apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-21 14:05:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

gradcheck fixes are not easy

Browse source
This commit is contained in:
Max Zwiessele 2014-02-13 21:14:08 +00:00
parent c78ddde6de
commit 9af4c34f90
4 changed files with 42 additions and 29 deletions
Download patch file Download diff file Expand all files Collapse all files

53
GPy/core/model.py
View file

@ -411,12 +411,21 @@ class Model(Parameterized):
f2 = self.objective_function(x - dx)
gradient = self.objective_function_gradients(x)
param_list = self._raveled_index_for(target_param)
if self._has_fixes():
param_list = np.intersect1d(np.r_[:self.size][self._fixes_], param_list, True)
if target_param is None:
transformed_index = range(len(x))
else:
transformed_index = self._raveled_index_for(target_param)
if self._has_fixes():
indices = np.r_[:self.size]
which = (transformed_index[:,None]==indices[self._fixes_]).nonzero()[0]
indices -= (~self._fixes_).cumsum()
transformed_index = indices[which]
if transformed_index.size == 0:
print "No free parameters to check"
return
gradient = gradient[transformed_index]
numerical_gradient = (f1 - f2) / (2 * dx)
global_ratio = (f1 - f2) / (2 * np.dot(dx, np.where(gradient == 0, 1e-32, gradient)))
@ -439,40 +448,46 @@ class Model(Parameterized):
separator = '-' * len(header_string[0])
print '\n'.join([header_string[0], separator])
if target_param is None:
param_list = range(len(x))
param_index = range(len(x))
transformed_index = param_index
else:
param_list = self._raveled_index_for(target_param)
param_index = self._raveled_index_for(target_param)
if self._has_fixes():
param_list = np.intersect1d(np.r_[:self.size][self._fixes_], param_list, True)
if param_list.size == 0:
indices = np.r_[:self.size]
which = (param_index[:,None]==indices[self._fixes_][None,:]).nonzero()
transformed_index = (indices-(~self._fixes_).cumsum())[which[1]]
param_index = indices[which[0]]
print param_index, transformed_index
else:
transformed_index = param_index
if param_index.size == 0:
print "No free parameters to check"
return
gradient = self.objective_function_gradients(x)
np.where(gradient == 0, 1e-312, gradient)
ret = True
for i, ind in enumerate(param_list):
for nind, xind in itertools.izip(param_index, transformed_index):
xx = x.copy()
xx[ind] += step
xx[xind] += step
f1 = self.objective_function(xx)
xx[ind] -= 2.*step
xx[xind] -= 2.*step
f2 = self.objective_function(xx)
print ind
numerical_gradient = (f1 - f2) / (2 * step)
ratio = (f1 - f2) / (2 * step * gradient[ind])
difference = np.abs((f1 - f2) / 2 / step - gradient[ind])
ratio = (f1 - f2) / (2 * step * gradient[xind])
difference = np.abs((f1 - f2) / 2 / step - gradient[xind])
if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance:
formatted_name = "\033[92m {0} \033[0m".format(names[ind])
formatted_name = "\033[92m {0} \033[0m".format(names[nind])
ret &= True
else:
formatted_name = "\033[91m {0} \033[0m".format(names[ind])
formatted_name = "\033[91m {0} \033[0m".format(names[nind])
ret &= False
r = '%.6f' % float(ratio)
d = '%.6f' % float(difference)
g = '%.6f' % gradient[ind]
g = '%.6f' % gradient[xind]
ng = '%.6f' % float(numerical_gradient)
grad_string = "{0:<{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name, r, d, g, ng, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4])
print grad_string