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rename dK_dtheta > gradients_X

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Max Zwiessele 2014-02-11 12:20:57 +00:00
parent 6a1c700c03
commit b944427733
35 changed files with 136 additions and 129 deletions
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44
GPy/core/model.py
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@ -260,17 +260,18 @@ class Model(Parameterized):
"""
positive_strings = ['variance', 'lengthscale', 'precision', 'kappa', 'sensitivity']
# param_names = self._get_param_names()
for s in positive_strings:
paramlist = self.grep_param_names(".*"+s)
for param in paramlist:
for p in param.flattened_parameters:
rav_i = set(self._raveled_index_for(p))
for constraint in self.constraints.iter_properties():
rav_i -= set(self._constraint_indices(p, constraint))
rav_i -= set(np.nonzero(self._fixes_for(p)!=UNFIXED)[0])
ind = self._backtranslate_index(p, np.array(list(rav_i), dtype=int))
if ind.size != 0:
p[np.unravel_index(ind, p.shape)].constrain_positive()
# for s in positive_strings:
# paramlist = self.grep_param_names(".*"+s)
# for param in paramlist:
# for p in param.flattened_parameters:
# rav_i = set(self._raveled_index_for(p))
# for constraint in self.constraints.iter_properties():
# rav_i -= set(self._constraint_indices(p, constraint))
# rav_i -= set(np.nonzero(self._fixes_for(p)!=UNFIXED)[0])
# ind = self._backtranslate_index(p, np.array(list(rav_i), dtype=int))
# if ind.size != 0:
# p[np.unravel_index(ind, p.shape)].constrain_positive(warning=warning)
# if paramlist:
# self.__getitem__(None, paramlist).constrain_positive(warning=warning)
# currently_constrained = self.all_constrained_indices()
@ -405,13 +406,13 @@ class Model(Parameterized):
dx = step * np.sign(np.random.uniform(-1, 1, x.size))
# evaulate around the point x
f1, g1 = self.objective_and_gradients(x + dx)
f2, g2 = self.objective_and_gradients(x - dx)
f1 = self.objective_function(x + dx)
f2 = self.objective_function(x - dx)
gradient = self.objective_function_gradients(x)
numerical_gradient = (f1 - f2) / (2 * dx)
global_ratio = (f1 - f2) / (2 * np.dot(dx, np.where(gradient==0, 1e-32, gradient)))
return (np.abs(1. - global_ratio) < tolerance) or (np.abs(gradient - numerical_gradient).mean() < tolerance)
else:
# check the gradient of each parameter individually, and do some pretty printing
@ -430,7 +431,6 @@ class Model(Parameterized):
header_string = map(lambda x: '|'.join(x), [header_string])
separator = '-' * len(header_string[0])
print '\n'.join([header_string[0], separator])
if target_param is None:
param_list = range(len(x))
else:
@ -438,19 +438,19 @@ class Model(Parameterized):
if self._has_fixes():
param_list = np.intersect1d(param_list, np.r_[:self.size][self._fixes_], True)
if not np.any(param_list):
if param_list.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):
xx = x.copy()
xx[ind] += step
f1, g1 = self.objective_and_gradients(xx)
f1 = self.objective_function(xx)
xx[ind] -= 2.*step
f2, g2 = self.objective_and_gradients(xx)
f2 = self.objective_function(xx)
numerical_gradient = (f1 - f2) / (2 * step)
ratio = (f1 - f2) / (2 * step * gradient[ind])
@ -458,15 +458,19 @@ class Model(Parameterized):
if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance:
formatted_name = "\033[92m {0} \033[0m".format(names[ind])
ret &= True
else:
formatted_name = "\033[91m {0} \033[0m".format(names[ind])
ret &= False
r = '%.6f' % float(ratio)
d = '%.6f' % float(difference)
g = '%.6f' % gradient[ind]
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
return ret
def input_sensitivity(self):
"""
return an array describing the sesitivity of the model to each input