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""" Test functions for stats module
"""
from numpy.testing import *
import numpy
from numpy import typecodes, array
import scipy.stats as stats
def kolmogorov_check(diststr,args=(),N=20,significance=0.01):
qtest = stats.ksoneisf(significance,N)
cdf = eval('stats.'+diststr+'.cdf')
dist = eval('stats.'+diststr)
# Get random numbers
kwds = {'size':N}
vals = numpy.sort(dist.rvs(*args,**kwds))
cdfvals = cdf(vals,*args)
q = max(abs(cdfvals-arange(1.0,N+1)/N))
assert (q < qtest), "Failed q=%f, bound=%f, alpha=%f" % (q, qtest, significance)
return
# generate test cases to test cdf and distribution consistency
dists = ['uniform','norm','lognorm','expon','beta',
'powerlaw','bradford','burr','fisk','cauchy','halfcauchy',
'foldcauchy','gamma','gengamma','loggamma',
'alpha','anglit','arcsine','betaprime','erlang',
'dgamma','exponweib','exponpow','frechet_l','frechet_r',
'gilbrat','f','ncf','chi2','chi','nakagami','genpareto',
'genextreme','genhalflogistic','pareto','lomax','halfnorm',
'halflogistic','fatiguelife','foldnorm','ncx2','t','nct',
'weibull_min','weibull_max','dweibull','maxwell','rayleigh',
'genlogistic', 'logistic','gumbel_l','gumbel_r','gompertz',
'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), "D = " + str(D) + "; pval = " + str(pval) + \
"; alpha = " + str(alpha) + "\nargs = " + str(args)
# nose test generator
def test_all_distributions():
for dist in dists:
distfunc = getattr(stats, dist)
nargs = distfunc.numargs
alpha = 0.01
if dist == 'fatiguelife':
alpha = 0.001
if dist == 'erlang':
args = (4,)+tuple(rand(2))
elif dist == 'frechet':
args = tuple(2*rand(1))+(0,)+tuple(2*rand(2))
elif dist == 'triang':
args = tuple(rand(nargs))
elif dist == 'reciprocal':
vals = rand(nargs)
vals[1] = vals[0] + 1.0
args = tuple(vals)
elif dist == 'vonmises':
yield check_distribution, dist, (100,), alpha
args = tuple(1.0+rand(nargs))
else:
args = tuple(1.0+rand(nargs))
yield check_distribution, dist, args, alpha
class TestRandInt(TestCase):
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),`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 = numpy.r_[0:36:100j]
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(TestCase):
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'])
class TestBernoulli(TestCase):
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'])
class TestNBinom(TestCase):
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'])
class TestGeom(TestCase):
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_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)
class TestHypergeom(TestCase):
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'])
class TestLogser(TestCase):
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'])
class TestPoisson(TestCase):
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'])
class TestZipf(TestCase):
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'])
class TestDLaplace(TestCase):
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'])
class TestRvDiscrete(TestCase):
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))
class TestExpon(TestCase):
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)
class TestGenExpon(TestCase):
def test_pdf_unity_area(self):
from scipy.integrate import simps
# PDF should integrate to one
assert_almost_equal(simps(stats.genexpon.pdf(numpy.arange(0,10,0.01),
0.5, 0.5, 2.0),
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(TestCase):
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 TestDocstring(TestCase):
def test_docstrings(self):
"""See ticket #761"""
if stats.rayleigh.__doc__ is not None:
self.failUnless("rayleigh" in stats.rayleigh.__doc__.lower())
if stats.bernoulli.__doc__ is not None:
self.failUnless("bernoulli" in stats.bernoulli.__doc__.lower())
class TestEntropy(TestCase):
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)
if __name__ == "__main__":
run_module_suite()
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