dimensionality-reduction
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Following up on the discussion here, it would be good to document how to get reproducible results with UMAP.
I think we should consider changing random_state in the UMAP constructor to a seed (e.g. 42, like the new transform_seed default) so that UMAP is reproducible by default.
We should document that users can set `ran
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@sirusb, @ttriche: as contributors of PRs to this package, would you like to be acknowledged as such in the Authors@R field of the DESCRIPTION? You don't need to provide an email address, just a suitable identifier, e.g. first name and last name. For reference, the field currently looks like:
c(person("James", "Melville", email = "jlmelville@gmail.com", role = c("aut", "cre")),
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May 13, 2020 - Python
Unless I'm missing something, the implementation (and docs) of llsq don't agree with the statement on the documentation index that data matrices have features as rows and observations as columns.
In the following code from the llsq documentation, the number of observations is 1000, and the number of features is 3, but the observation matrix X has 1000 rows and 3 columns, and the output fr
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Oct 31, 2018 - Jupyter Notebook
It would be nice if it supports Isomap
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Apparently, the absolute path of the Travis build is used (/home/travis/...) instead of the relative path to the current page.
For example, Fs Peptide (in RAM) links (in the bottom) to [this page](http://msmbuilder.org/home/travis/build/msmbuilder/msmbuilder/docs/_build/html/examples/Fs-Peptide-in-RAM/Fs-Peptide-in-RAM.ipynb
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i'm a newbie in programming. I try to use this library. it's very useful for me.
I want to show centroid in K-means clustering. how to show it? thank u so much..