def __str__(self):
s = Parameterised.__str__(self).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())
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 = Parameterised.__str__(self).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())
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 __init__(self):
Parameterised.__init__(self)
self.priors = None
self.optimization_runs = []
self.sampling_runs = []
self.preferred_optimizer = 'scg'
def __init__(self):
Parameterised.__init__(self)
self.priors = None
self.optimization_runs = []
self.sampling_runs = []
self.preferred_optimizer = 'scg'
