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Try to silence optimizer.

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master
pbrod 9 years ago
parent b43b6dd445
commit b0ef188cf3
1 changed files with 18 additions and 15 deletions
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33
wafo/stats/estimation.py
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@ -335,8 +335,8 @@ class Profile(object):
'Something wrong with fit (par = {})'.format(str(par))) 'Something wrong with fit (par = {})'.format(str(par)))
def _profile_optimum(self, phatfree0, p_opt): def _profile_optimum(self, phatfree0, p_opt):
phatfree = optimize.fmin( phatfree = optimize.fmin(self._profile_fun, phatfree0, args=(p_opt,),
self._profile_fun, phatfree0, args=(p_opt,), disp=0) disp=0)
Lmax = -self._profile_fun(phatfree, p_opt) Lmax = -self._profile_fun(phatfree, p_opt)
self._correct_Lmax(Lmax, phatfree, p_opt) self._correct_Lmax(Lmax, phatfree, p_opt)
return Lmax, phatfree return Lmax, phatfree
@ -740,8 +740,8 @@ class ProfileOld(object):
'Something wrong with fit (par = {})'.format(str(par))) 'Something wrong with fit (par = {})'.format(str(par)))
def _profile_optimum(self, phatfree0, p_opt): def _profile_optimum(self, phatfree0, p_opt):
phatfree = optimize.fmin( phatfree = optimize.fmin(self._profile_fun, phatfree0, args=(p_opt,),
self._profile_fun, phatfree0, args=(p_opt,), disp=0) disp=0)
Lmax = -self._profile_fun(phatfree, p_opt) Lmax = -self._profile_fun(phatfree, p_opt)
self._correct_Lmax(Lmax, phatfree, p_opt) self._correct_Lmax(Lmax, phatfree, p_opt)
return Lmax, phatfree return Lmax, phatfree
@ -1353,7 +1353,8 @@ class FitDistribution(rv_frozen):
Narg = len(args) Narg = len(args)
if Narg > dist.numargs + 2: if Narg > dist.numargs + 2:
raise ValueError("Too many input arguments.") raise ValueError("Too many input arguments.")
if (Narg < dist.numargs + 2) or not ('loc' in kwds and 'scale' in kwds): if (Narg < dist.numargs + 2) or not ('loc' in kwds and
'scale' in kwds):
# get distribution specific starting locations # get distribution specific starting locations
start = dist._fitstart(data) start = dist._fitstart(data)
args += start[Narg:] args += start[Narg:]
@ -1376,14 +1377,17 @@ class FitDistribution(rv_frozen):
# by now kwds must be empty, since everybody took what they needed # by now kwds must be empty, since everybody took what they needed
if kwds: if kwds:
raise TypeError("Unknown arguments: %s." % kwds) raise TypeError("Unknown arguments: %s." % kwds)
output = optimizer(func, x0, args=(data,), full_output=True) output = optimizer(func, x0, args=(data,), full_output=True,
disp=0)
# output = optimize.fmin_bfgs(func, vals, args=(data,), full_output=True) # output = optimize.fmin_bfgs(func, vals, args=(data,),
# full_output=True)
# dfunc = nd.Gradient(func)(vals, data) # dfunc = nd.Gradient(func)(vals, data)
# nd.directionaldiff(f, x0, vec) # nd.directionaldiff(f, x0, vec)
warnflag = output[-1] warnflag = output[-1]
if warnflag == 1: if warnflag == 1:
output = optimizer(func, output[0], args=(data,), full_output=True) output = optimizer(func, output[0], args=(data,),
full_output=True)
warnflag = output[-1] warnflag = output[-1]
vals = tuple(output[0]) vals = tuple(output[0])
if warnflag == 1: if warnflag == 1:
@ -1550,13 +1554,12 @@ class FitDistribution(rv_frozen):
fixstr = 'Fixed: phat[{0:s}] = {1:s} '.format(phatistr, fixstr = 'Fixed: phat[{0:s}] = {1:s} '.format(phatistr,
phatvstr) phatvstr)
infostr = 'Fit method: {0:s}, Fit p-value: {1:2.2f} {2:s}, phat=[{3:s}]' subtxt = 'Fit method: {0:s}, Fit p-value: {1:2.2f} {2:s}, phat=[{3:s}]'
par_txt = ('{:1.2g}, '*len(self.par))[:-2].format(*self.par) par_txt = ('{:1.2g}, '*len(self.par))[:-2].format(*self.par)
try: try:
plotbackend.figtext(0.05, 0.01, infostr.format(self.method.upper(), plotbackend.figtext(0.05, 0.01, subtxt.format(self.method.upper(),
self.pvalue, self.pvalue, fixstr,
fixstr, par_txt))
par_txt))
except: except:
pass pass
@ -1727,10 +1730,10 @@ def test_doctstrings():
def test1(): def test1():
import wafo.stats as ws import wafo.stats as ws
dist = ws.weibull_min # dist = ws.weibull_min
# dist = ws.bradford # dist = ws.bradford
dist = ws.gengamma dist = ws.gengamma
R = dist.rvs(2,.5, size=500) R = dist.rvs(2, .5, size=500)
phat = FitDistribution(dist, R, method='ml') phat = FitDistribution(dist, R, method='ml')
phats = FitDistribution(dist, R, method='mps') phats = FitDistribution(dist, R, method='mps')
import matplotlib.pyplot as plt import matplotlib.pyplot as plt

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