Hi all,
I have implemented a proposed enhancement for the np.gradient function that
allows to compute the gradient on non uniform grids. (PR:
https://github.com/numpy/numpy/pull/8446)
The proposed implementation has a behaviour/signature is similar to that of
Matlab/Octave. As argument it can take
Hi Nadav,
I may be wrong, but I think that the result of the current implementation
is actually the expected one.
Using you example: probabilities for item 1, 2 and 3 are: 0.2, 0.4 and 0.4
P([1,2]) = P([2] | 1st=[1]) P([1]) + P([1] | 1st=[2]) P([2])
Now, P([1]) = 0.2 and P([2]) = 0.4. However:
P
2017-01-17 22:13 GMT+01:00 Nadav Har'El :
>
> On Tue, Jan 17, 2017 at 7:18 PM, aleba...@gmail.com
> wrote:
>
>> Hi Nadav,
>>
>> I may be wrong, but I think that the result of the current implementation
>> is actually the expected one.
>> Using you
2017-01-18 9:35 GMT+01:00 Nadav Har'El :
>
> On Wed, Jan 18, 2017 at 1:58 AM, aleba...@gmail.com
> wrote:
>
>>
>>
>> 2017-01-17 22:13 GMT+01:00 Nadav Har'El :
>>
>>>
>>> On Tue, Jan 17, 2017 at 7:18 PM, aleba...@gmail.com
>>
2017-01-23 15:33 GMT+01:00 Robert Kern :
> On Mon, Jan 23, 2017 at 6:27 AM, Anne Archibald
> wrote:
> >
> > On Wed, Jan 18, 2017 at 4:13 PM Nadav Har'El wrote:
> >>
> >> On Wed, Jan 18, 2017 at 4:30 PM, wrote:
> >>>
> Having more sampling schemes would be useful, but it's not possible
> to
