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gradcheck now fully functional

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Max Zwiessele 2014-02-13 20:24:45 +00:00
parent c84372127f
commit c78ddde6de
1 changed files with 13 additions and 7 deletions
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20
GPy/core/model.py
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@ -410,7 +410,13 @@ class Model(Parameterized):
f1 = self.objective_function(x + dx) f1 = self.objective_function(x + dx)
f2 = self.objective_function(x - dx) f2 = self.objective_function(x - dx)
gradient = self.objective_function_gradients(x) 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)
numerical_gradient = (f1 - f2) / (2 * dx) numerical_gradient = (f1 - f2) / (2 * dx)
global_ratio = (f1 - f2) / (2 * np.dot(dx, np.where(gradient == 0, 1e-32, gradient))) global_ratio = (f1 - f2) / (2 * np.dot(dx, np.where(gradient == 0, 1e-32, gradient)))
@ -448,14 +454,14 @@ class Model(Parameterized):
ret = True ret = True
for i, ind in enumerate(param_list): for i, ind in enumerate(param_list):
xx = x.copy() xx = x.copy()
xx[i] += step xx[ind] += step
f1 = self.objective_function(xx) f1 = self.objective_function(xx)
xx[i] -= 2.*step xx[ind] -= 2.*step
f2 = self.objective_function(xx) f2 = self.objective_function(xx)
print ind
numerical_gradient = (f1 - f2) / (2 * step) numerical_gradient = (f1 - f2) / (2 * step)
ratio = (f1 - f2) / (2 * step * gradient[i]) ratio = (f1 - f2) / (2 * step * gradient[ind])
difference = np.abs((f1 - f2) / 2 / step - gradient[i]) difference = np.abs((f1 - f2) / 2 / step - gradient[ind])
if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance: 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[ind])
@ -466,7 +472,7 @@ class Model(Parameterized):
r = '%.6f' % float(ratio) r = '%.6f' % float(ratio)
d = '%.6f' % float(difference) d = '%.6f' % float(difference)
g = '%.6f' % gradient[i] g = '%.6f' % gradient[ind]
ng = '%.6f' % float(numerical_gradient) 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]) 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 print grad_string