[Numpy-discussion] Combining covariance and correlation coefficient into one numpy.cov call
Mathew S. Madhavacheril
mathewsyriac at gmail.com
Wed Oct 26 14:03:36 EDT 2016
On Wed, Oct 26, 2016 at 1:46 PM, Stephan Hoyer <shoyer at 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.
> On Wed, Oct 26, 2016 at 10:27 AM, Mathew S. Madhavacheril <
> mathewsyriac at gmail.com> wrote:
>> Hi all,
>> I posted a pull request:
>> 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.
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