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Embedded bias initial implementation #114
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Updated to last state
…rcones/probeye into koh_implementation
This update also implements the proposed solution of Issue #90 . |
Tests cover is not 100% on the new implementations as there probably will be changes in the definition of the bias in the near future. The lack of cover is precisely on that front, the checks that some parameter definitions are correctly imported to the models. Nevertheless, they can be tested if considered necessary. |
docs/examples/bias.py
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b_dist = chaospy.Normal(0.0, b) | ||
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# generate the polynomial chaos expansion | ||
expansion = chaospy.generate_expansion(self.pce_order, bias_dist) |
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It should be b_dist instead of bias_dist
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I missed that totally, thanks!
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Thanks Daniel,
I had problems running the example file, because of a typo (see comment)
Furthermore, during creating a new env I run into an error with python 3.13. Changing to 3.10 solved it.
bias_std -> b_dist
Do you remember what whas the error with python 3.13? I think probeye had some problems with it at some point, not necessarily the embedded bias implementation only. |
Yes it was something with the required packages (but I didn't saved the message sorry). So I think it's a general problem of probeye and not with your implementation. |
…into embedded_bias
This is the initial implementation of embedded bias approaches to be integrated in probeye. It provides a working implementation for the computation of inverse problems with embedded bias.
chaospy
has been added as a dependency. Probably should be an optional one, but kept as compulsory for now.mypy
was updated to the latest version