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Fix for model gradients

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Alan Saul 2015-04-10 15:03:07 +01:00
parent 5c9587404d
commit 8f34bed6d7
1 changed files with 7 additions and 2 deletions
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9
GPy/core/model.py
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@ -256,7 +256,7 @@ class Model(Parameterized):
else:
optimizer = optimization.get_optimizer(optimizer)
opt = optimizer(start, model=self, max_iters=max_iters, **kwargs)
with VerboseOptimization(self, opt, maxiters=max_iters, verbose=messages, ipython_notebook=ipython_notebook) as vo:
opt.run(f_fp=self._objective_grads, f=self._objective, fp=self._grads)
vo.finish(opt)
@ -371,7 +371,12 @@ class Model(Parameterized):
f1 = self._objective(xx)
xx[xind] -= 2.*step
f2 = self._objective(xx)
df_ratio = np.abs((f1 - f2) / min(f1, f2))
#Avoid divide by zero, if any of the values are above 1e-15, otherwise both values are essentiall
#the same
if f1 > 1e-15 or f1 < -1e-15 or f2 > 1e-15 or f2 < -1e-15:
df_ratio = np.abs((f1 - f2) / min(f1, f2))
else:
df_ratio = 1.0
df_unstable = df_ratio < df_tolerance
numerical_gradient = (f1 - f2) / (2 * step)
if np.all(gradient[xind] == 0): ratio = (f1 - f2) == gradient[xind]