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nan-propagation errors (ticket #835)
stats.lognorm.pdf(0,s) reports nan (ticket #1471)
master
per.andreas.brodtkorb 13 years ago
parent 296f42d059
commit caa72fe9ee
3 changed files with 183 additions and 89 deletions
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2
pywafo/.pydevproject
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@ -3,7 +3,7 @@
<pydev_project>
<pydev_pathproperty name="org.python.pydev.PROJECT_SOURCE_PATH">
<path>/google_pywafo/src</path>
<path>/pywafo/src</path>
</pydev_pathproperty>
<pydev_property name="org.python.pydev.PYTHON_PROJECT_VERSION">python 2.6</pydev_property>
<pydev_property name="org.python.pydev.PYTHON_PROJECT_INTERPRETER">Default</pydev_property>

54
pywafo/src/wafo/stats/distributions.py
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@ -1642,7 +1642,7 @@ class rv_continuous(rv_generic):
cond1 = (scale > 0) & (x >= self.a) & (x <= self.b)
cond = cond0 & cond1
output = zeros(shape(cond),'d')
putmask(output,(1-cond0)*array(cond1,bool),self.badvalue)
putmask(output,(1-cond0)+np.isnan(x),self.badvalue)
if any(cond):
goodargs = argsreduce(cond, *((x,)+args+(scale,)))
scale, goodargs = goodargs[-1], goodargs[:-1]
@ -1685,7 +1685,7 @@ class rv_continuous(rv_generic):
cond = cond0 & cond1
output = empty(shape(cond),'d')
output.fill(NINF)
putmask(output,(1-cond0)*array(cond1,bool),self.badvalue)
putmask(output,(1-cond0)+np.isnan(x),self.badvalue)
if any(cond):
goodargs = argsreduce(cond, *((x,)+args+(scale,)))
scale, goodargs = goodargs[-1], goodargs[:-1]
@ -1727,7 +1727,8 @@ class rv_continuous(rv_generic):
cond2 = (x >= self.b) & cond0
cond = cond0 & cond1
output = zeros(shape(cond),'d')
place(output,(1-cond0)*(cond1==cond1),self.badvalue)
#place(output,(1-cond0)*(cond1==cond1),self.badvalue)
place(output,(1-cond0)+np.isnan(x),self.badvalue)
place(output,cond2,1.0)
if any(cond): #call only if at least 1 entry
goodargs = argsreduce(cond, *((x,)+args))
@ -1770,7 +1771,7 @@ class rv_continuous(rv_generic):
cond = cond0 & cond1
output = empty(shape(cond),'d')
output.fill(NINF)
place(output,(1-cond0)*(cond1==cond1),self.badvalue)
place(output,(1-cond0)+np.isnan(x),self.badvalue)
place(output,cond2,0.0)
if any(cond): #call only if at least 1 entry
goodargs = argsreduce(cond, *((x,)+args))
@ -1811,7 +1812,7 @@ class rv_continuous(rv_generic):
cond2 = cond0 & (x <= self.a)
cond = cond0 & cond1
output = zeros(shape(cond),'d')
place(output,(1-cond0)*(cond1==cond1),self.badvalue)
place(output,(1-cond0)+np.isnan(x),self.badvalue)
place(output,cond2,1.0)
if any(cond):
goodargs = argsreduce(cond, *((x,)+args))
@ -1856,7 +1857,7 @@ class rv_continuous(rv_generic):
cond = cond0 & cond1
output = empty(shape(cond),'d')
output.fill(NINF)
place(output,(1-cond0)*(cond1==cond1),self.badvalue)
place(output,(1-cond0)+np.isnan(x),self.badvalue)
place(output,cond2,0.0)
if any(cond):
goodargs = argsreduce(cond, *((x,)+args))
@ -3897,10 +3898,10 @@ class genpareto_gen(rv_continuous):
self.b = where(c < 0, 1.0 / abs(c), inf)
return where(abs(c) == inf, 0, 1)
def _pdf(self, x, c):
#%f = exp(-xn)/s; % for k==0
#%f = (1+k.*xn)**(-1./k-1)/s; % for k~=0
#%f = exp((-1./k-1)*log1p(kxn))/s % for k~=0
#%f = exp((-xn-kxn)*log1p(kxn)/(kxn))/s % for any k kxn~=inf
#f = exp(-xn)/s; % for k==0
#f = (1+k.*xn)**(-1./k-1)/s; % for k~=0
#f = exp((-1./k-1)*log1p(kxn))/s % for k~=0
#f = exp((-xn-kxn)*log1p(kxn)/(kxn))/s % for any k kxn~=inf
return exp(self._logpdf(x, c))
#Px = pow(1+c*x,arr(-1.0-1.0/c))
#return Px
@ -3920,14 +3921,12 @@ class genpareto_gen(rv_continuous):
def _sf(self, x, c):
log_sf = -self._chf(x, c)
return exp(log_sf)
def _isf2(self, log_sf, c):
return where((c != 0) & (-inf < log_sf), expm1(-c * log_sf) / c, -log_sf)
def _ppf(self, q, c):
log_sf = log1p(-q)
return self._isf2(log_sf, c)
return where((c != 0) & (-inf < log_sf), expm1(-c * log_sf) / c, -log_sf)
def _isf(self, q, c):
log_sf = log(q)
return self._isf2(log_sf, c)
return where((c != 0) & (-inf < log_sf), expm1(-c * log_sf) / c, -log_sf)
#vals = 1.0/c * (pow(1-q, -c)-1)
#return vals
@ -5071,8 +5070,11 @@ class lognorm_gen(rv_continuous):
def _rvs(self, s):
return exp(s * norm.rvs(size=self._size))
def _pdf(self, x, s):
Px = exp(-log(x)**2 / (2*s**2))
return Px / (s*x*sqrt(2*pi))
return exp(self._logpdf(x, s))
#Px = exp(-log(x)**2 / (2*s**2)) -Px*2*log(x)/x
#return Px / (s*x*sqrt(2*pi))
def _logpdf(self, x, s):
return -log(x)**2 / (2*s**2) + np.where((x==0)*(s==s) , 0, - log(s*x*sqrt(2*pi)))
def _cdf(self, x, s):
return norm.cdf(log(x)/s)
def _ppf(self, q, s):
@ -6783,7 +6785,7 @@ class rv_discrete(rv_generic):
cond1 = (k >= self.a) & (k <= self.b) & self._nonzero(k,*args)
cond = cond0 & cond1
output = zeros(shape(cond),'d')
place(output,(1-cond0)*(cond1==cond1),self.badvalue)
place(output,(1-cond0) + np.isnan(k),self.badvalue)
if any(cond):
goodargs = argsreduce(cond, *((k,)+args))
place(output,cond,self._pmf(*goodargs))
@ -6822,7 +6824,7 @@ class rv_discrete(rv_generic):
cond = cond0 & cond1
output = empty(shape(cond),'d')
output.fill(NINF)
place(output,(1-cond0)*(cond1==cond1),self.badvalue)
place(output,(1-cond0) + np.isnan(k),self.badvalue)
if any(cond):
goodargs = argsreduce(cond, *((k,)+args))
place(output,cond,self._logpmf(*goodargs))
@ -6860,7 +6862,7 @@ class rv_discrete(rv_generic):
cond2 = (k >= self.b)
cond = cond0 & cond1
output = zeros(shape(cond),'d')
place(output,(1-cond0)*(cond1==cond1),self.badvalue)
place(output,(1-cond0) + np.isnan(k),self.badvalue)
place(output,cond2*(cond0==cond0), 1.0)
if any(cond):
@ -6901,7 +6903,7 @@ class rv_discrete(rv_generic):
cond = cond0 & cond1
output = empty(shape(cond),'d')
output.fill(NINF)
place(output,(1-cond0)*(cond1==cond1),self.badvalue)
place(output,(1-cond0) + np.isnan(k),self.badvalue)
place(output,cond2*(cond0==cond0), 0.0)
if any(cond):
@ -6941,7 +6943,7 @@ class rv_discrete(rv_generic):
cond2 = (k < self.a) & cond0
cond = cond0 & cond1
output = zeros(shape(cond),'d')
place(output,(1-cond0)*(cond1==cond1),self.badvalue)
place(output,(1-cond0) + np.isnan(k),self.badvalue)
place(output,cond2,1.0)
if any(cond):
goodargs = argsreduce(cond, *((k,)+args))
@ -6981,7 +6983,7 @@ class rv_discrete(rv_generic):
cond = cond0 & cond1
output = empty(shape(cond),'d')
output.fill(NINF)
place(output,(1-cond0)*(cond1==cond1),self.badvalue)
place(output,(1-cond0) + np.isnan(k),self.badvalue)
place(output,cond2,0.0)
if any(cond):
goodargs = argsreduce(cond, *((k,)+args))
@ -7993,6 +7995,11 @@ Skellam distribution
"""
)
def test_lognorm():
lognorm.cdf(np.nan, 0.5, loc=3, scale=1)
lognorm.ppf(np.nan, 0.5, loc=3, scale=1)
lognorm.cdf(lognorm.ppf([-0.5,0,1e-4,0.5,1-1e-4,1,2],0.5,loc=3,
scale=1),0.5,loc=3,scale=1)
def test_truncrayleigh():
import matplotlib.pyplot as plt
@ -8070,4 +8077,5 @@ def main():
lp = pht.profile()
if __name__ == '__main__':
#main()
test_truncrayleigh()
#test_truncrayleigh()
test_lognorm()

204
pywafo/src/wafo/stats/tests/test_distributions.py
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@ -4,7 +4,7 @@
from numpy.testing import TestCase, run_module_suite, assert_equal, \
assert_array_equal, assert_almost_equal, assert_array_almost_equal, \
assert_, rand, dec
assert_allclose, assert_, rand, dec
import numpy
@ -40,15 +40,7 @@ dists = ['uniform','norm','lognorm','expon','beta',
'hypsecant', 'laplace', 'reciprocal','triang','tukeylambda',
'vonmises']
# check function for test generator
def check_distribution(dist, args, alpha):
D,pval = stats.kstest(dist,'', args=args, N=1000)
if (pval < alpha):
D,pval = stats.kstest(dist,'',args=args, N=1000)
#if (pval < alpha):
# D,pval = stats.kstest(dist,'',args=args, N=1000)
assert_(pval > alpha, msg="D = " + str(D) + "; pval = " + str(pval) + \
"; alpha = " + str(alpha) + "\nargs = " + str(args))
# nose test generator
def test_all_distributions():
@ -76,6 +68,67 @@ def test_all_distributions():
args = tuple(1.0+rand(nargs))
yield check_distribution, dist, args, alpha
class TestFitMethod(TestCase):
skip = ['ncf']
@dec.slow
def test_fit(self):
for func, dist, args, alpha in test_all_distributions():
if dist in self.skip:
continue
distfunc = getattr(stats, dist)
res = distfunc.rvs(*args, **{'size':200})
vals = distfunc.fit(res)
vals2 = distfunc.fit(res, optimizer='powell')
# Only check the length of the return
# FIXME: should check the actual results to see if we are 'close'
# to what was created --- but what is 'close' enough
if dist in ['erlang', 'frechet']:
assert_(len(vals)==len(args))
assert_(len(vals2)==len(args))
else:
assert_(len(vals) == 2+len(args))
assert_(len(vals2)==2+len(args))
@dec.slow
def test_fix_fit(self):
for func, dist, args, alpha in test_all_distributions():
# Not sure why 'ncf', and 'beta' are failing
# erlang and frechet have different len(args) than distfunc.numargs
if dist in self.skip + ['erlang', 'frechet', 'beta']:
continue
distfunc = getattr(stats, dist)
res = distfunc.rvs(*args, **{'size':200})
vals = distfunc.fit(res,floc=0)
vals2 = distfunc.fit(res,fscale=1)
assert_(len(vals) == 2+len(args))
assert_(vals[-2] == 0)
assert_(vals2[-1] == 1)
assert_(len(vals2) == 2+len(args))
if len(args) > 0:
vals3 = distfunc.fit(res, f0=args[0])
assert_(len(vals3) == 2+len(args))
assert_(vals3[0] == args[0])
if len(args) > 1:
vals4 = distfunc.fit(res, f1=args[1])
assert_(len(vals4) == 2+len(args))
assert_(vals4[1] == args[1])
if len(args) > 2:
vals5 = distfunc.fit(res, f2=args[2])
assert_(len(vals5) == 2+len(args))
assert_(vals5[2] == args[2])
# check function for test generator
def check_distribution(dist, args, alpha):
D,pval = stats.kstest(dist,'', args=args, N=1000)
if (pval < alpha):
D,pval = stats.kstest(dist,'',args=args, N=1000)
#if (pval < alpha):
# D,pval = stats.kstest(dist,'',args=args, N=1000)
assert_(pval > alpha, msg="D = " + str(D) + "; pval = " + str(pval) + \
"; alpha = " + str(alpha) + "\nargs = " + str(args))
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)))
@ -195,6 +248,37 @@ class TestHypergeom(TestCase):
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_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 = []
for eaten in fruits_eaten:
res.append(stats.hypergeom.sf(quantile, oranges + pears, oranges, 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)
class TestLogser(TestCase):
def test_rvs(self):
vals = stats.logser.rvs(0.75, size=(2, 50))
@ -246,6 +330,26 @@ def test_rvgeneric_std():
"""Regression test for #1191"""
assert_array_almost_equal(stats.t.std([5, 6]), [1.29099445, 1.22474487])
def test_nan_arguments_ticket835():
assert_(np.isnan(stats.t.logcdf(np.nan)))
assert_(np.isnan(stats.t.cdf(np.nan)))
assert_(np.isnan(stats.t.logsf(np.nan)))
assert_(np.isnan(stats.t.sf(np.nan)))
assert_(np.isnan(stats.t.pdf(np.nan)))
assert_(np.isnan(stats.t.logpdf(np.nan)))
assert_(np.isnan(stats.t.ppf(np.nan)))
assert_(np.isnan(stats.t.isf(np.nan)))
assert_(np.isnan(stats.bernoulli.logcdf(np.nan)))
assert_(np.isnan(stats.bernoulli.cdf(np.nan)))
assert_(np.isnan(stats.bernoulli.logsf(np.nan)))
assert_(np.isnan(stats.bernoulli.sf(np.nan)))
assert_(np.isnan(stats.bernoulli.pdf(np.nan)))
assert_(np.isnan(stats.bernoulli.logpdf(np.nan)))
assert_(np.isnan(stats.bernoulli.ppf(np.nan)))
assert_(np.isnan(stats.bernoulli.isf(np.nan)))
class TestRvDiscrete(TestCase):
def test_rvs(self):
states = [-1,0,1,2,3,4]
@ -332,6 +436,16 @@ class TestSkellam(TestCase):
assert_almost_equal(stats.skellam.cdf(k, mu1, mu2), skcdfR, decimal=5)
class TestGamma(TestCase):
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)
class TestHypergeom2(TestCase):
def test_precision(self):
# comparison number from mpmath
@ -364,6 +478,12 @@ class TestDocstring(TestCase):
if stats.bernoulli.__doc__ is not None:
self.assertTrue("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(TestCase):
def test_entropy_positive(self):
"""See ticket #497"""
@ -390,55 +510,6 @@ def TestArgsreduce():
assert_array_equal(c, [2] * numpy.size(a))
class TestFitMethod(TestCase):
skip = ['ncf']
@dec.slow
def test_fit(self):
for func, dist, args, alpha in test_all_distributions():
if dist in self.skip:
continue
distfunc = getattr(stats, dist)
res = distfunc.rvs(*args, **{'size':200})
vals = distfunc.fit(res)
vals2 = distfunc.fit(res, optimizer='powell')
# Only check the length of the return
# FIXME: should check the actual results to see if we are 'close'
# to what was created --- but what is 'close' enough
if dist in ['erlang', 'frechet']:
assert_(len(vals)==len(args))
assert_(len(vals2)==len(args))
else:
assert_(len(vals) == 2+len(args))
assert_(len(vals2)==2+len(args))
@dec.slow
def test_fix_fit(self):
for func, dist, args, alpha in test_all_distributions():
# Not sure why 'ncf', and 'beta' are failing
# erlang and frechet have different len(args) than distfunc.numargs
if dist in self.skip + ['erlang', 'frechet', 'beta']:
continue
distfunc = getattr(stats, dist)
res = distfunc.rvs(*args, **{'size':200})
vals = distfunc.fit(res,floc=0)
vals2 = distfunc.fit(res,fscale=1)
assert_(len(vals) == 2+len(args))
assert_(vals[-2] == 0)
assert_(vals2[-1] == 1)
assert_(len(vals2) == 2+len(args))
if len(args) > 0:
vals3 = distfunc.fit(res, f0=args[0])
assert_(len(vals3) == 2+len(args))
assert_(vals3[0] == args[0])
if len(args) > 1:
vals4 = distfunc.fit(res, f1=args[1])
assert_(len(vals4) == 2+len(args))
assert_(vals4[1] == args[1])
if len(args) > 2:
vals5 = distfunc.fit(res, f2=args[2])
assert_(len(vals5) == 2+len(args))
assert_(vals5[2] == args[2])
class TestFrozen(TestCase):
"""Test that a frozen distribution gives the same results as the original object.
@ -666,21 +737,36 @@ 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)
olderr = np.seterr(divide='ignore')
try:
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)
finally:
np.seterr(**olderr)
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())
def test_regression_ticket_1421():
"""Regression test for ticket #1421 - correction discrete docs."""
assert_('pdf(x, mu, loc=0, scale=1)' not in stats.poisson.__doc__)
assert_('pmf(x,' in stats.poisson.__doc__)
if __name__ == "__main__":
run_module_suite()

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