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Add a simple implementation for linear regression, using the optimization methods discussed in #3. I suggest starting with a very simple implementation, possibly including regularization.
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At the moment, our only optimized decompression is for `SparseMatrixCSC` in `:direct` mode: we store a vector of `compressed_indices` such that `nonzeros(A) = vec(B)[compressed_indices]`.
We can prob…
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For many applications, one needs to pass a function that evaluates the cost (or the log-posterior) from the control vector. For instance:
1. MCMC sampling: samples=MCMC(logpost_function, starting_poi…
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Function `par_interpolate` has weird behavior for small domain sizes. In particular, it is faster when (some of) its subroutines are sequential.
There is a lot of potential for optimization here. I…
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Hi there,
Thanks for the great package. I am currently using default GPyOpt.methods.BayesianOptimization() with default GP model for optimizing a set of parameters x (cost function is expensive to …
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Would it be possible to make `propertyChanged` / `propertyChanging` default value `null`, and generate the code only when `OnXXXChanged` / `OnXXXChanging` method is implemented?
It's purely an opti…
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Greetings,
I'm just wondering if you have thought about submitting the two algorithms as optimization methods for scipy? Those would make a great addition.
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### Description
## Background
The semantics of left semi join involve retaining rows from the probe side that have matching join keys with the build side. In constrast, anti join retains rows from t…
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- [ ] which label to use for "parameter optimization"? "optimization" is taken by "CPU performance optimizations", NuPIC used "swarming" which is more specific than necessary, but already used in the …
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1. The documentation at http://gpyopt.readthedocs.io/en/latest/GPyOpt.methods.html#module-GPyOpt.methods.bayesian_optimization has a typo in **Model_type** - it is not "warperdGP" but "warpedGP"
2. U…