I like D quite a bit, and there's clearly been some convergent evolution (UFCS)
with Nim. The D story with respect to memory management is still unfolding. As
was already said, I think the language could have been designed to make GC
easier, perhaps separating **ref** and **ptr** like Nim. Then
I've used Nim's Docgen to generate a tutorial + documentation.
End result is [here](https://mratsim.github.io/Arraymancer/) which I'm quite
happy with for the time invested.
A few remarks to ease its use:
* It would be nice to prepend the autogenerated "API" doc with rst tutorials.
As
The [NESM](https://github.com/xomachine/NESM) library may also give you a hint.
It [de]serializes strings into|from structure you described. [Documentation
here](https://xomachine.github.io/NESM/)
So the code will be:
from nesm import serializable
serializable:
type
> I guess I could add a NUL character to the end of the input strings, which
> would allow me to cast to cstring on retrieval.
If you can add the '0' character with which C strings are generally terminated,
then you do not need alloc() and copyMem(). Bytes sequences terminated with '0'
looks
Hi Stefan, the database I connected to was populated using strings, so no NUL
terminators. If I have control of database load, I guess I could add a NUL
character to the end of the input strings, which would allow me to cast to
cstring on retrieval.
Thank you for the response...it was helpful!
This recursion unpacking/unrolling trick that gcc does (at call-site if
insulated by call via volatile function ptr, and always inside the recursive
impl) is, in my experience, a rare compiler optimization, but maybe it will
catch on. clang does _neither_. If you `objdump -D` the executable (or
`get_data_ptr` is now public .
For now, I will add the neural network functionality directly in Arraymancer.
The directory structure will probably be:
* src/arraymancer ==> core Tensor stuff
* src/autograd ==> automatic gradient computation (i.e.
I've been following this for a while on GitHub and I think it is a very
impressive project. Nim would be a great language for scientific computing, but
it needs to have the numerical libraries and this is an excellent first step in
creating them.
A couple of questions. First, are you planning
@euant no worries !
@Araq I had to update to the latest devel. It didn`t work with my nim install
from the middle of May.
I checked solutions that could be used to upload to Github Pages easily and was
recommended [Couscous.io](http://couscous.io/).
Workflow seems to be:
* Have a directory
What do you desire as the result?
When it is a Nim string, then you could create a new Nim String
(newString(size)) of at least the desired size, and fill in the characters. To
access the source characters, you may temporary cast your data to a cstring and
access the cstring with the []
I'm testing the LMDB package and I'm stuck. When I retrieve key/value pairs
they are saved to MDB_val structures:
type
Val* = object
mvSize*: csize #*< size of the data item
mvData*: pointer#*< address of the data item
I tried casting the generic
I am using mingw64/clang64 in Msys2. But I found that the exe file build by
clang runs very slower than exe by gcc.
The nim is a fresh build from github source yesterday
$ nim
Nim Compiler Version 0.17.1 (2017-07-07) [Windows: amd64]
Copyright (c) 2006-2017 by Andreas
I am still around and do plan to eventually find time to work further on the
GitBook project I started, just been super busy at work for the last several
months.
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