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renamed opimizer methods to unobscure gradients and objective

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Max Zwiessele 2015-01-12 11:35:57 +00:00
parent ec42011617
commit dff577d22b
1 changed files with 8 additions and 10 deletions
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18
GPy/core/model.py
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@ -151,7 +151,7 @@ class Model(Parameterized):
"""
return -(self._log_likelihood_gradients() + self._log_prior_gradients())
def _grads(self, x):
def _objective_grads(self, x):
"""
Gets the gradients from the likelihood and the priors.
@ -197,7 +197,7 @@ class Model(Parameterized):
return np.inf
return obj
def _objective_grads(self, x):
def _objective_and_grads(self, x):
try:
self.optimizer_array = x
obj_f, obj_grads = self.objective_function(), self._transform_gradients(self.objective_function_gradients())
@ -233,9 +233,7 @@ class Model(Parameterized):
"""
if self.is_fixed:
print 'nothing to optimize'
if self.size == 0:
if self.is_fixed or self.size == 0:
print 'nothing to optimize'
if not self.update_model():
@ -255,7 +253,7 @@ class Model(Parameterized):
optimizer = optimization.get_optimizer(optimizer)
opt = optimizer(start, model=self, **kwargs)
opt.run(f_fp=self._objective_grads, f=self._objective, fp=self._grads)
opt.run(f_fp=self._objective_and_grads, f=self._objective, fp=self._objective_grads)
self.optimization_runs.append(opt)
@ -312,7 +310,7 @@ class Model(Parameterized):
# evaulate around the point x
f1 = self._objective(x + dx)
f2 = self._objective(x - dx)
gradient = self._grads(x)
gradient = self._objective_grads(x)
dx = dx[transformed_index]
gradient = gradient[transformed_index]
@ -358,7 +356,7 @@ class Model(Parameterized):
print "No free parameters to check"
return
gradient = self._grads(x).copy()
gradient = self._objective_grads(x).copy()
np.where(gradient == 0, 1e-312, gradient)
ret = True
for nind, xind in itertools.izip(param_index, transformed_index):
@ -367,8 +365,8 @@ class Model(Parameterized):
f1 = self._objective(xx)
xx[xind] -= 2.*step
f2 = self._objective(xx)
df_ratio = np.abs((f1-f2)/min(f1,f2))
df_unstable = df_ratio<df_tolerance
df_ratio = np.abs((f1 - f2) / min(f1, f2))
df_unstable = df_ratio < df_tolerance
numerical_gradient = (f1 - f2) / (2 * step)
if np.all(gradient[xind] == 0): ratio = (f1 - f2) == gradient[xind]
else: ratio = (f1 - f2) / (2 * step * gradient[xind])