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update docstring for checkgrad

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Zhenwen Dai 2014-11-05 16:42:28 +00:00
parent 3a97e253bf
commit 1dbe3e34b0
2 changed files with 14 additions and 4 deletions
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4
GPy/core/model.py
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@ -279,6 +279,10 @@ class Model(Parameterized):
Note:-
The gradient is considered correct if the ratio of the analytical
and numerical gradients is within <tolerance> of unity.
The *dF_ratio* indicates the limit of numerical accuracy of numerical gradients.
If it is too small, e.g., smaller than 1e-12, the numerical gradients are usually
not accurate enough for the tests (shown with blue).
"""
x = self.optimizer_array.copy()

14
GPy/core/parameterization/parameter_core.py
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@ -294,7 +294,7 @@ class Gradcheckable(Pickleable, Parentable):
def __init__(self, *a, **kw):
super(Gradcheckable, self).__init__(*a, **kw)
def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3):
def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3, df_tolerance=1e-12):
"""
Check the gradient of this parameter with respect to the highest parent's
objective function.
@ -305,11 +305,17 @@ class Gradcheckable(Pickleable, Parentable):
:param bool verbose: whether each parameter shall be checked individually.
:param float step: the stepsize for the numerical three point gradient estimate.
:param flaot tolerance: the tolerance for the gradient ratio or difference.
:param float tolerance: the tolerance for the gradient ratio or difference.
:param float df_tolerance: the tolerance for df_tolerance
Note:-
The *dF_ratio* indicates the limit of accuracy of numerical gradients.
If it is too small, e.g., smaller than 1e-12, the numerical gradients
are usually not accurate enough for the tests (shown with blue).
"""
if self.has_parent():
return self._highest_parent_._checkgrad(self, verbose=verbose, step=step, tolerance=tolerance)
return self._checkgrad(self, verbose=verbose, step=step, tolerance=tolerance)
return self._highest_parent_._checkgrad(self, verbose=verbose, step=step, tolerance=tolerance, df_tolerance=df_tolerance)
return self._checkgrad(self, verbose=verbose, step=step, tolerance=tolerance, df_tolerance=df_tolerance)
def _checkgrad(self, param, verbose=0, step=1e-6, tolerance=1e-3):
"""