[Numpy-discussion] Combining covariance and correlation coefficient into one numpy.cov call
njs at pobox.com
Wed Oct 26 14:56:41 EDT 2016
On Wed, Oct 26, 2016 at 11:13 AM, Stephan Hoyer <shoyer at gmail.com> wrote:
> On Wed, Oct 26, 2016 at 11:03 AM, Mathew S. Madhavacheril
> <mathewsyriac at gmail.com> wrote:
>> 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.
> With the API I was envisioning (or even your proposed API, for that matter),
> this function would only be a few lines, e.g.,
> def covcorr(x):
> cov = np.cov(x)
> corr = np.corrcoef(x, cov=cov)
IIUC, if you have a covariance matrix then you can compute the
correlation matrix directly, without looking at 'x', so corrcoef(x,
cov=cov) is a bit odd-looking. I think probably the API that makes the
most sense is just to expose something like the covtocorr function
(maybe it could have a less telegraphic name?)? And then, yeah, users
can use that to build their own covcorr or whatever if they want it.
Nathaniel J. Smith -- https://vorpus.org
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