def test_loggamma():
#'Results for expgamma'
loggammaexpg = LogTransf_gen(stats.gamma)
cdftr = loggammaexpg._cdf(1,10)
cdfst = stats.loggamma.cdf(1,10)
assert_almost_equal(cdfst, cdftr, 14)
cdftr = loggammaexpg._cdf(2,15)
cdfst = stats.loggamma.cdf(2,15)
assert_almost_equal(cdfst, cdftr, 14)
def test_loggamma():
#'Results for expgamma'
loggammaexpg = LogTransf_gen(stats.gamma)
cdftr = loggammaexpg._cdf(1, 10)
cdfst = stats.loggamma.cdf(1, 10)
assert_almost_equal(cdfst, cdftr, 14)
cdftr = loggammaexpg._cdf(2, 15)
cdfst = stats.loggamma.cdf(2, 15)
assert_almost_equal(cdfst, cdftr, 14)
def __init__(self):
self.dist = LogTransf_gen(stats.gamma)
self.trargs = (10, )
self.trkwds = {}
self.statsdist = stats.loggamma
self.stargs = (10, )
self.stkwds = {}
def examples_transf():
##lognormal = ExpTransf(a=0.0, xa=-10.0, name = 'Log transformed normal')
##print lognormal.cdf(1)
##print stats.lognorm.cdf(1,1)
##print lognormal.stats()
##print stats.lognorm.stats(1)
##print lognormal.rvs(size=10)
print 'Results for lognormal'
lognormalg = ExpTransf_gen(stats.norm, a=0, name = 'Log transformed normal general')
print lognormalg.cdf(1)
print stats.lognorm.cdf(1,1)
print lognormalg.stats()
print stats.lognorm.stats(1)
print lognormalg.rvs(size=5)
##print 'Results for loggamma'
##loggammag = ExpTransf_gen(stats.gamma)
##print loggammag._cdf(1,10)
##print stats.loggamma.cdf(1,10)
print 'Results for expgamma'
loggammaexpg = LogTransf_gen(stats.gamma)
print loggammaexpg._cdf(1,10)
print stats.loggamma.cdf(1,10)
print loggammaexpg._cdf(2,15)
print stats.loggamma.cdf(2,15)
# this requires change in scipy.stats.distribution
#print loggammaexpg.cdf(1,10)
print 'Results for loglaplace'
loglaplaceg = LogTransf_gen(stats.laplace)
print loglaplaceg._cdf(2)
print stats.loglaplace.cdf(2,1)
loglaplaceexpg = ExpTransf_gen(stats.laplace)
print loglaplaceexpg._cdf(2)
stats.loglaplace.cdf(3,3)
#0.98148148148148151
loglaplaceexpg._cdf(3,0,1./3)
def examples_transf():
##lognormal = ExpTransf(a=0.0, xa=-10.0, name = 'Log transformed normal')
##print(lognormal.cdf(1))
##print(stats.lognorm.cdf(1,1))
##print(lognormal.stats())
##print(stats.lognorm.stats(1))
##print(lognormal.rvs(size=10))
print('Results for lognormal')
lognormalg = ExpTransf_gen(stats.norm, a=0, name = 'Log transformed normal general')
print(lognormalg.cdf(1))
print(stats.lognorm.cdf(1,1))
print(lognormalg.stats())
print(stats.lognorm.stats(1))
print(lognormalg.rvs(size=5))
##print('Results for loggamma')
##loggammag = ExpTransf_gen(stats.gamma)
##print(loggammag._cdf(1,10))
##print(stats.loggamma.cdf(1,10))
print('Results for expgamma')
loggammaexpg = LogTransf_gen(stats.gamma)
print(loggammaexpg._cdf(1,10))
print(stats.loggamma.cdf(1,10))
print(loggammaexpg._cdf(2,15))
print(stats.loggamma.cdf(2,15))
# this requires change in scipy.stats.distribution
#print(loggammaexpg.cdf(1,10))
print('Results for loglaplace')
loglaplaceg = LogTransf_gen(stats.laplace)
print(loglaplaceg._cdf(2))
print(stats.loglaplace.cdf(2,1))
loglaplaceexpg = ExpTransf_gen(stats.laplace)
print(loglaplaceexpg._cdf(2))
stats.loglaplace.cdf(3,3)
#0.98148148148148151
loglaplaceexpg._cdf(3,0,1./3)