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starting to sort out likelihoods WARNING not working

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Max Zwiessele 2013-10-17 14:38:43 +01:00
parent bc765134c0
commit 055eb3b70b
10 changed files with 247 additions and 142 deletions
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67
GPy/core/model.py
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@ -5,12 +5,13 @@
from .. import likelihoods
from ..inference import optimization
from ..util.linalg import jitchol
from GPy.util.misc import opt_wrapper
from ..util.misc import opt_wrapper
from parameterized import Parameterized
import multiprocessing as mp
import numpy as np
from GPy.core.domains import _POSITIVE, _REAL
from domains import _POSITIVE, _REAL
from numpy.linalg.linalg import LinAlgError
from index_operations import ParameterIndexOperations
# import numdifftools as ndt
class Model(Parameterized):
@ -18,7 +19,7 @@ class Model(Parameterized):
_allowed_failures = 10 # number of allowed failures
def __init__(self):
Parameterized.__init__(self)
self.priors = None
self._priors = ParameterIndexOperations()
self.optimization_runs = []
self.sampling_runs = []
self.preferred_optimizer = 'scg'
@ -399,36 +400,36 @@ class Model(Parameterized):
return np.nan
return 0.5 * self._get_params().size * np.log(2 * np.pi) + self.log_likelihood() - hld
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 checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3):