def myneldermead(xxx,
mymaxfev,
mystep,
fold=numpy.float_(numpy.finfo(numpy.float_).max)):
result = myloop(xxx, mymaxfev, mystep)
mynfev = result[3]
fnew = result[2]
if fnew < fold * 0.995 and 0 == Knuth_close( fnew, fold, ftol ) and \
0 == Knuth_close( fnew, fold, ftol ) and \
0 == result[0] and mynfev < mymaxfev:
if debug:
print 'neldermead: fnew = ', fnew, '\tfold = ', fold, \
'\tx = ', result[ 1 ]
result = myneldermead(result[1],
mymaxfev - mynfev,
step,
fold=fnew)
mynfev += result[3]
return result[0], result[1], result[2], mynfev
def find(self,
dir,
iter,
step_size,
open_interval,
maxiters,
tol,
bloginfo,
base=2.0):
assert self.fcn is not None, 'callback func has not been set'
hlimit = [
ConfBracket.LowerLimit(self.myargs.get_hlimit(dir)),
ConfBracket.UpperLimit(self.myargs.get_hlimit(dir))
]
slimit = [
ConfBracket.LowerLimit(self.myargs.get_slimit(dir)),
ConfBracket.UpperLimit(self.myargs.get_slimit(dir))
]
xxx = self.trial_points[0]
fff = self.trial_points[1]
conf_step = ConfStep(xxx, fff)
mymaxiters = maxiters
if mymaxiters > 16:
mymaxiters = 16
plateau = 0
max_plateau_iter = 5
try:
for iter in range(mymaxiters):
if 0 == iter:
x = conf_step.covar(dir, iter, step_size, base)
elif 1 == iter:
x = conf_step.secant(dir, iter, step_size, base)
#x = conf_step.covar( dir, iter, step_size, base )
else:
x = conf_step.quad(dir, iter, step_size, base, bloginfo)
if x is None or numpy.isnan(x):
return ConfRootNone()
# Make sure x is not beyond the **hard** limit
if hlimit[dir].is_beyond_limit(x):
x = hlimit[dir].limit
f = self.fcn(x, self.myargs())
# print 'find(): beyond hard limit: f(%.14e)=%.14e' % (x,f)
if abs(f) <= tol:
return ConfRootZero(x)
if f >= 0.0:
return ConfRootBracket(self.fcn, self.trial_points,
open_interval)
else:
return ConfRootNone()
elif slimit[dir].is_beyond_limit(x):
f = self.fcn(x, self.myargs())
# print 'find(): beyond soft limit: f(%.14e)=%.14e' % (x,f)
if abs(f) <= tol:
return ConfRootZero(x)
if f >= 0.0:
return ConfRootBracket(self.fcn, self.trial_points,
open_interval)
elif f < fff[-2]:
# if the fit beyond the soft limit is a better fit
# then the confidence for the parameter does not exist
return ConfRootNone()
else:
f = self.fcn(x, self.myargs())
# print 'find(): f(%.14e)=%.14e' % (x,f)
if abs(f) <= tol:
return ConfRootZero(x)
elif f >= 0.0:
return ConfRootBracket(self.fcn, self.trial_points,
open_interval)
if Knuth_close(fff[-2], fff[-1], 1.0e-6):
plateau += 1
if plateau > max_plateau_iter:
# print 'find( %d ): plateau = %d', (iter,plateau)
return ConfRootNone(None)
# else:
# if plateau > 0:
# plateau -= 1
return ConfRootNone(None)
except OutOfBoundErr:
return ConfRootNone()
def are_func_vals_close_enough(tolerance):
smallest_fct_val = self.simplex[0, -1]
largest_fct_val = self.simplex[-1, -1]
return Knuth_close(smallest_fct_val, largest_fct_val, tolerance)