Hello,
indeed I was looking for the cartesian product.
I timed the two stackoverflow answers and the winner is not quite as clear:
n_elements:10 cartesian 0.00427 cartesian2 0.00172
n_elements: 100 cartesian 0.02758 cartesian2 0.01044
n_elements: 1000 cartesian 0.97628 cartesian2
In order to make sure all my random number generators have good
independence, it is a good practice to use a single shared instance (because
it is already known to have good properties). A less-desirable alternative
is to used rng's seeded with different starting states - in this case the
Hi all,
I wanted to let the community know that we are currently hiring 3 full time
software engineers to work full time on Project Jupyter/IPython. These
positions will be in my group at Cal Poly in San Luis Obispo, CA. We are
looking for frontend and backend software engineers with lots of
Roland Schulz wrote:
Hi,
I think the best way to solve this issue to not use a state at all. It is
fast, reproducible even in parallel (if wanted), and doesn't suffer from
the shared issue. Would be nice if numpy provided such a stateless RNG as
implemented in Random123:
Hi,
I think the best way to solve this issue to not use a state at all. It is
fast, reproducible even in parallel (if wanted), and doesn't suffer from
the shared issue. Would be nice if numpy provided such a stateless RNG as
implemented in Random123:
On Tue, May 12, 2015 at 12:41 PM, Pierson, Oliver C o...@gatech.edu wrote:
Hi All,
Awhile back I had written some code to solve Volterra integral equations
(integral equations where one of the integration bounds is a variable).
The code is available on Github
Hi All,
Awhile back I had written some code to solve Volterra integral equations
(integral equations where one of the integration bounds is a variable). The
code is available on Github (https://github.com/oliverpierson/volterra). Just
curious if there'd be any interest in adding this to
I'm totally in favor of the 'gridspace(linspaces)' version, as you probably end
up wanting to create grids of other things than linspaces (e.g. a logspace grid,
or a grid of random points etc.).
It should be called somewhat different though. Maybe 'cartesian(arrays)'?
Best,
Johannes
Quoting
Hi all,
I'm pleased to announce the availability of the first beta release of Scipy
0.16.0. Please try this beta and report any issues on the Github issue
tracker or on the scipy-dev mailing list.
This first beta is a source-only release; binary installers will follow
(probably next week).
On Tue, May 12, 2015 at 1:17 AM, Stefan Otte stefan.o...@gmail.com wrote:
Hello,
indeed I was looking for the cartesian product.
I timed the two stackoverflow answers and the winner is not quite as clear:
n_elements:10 cartesian 0.00427 cartesian2 0.00172
n_elements: 100
Hey,
here is an ipython notebook with benchmarks of all implementations (scroll
to the bottom for plots):
https://github.com/sotte/ipynb_snippets/blob/master/2015-05%20gridspace%20-%20cartesian.ipynb
Overall, Jaime's version is the fastest.
On Tue, May 12, 2015 at 2:01 PM Jaime Fernández
Agreed that indexing functions should return bare `ndarray`. Note that in
Jaime's PR one can override it anyway by defining __nonzero__. -- Marten
On Sat, May 9, 2015 at 9:53 PM, Stephan Hoyer sho...@gmail.com wrote:
With regards to np.where -- shouldn't where be a ufunc, so subclasses or
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