Lets say you are optimizing three parameters,
by grid searching,
you obtain:
f as a function of parameters p1, p2, p3, i.e. f(p1, p2, p3)
where p1 takes value on a set of n1 values, p2 takes value on a set of
n2 values, and p3 takes value on a set of n3 values.
Therefore, you obtain the f's which is of size n1 x n2 x n3...
What's the best way to visualize these data, suppose our goal is to
check the smoothness of the surface?
Thanks a lot!
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 lunamoonmoon (258)
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9/9/2010 6:28:07 PM |
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Luna Moon wrote:
> Lets say you are optimizing three parameters,
> by grid searching,
> you obtain:
>
> f as a function of parameters p1, p2, p3, i.e. f(p1, p2, p3)
>
> where p1 takes value on a set of n1 values, p2 takes value on a set of
> n2 values, and p3 takes value on a set of n3 values.
>
> Therefore, you obtain the f's which is of size n1 x n2 x n3...
>
> What's the best way to visualize these data, suppose our goal is to
> check the smoothness of the surface?
>
> Thanks a lot!
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 Vladimir
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9/9/2010 6:39:54 PM
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On 10-09-09 01:28 PM, Luna Moon wrote:
> Lets say you are optimizing three parameters,
> by grid searching,
> you obtain:
>
> f as a function of parameters p1, p2, p3, i.e. f(p1, p2, p3)
>
> where p1 takes value on a set of n1 values, p2 takes value on a set of
> n2 values, and p3 takes value on a set of n3 values.
>
> Therefore, you obtain the f's which is of size n1 x n2 x n3...
>
> What's the best way to visualize these data, suppose our goal is to
> check the smoothness of the surface?
You might be able to get somewhere by using gradient(), and for each of the
matrices so returned, max() it along an appropriate dimension, and surf() the
results. This will give you an idea of the maximum rate of change looking long
a particular axes.
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Walter
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9/9/2010 7:07:29 PM
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2 Replies
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