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domains added and class names in priors capitalized

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Max Zwiessele 2013-06-04 17:21:56 +01:00
parent 3546650d15
commit c7ac1ed9d8
7 changed files with 61 additions and 49 deletions
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38
GPy/core/model.py
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@ -41,16 +41,16 @@ class model(parameterised):
Arguments
---------
which -- string, regexp, or integer array
what -- instance of a prior class
what -- instance of a Prior class
Notes
-----
Asserts that the prior is suitable for the constraint. If the
Asserts that the Prior is suitable for the constraint. If the
wrong constraint is in place, an error is raised. If no
constraint is in place, one is added (warning printed).
For tied parameters, the prior will only be "counted" once, thus
a prior object is only inserted on the first tied index
For tied parameters, the Prior will only be "counted" once, thus
a Prior object is only inserted on the first tied index
"""
which = self.grep_param_names(which)
@ -58,24 +58,24 @@ class model(parameterised):
# check tied situation
tie_partial_matches = [tie for tie in self.tied_indices if (not set(tie).isdisjoint(set(which))) & (not set(tie) == set(which))]
if len(tie_partial_matches):
raise ValueError, "cannot place prior across partial ties"
raise ValueError, "cannot place Prior across partial ties"
tie_matches = [tie for tie in self.tied_indices if set(which) == set(tie) ]
if len(tie_matches) > 1:
raise ValueError, "cannot place prior across multiple ties"
raise ValueError, "cannot place Prior across multiple ties"
elif len(tie_matches) == 1:
which = which[:1] # just place a prior object on the first parameter
which = which[:1] # just place a Prior object on the first parameter
# check constraints are okay
if what.domain is POSITIVE:
constrained_positive_indices = [i for i, t in zip(self.constrained_indices, self.constraints) if t.domain == POSITIVE]
constrained_positive_indices = [i for i, t in zip(self.constrained_indices, self.constraints) if t.domain is POSITIVE]
if len(constrained_positive_indices):
constrained_positive_indices = np.hstack(constrained_positive_indices)
else:
constrained_positive_indices = np.zeros(shape=(0,))
bad_constraints = np.setdiff1d(self.all_constrained_indices(), constrained_positive_indices)
assert not np.any(which[:, None] == bad_constraints), "constraint and prior incompatible"
assert not np.any(which[:, None] == bad_constraints), "constraint and Prior incompatible"
unconst = np.setdiff1d(which, constrained_positive_indices)
if len(unconst):
print "Warning: constraining parameters to be positive:"
@ -83,11 +83,11 @@ class model(parameterised):
print '\n'
self.constrain_positive(unconst)
elif what.domain is REAL:
assert not np.any(which[:, None] == self.all_constrained_indices()), "constraint and prior incompatible"
assert not np.any(which[:, None] == self.all_constrained_indices()), "constraint and Prior incompatible"
else:
raise ValueError, "prior not recognised"
raise ValueError, "Prior not recognised"
# store the prior in a local list
# store the Prior in a local list
for w in which:
self.priors[w] = what
@ -105,7 +105,7 @@ class model(parameterised):
raise AttributeError, "no parameter matches %s" % name
def log_prior(self):
"""evaluate the prior"""
"""evaluate the Prior"""
return np.sum([p.lnpdf(x) for p, x in zip(self.priors, self._get_params()) if p is not None])
def _log_prior_gradients(self):
@ -129,17 +129,17 @@ class model(parameterised):
def randomize(self):
"""
Randomize the model.
Make this draw from the prior if one exists, else draw from N(0,1)
Make this draw from the Prior if one exists, else draw from N(0,1)
"""
# first take care of all parameters (from N(0,1))
x = self._get_params_transformed()
x = np.random.randn(x.size)
self._set_params_transformed(x)
# now draw from prior where possible
# now draw from Prior where possible
x = self._get_params()
[np.put(x, i, p.rvs(1)) for i, p in enumerate(self.priors) if not p is None]
self._set_params(x)
self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one prior object...)
self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one Prior object...)
def optimize_restarts(self, Nrestarts=10, robust=False, verbose=True, parallel=False, num_processes=None, **kwargs):
@ -279,7 +279,7 @@ class model(parameterised):
def Laplace_covariance(self):
"""return the covariance matric of a Laplace approximatino at the current (stationary) point"""
# TODO add in the prior contributions for MAP estimation
# TODO add in the Prior contributions for MAP estimation
# TODO fix the hessian for tied, constrained and fixed components
if hasattr(self, 'log_likelihood_hessian'):
A = -self.log_likelihood_hessian()
@ -318,14 +318,14 @@ class model(parameterised):
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 += ', 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)
s[p] += '|{Prior:^{width}}'.format(Prior=strs[p - 2], width=width)
return '\n'.join(s)