apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-24 14:15:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

added absolute difference check to gradcheck

Browse source
This commit is contained in:
Max Zwiessele 2013-05-03 13:35:41 +01:00
parent 40c9790529
commit 914bdc73d8
1 changed files with 8 additions and 11 deletions
Download patch file Download diff file Expand all files Collapse all files

19
GPy/core/model.py
View file

@ -67,12 +67,12 @@ class model(parameterised):
# check constraints are okay
if isinstance(what, (priors.gamma, priors.log_Gaussian)):
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 == '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)
bad_constraints = np.setdiff1d(self.all_constrained_indices(), constrained_positive_indices)
assert not np.any(which[:, None] == bad_constraints), "constraint and prior incompatible"
unconst = np.setdiff1d(which, constrained_positive_indices)
if len(unconst):
@ -115,12 +115,12 @@ class model(parameterised):
def _transform_gradients(self, g):
x = self._get_params()
for index,constraint in zip(self.constrained_indices, self.constraints):
for index, constraint in zip(self.constrained_indices, self.constraints):
g[index] = g[index] * constraint.gradfactor(x[index])
[np.put(g, i, v) for i, v in [(t[0], np.sum(g[t])) for t in self.tied_indices]]
if len(self.tied_indices) or len(self.fixed_indices):
to_remove = np.hstack((self.fixed_indices+[t[1:] for t in self.tied_indices]))
return np.delete(g,to_remove)
to_remove = np.hstack((self.fixed_indices + [t[1:] for t in self.tied_indices]))
return np.delete(g, to_remove)
else:
return g
@ -207,7 +207,7 @@ class model(parameterised):
"""
Ensure that any variables which should clearly be positive have been constrained somehow.
"""
positive_strings = ['variance','lengthscale', 'precision', 'kappa']
positive_strings = ['variance', 'lengthscale', 'precision', 'kappa']
param_names = self._get_param_names()
currently_constrained = self.all_constrained_indices()
to_make_positive = []
@ -359,10 +359,7 @@ class model(parameterised):
numerical_gradient = (f1 - f2) / (2 * dx)
global_ratio = (f1 - f2) / (2 * np.dot(dx, gradient))
if (np.abs(1. - global_ratio) < tolerance) and not np.isnan(global_ratio):
return True
else:
return False
return (np.abs(1. - global_ratio) < tolerance) or (np.abs(gradient - numerical_gradient).mean() - 1) < tolerance
else:
# check the gradient of each parameter individually, and do some pretty printing
try:
@ -399,7 +396,7 @@ class model(parameterised):
ratio = (f1 - f2) / (2 * step * gradient)
difference = np.abs((f1 - f2) / 2 / step - gradient)
if (np.abs(ratio - 1) < tolerance):
if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance:
formatted_name = "\033[92m {0} \033[0m".format(names[i])
else:
formatted_name = "\033[91m {0} \033[0m".format(names[i])