FYI: My article with @9il "Interfacing D with C and Fortran" is now up http://www.active-analytics.com/blog/interface-d-with-c-fortran/

Thanks to those that made suggestions that informed the article.

You may also want to check out "A quick look at D" article http://www.active-analytics.com/blog/a-quick-look-at-d/ which is a "flyby" visitation of D from a numeric point of view.

Thanks

p.s. Sorry for originally positing this in the General Forum, it should be here instead

---
DP

On Thursday, 13 April 2017 at 09:19:05 UTC, data pulverizer wrote:
> FYI: My article with @9il "Interfacing D with C and Fortran" is now up http://www.active-analytics.com/blog/interface-d-with-c-fortran/
>
> Thanks to those that made suggestions that informed the article.
>
> You may also want to check out "A quick look at D" article http://www.active-analytics.com/blog/a-quick-look-at-d/ which is a "flyby" visitation of D from a numeric point of view.
>
> Thanks
>
> p.s. Sorry for originally positing this in the General Forum, it should be here instead
>
> ---
> DP

Looks good.

Also, I tried to add the blog to feedly, but it wasn't having any of it. You might want to create an RSS feed for it.

On Thursday, 13 April 2017 at 09:19:05 UTC, data pulverizer wrote:
> FYI: My article with @9il "Interfacing D with C and Fortran" is now up http://www.active-analytics.com/blog/interface-d-with-c-fortran/
>
> Thanks to those that made suggestions that informed the article.
>
> You may also want to check out "A quick look at D" article http://www.active-analytics.com/blog/a-quick-look-at-d/ which is a "flyby" visitation of D from a numeric point of view.
>
> Thanks
>
> p.s. Sorry for originally positing this in the General Forum, it should be here instead
>
> ---
> DP

https://www.reddit.com/r/programming/comments/655ilu/interfacing_d_with_c_and_fortran/

On Thursday, 13 April 2017 at 13:40:48 UTC, Mike Parker wrote:

> https://www.reddit.com/r/programming/comments/655ilu/interfacing_d_with_c_and_fortran/

On lobste.rs: https://lobste.rs/s/pidpz1/interfacing_d_with_c_fortran_use_d_as

On Thursday, 13 April 2017 at 11:23:32 UTC, jmh530 wrote:
>
> Looks good.
>
> Also, I tried to add the blog to feedly, but it wasn't having any of it. You might want to create an RSS feed for it.

Just an FYI, I was looking at another post

http://www.active-analytics.com/blog/fitting-glm-with-large-datasets/

and the top part is a little confusing because the code below switches it up to do CC=BB*AA instead of CC=AA*BB.

If I'm understanding it correctly, you originally have an mXn matrix times an nXp matrix, then you partition the left hand side to be mXk and the right hand to kXp and loop through and add them up. However, at the top you say that A (which at the top is the left hand variable) is split up by rows. However, the code clearly splits the left hand side (B here) by columns (BB is 5X100 and B is a 10-dimensional list of 5X10 matrices).

On Friday, 14 April 2017 at 17:55:54 UTC, jmh530 wrote:
> On Thursday, 13 April 2017 at 11:23:32 UTC, jmh530 wrote:
>
> Just an FYI, I was looking at another post
>
> http://www.active-analytics.com/blog/fitting-glm-with-large-datasets/
>
> and the top part is a little confusing because the code below switches it up to do CC=BB*AA instead of CC=AA*BB.
>
> If I'm understanding it correctly, you originally have an mXn matrix times an nXp matrix, then you partition the left hand side to be mXk and the right hand to kXp and loop through and add them up. However, at the top you say that A (which at the top is the left hand variable) is split up by rows. However, the code clearly splits the left hand side (B here) by columns (BB is 5X100 and B is a 10-dimensional list of 5X10 matrices).

Sorry, I didn't see your question until now. That article was something I worked on years earlier. The main principle is that you split and aggregate over repeated indices. The code is intended to be illustrative of the principle. Don't get too hung up with equating the the code symbols with equation - the principle is the main thing. I wrote an R package where the important bits is written in C++: https://cran.r-project.org/web/packages/bigReg/index.html using the principle in GLM

MORE IMPORTANTLY, however is that that algorithm is not efficient! At least not as efficient as gradient descent or even better stochastic gradient descent or their respective modifications.