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)