Mean_var, mean_std and tests are now ready.
(https://github.com/soundappraisal/numpy/tree/stdmean-dev-001)
Some decisions made during implementation:
- the output shape of mean follows the output shape of the variance or the
standard deviation. So it responds in the same way to the keepdims fl
On 2/6/23 13:09, Ronald van Elburg wrote:
Mean_var, mean_std and tests are now ready.
(https://github.com/soundappraisal/numpy/tree/stdmean-dev-001)
Some decisions made during implementation:
- the output shape of mean follows the output shape of the variance or the
standard deviation. So
On 2/6/23 13:41, Matti Picus wrote:
On 2/6/23 13:09, Ronald van Elburg wrote:
Mean_var, mean_std and tests are now ready.
(https://github.com/soundappraisal/numpy/tree/stdmean-dev-001)
Some decisions made during implementation:
- the output shape of mean follows the output shape of the
v
I think I left those aspects of the implementation untouched. But having
someone more experienced look at it is probably a good idea.
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Aha, the unnecessary copy mentioned in the
https://dbs.ifi.uni-heidelberg.de/files/Team/eschubert/publications/SSDBM18-covariance-authorcopy.pdf.
paper is a copy of the input. Here it is about discarding a valuable output
(the mean) and then calculating that result separately.
Not throwing the
Dear mentors,
I have been trying to solve this issue(for round_). I found that this(round_)
is not included into the latest documentation of version 1.24 and it was last
time introduced into version 1.13 documentation. As I can see round_ is working
for 1.24.2 and it will be removed in version
On Fri, 02 Jun 2023 11:47:14 -
"Ronald van Elburg" wrote:
> Aha, the unnecessary copy mentioned in the
> https://dbs.ifi.uni-heidelberg.de/files/Team/eschubert/publications/SSDBM18-covariance-authorcopy.pdf.
> paper is a copy of the input. Here it is about discarding a valuable output
> (
On Fri, Jun 2, 2023 at 1:51 PM Ronald van Elburg
wrote:
> Aha, the unnecessary copy mentioned in the
> https://dbs.ifi.uni-heidelberg.de/files/Team/eschubert/publications/SSDBM18-covariance-authorcopy.pdf.
> paper is a copy of the input. Here it is about discarding a valuable output
> (the mean)
I am agnostic to the order of those changes. Also this is my first attempt to
contribute to numpy, so I am not aware of all the ongoing discussions. I'll try
to read the issue you just mentioned.
But in the code I rewrote replacing _mean_var with a faster version would
benefit var, std, mean_va
I had a closer look at the paper. When I have more brain and time I may check
the mathematics. The focus is however more on streaming data, which is an
application with completely different demands. I think that here we can not
afford to sample the data, which is an option in streaming database
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