def check(method):
x0 = np.array([2.0])
f0 = func(x0)
jac = bad_grad
if method in ["nelder-mead", "powell", "cobyla"]:
jac = None
sol = optimize.minimize(func, x0, jac=jac, method=method, options=dict(maxiter=20))
assert_equal(func(sol.x), sol.fun)
dec.knownfailureif(method == "slsqp", "SLSQP returns slightly worse")(lambda: None)()
assert_(func(sol.x) <= f0)
def check(method):
x0 = np.array([2.0])
f0 = func(x0)
jac = bad_grad
if method in ['nelder-mead', 'powell', 'anneal', 'cobyla']:
jac = None
sol = optimize.minimize(func, x0, jac=jac, method=method,
options=dict(maxiter=20))
assert_equal(func(sol.x), sol.fun)
dec.knownfailureif(method == 'slsqp', "SLSQP returns slightly worse")(lambda: None)()
assert_(func(sol.x) <= f0)
def deco(func):
try:
if bool(os.environ['SCIPY_XFAIL']):
return func
except (ValueError, KeyError):
pass
return dec.knownfailureif(True, msg)(func)
def deco(func):
try:
if bool(os.environ['SCIPY_XFAIL']):
return func
except (ValueError, KeyError):
pass
return dec.knownfailureif(True, msg)(func)
def check_cont_fit(distname, arg):
if distname in failing_fits:
# Skip failing fits unless overridden
xfail = True
try:
xfail = not int(os.environ['SCIPY_XFAIL'])
except:
pass
if xfail:
msg = "Fitting %s doesn't work reliably yet" % distname
msg += " [Set environment variable SCIPY_XFAIL=1 to run this test nevertheless.]"
dec.knownfailureif(True, msg)(lambda: None)()
distfn = getattr(stats, distname)
truearg = np.hstack([arg, [0.0, 1.0]])
diffthreshold = np.max(
np.vstack([
truearg * thresh_percent,
np.ones(distfn.numargs + 2) * thresh_min
]), 0)
for fit_size in fit_sizes:
# Note that if a fit succeeds, the other fit_sizes are skipped
np.random.seed(1234)
with np.errstate(all='ignore'):
rvs = distfn.rvs(size=fit_size, *arg)
est = distfn.fit(rvs) # start with default values
diff = est - truearg
# threshold for location
diffthreshold[-2] = np.max(
[np.abs(rvs.mean()) * thresh_percent, thresh_min])
if np.any(np.isnan(est)):
raise AssertionError('nan returned in fit')
else:
if np.all(np.abs(diff) <= diffthreshold):
break
else:
txt = 'parameter: %s\n' % str(truearg)
txt += 'estimated: %s\n' % str(est)
txt += 'diff : %s\n' % str(diff)
raise AssertionError('fit not very good in %s\n' % distfn.name + txt)
def check_cont_fit(distname,arg):
if distname in failing_fits:
# Skip failing fits unless overridden
xfail = True
try:
xfail = not int(os.environ['SCIPY_XFAIL'])
except:
pass
if xfail:
msg = "Fitting %s doesn't work reliably yet" % distname
msg += " [Set environment variable SCIPY_XFAIL=1 to run this test nevertheless.]"
dec.knownfailureif(True, msg)(lambda: None)()
distfn = getattr(stats, distname)
truearg = np.hstack([arg, [0.0, 1.0]])
diffthreshold = np.max(np.vstack([truearg*thresh_percent,
np.ones(distfn.numargs+2)*thresh_min]),
0)
for fit_size in fit_sizes:
# Note that if a fit succeeds, the other fit_sizes are skipped
np.random.seed(1234)
with np.errstate(all='ignore'):
rvs = distfn.rvs(size=fit_size, *arg)
est = distfn.fit(rvs) # start with default values
diff = est - truearg
# threshold for location
diffthreshold[-2] = np.max([np.abs(rvs.mean())*thresh_percent,thresh_min])
if np.any(np.isnan(est)):
raise AssertionError('nan returned in fit')
else:
if np.all(np.abs(diff) <= diffthreshold):
break
else:
txt = 'parameter: %s\n' % str(truearg)
txt += 'estimated: %s\n' % str(est)
txt += 'diff : %s\n' % str(diff)
raise AssertionError('fit not very good in %s\n' % distfn.name + txt)
def knownfailure(f):
return dec.knownfailureif(True)(f)
def knownfailure(f):
return dec.knownfailureif(True)(f)
