ChanServ changed the topic of #mlpack to: "mlpack: a fast, flexible machine learning library :: We don't always respond instantly, but we will respond; please be patient :: Logs at http://www.mlpack.org/irc/
< brongulusGitter[> Wouldn't it be better if you opened a PR and shared a link to that, would make for better and easier code reviews too?
< ShahAnwaarKhalid> I was actually talking about something that's already been implemented.
< jonpsy[m]> is there a way to convert ```std::vector<arma::Col<ElemType>>``` to ```arma::Mat<ElemType>``` without copying memory?
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< Shadow3049> Hi, I am shivank. Is anyone working on the parallel profiling project idea?
< Shadow3049> If so, what tool are you using for profiling?
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< jeffin143[m]> Rishabh Garg (Gitter): haha , no I just brought it here so that everyone can have a smile about it
< jeffin143[m]> There are many instances before where we have talked about the huge amount of ram mlpack takes 😂😂😂
< jeffin143[m]> So there is no hurt in it
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< ABHINAVANAND[m]> <ABHINAVANAND[m] "I was going through the code of "> `arma::mat input = arma::mat(28, 1);`
< zoq> jonpsy[m]: You can use stdvector.data() and then you can use the advanced constructor.
< zoq> ShahAnwaarKhalid: Can you add a comment on the dual optimizer PR, that way I don't miss to take a look :)
< jonpsy[m]> I'll probably have to wrap that in a class
< ShahAnwaarKhalid> zoq: I opened an issue. Maybe, we can discuss there?
< rcurtin[m]> jonpsy: the only problem is, to convert to a `Mat` without copying memory, we would need the guarantee that the data of every `arma::Col<>` is contiguous---and we don't have that guarantee. so unless you were able to set up the `std::vector` very specifically so that each `Col` was using contiguous blocks of memory, then a copy would be unavoidable
< zoq> Ohh right.
< jonpsy[m]> <rcurtin[m] "jonpsy: the only problem is, to "> There's goes my dream of micro-optimization :)
< jonpsy[m]> * There goes my dream of micro-optimization :)
< rcurtin[m]> yeah, I know how you feel, I have been bitten by that too many times...
< rcurtin[m]> if I remember right, std::vector doesn't make any guarantees of data being contiguous either, so even wrapping an Armadillo matrix around the memory of a `std::vector<double>` is not safe across all platforms... but I may not be remembering exactly right
< jonpsy[m]> Yeah std::array is our buddy
< jonpsy[m]> Although, if we preallocate, will it be contiguous?
< jonpsy[m]> Argh.... I think it's giddied up, might confused people who would read code
< rcurtin[m]> I'm not sure what guarantees preallocation would give; we'd have to read the standard to see if we can count on it
< jonpsy[m]> Okay here's an idea, I'm working hard on my proposal atm (codes and all) when I'm done I'll start a PR
< jonpsy[m]> So maybe we can optimize when we have better context of the whole algorithm.
< zoq> ABHINAVANAND[m]: Same thing for stride yes, but we don't support asymetric stride right now.
< jonpsy[m]> > if I remember right, std::vector doesn't make any guarantees of data being contiguous either, so even wrapping an Armadillo matrix around the memory of a `std::vector<double>` is not safe across all platforms... but I may not be remembering exactly right
< jonpsy[m]> rcurtin So I did some digging. It seems the standard do [guarantee](https://stackoverflow.com/a/849190) it being contiguous. I think it makes sense because, as you said, converting ```Col``` is possible only if you can guarantee your elements are contigous.
< rcurtin[m]> that's good to know, but even if you have a `std::vector<arma::vec>`, the memory for each `arma::vec` isn't contiguous, because each `arma::vec` allocates its own memory somewhere else
< rcurtin[m]> so this does mean that you can take a `std::vector<double>` and wrap an `arma::vec` around it
< rcurtin[m]> but... ownership of the memory gets a little ugly... I don't know that you can tell a `std::vector<double>` that it no longer owns its memory... so the `arma::vec` could only be used if we knew that the `std::vector<double>` would never be freed or go out of scope
< jonpsy[m]> hm, a lot to take in
< zoq> What is the reason to use std instead of arma in the first place?
< jonpsy[m]> Personally, my first rule of work is to use library provided functions over any other
< jonpsy[m]> BUT, I'm using vector because columns are randomly dropped and inserted in the algo
< jonpsy[m]> * BUT, I'm using std::vector because columns are randomly dropped and inserted in the algo
< jonpsy[m]> * Personally, my first rule of work is to use library-provided-functions over any other
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< travis-ci> mlpack/ensmallen#1271 (2.16.2 - 259da3d : Ryan Curtin): The build passed.
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< zoq> But I guess your type is std::vector<arma::mat>?
< jonpsy[m]> yeah
< jonpsy[m]> (its mostly vec so i use them interchangeably :) )
< zoq> I guess I would have to look into the code, but wondering why you would have to convert it later.
< jonpsy[m]> i was just discussin a possibility, (for SPEA-II) haven't written a concrete code yet
< rcurtin[m]> Yeah, I would generally recommend using an `arma::cube` instead of `std::vector` where you can
< jonpsy[m]> can i drop columns from a random index?
< jonpsy[m]> nvm, i'll figure that out. Interesting discussion though :)
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< zoq> OleksandrNikolsk: Probably a timeout, you can increase the 5000 here - https://github.com/zoq/gym_tcp_api/blob/master/lib/server.ex#L35
< zoq> line 61 as well
< zoq> Or remove the third parameter
< zoq> Or use the python server, that works as well