def test_loglaplace():
#if x is laplace then y = exp(x) is loglaplace
#parameters are tricky
#the stats.loglaplace parameter is the inverse scale of x
loglaplaceexpg = ExpTransf_gen(stats.laplace)
cdfst = stats.loglaplace.cdf(3, 3)
#0.98148148148148151
#the parameters are shape, loc and scale of underlying laplace
cdftr = loglaplaceexpg._cdf(3, 0, 1. / 3)
assert_almost_equal(cdfst, cdftr, 14)
def test_loglaplace():
#if x is laplace then y = exp(x) is loglaplace
#parameters are tricky
#the stats.loglaplace parameter is the inverse scale of x
loglaplaceexpg = ExpTransf_gen(stats.laplace)
cdfst = stats.loglaplace.cdf(3,3)
#0.98148148148148151
#the parameters are shape, loc and scale of underlying laplace
cdftr = loglaplaceexpg._cdf(3,0,1./3)
assert_almost_equal(cdfst, cdftr, 14)
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)