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Hello,
Thanks for your amazing works.When I use the GPyOpt, I found a question,which makes me confused. I defined a func with a parameter r. When I run the BayesianOptimization(), the results shows t…
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Moved to https://github.com/SheffieldML/GPyOpt/issues/237
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Wondering if anyone else has observed this. I have 128GB RAM computer and trying to optimize ~6 parameters. Initially the function call triggers the child function and next set of parameters within a …
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Hi,
I see an example in here: https://www.blopig.com/blog/wp-content/uploads/2019/10/GPyOpt-Tutorial1.html
The codes are:
#Import Modules
#GPyOpt - Cases are important, for some reason
import G…
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Hello everyone,
In my understanding, .x_opt returns the optimum of a BayesianOptimization object, where the optimum is chosen from the set of objective function evaluations, e.g.
`self.x_opt = s…
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A dump of solvers/algorithms that might be useful to have in pagmo:
- [x] https://projects.coin-or.org/Ipopt ipopt (this was available in pagmo 1.x, potential issues compiling it on windows as it r…
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Hi,
I am also trying to apply the Bayesian Optimization in CNN. I have looked through your codes. It seems that you use Grid Search for hyperparameters. I am not sure whether I understand right. Wou…
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I've been trying out the search functionality at juliahub.com, and find that the search is not very good. If I try a few things where I know what I would like to find, the desired result often ends up…
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The following example demonstrates the issue:
```
import GPyOpt
from numpy.random import seed
seed(1234)
def myf(args):
print(args)
x, y = args[0]
return (2*x)**2 + y
boun…
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