def opt(self, f_fp=None, f=None, fp=None):
"""
Run Rasmussen's Conjugate Gradient optimizer
"""
assert f_fp != None, "Rasmussen's minimizer requires f_fp"
statuses = [
'Converged', 'Line search failed',
'Maximum number of f evaluations reached', 'NaNs in optimization'
]
opt_dict = {}
if self.xtol is not None:
print(
"WARNING: minimize doesn't have an xtol arg, so I'm going to ignore it"
)
if self.ftol is not None:
print(
"WARNING: minimize doesn't have an ftol arg, so I'm going to ignore it"
)
if self.gtol is not None:
print(
"WARNING: minimize doesn't have an gtol arg, so I'm going to ignore it"
)
opt_result = rasm.minimize(self.x_init,
f_fp, (),
messages=self.messages,
maxnumfuneval=self.max_f_eval)
self.x_opt = opt_result[0]
self.f_opt = opt_result[1][-1]
self.funct_eval = opt_result[2]
self.status = statuses[opt_result[3]]
self.trace = opt_result[1]
def opt(self, f_fp=None, f=None, fp=None):
"""
Run Rasmussen's Conjugate Gradient optimizer
"""
assert f_fp != None, "Rasmussen's minimizer requires f_fp"
statuses = ['Converged', 'Line search failed', 'Maximum number of f evaluations reached',
'NaNs in optimization']
opt_dict = {}
if self.xtol is not None:
print "WARNING: minimize doesn't have an xtol arg, so I'm going to ignore it"
if self.ftol is not None:
print "WARNING: minimize doesn't have an ftol arg, so I'm going to ignore it"
if self.gtol is not None:
print "WARNING: minimize doesn't have an gtol arg, so I'm going to ignore it"
opt_result = rasm.minimize(self.x_init, f_fp, (), messages=self.messages,
maxnumfuneval=self.max_f_eval)
self.x_opt = opt_result[0]
self.f_opt = opt_result[1][-1]
self.funct_eval = opt_result[2]
self.status = statuses[opt_result[3]]
self.trace = opt_result[1]