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Nparams > num_params and Nparam_tranformed > num_params_transformed

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Max Zwiessele 2013-06-05 15:29:18 +01:00
parent 35c2a8b521
commit db78b233b8
25 changed files with 119 additions and 121 deletions
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32
GPy/core/model.py
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@ -24,7 +24,7 @@ class model(parameterised):
self.optimization_runs = []
self.sampling_runs = []
self.preferred_optimizer = 'scg'
#self._set_params(self._get_params()) has been taken out as it should only be called on leaf nodes
# self._set_params(self._get_params()) has been taken out as it should only be called on leaf nodes
def _get_params(self):
raise NotImplementedError, "this needs to be implemented to use the model class"
def _set_params(self, x):
@ -65,7 +65,7 @@ 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
@ -147,10 +147,10 @@ class model(parameterised):
if self.priors is not None:
[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):
def optimize_restarts(self, num_restarts=10, robust=False, verbose=True, parallel=False, num_processes=None, **kwargs):
"""
Perform random restarts of the model, and set the model to the best
seen solution.
@ -179,19 +179,19 @@ class model(parameterised):
try:
jobs = []
pool = mp.Pool(processes=num_processes)
for i in range(Nrestarts):
for i in range(num_restarts):
self.randomize()
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()
pool.join()
for i in range(Nrestarts):
for i in range(num_restarts):
try:
if not parallel:
self.randomize()
@ -200,10 +200,10 @@ class model(parameterised):
self.optimization_runs.append(jobs[i].get())
if verbose:
print("Optimization restart {0}/{1}, f = {2}".format(i + 1, Nrestarts, self.optimization_runs[-1].f_opt))
print("Optimization restart {0}/{1}, f = {2}".format(i + 1, num_restarts, self.optimization_runs[-1].f_opt))
except Exception as e:
if robust:
print("Warning - optimization restart {0}/{1} failed".format(i + 1, Nrestarts))
print("Warning - optimization restart {0}/{1} failed".format(i + 1, num_restarts))
else:
raise e
@ -222,7 +222,7 @@ class model(parameterised):
currently_constrained = self.all_constrained_indices()
to_make_positive = []
for s in positive_strings:
for i in self.grep_param_names(".*"+s):
for i in self.grep_param_names(".*" + s):
if not (i in currently_constrained):
to_make_positive.append(i)
if len(to_make_positive):
@ -240,13 +240,13 @@ class model(parameterised):
Gets the gradients from the likelihood and the priors.
"""
self._set_params_transformed(x)
obj_grads = - self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
return obj_grads
def objective_and_gradients(self, x):
self._set_params_transformed(x)
obj_f = -self.log_likelihood() - self.log_prior()
obj_grads = - self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
return obj_f, obj_grads
def optimize(self, optimizer=None, start=None, **kwargs):
@ -315,7 +315,7 @@ class model(parameterised):
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_params())
width = np.array(max([len(p) for p in strs] + [5])) + 4
log_like = self.log_likelihood()
@ -474,8 +474,8 @@ 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: