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numerical global diff in gradcheck

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Max Zwiessele 2014-03-05 12:50:46 +00:00
parent b1ebeea912
commit 988bad88a3
1 changed files with 2 additions and 2 deletions
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4
GPy/core/model.py
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@ -300,9 +300,9 @@ class Model(Parameterized):
gradient = gradient[transformed_index]
global_ratio = (f1 - f2) / (2 * np.dot(dx, np.where(gradient == 0, 1e-32, gradient)))
num_grad =(np.abs((f1-f2)/-(2*dx)*np.where(gradient == 0, 1e-32, gradient))).mean()
gloabl_diff = (f1 - f2) - (2 * np.dot(dx, np.where(gradient == 0, 1e-32, gradient)))
return (np.abs(1. - global_ratio) < tolerance) or (num_grad < tolerance)
return (np.abs(1. - global_ratio) < tolerance) or (np.abs(gloabl_diff) < tolerance)
else:
# check the gradient of each parameter individually, and do some pretty printing
try: