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REVERT a53690ab7f, flapack back substitued in

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Max Zwiessele 2013-05-31 12:09:11 +01:00
parent 43c2f8af87
commit 4eebf99bfb
15 changed files with 96 additions and 101 deletions
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28
GPy/core/model.py
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@ -62,7 +62,7 @@ class model(parameterised):
if len(tie_matches) > 1:
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
@ -89,18 +89,18 @@ class model(parameterised):
for w in which:
self.priors[w] = what
def get_gradient(self, cd48_name, return_names=False):
def get_gradient(self, name, return_names=False):
"""
Get model gradient(s) by cd48_name. The cd48_name is applied as a regular expression and all parameters that match that regular expression are returned.
Get model gradient(s) by name. The name is applied as a regular expression and all parameters that match that regular expression are returned.
"""
matches = self.grep_param_names(cd48_name)
matches = self.grep_param_names(name)
if len(matches):
if return_names:
return self._log_likelihood_gradients()[matches], np.asarray(self._get_param_names())[matches].tolist()
else:
return self._log_likelihood_gradients()[matches]
else:
raise AttributeError, "no parameter matches %s" % cd48_name
raise AttributeError, "no parameter matches %s" % name
def log_prior(self):
"""evaluate the prior"""
@ -137,7 +137,7 @@ class model(parameterised):
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):
@ -174,8 +174,8 @@ class model(parameterised):
job = pool.apply_async(opt_wrapper, args=(self,), kwds=kwargs)
jobs.append(job)
pool.close() # signal that no more data coming in
pool.join() # wait for all the tasks to complete
pool.close() # signal that no more data coming in
pool.join() # wait for all the tasks to complete
except KeyboardInterrupt:
print "Ctrl+c received, terminating and joining pool."
pool.terminate()
@ -214,10 +214,10 @@ class model(parameterised):
for s in positive_strings:
for i in self.grep_param_names(s):
if not (i in currently_constrained):
# to_make_positive.append(re.escape(param_names[i]))
#to_make_positive.append(re.escape(param_names[i]))
to_make_positive.append(i)
if len(to_make_positive):
# self.constrain_positive('(' + '|'.join(to_make_positive) + ')')
#self.constrain_positive('(' + '|'.join(to_make_positive) + ')')
self.constrain_positive(np.asarray(to_make_positive))
@ -430,7 +430,7 @@ class model(parameterised):
return 1. / k.variances
def pseudo_EM(self, epsilon=.1, max_EM_iterations=np.inf, **kwargs):
def pseudo_EM(self, epsilon=.1, **kwargs):
"""
TODO: Should this not bein the GP class?
EM - like algorithm for Expectation Propagation and Laplace approximation
@ -453,7 +453,7 @@ class model(parameterised):
alpha = 0
stop = False
while not stop and iteration < max_EM_iterations:
while not stop:
last_approximation = self.likelihood.copy()
last_params = self._get_params()
@ -464,8 +464,8 @@ class model(parameterised):
ll_change = new_ll - last_ll
if ll_change < 0:
self.likelihood = last_approximation # restore previous likelihood approximation
self._set_params(last_params) # restore model parameters
self.likelihood = last_approximation # restore previous likelihood approximation
self._set_params(last_params) # restore model parameters
print "Log-likelihood decrement: %s \nLast likelihood update discarded." % ll_change
stop = True
else: