def setstate(self, state):
"""
set state from previous call to getstate
call Parameterized with the rest of the state
:param state: the state of the model.
:type state: list as returned from getstate.
"""
self.preferred_optimizer = state.pop()
self.sampling_runs = state.pop()
self.optimization_runs = state.pop()
self.priors = state.pop()
Parameterized.setstate(self, state)
def setstate(self, state):
"""
set state from previous call to getstate
call Parameterized with the rest of the state
:param state: the state of the model.
:type state: list as returned from getstate.
"""
self.preferred_optimizer = state.pop()
self.sampling_runs = state.pop()
self.optimization_runs = state.pop()
self.priors = state.pop()
Parameterized.setstate(self, state)
def __str__(self):
s = Parameterized.__str__(self).split('\n')
#def __str__(self, names=None):
# if names is None:
# names = self._get_print_names()
#s = Parameterized.__str__(self, names=names).split('\n')
# add priors to the string
if self.priors is not None:
strs = [str(p) if p is not None else '' for p in self.priors]
else:
strs = [''] * len(self._get_params())
# strs = [''] * len(self._get_param_names())
# name_indices = self.grep_param_names("|".join(names))
# strs = np.array(strs)[name_indices]
width = np.array(max([len(p) for p in strs] + [5])) + 4
log_like = self.log_likelihood()
log_prior = self.log_prior()
obj_funct = '\nLog-likelihood: {0:.3e}'.format(log_like)
if len(''.join(strs)) != 0:
obj_funct += ', Log prior: {0:.3e}, LL+prior = {0:.3e}'.format(
log_prior, log_like + log_prior)
obj_funct += '\n\n'
s[0] = obj_funct + s[0]
s[0] += "|{h:^{col}}".format(h='prior', col=width)
s[1] += '-' * (width + 1)
for p in range(2, len(strs) + 2):
s[p] += '|{prior:^{width}}'.format(prior=strs[p - 2], width=width)
return '\n'.join(s)
def __str__(self):
s = Parameterized.__str__(self).split('\n')
#def __str__(self, names=None):
# if names is None:
# names = self._get_print_names()
#s = Parameterized.__str__(self, names=names).split('\n')
# add priors to the string
if self.priors is not None:
strs = [str(p) if p is not None else '' for p in self.priors]
else:
strs = [''] * len(self._get_params())
# strs = [''] * len(self._get_param_names())
# name_indices = self.grep_param_names("|".join(names))
# strs = np.array(strs)[name_indices]
width = np.array(max([len(p) for p in strs] + [5])) + 4
log_like = self.log_likelihood()
log_prior = self.log_prior()
obj_funct = '\nLog-likelihood: {0:.3e}'.format(log_like)
if len(''.join(strs)) != 0:
obj_funct += ', Log prior: {0:.3e}, LL+prior = {0:.3e}'.format(log_prior, log_like + log_prior)
obj_funct += '\n\n'
s[0] = obj_funct + s[0]
s[0] += "|{h:^{col}}".format(h='prior', col=width)
s[1] += '-' * (width + 1)
for p in range(2, len(strs) + 2):
s[p] += '|{prior:^{width}}'.format(prior=strs[p - 2], width=width)
return '\n'.join(s)
def __set__(self, obj, val):
"""
Call Parameter's __set__, but warn if the file cannot be found.
"""
try:
self._resolve(val)
except IOError, e:
Parameterized(name="%s.%s"%(obj.name,self._attrib_name)).warning('%s'%(e.args[0]))
def getstate(self):
"""
Get the current state of the class.
Inherited from Parameterized, so add those parameters to the state
:return: list of states from the model.
"""
return Parameterized.getstate(self) + \
[self.priors, self.optimization_runs,
self.sampling_runs, self.preferred_optimizer]
def getstate(self):
"""
Get the current state of the class.
Inherited from Parameterized, so add those parameters to the state
:return: list of states from the model.
"""
return Parameterized.getstate(self) + \
[self.priors, self.optimization_runs,
self.sampling_runs, self.preferred_optimizer]
def __init__(self):
Parameterized.__init__(self)
self.priors = None
self.optimization_runs = []
self.sampling_runs = []
self.preferred_optimizer = 'scg'
def __init__(self):
Parameterized.__init__(self)
self.priors = None
self.optimization_runs = []
self.sampling_runs = []
self.preferred_optimizer = 'scg'
