Comments (5)
pyDOE3 1.0 is out!
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Hi Rob,
Yes, sampling methods API in SMT is exactly the topic of pyDOE2. In SMT we just provide random, fullfactorial and optimized LHS sampling methods and we depend on pyDOE2 as well. Even if we have a nice API, at the moment the sampling methods are more a utility for testing and showing surrogates capabilities than being the core of the library which is acually the purpose of pyDOE.
So should pyDOE2 not be maintained, we could :
a - Just copy/adapt the relevant part under sampling methods (ie doe_lhs, bbdesign) to just keep current SMT in shape
b - or fork pyDOE2 under SMTOrg and package it as let say pyDOEtoo and just use it,
c - or integrate all pyDOE2 methods under sampling methods, with tests, documentation, etc.
Ignoring the selfish option a), I think we could implement b) and if there is a compelling need maybe gradually implement c) with the help of the community ?
from smt.
If you're willing to accept the management of it, then I'd say option B probably makes the most sense. pyDOE3? 🙂
from smt.
Thank you!
from smt.
Thank you
from smt.
Related Issues (20)
- data-driven Multi-Fidelity Kriging HOT 1
- ValueError by passing an int instead of a float HOT 3
- SMT 2.5 is not available on conda-forge HOT 2
- Error from SMT --> ValueError: setting an array element with a sequence. HOT 4
- Question on new syntax to train GENN (JENN) models HOT 6
- Meta: add "Discussions" to GitHub repo? HOT 1
- Saving models without using pickle? HOT 1
- Number of parameters can be handled by KPLSK HOT 3
- Deprecation warning HOT 1
- Document mapping between function arguments and equations HOT 2
- Hessian computation/Compatibility with dual numbers HOT 4
- SIGBUS (Misaligned Address Error) HOT 1
- negative axis 1 index
- Training two samples take ~480 seconds HOT 3
- OOB access in RMTB HOT 1
- SGP Gradients unsupported HOT 1
- Relevance of the warning `R is too ill conditioned...` HOT 1
- ConfigSpace vs Numpy 2.0 compatibility HOT 1
- Document parameters to improve accuracy of SGP
- Recommendation for surrogate optimization
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