def test_distribution_rvs(self):
alpha = 0.01
loc = 0
scale = 1
arg = (loc, scale)
distfn = lognorm_gen(0, 1)
D,pval = stats.kstest(distfn.rvs, distfn.cdf, args=arg, N=1000)
if (pval < alpha):
npt.assert_(pval > alpha,
"D = %f; pval = %f; alpha = %f; args=%s" % (
D, pval, alpha, arg))
def test_distribution_rvs(self):
return
alpha = 0.01
loc = 0
scale = 1
arg = (loc, scale)
distfn = lognorm_gen(0, 1)
D, pval = stats.kstest(distfn.rvs, distfn.cdf, args=arg, N=1000)
if (pval < alpha):
npt.assert_(pval > alpha,
"D = %f; pval = %f; alpha = %f; args=%s" % (
D, pval, alpha, arg))
def test_distribution_rvs(self):
import warnings
warnings.warn("test_distribution_rvs is being skipped!")
return # XXX
alpha = 0.01
loc = 0
scale = 1
arg = (loc, scale)
distfn = lognorm_gen(0, 1)
D, pval = stats.kstest(distfn.rvs, distfn.cdf, args=arg, N=1000)
if pval < alpha:
npt.assert_(
pval > alpha,
f"D = {D:f}; pval = {pval:f}; alpha = {alpha:f}; args={arg}",
)
def test_pdf(self):
check_pdf(lognorm_gen(0, 1), (0, 1), '')
check_pdf(lognorm_gen(mu=-4, sigma=2), (-3, 2), '')
def test_pdf_logpdf(self):
check_pdf_logpdf(lognorm_gen(0, 1), (0, 1), '')
check_pdf_logpdf(lognorm_gen(mu=-4, sigma=0.5), (-2, 1), '')
def test_cdf_ppf(self):
check_cdf_ppf(lognorm_gen(0, 1), (0, 1), '')
check_cdf_ppf(lognorm_gen(-2, 1), (-5, 5), '')
def test_pdf_logpdf(self):
check_pdf_logpdf(lognorm_gen(0, 1), args=(),
msg='base case')
check_pdf_logpdf(lognorm_gen(mu=-4, sigma=0.5), args=(),
msg='non-default mu, sigma')
def test_cdf_logcdf(self):
check_cdf_logcdf(lognorm_gen(0, 1), (), '')
check_cdf_logcdf(lognorm_gen(0, 1), (), '')
def test_pdf(self):
check_pdf(lognorm_gen(0, 1), (), "")
check_pdf(lognorm_gen(mu=-4, sigma=2), (), "")
def test_cdf_ppf(self):
check_cdf_ppf(lognorm_gen(0, 1), (), "")
check_cdf_ppf(lognorm_gen(-2, 1), (), "")
