On Wed, Oct 26, 2016 at 1:46 PM, Stephan Hoyer <sho...@gmail.com> wrote:
> I wonder if the goals of this addition could be achieved by simply adding > an optional `cov` argument > to np.corr, which would provide a pre-computed covariance. > That's a fair suggestion which I'm happy to switch to. This eliminates the need for two new functions. I'll add an optional `cov = False` argument to numpy.corrcoef that returns a tuple (corr, cov) instead. > > Either way, `covcorr` feels like a helper function that could exist in > user code rather than numpy proper. > The user would have to re-implement the part that converts the covariance matrix to a correlation coefficient. I made this PR to avoid that code duplication. Mathew > > On Wed, Oct 26, 2016 at 10:27 AM, Mathew S. Madhavacheril < > mathewsyr...@gmail.com> wrote: > >> Hi all, >> >> I posted a pull request: >> https://github.com/numpy/numpy/pull/8211 >> >> which adds a function `numpy.covcorr` that calculates both >> the covariance matrix and correlation coefficient with a single >> call to `numpy.cov` (which is often an expensive call for large >> data-sets). A function `numpy.covtocorr` has also been added >> that converts a covariance matrix to a correlation coefficent, >> and `numpy.corrcoef` has been modified to call this. The >> motivation here is that one often needs the covariance for >> subsequent analysis and the correlation coefficient for >> visualization, so instead of forcing the user to write their own >> code to convert one to the other, we want to allow both to >> be obtained from `numpy` as efficiently as possible. >> >> Best, >> Mathew >> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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