n * (-psiab + special.psi(b))]
return func
# Basic test with floc and fscale given.
x = np.array([0.125, 0.25, 0.5])
a, b, loc, scale = stats.beta.fit(x, floc=0, fscale=1)
assert_equal(loc, 0)
assert_equal(scale, 1)
assert_allclose(mlefunc(a, b, x), [0, 0], atol=1e-6)
# Basic test with f0, floc and fscale given.
# This is also a regression test for gh-2514.
x = np.array([0.125, 0.25, 0.5])
a, b, loc, scale = stats.beta.fit(x, f0=2, floc=0, fscale=1)
assert_equal(a, 2)
assert_equal(loc, 0)
assert_equal(scale, 1)
da, db = mlefunc(a, b, x)
assert_allclose(db, 0, atol=1e-5)
# Same floc and fscale values as above, but reverse the data
# and fix b (f1).
x2 = 1 - x
a2, b2, loc2, scale2 = stats.beta.fit(x2, f1=2, floc=0, fscale=1)
assert_equal(b2, 2)
assert_equal(loc2, 0)
assert_equal(scale2, 1)
da, db = mlefunc(a2, b2, x2)
assert_allclose(da, 0, atol=1e-5)
# a2 of this test should equal b from above.
assert_almost_equal(a2, b)
# Check for detection of data out of bounds when floc and fscale
# are given.
assert_raises(ValueError, stats.beta.fit, x, floc=0.5, fscale=1)
y = np.array([0, .5, 1])
assert_raises(ValueError, stats.beta.fit, y, floc=0, fscale=1)
assert_raises(ValueError, stats.beta.fit, y, floc=0, fscale=1, f0=2)
assert_raises(ValueError, stats.beta.fit, y, floc=0, fscale=1, f1=2)
# Check that attempting to fix all the parameters raises a ValueError.
assert_raises(ValueError, stats.beta.fit, y, f0=0, f1=1,
floc=2, fscale=3)
def test_expon_fit(self):
x = np.array([2, 2, 4, 4, 4, 4, 4, 8])
loc, scale = stats.expon.fit(x)
assert_equal(loc, 2) # x.min()
assert_equal(scale, 2) # x.mean() - x.min()
loc, scale = stats.expon.fit(x, fscale=3)
assert_equal(loc, 2) # x.min()
assert_equal(scale, 3) # fscale
loc, scale = stats.expon.fit(x, floc=0)
assert_equal(loc, 0) # floc
assert_equal(scale, 4) # x.mean() - loc
def test_lognorm_fit(self):
x = np.array([1.5, 3, 10, 15, 23, 59])
lnxm1 = np.log(x - 1)
shape, loc, scale = stats.lognorm.fit(x, floc=1)
assert_allclose(shape, lnxm1.std(), rtol=1e-12)
assert_equal(loc, 1)
assert_allclose(scale, np.exp(lnxm1.mean()), rtol=1e-12)
shape, loc, scale = stats.lognorm.fit(x, floc=1, fscale=6)
assert_allclose(shape, np.sqrt(((lnxm1 - np.log(6))**2).mean()),
rtol=1e-12)
assert_equal(loc, 1)
assert_equal(scale, 6)
shape, loc, scale = stats.lognorm.fit(x, floc=1, fix_s=0.75)
assert_equal(shape, 0.75)
assert_equal(loc, 1)
assert_allclose(scale, np.exp(lnxm1.mean()), rtol=1e-12)
def test_uniform_fit(self):
x = np.array([1.0, 1.1, 1.2, 9.0])
loc, scale = stats.uniform.fit(x)
assert_equal(loc, x.min())
assert_equal(scale, x.ptp())
loc, scale = stats.uniform.fit(x, floc=0)
assert_equal(loc, 0)
assert_equal(scale, x.max())
loc, scale = stats.uniform.fit(x, fscale=10)
assert_equal(loc, 0)
assert_equal(scale, 10)
assert_raises(ValueError, stats.uniform.fit, x, floc=2.0)
assert_raises(ValueError, stats.uniform.fit, x, fscale=5.0)
def test_fshapes(self):
# take a beta distribution, with shapes='a, b', and make sure that
# fa is equivalent to f0, and fb is equivalent to f1
a, b = 3., 4.
x = stats.beta.rvs(a, b, size=100, random_state=1234)
res_1 = stats.beta.fit(x, f0=3.)
res_2 = stats.beta.fit(x, fa=3.)
assert_allclose(res_1, res_2, atol=1e-12, rtol=1e-12)
res_2 = stats.beta.fit(x, fix_a=3.)
assert_allclose(res_1, res_2, atol=1e-12, rtol=1e-12)
res_3 = stats.beta.fit(x, f1=4.)
res_4 = stats.beta.fit(x, fb=4.)
assert_allclose(res_3, res_4, atol=1e-12, rtol=1e-12)
res_4 = stats.beta.fit(x, fix_b=4.)
assert_allclose(res_3, res_4, atol=1e-12, rtol=1e-12)
# cannot specify both positional and named args at the same time
assert_raises(ValueError, stats.beta.fit, x, fa=1, f0=2)
# check that attempting to fix all parameters raises a ValueError
assert_raises(ValueError, stats.beta.fit, x, fa=0, f1=1,
floc=2, fscale=3)
# check that specifying floc, fscale and fshapes works for
# beta and gamma which override the generic fit method
res_5 = stats.beta.fit(x, fa=3., floc=0, fscale=1)
aa, bb, ll, ss = res_5
assert_equal([aa, ll, ss], [3., 0, 1])
# gamma distribution
a = 3.
data = stats.gamma.rvs(a, size=100)
aa, ll, ss = stats.gamma.fit(data, fa=a)
assert_equal(aa, a)
def test_extra_params(self):
# unknown parameters should raise rather than be silently ignored
dist = stats.exponnorm
data = dist.rvs(K=2, size=100)
dct = dict(enikibeniki=-101)
assert_raises(TypeError, dist.fit, data, **dct)
class TestFrozen(object):
def setup_method(self):
np.random.seed(1234)
# Test that a frozen distribution gives the same results as the original
# object.
#
# Only tested for the normal distribution (with loc and scale specified)
# and for the gamma distribution (with a shape parameter specified).
def test_norm(self):
dist = stats.norm
frozen = stats.norm(loc=10.0, scale=3.0)
result_f = frozen.pdf(20.0)
result = dist.pdf(20.0, loc=10.0, scale=3.0)
assert_equal(result_f, result)
result_f = frozen.cdf(20.0)
result = dist.cdf(20.0, loc=10.0, scale=3.0)
assert_equal(result_f, result)
result_f = frozen.ppf(0.25)
result = dist.ppf(0.25, loc=10.0, scale=3.0)
assert_equal(result_f, result)
result_f = frozen.isf(0.25)
result = dist.isf(0.25, loc=10.0, scale=3.0)
assert_equal(result_f, result)
result_f = frozen.sf(10.0)
result = dist.sf(10.0, loc=10.0, scale=3.0)
assert_equal(result_f, result)
result_f = frozen.median()
result = dist.median(loc=10.0, scale=3.0)
assert_equal(result_f, result)
result_f = frozen.mean()
result = dist.mean(loc=10.0, scale=3.0)
assert_equal(result_f, result)
result_f = frozen.var()
result = dist.var(loc=10.0, scale=3.0)
assert_equal(result_f, result)
result_f = frozen.std()
result = dist.std(loc=10.0, scale=3.0)
assert_equal(result_f, result)
result_f = frozen.entropy()
result = dist.entropy(loc=10.0, scale=3.0)
assert_equal(result_f, result)
result_f = frozen.moment(2)
result = dist.moment(2, loc=10.0, scale=3.0)
assert_equal(result_f, result)
assert_equal(frozen.a, dist.a)
assert_equal(frozen.b, dist.b)
def test_gamma(self):
a = 2.0
dist = stats.gamma
frozen = stats.gamma(a)
result_f = frozen.pdf(20.0)
result = dist.pdf(20.0, a)
assert_equal(result_f, result)
result_f = frozen.cdf(20.0)
result = dist.cdf(20.0, a)
assert_equal(result_f, result)
result_f = frozen.ppf(0.25)
result = dist.ppf(0.25, a)
assert_equal(result_f, result)
result_f = frozen.isf(0.25)
result = dist.isf(0.25, a)
assert_equal(result_f, result)
result_f = frozen.sf(10.0)
result = dist.sf(10.0, a)
assert_equal(result_f, result)
result_f = frozen.median()
result = dist.median(a)
assert_equal(result_f, result)
result_f = frozen.mean()
result = dist.mean(a)
assert_equal(result_f, result)
result_f = frozen.var()
result = dist.var(a)
assert_equal(result_f, result)
result_f = frozen.std()
result = dist.std(a)
assert_equal(result_f, result)
result_f = frozen.entropy()
result = dist.entropy(a)
assert_equal(result_f, result)
result_f = frozen.moment(2)
result = dist.moment(2, a)
assert_equal(result_f, result)
assert_equal(frozen.a, frozen.dist.a)
assert_equal(frozen.b, frozen.dist.b)
def test_regression_ticket_1293(self):
# Create a frozen distribution.
frozen = stats.lognorm(1)
# Call one of its methods that does not take any keyword arguments.
m1 = frozen.moment(2)
# Now call a method that takes a keyword argument.
frozen.stats(moments='mvsk')
# Call moment(2) again.
# After calling stats(), the following was raising an exception.
# So this test passes if the following does not raise an exception.
m2 = frozen.moment(2)
# The following should also be true, of course. But it is not
# the focus of this test.
assert_equal(m1, m2)
def test_ab(self):
# test that the support of a frozen distribution
# (i) remains frozen even if it changes for the original one
# (ii) is actually correct if the shape parameters are such that
# the values of [a, b] are not the default [0, inf]
# take a genpareto as an example where the support
# depends on the value of the shape parameter:
# for c > 0: a, b = 0, inf
# for c < 0: a, b = 0, -1/c
c = -0.1
rv = stats.genpareto(c=c)
a, b = rv.dist._get_support(c)
assert_equal([a, b], [0., 10.])
c = 0.1
stats.genpareto.pdf(0, c=c)
assert_equal(rv.dist._get_support(c), [0, np.inf])
c = -0.1
rv = stats.genpareto(c=c)
a, b = rv.dist._get_support(c)
assert_equal([a, b], [0., 10.])
c = 0.1
stats.genpareto.pdf(0, c) # this should NOT change genpareto.b
assert_equal((rv.dist.a, rv.dist.b), stats.genpareto._get_support(c))
rv1 = stats.genpareto(c=0.1)
assert_(rv1.dist is not rv.dist)
# c >= 0: a, b = [0, inf]
for c in [1., 0.]:
c = np.asarray(c)
rv = stats.genpareto(c=c)
a, b = rv.a, rv.b
assert_equal(a, 0.)
assert_(np.isposinf(b))
# c < 0: a=0, b=1/|c|
c = np.asarray(-2.)
a, b = stats.genpareto._get_support(c)
assert_allclose([a, b], [0., 0.5])
def test_rv_frozen_in_namespace(self):
# Regression test for gh-3522
assert_(hasattr(stats.distributions, 'rv_frozen'))
def test_random_state(self):
# only check that the random_state attribute exists,
frozen = stats.norm()
assert_(hasattr(frozen, 'random_state'))
# ... that it can be set,
frozen.random_state = 42
assert_equal(frozen.random_state.get_state(),
np.random.RandomState(42).get_state())
# ... and that .rvs method accepts it as an argument
rndm = np.random.RandomState(1234)
frozen.rvs(size=8, random_state=rndm)
def test_pickling(self):
# test that a frozen instance pickles and unpickles
# (this method is a clone of common_tests.check_pickling)
beta = stats.beta(2.3098496451481823, 0.62687954300963677)
poiss = stats.poisson(3.)
sample = stats.rv_discrete(values=([0, 1, 2, 3],
[0.1, 0.2, 0.3, 0.4]))
for distfn in [beta, poiss, sample]:
distfn.random_state = 1234
distfn.rvs(size=8)
s = pickle.dumps(distfn)
r0 = distfn.rvs(size=8)
unpickled = pickle.loads(s)
r1 = unpickled.rvs(size=8)
assert_equal(r0, r1)
# also smoke test some methods
medians = [distfn.ppf(0.5), unpickled.ppf(0.5)]
assert_equal(medians[0], medians[1])
assert_equal(distfn.cdf(medians[0]),
unpickled.cdf(medians[1]))
def test_expect(self):
# smoke test the expect method of the frozen distribution
# only take a gamma w/loc and scale and poisson with loc specified
def func(x):
return x
gm = stats.gamma(a=2, loc=3, scale=4)
gm_val = gm.expect(func, lb=1, ub=2, conditional=True)
gamma_val = stats.gamma.expect(func, args=(2,), loc=3, scale=4,
lb=1, ub=2, conditional=True)
assert_allclose(gm_val, gamma_val)
p = stats.poisson(3, loc=4)
p_val = p.expect(func)
poisson_val = stats.poisson.expect(func, args=(3,), loc=4)
assert_allclose(p_val, poisson_val)
class TestExpect(object):
# Test for expect method.
#
# Uses normal distribution and beta distribution for finite bounds, and
# hypergeom for discrete distribution with finite support
def test_norm(self):
v = stats.norm.expect(lambda x: (x-5)*(x-5), loc=5, scale=2)
assert_almost_equal(v, 4, decimal=14)
m = stats.norm.expect(lambda x: (x), loc=5, scale=2)
assert_almost_equal(m, 5, decimal=14)
lb = stats.norm.ppf(0.05, loc=5, scale=2)
ub = stats.norm.ppf(0.95, loc=5, scale=2)
prob90 = stats.norm.expect(lambda x: 1, loc=5, scale=2, lb=lb, ub=ub)
assert_almost_equal(prob90, 0.9, decimal=14)
prob90c = stats.norm.expect(lambda x: 1, loc=5, scale=2, lb=lb, ub=ub,
conditional=True)
assert_almost_equal(prob90c, 1., decimal=14)
def test_beta(self):
# case with finite support interval
v = stats.beta.expect(lambda x: (x-19/3.)*(x-19/3.), args=(10, 5),
loc=5, scale=2)
assert_almost_equal(v, 1./18., decimal=13)
m = stats.beta.expect(lambda x: x, args=(10, 5), loc=5., scale=2.)
assert_almost_equal(m, 19/3., decimal=13)
ub = stats.beta.ppf(0.95, 10, 10, loc=5, scale=2)
lb = stats.beta.ppf(0.05, 10, 10, loc=5, scale=2)
prob90 = stats.beta.expect(lambda x: 1., args=(10, 10), loc=5.,
scale=2., lb=lb, ub=ub, conditional=False)
assert_almost_equal(prob90, 0.9, decimal=13)
prob90c = stats.beta.expect(lambda x: 1, args=(10, 10), loc=5,
scale=2, lb=lb, ub=ub, conditional=True)
assert_almost_equal(prob90c, 1., decimal=13)
def test_hypergeom(self):
# test case with finite bounds
# without specifying bounds
m_true, v_true = stats.hypergeom.stats(20, 10, 8, loc=5.)
m = stats.hypergeom.expect(lambda x: x, args=(20, 10, 8), loc=5.)
assert_almost_equal(m, m_true, decimal=13)
v = stats.hypergeom.expect(lambda x: (x-9.)**2, args=(20, 10, 8),
loc=5.)
assert_almost_equal(v, v_true, decimal=14)
# with bounds, bounds equal to shifted support
v_bounds = stats.hypergeom.expect(lambda x: (x-9.)**2,
args=(20, 10, 8),
loc=5., lb=5, ub=13)
assert_almost_equal(v_bounds, v_true, decimal=14)
# drop boundary points
prob_true = 1-stats.hypergeom.pmf([5, 13], 20, 10, 8, loc=5).sum()
prob_bounds = stats.hypergeom.expect(lambda x: 1, args=(20, 10, 8),
loc=5., lb=6, ub=12)
assert_almost_equal(prob_bounds, prob_true, decimal=13)
# conditional
prob_bc = stats.hypergeom.expect(lambda x: 1, args=(20, 10, 8), loc=5.,
lb=6, ub=12, conditional=True)
assert_almost_equal(prob_bc, 1, decimal=14)
# check simple integral
prob_b = stats.hypergeom.expect(lambda x: 1, args=(20, 10, 8),
lb=0, ub=8)
assert_almost_equal(prob_b, 1, decimal=13)
def test_poisson(self):
# poisson, use lower bound only
prob_bounds = stats.poisson.expect(lambda x: 1, args=(2,), lb=3,
conditional=False)
prob_b_true = 1-stats.poisson.cdf(2, 2)
assert_almost_equal(prob_bounds, prob_b_true, decimal=14)
prob_lb = stats.poisson.expect(lambda x: 1, args=(2,), lb=2,
conditional=True)
assert_almost_equal(prob_lb, 1, decimal=14)
def test_genhalflogistic(self):
# genhalflogistic, changes upper bound of support in _argcheck
# regression test for gh-2622
halflog = stats.genhalflogistic
# check consistency when calling expect twice with the same input
res1 = halflog.expect(args=(1.5,))
halflog.expect(args=(0.5,))
res2 = halflog.expect(args=(1.5,))
assert_almost_equal(res1, res2, decimal=14)
def test_rice_overflow(self):
# rice.pdf(999, 0.74) was inf since special.i0 silentyly overflows
# check that using i0e fixes it
assert_(np.isfinite(stats.rice.pdf(999, 0.74)))
assert_(np.isfinite(stats.rice.expect(lambda x: 1, args=(0.74,))))
assert_(np.isfinite(stats.rice.expect(lambda x: 2, args=(0.74,))))
assert_(np.isfinite(stats.rice.expect(lambda x: 3, args=(0.74,))))
def test_logser(self):
# test a discrete distribution with infinite support and loc
p, loc = 0.3, 3
res_0 = stats.logser.expect(lambda k: k, args=(p,))
# check against the correct answer (sum of a geom series)
assert_allclose(res_0,
p / (p - 1.) / np.log(1. - p), atol=1e-15)
# now check it with `loc`
res_l = stats.logser.expect(lambda k: k, args=(p,), loc=loc)
assert_allclose(res_l, res_0 + loc, atol=1e-15)
def test_skellam(self):
# Use a discrete distribution w/ bi-infinite support. Compute two first
# moments and compare to known values (cf skellam.stats)
p1, p2 = 18, 22
m1 = stats.skellam.expect(lambda x: x, args=(p1, p2))
m2 = stats.skellam.expect(lambda x: x**2, args=(p1, p2))
assert_allclose(m1, p1 - p2, atol=1e-12)
assert_allclose(m2 - m1**2, p1 + p2, atol=1e-12)
def test_randint(self):
# Use a discrete distribution w/ parameter-dependent support, which
# is larger than the default chunksize
lo, hi = 0, 113
res = stats.randint.expect(lambda x: x, (lo, hi))
assert_allclose(res,
sum(_ for _ in range(lo, hi)) / (hi - lo), atol=1e-15)
def test_zipf(self):
# Test that there is no infinite loop even if the sum diverges
assert_warns(RuntimeWarning, stats.zipf.expect,
lambda x: x**2, (2,))
def test_discrete_kwds(self):
# check that discrete expect accepts keywords to control the summation
n0 = stats.poisson.expect(lambda x: 1, args=(2,))
n1 = stats.poisson.expect(lambda x: 1, args=(2,),
maxcount=1001, chunksize=32, tolerance=1e-8)
assert_almost_equal(n0, n1, decimal=14)
def test_moment(self):
# test the .moment() method: compute a higher moment and compare to
# a known value
def poiss_moment5(mu):
return mu**5 + 10*mu**4 + 25*mu**3 + 15*mu**2 + mu
for mu in [5, 7]:
m5 = stats.poisson.moment(5, mu)
assert_allclose(m5, poiss_moment5(mu), rtol=1e-10)
class TestNct(object):
def test_nc_parameter(self):
# Parameter values c<=0 were not enabled (gh-2402).
# For negative values c and for c=0 results of rv.cdf(0) below were nan
rv = stats.nct(5, 0)
assert_equal(rv.cdf(0), 0.5)
rv = stats.nct(5, -1)
assert_almost_equal(rv.cdf(0), 0.841344746069, decimal=10)
def test_broadcasting(self):
res = stats.nct.pdf(5, np.arange(4, 7)[:, None],
np.linspace(0.1, 1, 4))
expected = array([[0.00321886, 0.00557466, 0.00918418, 0.01442997],
[0.00217142, 0.00395366, 0.00683888, 0.01126276],
[0.00153078, 0.00291093, 0.00525206, 0.00900815]])
assert_allclose(res, expected, rtol=1e-5)
def test_variance_gh_issue_2401(self):
# Computation of the variance of a non-central t-distribution resulted
# in a TypeError: ufunc 'isinf' not supported for the input types,
# and the inputs could not be safely coerced to any supported types
# according to the casting rule 'safe'
rv = stats.nct(4, 0)
assert_equal(rv.var(), 2.0)
def test_nct_inf_moments(self):
# n-th moment of nct only exists for df > n
m, v, s, k = stats.nct.stats(df=0.9, nc=0.3, moments='mvsk')
assert_equal([m, v, s, k], [np.nan, np.nan, np.nan, np.nan])
m, v, s, k = stats.nct.stats(df=1.9, nc=0.3, moments='mvsk')
assert_(np.isfinite(m))
assert_equal([v, s, k], [np.nan, np.nan, np.nan])
m, v, s, k = stats.nct.stats(df=3.1, nc=0.3, moments='mvsk')
assert_(np.isfinite([m, v, s]).all())
assert_equal(k, np.nan)
def test_nct_stats_large_df_values(self):
# previously gamma function was used which lost precision at df=345
# cf. https://github.com/scipy/scipy/issues/12919 for details
nct_mean_df_1000 = stats.nct.mean(1000, 2)
nct_stats_df_1000 = stats.nct.stats(1000, 2)
# These expected values were computed with mpmath. They were also
# verified with the Wolfram Alpha expressions:
# Mean[NoncentralStudentTDistribution[1000, 2]]
# Var[NoncentralStudentTDistribution[1000, 2]]
expected_stats_df_1000 = [2.0015015641422464, 1.0040115288163005]
assert_allclose(nct_mean_df_1000, expected_stats_df_1000[0],
rtol=1e-10)
assert_allclose(nct_stats_df_1000, expected_stats_df_1000,
rtol=1e-10)
# and a bigger df value
nct_mean = stats.nct.mean(100000, 2)
nct_stats = stats.nct.stats(100000, 2)
# These expected values were computed with mpmath.
expected_stats = [2.0000150001562518, 1.0000400011500288]
assert_allclose(nct_mean, expected_stats[0], rtol=1e-10)
assert_allclose(nct_stats, expected_stats, rtol=1e-9)
class TestRice(object):
def test_rice_zero_b(self):
# rice distribution should work with b=0, cf gh-2164
x = [0.2, 1., 5.]
assert_(np.isfinite(stats.rice.pdf(x, b=0.)).all())
assert_(np.isfinite(stats.rice.logpdf(x, b=0.)).all())
assert_(np.isfinite(stats.rice.cdf(x, b=0.)).all())
assert_(np.isfinite(stats.rice.logcdf(x, b=0.)).all())
q = [0.1, 0.1, 0.5, 0.9]
assert_(np.isfinite(stats.rice.ppf(q, b=0.)).all())
mvsk = stats.rice.stats(0, moments='mvsk')
assert_(np.isfinite(mvsk).all())
# furthermore, pdf is continuous as b\to 0
# rice.pdf(x, b\to 0) = x exp(-x^2/2) + O(b^2)
# see e.g. Abramovich & Stegun 9.6.7 & 9.6.10
b = 1e-8
assert_allclose(stats.rice.pdf(x, 0), stats.rice.pdf(x, b),
atol=b, rtol=0)
def test_rice_rvs(self):
rvs = stats.rice.rvs
assert_equal(rvs(b=3.).size, 1)
assert_equal(rvs(b=3., size=(3, 5)).shape, (3, 5))
class TestErlang(object):
def setup_method(self):
np.random.seed(1234)
def test_erlang_runtimewarning(self):
# erlang should generate a RuntimeWarning if a non-integer
# shape parameter is used.
with warnings.catch_warnings():
warnings.simplefilter("error", RuntimeWarning)
# The non-integer shape parameter 1.3 should trigger a
# RuntimeWarning
assert_raises(RuntimeWarning,
stats.erlang.rvs, 1.3, loc=0, scale=1, size=4)
# Calling the fit method with `f0` set to an integer should
# *not* trigger a RuntimeWarning. It should return the same
# values as gamma.fit(...).
data = [0.5, 1.0, 2.0, 4.0]
result_erlang = stats.erlang.fit(data, f0=1)
result_gamma = stats.gamma.fit(data, f0=1)
assert_allclose(result_erlang, result_gamma, rtol=1e-3)
def test_gh_pr_10949_argcheck(self):
assert_equal(stats.erlang.pdf(0.5, a=[1, -1]),
stats.gamma.pdf(0.5, a=[1, -1]))
class TestRayleigh(object):
def setup_method(self):
np.random.seed(987654321)
# gh-6227
def test_logpdf(self):
y = stats.rayleigh.logpdf(50)
assert_allclose(y, -1246.0879769945718)
def test_logsf(self):
y = stats.rayleigh.logsf(50)
assert_allclose(y, -1250)
@pytest.mark.parametrize("rvs_loc,rvs_scale", [np.random.rand(2)])
def test_fit(self, rvs_loc, rvs_scale):
data = stats.rayleigh.rvs(size=250, loc=rvs_loc, scale=rvs_scale)
def scale_mle(data, floc):
return (np.sum((data - floc) ** 2) / (2 * len(data))) ** .5
# when `floc` is provided, `scale` is found with an analytical formula
scale_expect = scale_mle(data, rvs_loc)
loc, scale = stats.rayleigh.fit(data, floc=rvs_loc)
assert_equal(loc, rvs_loc)
assert_equal(scale, scale_expect)
# when `fscale` is fixed, superclass fit is used to determine `loc`.
loc, scale = stats.rayleigh.fit(data, fscale=.6)
assert_equal(scale, .6)
# with both parameters free, one dimensional optimization is done
# over a new function that takes into account the dependent relation
# of `scale` to `loc`.
loc, scale = stats.rayleigh.fit(data)
# test that `scale` is defined by its relation to `loc`
assert_equal(scale, scale_mle(data, loc))
@pytest.mark.parametrize("rvs_loc,rvs_scale", [[0.74, 0.01],
np.random.rand(2)])
def test_fit_comparison_super_method(self, rvs_loc, rvs_scale):
# test that the objective function result of the analytical MLEs is
# less than or equal to that of the numerically optimized estimate
data = stats.rayleigh.rvs(size=250, loc=rvs_loc, scale=rvs_scale)
# obtain objective function with same method as `rv_continuous.fit`
args = [data, (stats.rayleigh._fitstart(data), )]
func = stats.rayleigh._reduce_func(args, {})[1]
_assert_less_or_close_loglike(stats.rayleigh, data, func)
def test_fit_warnings(self):
assert_fit_warnings(stats.rayleigh)
class TestExponWeib(object):
def test_pdf_logpdf(self):
# Regression test for gh-3508.
x = 0.1
a = 1.0
c = 100.0
p = stats.exponweib.pdf(x, a, c)
logp = stats.exponweib.logpdf(x, a, c)
# Expected values were computed with mpmath.
assert_allclose([p, logp],
[1.0000000000000054e-97, -223.35075402042244])
def test_a_is_1(self):
# For issue gh-3508.
# Check that when a=1, the pdf and logpdf methods of exponweib are the
# same as those of weibull_min.
x = np.logspace(-4, -1, 4)
a = 1
c = 100
p = stats.exponweib.pdf(x, a, c)
expected = stats.weibull_min.pdf(x, c)
assert_allclose(p, expected)
logp = stats.exponweib.logpdf(x, a, c)
expected = stats.weibull_min.logpdf(x, c)
assert_allclose(logp, expected)
def test_a_is_1_c_is_1(self):
# When a = 1 and c = 1, the distribution is exponential.
x = np.logspace(-8, 1, 10)
a = 1
c = 1
p = stats.exponweib.pdf(x, a, c)
expected = stats.expon.pdf(x)
assert_allclose(p, expected)
logp = stats.exponweib.logpdf(x, a, c)
expected = stats.expon.logpdf(x)
assert_allclose(logp, expected)
class TestWeibull(object):
def test_logpdf(self):
# gh-6217
y = stats.weibull_min.logpdf(0, 1)
assert_equal(y, 0)
def test_with_maxima_distrib(self):
# Tests for weibull_min and weibull_max.
# The expected values were computed using the symbolic algebra
# program 'maxima' with the package 'distrib', which has
# 'pdf_weibull' and 'cdf_weibull'. The mapping between the
# scipy and maxima functions is as follows:
# -----------------------------------------------------------------
# scipy maxima
# --------------------------------- ------------------------------
# weibull_min.pdf(x, a, scale=b) pdf_weibull(x, a, b)
# weibull_min.logpdf(x, a, scale=b) log(pdf_weibull(x, a, b))
# weibull_min.cdf(x, a, scale=b) cdf_weibull(x, a, b)
# weibull_min.logcdf(x, a, scale=b) log(cdf_weibull(x, a, b))
# weibull_min.sf(x, a, scale=b) 1 - cdf_weibull(x, a, b)
# weibull_min.logsf(x, a, scale=b) log(1 - cdf_weibull(x, a, b))
#
# weibull_max.pdf(x, a, scale=b) pdf_weibull(-x, a, b)
# weibull_max.logpdf(x, a, scale=b) log(pdf_weibull(-x, a, b))
# weibull_max.cdf(x, a, scale=b) 1 - cdf_weibull(-x, a, b)
# weibull_max.logcdf(x, a, scale=b) log(1 - cdf_weibull(-x, a, b))
# weibull_max.sf(x, a, scale=b) cdf_weibull(-x, a, b)
# weibull_max.logsf(x, a, scale=b) log(cdf_weibull(-x, a, b))
# -----------------------------------------------------------------
x = 1.5
a = 2.0
b = 3.0
# weibull_min
p = stats.weibull_min.pdf(x, a, scale=b)
assert_allclose(p, np.exp(-0.25)/3)
lp = stats.weibull_min.logpdf(x, a, scale=b)
assert_allclose(lp, -0.25 - np.log(3))
c = stats.weibull_min.cdf(x, a, scale=b)
assert_allclose(c, -special.expm1(-0.25))
lc = stats.weibull_min.logcdf(x, a, scale=b)
assert_allclose(lc, np.log(-special.expm1(-0.25)))
s = stats.weibull_min.sf(x, a, scale=b)
assert_allclose(s, np.exp(-0.25))
ls = stats.weibull_min.logsf(x, a, scale=b)
assert_allclose(ls, -0.25)
# Also test using a large value x, for which computing the survival
# function using the CDF would result in 0.
s = stats.weibull_min.sf(30, 2, scale=3)
assert_allclose(s, np.exp(-100))
ls = stats.weibull_min.logsf(30, 2, scale=3)
assert_allclose(ls, -100)
# weibull_max
x = -1.5
p = stats.weibull_max.pdf(x, a, scale=b)
assert_allclose(p, np.exp(-0.25)/3)
lp = stats.weibull_max.logpdf(x, a, scale=b)
assert_allclose(lp, -0.25 - np.log(3))
c = stats.weibull_max.cdf(x, a, scale=b)
assert_allclose(c, np.exp(-0.25))
lc = stats.weibull_max.logcdf(x, a, scale=b)
assert_allclose(lc, -0.25)
s = stats.weibull_max.sf(x, a, scale=b)
assert_allclose(s, -special.expm1(-0.25))
ls = stats.weibull_max.logsf(x, a, scale=b)
assert_allclose(ls, np.log(-special.expm1(-0.25)))
# Also test using a value of x close to 0, for which computing the
# survival function using the CDF would result in 0.
s = stats.weibull_max.sf(-1e-9, 2, scale=3)
assert_allclose(s, -special.expm1(-1/9000000000000000000))
ls = stats.weibull_max.logsf(-1e-9, 2, scale=3)
assert_allclose(ls, np.log(-special.expm1(-1/9000000000000000000)))
class TestRdist(object):
def test_rdist_cdf_gh1285(self):
# check workaround in rdist._cdf for issue gh-1285.
distfn = stats.rdist
values = [0.001, 0.5, 0.999]
assert_almost_equal(distfn.cdf(distfn.ppf(values, 541.0), 541.0),
values, decimal=5)
def test_rdist_beta(self):
# rdist is a special case of stats.beta
x = np.linspace(-0.99, 0.99, 10)
c = 2.7
assert_almost_equal(0.5*stats.beta(c/2, c/2).pdf((x + 1)/2),
stats.rdist(c).pdf(x))
class TestTrapezoid(object):
def test_reduces_to_triang(self):
modes = [0, 0.3, 0.5, 1]
for mode in modes:
x = [0, mode, 1]
assert_almost_equal(stats.trapezoid.pdf(x, mode, mode),
stats.triang.pdf(x, mode))
assert_almost_equal(stats.trapezoid.cdf(x, mode, mode),
stats.triang.cdf(x, mode))
def test_reduces_to_uniform(self):
x = np.linspace(0, 1, 10)
assert_almost_equal(stats.trapezoid.pdf(x, 0, 1), stats.uniform.pdf(x))
assert_almost_equal(stats.trapezoid.cdf(x, 0, 1), stats.uniform.cdf(x))
def test_cases(self):
# edge cases
assert_almost_equal(stats.trapezoid.pdf(0, 0, 0), 2)
assert_almost_equal(stats.trapezoid.pdf(1, 1, 1), 2)
assert_almost_equal(stats.trapezoid.pdf(0.5, 0, 0.8),
1.11111111111111111)
assert_almost_equal(stats.trapezoid.pdf(0.5, 0.2, 1.0),
1.11111111111111111)
# straightforward case
assert_almost_equal(stats.trapezoid.pdf(0.1, 0.2, 0.8), 0.625)
assert_almost_equal(stats.trapezoid.pdf(0.5, 0.2, 0.8), 1.25)
assert_almost_equal(stats.trapezoid.pdf(0.9, 0.2, 0.8), 0.625)
assert_almost_equal(stats.trapezoid.cdf(0.1, 0.2, 0.8), 0.03125)
assert_almost_equal(stats.trapezoid.cdf(0.2, 0.2, 0.8), 0.125)
assert_almost_equal(stats.trapezoid.cdf(0.5, 0.2, 0.8), 0.5)
assert_almost_equal(stats.trapezoid.cdf(0.9, 0.2, 0.8), 0.96875)
assert_almost_equal(stats.trapezoid.cdf(1.0, 0.2, 0.8), 1.0)
def test_moments_and_entropy(self):
# issue #11795: improve precision of trapezoid stats
# Apply formulas from Wikipedia for the following parameters:
a, b, c, d = -3, -1, 2, 3 # => 1/3, 5/6, -3, 6
p1, p2, loc, scale = (b-a) / (d-a), (c-a) / (d-a), a, d-a
h = 2 / (d+c-b-a)
def moment(n):
return (h * ((d**(n+2) - c**(n+2)) / (d-c)
- (b**(n+2) - a**(n+2)) / (b-a)) /
(n+1) / (n+2))
mean = moment(1)
var = moment(2) - mean**2
entropy = 0.5 * (d-c+b-a) / (d+c-b-a) + np.log(0.5 * (d+c-b-a))
assert_almost_equal(stats.trapezoid.mean(p1, p2, loc, scale),
mean, decimal=13)
assert_almost_equal(stats.trapezoid.var(p1, p2, loc, scale),
var, decimal=13)
assert_almost_equal(stats.trapezoid.entropy(p1, p2, loc, scale),
entropy, decimal=13)
# Check boundary cases where scipy d=0 or d=1.
assert_almost_equal(stats.trapezoid.mean(0, 0, -3, 6), -1, decimal=13)
assert_almost_equal(stats.trapezoid.mean(0, 1, -3, 6), 0, decimal=13)
assert_almost_equal(stats.trapezoid.var(0, 1, -3, 6), 3, decimal=13)
def test_trapezoid_vect(self):
# test that array-valued shapes and arguments are handled
c = np.array([0.1, 0.2, 0.3])
d = np.array([0.5, 0.6])[:, None]
x = np.array([0.15, 0.25, 0.9])
v = stats.trapezoid.pdf(x, c, d)
cc, dd, xx = np.broadcast_arrays(c, d, x)
res = np.empty(xx.size, dtype=xx.dtype)
ind = np.arange(xx.size)
for i, x1, c1, d1 in zip(ind, xx.ravel(), cc.ravel(), dd.ravel()):
res[i] = stats.trapezoid.pdf(x1, c1, d1)
assert_allclose(v, res.reshape(v.shape), atol=1e-15)
# Check that the stats() method supports vector arguments.
v = np.asarray(stats.trapezoid.stats(c, d, moments="mvsk"))
cc, dd = np.broadcast_arrays(c, d)
res = np.empty((cc.size, 4)) # 4 stats returned per value
ind = np.arange(cc.size)
for i, c1, d1 in zip(ind, cc.ravel(), dd.ravel()):
res[i] = stats.trapezoid.stats(c1, d1, moments="mvsk")
assert_allclose(v, res.T.reshape(v.shape), atol=1e-15)
def test_trapz(self):
# Basic test for alias
x = np.linspace(0, 1, 10)
assert_almost_equal(stats.trapz.pdf(x, 0, 1), stats.uniform.pdf(x))
class TestTriang(object):
def test_edge_cases(self):
with np.errstate(all='raise'):
assert_equal(stats.triang.pdf(0, 0), 2.)
assert_equal(stats.triang.pdf(0.5, 0), 1.)
assert_equal(stats.triang.pdf(1, 0), 0.)
assert_equal(stats.triang.pdf(0, 1), 0)
assert_equal(stats.triang.pdf(0.5, 1), 1.)
assert_equal(stats.triang.pdf(1, 1), 2)
assert_equal(stats.triang.cdf(0., 0.), 0.)
assert_equal(stats.triang.cdf(0.5, 0.), 0.75)
assert_equal(stats.triang.cdf(1.0, 0.), 1.0)
assert_equal(stats.triang.cdf(0., 1.), 0.)
assert_equal(stats.triang.cdf(0.5, 1.), 0.25)
assert_equal(stats.triang.cdf(1., 1.), 1)
class TestMielke(object):
def test_moments(self):
k, s = 4.642, 0.597
# n-th moment exists only if n < s
assert_equal(stats.mielke(k, s).moment(1), np.inf)
assert_equal(stats.mielke(k, 1.0).moment(1), np.inf)
assert_(np.isfinite(stats.mielke(k, 1.01).moment(1)))
def test_burr_equivalence(self):
x = np.linspace(0.01, 100, 50)
k, s = 2.45, 5.32
assert_allclose(stats.burr.pdf(x, s, k/s), stats.mielke.pdf(x, k, s))
class TestBurr(object):
def test_endpoints_7491(self):
# gh-7491
# Compute the pdf at the left endpoint dst.a.
data = [
[stats.fisk, (1,), 1],
[stats.burr, (0.5, 2), 1],
[stats.burr, (1, 1), 1],
[stats.burr, (2, 0.5), 1],
[stats.burr12, (1, 0.5), 0.5],
[stats.burr12, (1, 1), 1.0],
[stats.burr12, (1, 2), 2.0]]
ans = [_f.pdf(_f.a, *_args) for _f, _args, _ in data]
correct = [_correct_ for _f, _args, _correct_ in data]
assert_array_almost_equal(ans, correct)
ans = [_f.logpdf(_f.a, *_args) for _f, _args, _ in data]
correct = [np.log(_correct_) for _f, _args, _correct_ in data]
assert_array_almost_equal(ans, correct)
def test_burr_stats_9544(self):
# gh-9544. Test from gh-9978
c, d = 5.0, 3
mean, variance = stats.burr(c, d).stats()
# mean = sc.beta(3 + 1/5, 1. - 1/5) * 3 = 1.4110263...
# var = sc.beta(3 + 2 / 5, 1. - 2 / 5) * 3 -
# (sc.beta(3 + 1 / 5, 1. - 1 / 5) * 3) ** 2
mean_hc, variance_hc = 1.4110263183925857, 0.22879948026191643
assert_allclose(mean, mean_hc)
assert_allclose(variance, variance_hc)
def test_burr_nan_mean_var_9544(self):
# gh-9544. Test from gh-9978
c, d = 0.5, 3
mean, variance = stats.burr(c, d).stats()
assert_(np.isnan(mean))
assert_(np.isnan(variance))
c, d = 1.5, 3
mean, variance = stats.burr(c, d).stats()
assert_(np.isfinite(mean))
assert_(np.isnan(variance))
c, d = 0.5, 3
e1, e2, e3, e4 = stats.burr._munp(np.array([1, 2, 3, 4]), c, d)
assert_(np.isnan(e1))
assert_(np.isnan(e2))
assert_(np.isnan(e3))
assert_(np.isnan(e4))
c, d = 1.5, 3
e1, e2, e3, e4 = stats.burr._munp([1, 2, 3, 4], c, d)
assert_(np.isfinite(e1))
assert_(np.isnan(e2))
assert_(np.isnan(e3))
assert_(np.isnan(e4))
c, d = 2.5, 3
e1, e2, e3, e4 = stats.burr._munp([1, 2, 3, 4], c, d)
assert_(np.isfinite(e1))
assert_(np.isfinite(e2))
assert_(np.isnan(e3))
assert_(np.isnan(e4))
c, d = 3.5, 3
e1, e2, e3, e4 = stats.burr._munp([1, 2, 3, 4], c, d)
assert_(np.isfinite(e1))
assert_(np.isfinite(e2))
assert_(np.isfinite(e3))
assert_(np.isnan(e4))
c, d = 4.5, 3
e1, e2, e3, e4 = stats.burr._munp([1, 2, 3, 4], c, d)
assert_(np.isfinite(e1))
assert_(np.isfinite(e2))
assert_(np.isfinite(e3))
assert_(np.isfinite(e4))
def test_540_567():
# test for nan returned in tickets 540, 567
assert_almost_equal(stats.norm.cdf(-1.7624320982), 0.03899815971089126,
decimal=10, err_msg='test_540_567')
assert_almost_equal(stats.norm.cdf(-1.7624320983), 0.038998159702449846,
decimal=10, err_msg='test_540_567')
assert_almost_equal(stats.norm.cdf(1.38629436112, loc=0.950273420309,
scale=0.204423758009),
0.98353464004309321,
decimal=10, err_msg='test_540_567')
def test_regression_ticket_1316():
# The following was raising an exception, because _construct_default_doc()
# did not handle the default keyword extradoc=None. See ticket #1316.
stats._continuous_distns.gamma_gen(name='gamma')
def test_regression_ticket_1326():
# adjust to avoid nan with 0*log(0)
assert_almost_equal(stats.chi2.pdf(0.0, 2), 0.5, 14)
def test_regression_tukey_lambda():
# Make sure that Tukey-Lambda distribution correctly handles
# non-positive lambdas.
x = np.linspace(-5.0, 5.0, 101)
with np.errstate(divide='ignore'):
for lam in [0.0, -1.0, -2.0, np.array([[-1.0], [0.0], [-2.0]])]:
p = stats.tukeylambda.pdf(x, lam)
assert_((p != 0.0).all())
assert_(~np.isnan(p).all())
lam = np.array([[-1.0], [0.0], [2.0]])
p = stats.tukeylambda.pdf(x, lam)
assert_(~np.isnan(p).all())
assert_((p[0] != 0.0).all())
assert_((p[1] != 0.0).all())
assert_((p[2] != 0.0).any())
assert_((p[2] == 0.0).any())
@pytest.mark.skipif(DOCSTRINGS_STRIPPED, reason="docstrings stripped")
def test_regression_ticket_1421():
assert_('pdf(x, mu, loc=0, scale=1)' not in stats.poisson.__doc__)
assert_('pmf(x,' in stats.poisson.__doc__)
def test_nan_arguments_gh_issue_1362():
with np.errstate(invalid='ignore'):
assert_(np.isnan(stats.t.logcdf(1, np.nan)))
assert_(np.isnan(stats.t.cdf(1, np.nan)))
assert_(np.isnan(stats.t.logsf(1, np.nan)))
assert_(np.isnan(stats.t.sf(1, np.nan)))
assert_(np.isnan(stats.t.pdf(1, np.nan)))
assert_(np.isnan(stats.t.logpdf(1, np.nan)))
assert_(np.isnan(stats.t.ppf(1, np.nan)))
assert_(np.isnan(stats.t.isf(1, np.nan)))
assert_(np.isnan(stats.bernoulli.logcdf(np.nan, 0.5)))
assert_(np.isnan(stats.bernoulli.cdf(np.nan, 0.5)))
assert_(np.isnan(stats.bernoulli.logsf(np.nan, 0.5)))
assert_(np.isnan(stats.bernoulli.sf(np.nan, 0.5)))
assert_(np.isnan(stats.bernoulli.pmf(np.nan, 0.5)))
assert_(np.isnan(stats.bernoulli.logpmf(np.nan, 0.5)))
assert_(np.isnan(stats.bernoulli.ppf(np.nan, 0.5)))
assert_(np.isnan(stats.bernoulli.isf(np.nan, 0.5)))
def test_frozen_fit_ticket_1536():
np.random.seed(5678)
true = np.array([0.25, 0., 0.5])
x = stats.lognorm.rvs(true[0], true[1], true[2], size=100)
with np.errstate(divide='ignore'):
params = np.array(stats.lognorm.fit(x, floc=0.))
assert_almost_equal(params, true, decimal=2)
params = np.array(stats.lognorm.fit(x, fscale=0.5, loc=0))
assert_almost_equal(params, true, decimal=2)
params = np.array(stats.lognorm.fit(x, f0=0.25, loc=0))
assert_almost_equal(params, true, decimal=2)
params = np.array(stats.lognorm.fit(x, f0=0.25, floc=0))
assert_almost_equal(params, true, decimal=2)
np.random.seed(5678)
loc = 1
floc = 0.9
x = stats.norm.rvs(loc, 2., size=100)
params = np.array(stats.norm.fit(x, floc=floc))
expected = np.array([floc, np.sqrt(((x-floc)**2).mean())])
assert_almost_equal(params, expected, decimal=4)
def test_regression_ticket_1530():
# Check the starting value works for Cauchy distribution fit.
np.random.seed(654321)
rvs = stats.cauchy.rvs(size=100)
params = stats.cauchy.fit(rvs)
expected = (0.045, 1.142)
assert_almost_equal(params, expected, decimal=1)
def test_gh_pr_4806():
# Check starting values for Cauchy distribution fit.
np.random.seed(1234)
x = np.random.randn(42)
for offset in 10000.0, 1222333444.0:
loc, scale = stats.cauchy.fit(x + offset)
assert_allclose(loc, offset, atol=1.0)
assert_allclose(scale, 0.6, atol=1.0)
def test_tukeylambda_stats_ticket_1545():
# Some test for the variance and kurtosis of the Tukey Lambda distr.
# See test_tukeylamdba_stats.py for more tests.
mv = stats.tukeylambda.stats(0, moments='mvsk')
# Known exact values:
expected = [0, np.pi**2/3, 0, 1.2]
assert_almost_equal(mv, expected, decimal=10)
mv = stats.tukeylambda.stats(3.13, moments='mvsk')
# 'expected' computed with mpmath.
expected = [0, 0.0269220858861465102, 0, -0.898062386219224104]
assert_almost_equal(mv, expected, decimal=10)
mv = stats.tukeylambda.stats(0.14, moments='mvsk')
# 'expected' computed with mpmath.
expected = [0, 2.11029702221450250, 0, -0.02708377353223019456]
assert_almost_equal(mv, expected, decimal=10)
def test_poisson_logpmf_ticket_1436():
assert_(np.isfinite(stats.poisson.logpmf(1500, 200)))
def test_powerlaw_stats():
"""Test the powerlaw stats function.
This unit test is also a regression test for ticket 1548.
The exact values are:
mean:
mu = a / (a + 1)
variance:
sigma**2 = a / ((a + 2) * (a + 1) ** 2)
skewness:
One formula (see https://en.wikipedia.org/wiki/Skewness) is
gamma_1 = (E[X**3] - 3*mu*E[X**2] + 2*mu**3) / sigma**3
A short calculation shows that E[X**k] is a / (a + k), so gamma_1
can be implemented as
n = a/(a+3) - 3*(a/(a+1))*a/(a+2) + 2*(a/(a+1))**3
d = sqrt(a/((a+2)*(a+1)**2)) ** 3
gamma_1 = n/d
Either by simplifying, or by a direct calculation of mu_3 / sigma**3,
one gets the more concise formula:
gamma_1 = -2.0 * ((a - 1) / (a + 3)) * sqrt((a + 2) / a)
kurtosis: (See https://en.wikipedia.org/wiki/Kurtosis)
The excess kurtosis is
gamma_2 = mu_4 / sigma**4 - 3
A bit of calculus and algebra (sympy helps) shows that
mu_4 = 3*a*(3*a**2 - a + 2) / ((a+1)**4 * (a+2) * (a+3) * (a+4))
so
gamma_2 = 3*(3*a**2 - a + 2) * (a+2) / (a*(a+3)*(a+4)) - 3
which can be rearranged to
gamma_2 = 6 * (a**3 - a**2 - 6*a + 2) / (a*(a+3)*(a+4))
"""
cases = [(1.0, (0.5, 1./12, 0.0, -1.2)),
(2.0, (2./3, 2./36, -0.56568542494924734, -0.6))]
for a, exact_mvsk in cases:
mvsk = stats.powerlaw.stats(a, moments="mvsk")
assert_array_almost_equal(mvsk, exact_mvsk)
def test_powerlaw_edge():
# Regression test for gh-3986.
p = stats.powerlaw.logpdf(0, 1)
assert_equal(p, 0.0)
def test_exponpow_edge():
# Regression test for gh-3982.
p = stats.exponpow.logpdf(0, 1)
assert_equal(p, 0.0)
# Check pdf and logpdf at x = 0 for other values of b.
p = stats.exponpow.pdf(0, [0.25, 1.0, 1.5])
assert_equal(p, [np.inf, 1.0, 0.0])
p = stats.exponpow.logpdf(0, [0.25, 1.0, 1.5])
assert_equal(p, [np.inf, 0.0, -np.inf])
def test_gengamma_edge():
# Regression test for gh-3985.
p = stats.gengamma.pdf(0, 1, 1)
assert_equal(p, 1.0)
# Regression tests for gh-4724.
p = stats.gengamma._munp(-2, 200, 1.)
assert_almost_equal(p, 1./199/198)
p = stats.gengamma._munp(-2, 10, 1.)
assert_almost_equal(p, 1./9/8)
def test_ksone_fit_freeze():
# Regression test for ticket #1638.
d = np.array(
[-0.18879233, 0.15734249, 0.18695107, 0.27908787, -0.248649,
-0.2171497, 0.12233512, 0.15126419, 0.03119282, 0.4365294,
0.08930393, -0.23509903, 0.28231224, -0.09974875, -0.25196048,
0.11102028, 0.1427649, 0.10176452, 0.18754054, 0.25826724,
0.05988819, 0.0531668, 0.21906056, 0.32106729, 0.2117662,
0.10886442, 0.09375789, 0.24583286, -0.22968366, -0.07842391,
-0.31195432, -0.21271196, 0.1114243, -0.13293002, 0.01331725,
-0.04330977, -0.09485776, -0.28434547, 0.22245721, -0.18518199,
-0.10943985, -0.35243174, 0.06897665, -0.03553363, -0.0701746,
-0.06037974, 0.37670779, -0.21684405])
with np.errstate(invalid='ignore'):
with suppress_warnings() as sup:
sup.filter(IntegrationWarning,
"The maximum number of subdivisions .50. has been "
"achieved.")
sup.filter(RuntimeWarning,
"floating point number truncated to an integer")
stats.ksone.fit(d)
def test_norm_logcdf():
# Test precision of the logcdf of the normal distribution.
# This precision was enhanced in ticket 1614.
x = -np.asarray(list(range(0, 120, 4)))
# Values from R
expected = [-0.69314718, -10.36010149, -35.01343716, -75.41067300,
-131.69539607, -203.91715537, -292.09872100, -396.25241451,
-516.38564863, -652.50322759, -804.60844201, -972.70364403,
-1156.79057310, -1356.87055173, -1572.94460885, -1805.01356068,
-2053.07806561, -2317.13866238, -2597.19579746, -2893.24984493,
-3205.30112136, -3533.34989701, -3877.39640444, -4237.44084522,
-4613.48339520, -5005.52420869, -5413.56342187, -5837.60115548,
-6277.63751711, -6733.67260303]
assert_allclose(stats.norm().logcdf(x), expected, atol=1e-8)
# also test the complex-valued code path
assert_allclose(stats.norm().logcdf(x + 1e-14j).real, expected, atol=1e-8)
# test the accuracy: d(logcdf)/dx = pdf / cdf \equiv exp(logpdf - logcdf)
deriv = (stats.norm.logcdf(x + 1e-10j)/1e-10).imag
deriv_expected = np.exp(stats.norm.logpdf(x) - stats.norm.logcdf(x))
assert_allclose(deriv, deriv_expected, atol=1e-10)
def test_levy_cdf_ppf():
# Test levy.cdf, including small arguments.
x = np.array([1000, 1.0, 0.5, 0.1, 0.01, 0.001])
# Expected values were calculated separately with mpmath.
# E.g.
# >>> mpmath.mp.dps = 100
# >>> x = mpmath.mp.mpf('0.01')
# >>> cdf = mpmath.erfc(mpmath.sqrt(1/(2*x)))
expected = np.array([0.9747728793699604,
0.3173105078629141,
0.1572992070502851,
0.0015654022580025495,
1.523970604832105e-23,
1.795832784800726e-219])
y = stats.levy.cdf(x)
assert_allclose(y, expected, rtol=1e-10)
# ppf(expected) should get us back to x.
xx = stats.levy.ppf(expected)
assert_allclose(xx, x, rtol=1e-13)
def test_levy_sf():
# Large values, far into the tail of the distribution.
x = np.array([1e15, 1e25, 1e35, 1e50])
# Expected values were calculated with mpmath.
expected = np.array([2.5231325220201597e-08,
2.52313252202016e-13,
2.52313252202016e-18,
7.978845608028653e-26])
y = stats.levy.sf(x)
assert_allclose(y, expected, rtol=1e-14)
def test_levy_l_sf():
# Test levy_l.sf for small arguments.
x = np.array([-0.016, -0.01, -0.005, -0.0015])
# Expected values were calculated with mpmath.
expected = np.array([2.6644463892359302e-15,
1.523970604832107e-23,
2.0884875837625492e-45,
5.302850374626878e-147])
y = stats.levy_l.sf(x)
assert_allclose(y, expected, rtol=1e-13)
def test_levy_l_isf():
# Test roundtrip sf(isf(p)), including a small input value.
p = np.array([3.0e-15, 0.25, 0.99])
x = stats.levy_l.isf(p)
q = stats.levy_l.sf(x)
assert_allclose(q, p, rtol=5e-14)
def test_hypergeom_interval_1802():
# these two had endless loops
assert_equal(stats.hypergeom.interval(.95, 187601, 43192, 757),
(152.0, 197.0))
assert_equal(stats.hypergeom.interval(.945, 187601, 43192, 757),
(152.0, 197.0))
# this was working also before
assert_equal(stats.hypergeom.interval(.94, 187601, 43192, 757),
(153.0, 196.0))
# degenerate case .a == .b
assert_equal(stats.hypergeom.ppf(0.02, 100, 100, 8), 8)
assert_equal(stats.hypergeom.ppf(1, 100, 100, 8), 8)
def test_distribution_too_many_args():
np.random.seed(1234)
# Check that a TypeError is raised when too many args are given to a method
# Regression test for ticket 1815.
x = np.linspace(0.1, 0.7, num=5)
assert_raises(TypeError, stats.gamma.pdf, x, 2, 3, loc=1.0)
assert_raises(TypeError, stats.gamma.pdf, x, 2, 3, 4, loc=1.0)
assert_raises(TypeError, stats.gamma.pdf, x, 2, 3, 4, 5)
assert_raises(TypeError, stats.gamma.pdf, x, 2, 3, loc=1.0, scale=0.5)
assert_raises(TypeError, stats.gamma.rvs, 2., 3, loc=1.0, scale=0.5)
assert_raises(TypeError, stats.gamma.cdf, x, 2., 3, loc=1.0, scale=0.5)
assert_raises(TypeError, stats.gamma.ppf, x, 2., 3, loc=1.0, scale=0.5)
assert_raises(TypeError, stats.gamma.stats, 2., 3, loc=1.0, scale=0.5)
assert_raises(TypeError, stats.gamma.entropy, 2., 3, loc=1.0, scale=0.5)
assert_raises(TypeError, stats.gamma.fit, x, 2., 3, loc=1.0, scale=0.5)
# These should not give errors
stats.gamma.pdf(x, 2, 3) # loc=3
stats.gamma.pdf(x, 2, 3, 4) # loc=3, scale=4
stats.gamma.stats(2., 3)
stats.gamma.stats(2., 3, 4)
stats.gamma.stats(2., 3, 4, 'mv')
stats.gamma.rvs(2., 3, 4, 5)
stats.gamma.fit(stats.gamma.rvs(2., size=7), 2.)
# Also for a discrete distribution
stats.geom.pmf(x, 2, loc=3) # no error, loc=3
assert_raises(TypeError, stats.geom.pmf, x, 2, 3, 4)
assert_raises(TypeError, stats.geom.pmf, x, 2, 3, loc=4)
# And for distributions with 0, 2 and 3 args respectively
assert_raises(TypeError, stats.expon.pdf, x, 3, loc=1.0)
assert_raises(TypeError, stats.exponweib.pdf, x, 3, 4, 5, loc=1.0)
assert_raises(TypeError, stats.exponweib.pdf, x, 3, 4, 5, 0.1, 0.1)
assert_raises(TypeError, stats.ncf.pdf, x, 3, 4, 5, 6, loc=1.0)
assert_raises(TypeError, stats.ncf.pdf, x, 3, 4, 5, 6, 1.0, scale=0.5)
stats.ncf.pdf(x, 3, 4, 5, 6, 1.0) # 3 args, plus loc/scale
def test_ncx2_tails_ticket_955():
# Trac #955 -- check that the cdf computed by special functions
# matches the integrated pdf
a = stats.ncx2.cdf(np.arange(20, 25, 0.2), 2, 1.07458615e+02)
b = stats.ncx2._cdfvec(np.arange(20, 25, 0.2), 2, 1.07458615e+02)
assert_allclose(a, b, rtol=1e-3, atol=0)
def test_ncx2_tails_pdf():
# ncx2.pdf does not return nans in extreme tails(example from gh-1577)
# NB: this is to check that nan_to_num is not needed in ncx2.pdf
with warnings.catch_warnings():
warnings.simplefilter('error', RuntimeWarning)
assert_equal(stats.ncx2.pdf(1, np.arange(340, 350), 2), 0)
logval = stats.ncx2.logpdf(1, np.arange(340, 350), 2)
assert_(np.isneginf(logval).all())
# Verify logpdf has extended precision when pdf underflows to 0
with warnings.catch_warnings():
warnings.simplefilter('error', RuntimeWarning)
assert_equal(stats.ncx2.pdf(10000, 3, 12), 0)
assert_allclose(stats.ncx2.logpdf(10000, 3, 12), -4662.444377524883)
@pytest.mark.parametrize('method, expected', [
('cdf', np.array([2.497951336e-09, 3.437288941e-10])),
('pdf', np.array([1.238579980e-07, 1.710041145e-08])),
('logpdf', np.array([-15.90413011, -17.88416331])),
('ppf', np.array([4.865182052, 7.017182271]))
])
def test_ncx2_zero_nc(method, expected):
# gh-5441
# ncx2 with nc=0 is identical to chi2
# Comparison to R (v3.5.1)
# > options(digits=10)
# > pchisq(0.1, df=10, ncp=c(0,4))
# > dchisq(0.1, df=10, ncp=c(0,4))
# > dchisq(0.1, df=10, ncp=c(0,4), log=TRUE)
# > qchisq(0.1, df=10, ncp=c(0,4))
result = getattr(stats.ncx2, method)(0.1, nc=[0, 4], df=10)
assert_allclose(result, expected, atol=1e-15)
def test_ncx2_zero_nc_rvs():
# gh-5441
# ncx2 with nc=0 is identical to chi2
result = stats.ncx2.rvs(df=10, nc=0, random_state=1)
expected = stats.chi2.rvs(df=10, random_state=1)
assert_allclose(result, expected, atol=1e-15)
def test_foldnorm_zero():
# Parameter value c=0 was not enabled, see gh-2399.
rv = stats.foldnorm(0, scale=1)
assert_equal(rv.cdf(0), 0) # rv.cdf(0) previously resulted in: nan
def test_stats_shapes_argcheck():
# stats method was failing for vector shapes if some of the values
# were outside of the allowed range, see gh-2678
mv3 = stats.invgamma.stats([0.0, 0.5, 1.0], 1, 0.5) # 0 is not a legal `a`
mv2 = stats.invgamma.stats([0.5, 1.0], 1, 0.5)
mv2_augmented = tuple(np.r_[np.nan, _] for _ in mv2)
assert_equal(mv2_augmented, mv3)
# -1 is not a legal shape parameter
mv3 = stats.lognorm.stats([2, 2.4, -1])
mv2 = stats.lognorm.stats([2, 2.4])
mv2_augmented = tuple(np.r_[_, np.nan] for _ in mv2)
assert_equal(mv2_augmented, mv3)
# FIXME: this is only a quick-and-dirty test of a quick-and-dirty bugfix.
# stats method with multiple shape parameters is not properly vectorized
# anyway, so some distributions may or may not fail.
# Test subclassing distributions w/ explicit shapes
class _distr_gen(stats.rv_continuous):
def _pdf(self, x, a):
return 42
class _distr2_gen(stats.rv_continuous):
def _cdf(self, x, a):
return 42 * a + x
class _distr3_gen(stats.rv_continuous):
def _pdf(self, x, a, b):
return a + b
def _cdf(self, x, a):
# Different # of shape params from _pdf, to be able to check that
# inspection catches the inconsistency."""
return 42 * a + x
class _distr6_gen(stats.rv_continuous):
# Two shape parameters (both _pdf and _cdf defined, consistent shapes.)
def _pdf(self, x, a, b):
return a*x + b
def _cdf(self, x, a, b):
return 42 * a + x
class TestSubclassingExplicitShapes(object):
# Construct a distribution w/ explicit shapes parameter and test it.
def test_correct_shapes(self):
dummy_distr = _distr_gen(name='dummy', shapes='a')
assert_equal(dummy_distr.pdf(1, a=1), 42)
def test_wrong_shapes_1(self):
dummy_distr = _distr_gen(name='dummy', shapes='A')
assert_raises(TypeError, dummy_distr.pdf, 1, **dict(a=1))
def test_wrong_shapes_2(self):
dummy_distr = _distr_gen(name='dummy', shapes='a, b, c')
dct = dict(a=1, b=2, c=3)
assert_raises(TypeError, dummy_distr.pdf, 1, **dct)
def test_shapes_string(self):
# shapes must be a string
dct = dict(name='dummy', shapes=42)
assert_raises(TypeError, _distr_gen, **dct)
def test_shapes_identifiers_1(self):
# shapes must be a comma-separated list of valid python identifiers
dct = dict(name='dummy', shapes='(!)')
assert_raises(SyntaxError, _distr_gen, **dct)
def test_shapes_identifiers_2(self):
dct = dict(name='dummy', shapes='4chan')
assert_raises(SyntaxError, _distr_gen, **dct)
def test_shapes_identifiers_3(self):
dct = dict(name='dummy', shapes='m(fti)')
assert_raises(SyntaxError, _distr_gen, **dct)
def test_shapes_identifiers_nodefaults(self):
dct = dict(name='dummy', shapes='a=2')
assert_raises(SyntaxError, _distr_gen, **dct)
def test_shapes_args(self):
dct = dict(name='dummy', shapes='*args')
assert_raises(SyntaxError, _distr_gen, **dct)
def test_shapes_kwargs(self):
dct = dict(name='dummy', shapes='**kwargs')
assert_raises(SyntaxError, _distr_gen, **dct)
def test_shapes_keywords(self):
# python keywords cannot be used for shape parameters
dct = dict(name='dummy', shapes='a, b, c, lambda')
assert_raises(SyntaxError, _distr_gen, **dct)
def test_shapes_signature(self):
# test explicit shapes which agree w/ the signature of _pdf
class _dist_gen(stats.rv_continuous):
def _pdf(self, x, a):
return stats.norm._pdf(x) * a
dist = _dist_gen(shapes='a')
assert_equal(dist.pdf(0.5, a=2), stats.norm.pdf(0.5)*2)
def test_shapes_signature_inconsistent(self):
# test explicit shapes which do not agree w/ the signature of _pdf
class _dist_gen(stats.rv_continuous):
def _pdf(self, x, a):
return stats.norm._pdf(x) * a
dist = _dist_gen(shapes='a, b')
assert_raises(TypeError, dist.pdf, 0.5, **dict(a=1, b=2))
def test_star_args(self):
# test _pdf with only starargs
# NB: **kwargs of pdf will never reach _pdf
class _dist_gen(stats.rv_continuous):
def _pdf(self, x, *args):
extra_kwarg = args[0]
return stats.norm._pdf(x) * extra_kwarg
dist = _dist_gen(shapes='extra_kwarg')
assert_equal(dist.pdf(0.5, extra_kwarg=33), stats.norm.pdf(0.5)*33)
assert_equal(dist.pdf(0.5, 33), stats.norm.pdf(0.5)*33)
assert_raises(TypeError, dist.pdf, 0.5, **dict(xxx=33))
def test_star_args_2(self):
# test _pdf with named & starargs
# NB: **kwargs of pdf will never reach _pdf
class _dist_gen(stats.rv_continuous):
def _pdf(self, x, offset, *args):
extra_kwarg = args[0]
return stats.norm._pdf(x) * extra_kwarg + offset
dist = _dist_gen(shapes='offset, extra_kwarg')
assert_equal(dist.pdf(0.5, offset=111, extra_kwarg=33),
stats.norm.pdf(0.5)*33 + 111)
assert_equal(dist.pdf(0.5, 111, 33),
stats.norm.pdf(0.5)*33 + 111)
def test_extra_kwarg(self):
# **kwargs to _pdf are ignored.
# this is a limitation of the framework (_pdf(x, *goodargs))
class _distr_gen(stats.rv_continuous):
def _pdf(self, x, *args, **kwargs):
# _pdf should handle *args, **kwargs itself. Here "handling"
# is ignoring *args and looking for ``extra_kwarg`` and using
# that.
extra_kwarg = kwargs.pop('extra_kwarg', 1)
return stats.norm._pdf(x) * extra_kwarg
dist = _distr_gen(shapes='extra_kwarg')
assert_equal(dist.pdf(1, extra_kwarg=3), stats.norm.pdf(1))
def shapes_empty_string(self):
# shapes='' is equivalent to shapes=None
class _dist_gen(stats.rv_continuous):
def _pdf(self, x):
return stats.norm.pdf(x)
dist = _dist_gen(shapes='')
assert_equal(dist.pdf(0.5), stats.norm.pdf(0.5))
class TestSubclassingNoShapes(object):
# Construct a distribution w/o explicit shapes parameter and test it.
def test_only__pdf(self):
dummy_distr = _distr_gen(name='dummy')
assert_equal(dummy_distr.pdf(1, a=1), 42)
def test_only__cdf(self):
# _pdf is determined from _cdf by taking numerical derivative
dummy_distr = _distr2_gen(name='dummy')
assert_almost_equal(dummy_distr.pdf(1, a=1), 1)
@pytest.mark.skipif(DOCSTRINGS_STRIPPED, reason="docstring stripped")
def test_signature_inspection(self):
# check that _pdf signature inspection works correctly, and is used in
# the class docstring
dummy_distr = _distr_gen(name='dummy')
assert_equal(dummy_distr.numargs, 1)
assert_equal(dummy_distr.shapes, 'a')
res = re.findall(r'logpdf\(x, a, loc=0, scale=1\)',
dummy_distr.__doc__)
assert_(len(res) == 1)
@pytest.mark.skipif(DOCSTRINGS_STRIPPED, reason="docstring stripped")
def test_signature_inspection_2args(self):
# same for 2 shape params and both _pdf and _cdf defined
dummy_distr = _distr6_gen(name='dummy')
assert_equal(dummy_distr.numargs, 2)
assert_equal(dummy_distr.shapes, 'a, b')
res = re.findall(r'logpdf\(x, a, b, loc=0, scale=1\)',
dummy_distr.__doc__)
assert_(len(res) == 1)
def test_signature_inspection_2args_incorrect_shapes(self):
# both _pdf and _cdf defined, but shapes are inconsistent: raises
assert_raises(TypeError, _distr3_gen, name='dummy')
def test_defaults_raise(self):
# default arguments should raise
class _dist_gen(stats.rv_continuous):
def _pdf(self, x, a=42):
return 42
assert_raises(TypeError, _dist_gen, **dict(name='dummy'))
def test_starargs_raise(self):
# without explicit shapes, *args are not allowed
class _dist_gen(stats.rv_continuous):
def _pdf(self, x, a, *args):
return 42
assert_raises(TypeError, _dist_gen, **dict(name='dummy'))
def test_kwargs_raise(self):
# without explicit shapes, **kwargs are not allowed
class _dist_gen(stats.rv_continuous):
def _pdf(self, x, a, **kwargs):
return 42
assert_raises(TypeError, _dist_gen, **dict(name='dummy'))
@pytest.mark.skipif(DOCSTRINGS_STRIPPED, reason="docstring stripped")
def test_docstrings():
badones = [r',\s*,', r'\(\s*,', r'^\s*:']
for distname in stats.__all__:
dist = getattr(stats, distname)
if isinstance(dist, (stats.rv_discrete, stats.rv_continuous)):
for regex in badones:
assert_(re.search(regex, dist.__doc__) is None)
def test_infinite_input():
assert_almost_equal(stats.skellam.sf(np.inf, 10, 11), 0)
assert_almost_equal(stats.ncx2._cdf(np.inf, 8, 0.1), 1)
def test_lomax_accuracy():
# regression test for gh-4033
p = stats.lomax.ppf(stats.lomax.cdf(1e-100, 1), 1)
assert_allclose(p, 1e-100)
def test_gompertz_accuracy():
# Regression test for gh-4031
p = stats.gompertz.ppf(stats.gompertz.cdf(1e-100, 1), 1)
assert_allclose(p, 1e-100)
def test_truncexpon_accuracy():
# regression test for gh-4035
p = stats.truncexpon.ppf(stats.truncexpon.cdf(1e-100, 1), 1)
assert_allclose(p, 1e-100)
def test_rayleigh_accuracy():
# regression test for gh-4034
p = stats.rayleigh.isf(stats.rayleigh.sf(9, 1), 1)
assert_almost_equal(p, 9.0, decimal=15)
def test_genextreme_give_no_warnings():
"""regression test for gh-6219"""
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
stats.genextreme.cdf(.5, 0)
stats.genextreme.pdf(.5, 0)
stats.genextreme.ppf(.5, 0)
stats.genextreme.logpdf(-np.inf, 0.0)
number_of_warnings_thrown = len(w)
assert_equal(number_of_warnings_thrown, 0)
def test_genextreme_entropy():
# regression test for gh-5181
euler_gamma = 0.5772156649015329
h = stats.genextreme.entropy(-1.0)
assert_allclose(h, 2*euler_gamma + 1, rtol=1e-14)
h = stats.genextreme.entropy(0)
assert_allclose(h, euler_gamma + 1, rtol=1e-14)
h = stats.genextreme.entropy(1.0)
assert_equal(h, 1)
h = stats.genextreme.entropy(-2.0, scale=10)
assert_allclose(h, euler_gamma*3 + np.log(10) + 1, rtol=1e-14)
h = stats.genextreme.entropy(10)
assert_allclose(h, -9*euler_gamma + 1, rtol=1e-14)
h = stats.genextreme.entropy(-10)
assert_allclose(h, 11*euler_gamma + 1, rtol=1e-14)
def test_genextreme_sf_isf():
# Expected values were computed using mpmath:
#
# import mpmath
#
# def mp_genextreme_sf(x, xi, mu=0, sigma=1):
# # Formula from wikipedia, which has a sign convention for xi that
# # is the opposite of scipy's shape parameter.
# if xi != 0:
# t = mpmath.power(1 + ((x - mu)/sigma)*xi, -1/xi)
# else:
# t = mpmath.exp(-(x - mu)/sigma)
# return 1 - mpmath.exp(-t)
#
# >>> mpmath.mp.dps = 1000
# >>> s = mp_genextreme_sf(mpmath.mp.mpf("1e8"), mpmath.mp.mpf("0.125"))
# >>> float(s)
# 1.6777205262585625e-57
# >>> s = mp_genextreme_sf(mpmath.mp.mpf("7.98"), mpmath.mp.mpf("-0.125"))
# >>> float(s)
# 1.52587890625e-21
# >>> s = mp_genextreme_sf(mpmath.mp.mpf("7.98"), mpmath.mp.mpf("0"))
# >>> float(s)
# 0.00034218086528426593
x = 1e8
s = stats.genextreme.sf(x, -0.125)
assert_allclose(s, 1.6777205262585625e-57)
x2 = stats.genextreme.isf(s, -0.125)
assert_allclose(x2, x)
x = 7.98
s = stats.genextreme.sf(x, 0.125)
assert_allclose(s, 1.52587890625e-21)
x2 = stats.genextreme.isf(s, 0.125)
assert_allclose(x2, x)
x = 7.98
s = stats.genextreme.sf(x, 0)
assert_allclose(s, 0.00034218086528426593)
x2 = stats.genextreme.isf(s, 0)
assert_allclose(x2, x)
def test_burr12_ppf_small_arg():
prob = 1e-16
quantile = stats.burr12.ppf(prob, 2, 3)
# The expected quantile was computed using mpmath:
# >>> import mpmath
# >>> mpmath.mp.dps = 100
# >>> prob = mpmath.mpf('1e-16')
# >>> c = mpmath.mpf(2)
# >>> d = mpmath.mpf(3)
# >>> float(((1-prob)**(-1/d) - 1)**(1/c))
# 5.7735026918962575e-09
assert_allclose(quantile, 5.7735026918962575e-09)
def test_crystalball_function():
"""
All values are calculated using the independent implementation of the
ROOT framework (see https://root.cern.ch/).
Corresponding ROOT code is given in the comments.
"""
X = np.linspace(-5.0, 5.0, 21)[:-1]
# for(float x = -5.0; x < 5.0; x+=0.5)
# std::cout << ROOT::Math::crystalball_pdf(x, 1.0, 2.0, 1.0) << ", ";
calculated = stats.crystalball.pdf(X, beta=1.0, m=2.0)
expected = np.array([0.0202867, 0.0241428, 0.0292128, 0.0360652, 0.045645,
0.059618, 0.0811467, 0.116851, 0.18258, 0.265652,
0.301023, 0.265652, 0.18258, 0.097728, 0.0407391,
0.013226, 0.00334407, 0.000658486, 0.000100982,
1.20606e-05])
assert_allclose(expected, calculated, rtol=0.001)
# for(float x = -5.0; x < 5.0; x+=0.5)
# std::cout << ROOT::Math::crystalball_pdf(x, 2.0, 3.0, 1.0) << ", ";
calculated = stats.crystalball.pdf(X, beta=2.0, m=3.0)
expected = np.array([0.0019648, 0.00279754, 0.00417592, 0.00663121,
0.0114587, 0.0223803, 0.0530497, 0.12726, 0.237752,
0.345928, 0.391987, 0.345928, 0.237752, 0.12726,
0.0530497, 0.0172227, 0.00435458, 0.000857469,
0.000131497, 1.57051e-05])
assert_allclose(expected, calculated, rtol=0.001)
# for(float x = -5.0; x < 5.0; x+=0.5) {
# std::cout << ROOT::Math::crystalball_pdf(x, 2.0, 3.0, 2.0, 0.5);
# std::cout << ", ";
# }
calculated = stats.crystalball.pdf(X, beta=2.0, m=3.0, loc=0.5, scale=2.0)
expected = np.array([0.00785921, 0.0111902, 0.0167037, 0.0265249,
0.0423866, 0.0636298, 0.0897324, 0.118876, 0.147944,
0.172964, 0.189964, 0.195994, 0.189964, 0.172964,
0.147944, 0.118876, 0.0897324, 0.0636298, 0.0423866,
0.0265249])
assert_allclose(expected, calculated, rtol=0.001)
# for(float x = -5.0; x < 5.0; x+=0.5)
# std::cout << ROOT::Math::crystalball_cdf(x, 1.0, 2.0, 1.0) << ", ";
calculated = stats.crystalball.cdf(X, beta=1.0, m=2.0)
expected = np.array([0.12172, 0.132785, 0.146064, 0.162293, 0.18258,
0.208663, 0.24344, 0.292128, 0.36516, 0.478254,
0.622723, 0.767192, 0.880286, 0.94959, 0.982834,
0.995314, 0.998981, 0.999824, 0.999976, 0.999997])
assert_allclose(expected, calculated, rtol=0.001)
# for(float x = -5.0; x < 5.0; x+=0.5)
# std::cout << ROOT::Math::crystalball_cdf(x, 2.0, 3.0, 1.0) << ", ";
calculated = stats.crystalball.cdf(X, beta=2.0, m=3.0)
expected = np.array([0.00442081, 0.00559509, 0.00730787, 0.00994682,
0.0143234, 0.0223803, 0.0397873, 0.0830763, 0.173323,
0.320592, 0.508717, 0.696841, 0.844111, 0.934357,
0.977646, 0.993899, 0.998674, 0.999771, 0.999969,
0.999997])
assert_allclose(expected, calculated, rtol=0.001)
# for(float x = -5.0; x < 5.0; x+=0.5) {
# std::cout << ROOT::Math::crystalball_cdf(x, 2.0, 3.0, 2.0, 0.5);
# std::cout << ", ";
# }
calculated = stats.crystalball.cdf(X, beta=2.0, m=3.0, loc=0.5, scale=2.0)
expected = np.array([0.0176832, 0.0223803, 0.0292315, 0.0397873, 0.0567945,
0.0830763, 0.121242, 0.173323, 0.24011, 0.320592,
0.411731, 0.508717, 0.605702, 0.696841, 0.777324,
0.844111, 0.896192, 0.934357, 0.960639, 0.977646])
assert_allclose(expected, calculated, rtol=0.001)
def test_crystalball_function_moments():
"""
All values are calculated using the pdf formula and the integrate function
of Mathematica
"""
# The Last two (alpha, n) pairs test the special case n == alpha**2
beta = np.array([2.0, 1.0, 3.0, 2.0, 3.0])
m = np.array([3.0, 3.0, 2.0, 4.0, 9.0])
# The distribution should be correctly normalised
expected_0th_moment = np.array([1.0, 1.0, 1.0, 1.0, 1.0])
calculated_0th_moment = stats.crystalball._munp(0, beta, m)
assert_allclose(expected_0th_moment, calculated_0th_moment, rtol=0.001)
# calculated using wolframalpha.com
# e.g. for beta = 2 and m = 3 we calculate the norm like this:
# integrate exp(-x^2/2) from -2 to infinity +
# integrate (3/2)^3*exp(-2^2/2)*(3/2-2-x)^(-3) from -infinity to -2
norm = np.array([2.5511, 3.01873, 2.51065, 2.53983, 2.507410455])
a = np.array([-0.21992, -3.03265, np.inf, -0.135335, -0.003174])
expected_1th_moment = a / norm
calculated_1th_moment = stats.crystalball._munp(1, beta, m)
assert_allclose(expected_1th_moment, calculated_1th_moment, rtol=0.001)
a = np.array([np.inf, np.inf, np.inf, 3.2616, 2.519908])
expected_2th_moment = a / norm
calculated_2th_moment = stats.crystalball._munp(2, beta, m)
assert_allclose(expected_2th_moment, calculated_2th_moment, rtol=0.001)
a = np.array([np.inf, np.inf, np.inf, np.inf, -0.0577668])
expected_3th_moment = a / norm
calculated_3th_moment = stats.crystalball._munp(3, beta, m)
assert_allclose(expected_3th_moment, calculated_3th_moment, rtol=0.001)
a = np.array([np.inf, np.inf, np.inf, np.inf, 7.78468])
expected_4th_moment = a / norm
calculated_4th_moment = stats.crystalball._munp(4, beta, m)
assert_allclose(expected_4th_moment, calculated_4th_moment, rtol=0.001)
a = np.array([np.inf, np.inf, np.inf, np.inf, -1.31086])
expected_5th_moment = a / norm
calculated_5th_moment = stats.crystalball._munp(5, beta, m)
assert_allclose(expected_5th_moment, calculated_5th_moment, rtol=0.001)
@pytest.mark.parametrize(
'df1,df2,x',
[(2, 2, [-0.5, 0.2, 1.0, 2.3]),
(4, 11, [-0.5, 0.2, 1.0, 2.3]),
(7, 17, [1, 2, 3, 4, 5])]
)
def test_ncf_edge_case(df1, df2, x):
# Test for edge case described in gh-11660.
# Non-central Fisher distribution when nc = 0
# should be the same as Fisher distribution.
nc = 0
expected_cdf = stats.f.cdf(x, df1, df2)
calculated_cdf = stats.ncf.cdf(x, df1, df2, nc)
assert_allclose(expected_cdf, calculated_cdf, rtol=1e-14)
# when ncf_gen._skip_pdf will be used instead of generic pdf,
# this additional test will be useful.
expected_pdf = stats.f.pdf(x, df1, df2)
calculated_pdf = stats.ncf.pdf(x, df1, df2, nc)
assert_allclose(expected_pdf, calculated_pdf, rtol=1e-6)
def test_ncf_variance():
# Regression test for gh-10658 (incorrect variance formula for ncf).
# The correct value of ncf.var(2, 6, 4), 42.75, can be verified with, for
# example, Wolfram Alpha with the expression
# Variance[NoncentralFRatioDistribution[2, 6, 4]]
# or with the implementation of the noncentral F distribution in the C++
# library Boost.
v = stats.ncf.var(2, 6, 4)
assert_allclose(v, 42.75, rtol=1e-14)
class TestHistogram(object):
def setup_method(self):
np.random.seed(1234)
# We have 8 bins
# [1,2), [2,3), [3,4), [4,5), [5,6), [6,7), [7,8), [8,9)
# But actually np.histogram will put the last 9 also in the [8,9) bin!
# Therefore there is a slight difference below for the last bin, from
# what you might have expected.
histogram = np.histogram([1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5,
6, 6, 6, 6, 7, 7, 7, 8, 8, 9], bins=8)
self.template = stats.rv_histogram(histogram)
data = stats.norm.rvs(loc=1.0, scale=2.5, size=10000, random_state=123)
norm_histogram = np.histogram(data, bins=50)
self.norm_template = stats.rv_histogram(norm_histogram)
def test_pdf(self):
values = np.array([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5,
5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5])
pdf_values = np.asarray([0.0/25.0, 0.0/25.0, 1.0/25.0, 1.0/25.0,
2.0/25.0, 2.0/25.0, 3.0/25.0, 3.0/25.0,
4.0/25.0, 4.0/25.0, 5.0/25.0, 5.0/25.0,
4.0/25.0, 4.0/25.0, 3.0/25.0, 3.0/25.0,
3.0/25.0, 3.0/25.0, 0.0/25.0, 0.0/25.0])
assert_allclose(self.template.pdf(values), pdf_values)
# Test explicitly the corner cases:
# As stated above the pdf in the bin [8,9) is greater than
# one would naively expect because np.histogram putted the 9
# into the [8,9) bin.
assert_almost_equal(self.template.pdf(8.0), 3.0/25.0)
assert_almost_equal(self.template.pdf(8.5), 3.0/25.0)
# 9 is outside our defined bins [8,9) hence the pdf is already 0
# for a continuous distribution this is fine, because a single value
# does not have a finite probability!
assert_almost_equal(self.template.pdf(9.0), 0.0/25.0)
assert_almost_equal(self.template.pdf(10.0), 0.0/25.0)
x = np.linspace(-2, 2, 10)
assert_allclose(self.norm_template.pdf(x),
stats.norm.pdf(x, loc=1.0, scale=2.5), rtol=0.1)
def test_cdf_ppf(self):
values = np.array([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5,
5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5])
cdf_values = np.asarray([0.0/25.0, 0.0/25.0, 0.0/25.0, 0.5/25.0,
1.0/25.0, 2.0/25.0, 3.0/25.0, 4.5/25.0,
6.0/25.0, 8.0/25.0, 10.0/25.0, 12.5/25.0,
15.0/25.0, 17.0/25.0, 19.0/25.0, 20.5/25.0,
22.0/25.0, 23.5/25.0, 25.0/25.0, 25.0/25.0])
assert_allclose(self.template.cdf(values), cdf_values)
# First three and last two values in cdf_value are not unique
assert_allclose(self.template.ppf(cdf_values[2:-1]), values[2:-1])
# Test of cdf and ppf are inverse functions
x = np.linspace(1.0, 9.0, 100)
assert_allclose(self.template.ppf(self.template.cdf(x)), x)
x = np.linspace(0.0, 1.0, 100)
assert_allclose(self.template.cdf(self.template.ppf(x)), x)
x = np.linspace(-2, 2, 10)
assert_allclose(self.norm_template.cdf(x),
stats.norm.cdf(x, loc=1.0, scale=2.5), rtol=0.1)
def test_rvs(self):
N = 10000
sample = self.template.rvs(size=N, random_state=123)
assert_equal(np.sum(sample < 1.0), 0.0)
assert_allclose(np.sum(sample <= 2.0), 1.0/25.0 * N, rtol=0.2)
assert_allclose(np.sum(sample <= 2.5), 2.0/25.0 * N, rtol=0.2)
assert_allclose(np.sum(sample <= 3.0), 3.0/25.0 * N, rtol=0.1)
assert_allclose(np.sum(sample <= 3.5), 4.5/25.0 * N, rtol=0.1)
assert_allclose(np.sum(sample <= 4.0), 6.0/25.0 * N, rtol=0.1)
assert_allclose(np.sum(sample <= 4.5), 8.0/25.0 * N, rtol=0.1)
assert_allclose(np.sum(sample <= 5.0), 10.0/25.0 * N, rtol=0.05)
assert_allclose(np.sum(sample <= 5.5), 12.5/25.0 * N, rtol=0.05)
assert_allclose(np.sum(sample <= 6.0), 15.0/25.0 * N, rtol=0.05)
assert_allclose(np.sum(sample <= 6.5), 17.0/25.0 * N, rtol=0.05)
assert_allclose(np.sum(sample <= 7.0), 19.0/25.0 * N, rtol=0.05)
assert_allclose(np.sum(sample <= 7.5), 20.5/25.0 * N, rtol=0.05)
assert_allclose(np.sum(sample <= 8.0), 22.0/25.0 * N, rtol=0.05)
assert_allclose(np.sum(sample <= 8.5), 23.5/25.0 * N, rtol=0.05)
assert_allclose(np.sum(sample <= 9.0), 25.0/25.0 * N, rtol=0.05)
assert_allclose(np.sum(sample <= 9.0), 25.0/25.0 * N, rtol=0.05)
assert_equal(np.sum(sample > 9.0), 0.0)
def test_munp(self):
for n in range(4):
assert_allclose(self.norm_template._munp(n),
stats.norm(1.0, 2.5).moment(n), rtol=0.05)
def test_entropy(self):
assert_allclose(self.norm_template.entropy(),
stats.norm.entropy(loc=1.0, scale=2.5), rtol=0.05)
def test_loguniform():
# This test makes sure the alias of "loguniform" is log-uniform
rv = stats.loguniform(10 ** -3, 10 ** 0)
rvs = rv.rvs(size=10000, random_state=42)
vals, _ = np.histogram(np.log10(rvs), bins=10)
assert 900 <= vals.min() <= vals.max() <= 1100
assert np.abs(np.median(vals) - 1000) <= 10
class TestArgus(object):
def test_argus_rvs_large_chi(self):
# test that the algorithm can handle large values of chi
x = stats.argus.rvs(50, size=500, random_state=325)
assert_almost_equal(stats.argus(50).mean(), x.mean(), decimal=4)
def test_argus_rvs_ratio_uniforms(self):
# test that the ratio of uniforms algorithms works for chi > 2.611
x = stats.argus.rvs(3.5, size=1500, random_state=1535)
assert_almost_equal(stats.argus(3.5).mean(), x.mean(), decimal=3)
assert_almost_equal(stats.argus(3.5).std(), x.std(), decimal=3)
# Expected values were computed with mpmath.
@pytest.mark.parametrize('chi, expected_mean',
[(1, 0.6187026683551835),
(10, 0.984805536783744),
(40, 0.9990617659702923),
(60, 0.9995831885165300),
(99, 0.9998469348663028)])
def test_mean(self, chi, expected_mean):
m = stats.argus.mean(chi, scale=1)
assert_allclose(m, expected_mean, rtol=1e-13)
# Expected values were computed with mpmath.
@pytest.mark.parametrize('chi, expected_var, rtol',
[(1, 0.05215651254197807, 1e-13),
(10, 0.00015805472008165595, 1e-11),
(40, 5.877763210262901e-07, 1e-8),
(60, 1.1590179389611416e-07, 1e-8),
(99, 1.5623277006064666e-08, 1e-8)])
def test_var(self, chi, expected_var, rtol):
v = stats.argus.var(chi, scale=1)
assert_allclose(v, expected_var, rtol=rtol)
class TestNakagami:
def test_logpdf(self):
# Test nakagami logpdf for an input where the PDF is smaller
# than can be represented with 64 bit floating point.
# The expected value of logpdf was computed with mpmath:
#
# def logpdf(x, nu):
# x = mpmath.mpf(x)
# nu = mpmath.mpf(nu)
# return (mpmath.log(2) + nu*mpmath.log(nu) -
# mpmath.loggamma(nu) + (2*nu - 1)*mpmath.log(x) -
# nu*x**2)
#
nu = 2.5
x = 25
logp = stats.nakagami.logpdf(x, nu)
assert_allclose(logp, -1546.9253055607549)
def test_sf_isf(self):
# Test nakagami sf and isf when the survival function
# value is very small.
# The expected value of the survival function was computed
# with mpmath:
#
# def sf(x, nu):
# x = mpmath.mpf(x)
# nu = mpmath.mpf(nu)
# return mpmath.gammainc(nu, nu*x*x, regularized=True)
#
nu = 2.5
x0 = 5.0
sf = stats.nakagami.sf(x0, nu)
assert_allclose(sf, 2.736273158588307e-25, rtol=1e-13)
# Check round trip back to x0.
x1 = stats.nakagami.isf(sf, nu)
assert_allclose(x1, x0, rtol=1e-13)
def test_rvs_no_size_warning():
class rvs_no_size_gen(stats.rv_continuous):
def _rvs(self):
return 1
rvs_no_size = rvs_no_size_gen(name='rvs_no_size')
with assert_warns(np.VisibleDeprecationWarning):
rvs_no_size.rvs()
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