mlpack IRC logs, 2017-03-07

Logs for the day 2017-03-07 (starts at 0:00 UTC) are shown below.

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--- Log opened Tue Mar 07 00:00:31 2017
01:32 -!- topology [3d0c4bd1@gateway/web/freenode/ip.61.12.75.209] has joined #mlpack
01:42 < topology> rcurtin: i am sorry i couldn't complete what i was saying yesterday.
01:42 < topology> in absolute valued approximate KDE, we require that the difference between our approximation and the true density estimate for each point be less than some parameter 'e'.
01:42 < topology> from this, i can understand why we should prune when the bound(for the maximum difference between 2 kernel values for a given combination of nodes) B >= e/|Sr|
01:43 < topology> where Sr is the reference set.
01:43 < topology> this is what is used in the ICML 2013 paper as well.
01:43 < topology> but the KDE score() function in your thesis prunes when the same bound is less than the parameter 'e'.
01:43 < topology> i am not able to understand how we reached that condition?
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05:14 < ravi__> hello
05:15 < ravi__> Am i talking to someone from mlpack
05:15 < ravi__> Sir
05:15 < ravi__> I tired mailing you regarding Gsoc
05:15 < ravi__> but it said only subscribers can mail
05:15 < ravi__> ??? please help
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07:36 < ravi__> ?HELP
07:36 < ravi__> ?/
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08:04 < ravi__> Hey! people , seniors , sirs and Coders .... This is Ravi sadhwani ... want to know about GSOC and all ... Can anyone please help me out
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13:17 < dashwood> heyy guys i am facing an error while running linear regression code
13:17 < dashwood> fatal error: 'mlpack/core.hpp' file not found
13:18 < dashwood> which directory should i place the mlpack directory
13:18 < dashwood> i am using Mac OS
13:22 < zoq> dashwood: You have to build mlpack, it's not header only: http://www.mlpack.org/docs/mlpack-2.1.1/doxygen.php?doc=build.html#build
13:22 < zoq> dashwood: Since you are on mac you can probably use homebrew to install all dependencies? There is also an mlpack package in homebrew science, if you just like to use mlpack e.g. linear regression.
13:23 < dashwood> i already installed all dependencies
13:23 < dashwood> armadillo
13:23 < dashwood> and everything thats mentioned
13:24 < dashwood> and it has installed it into /usr/include/mlpack
13:24 < dashwood> but for some reason my g++ compiler is showing error
13:25 < zoq> So I guess what you like to do is to build against mlpack and use the linear regression code in your own code?
13:27 < dashwood> yes i want to perform linear regression
13:30 < zoq> hm, just asking because you could use the executable that should be located in '/usr/local/bin/mlpack_linear_regression -h' or if you are in 'build bin/mlpack_linear_regression -h'
13:30 < zoq> anyway, how does your g++ command look like?
13:32 < dashwood> i wrote the code in a test.cpp file...and i am using g++ test.cpp
13:33 < zoq> So, yeah you have to build against mlpack something like: g++ test.cpp -std=c++11 -I/path/to/mlpack/build/include/ -L/path/to/mlpack/build/ -lmlpack -larmadillo
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13:41 < dashwood> fatal error: 'mlpack/methods/linear_regression/linear_regression.hpp' file not found i still get this error
13:41 < dashwood> i used this g++ command
13:41 < dashwood> g++ test.cpp -std=c++11 -I/usr/local/include/mlpack/build/include/ -L/usr/local/include/mlpack/build/ -lmlpack -larmadillo
13:43 < zoq> Who does your test.cpp look like? Maybe you can post it on pastebin?
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13:45 < dashwood> one minute
13:46 < dashwood> http://ideone.com/Z156X2
13:46 < dashwood> thats the code
13:48 < zoq> The link doesn't work for me.
13:49 < dashwood> http://ideone.com/lSKA3V
13:49 < dashwood> try this
13:52 < zoq> okay, that looks good, so can you check if you can find 'linear_regression.hpp' in /usr/local/include/mlpack/build/include/ and 'libmlpack' in /usr/local/include/mlpack/build/?
13:55 < zoq> g++ test.cpp -std=c++11 `pkg-config --libs mlpack` might also work
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13:59 < dashwood_> i got it running there but
13:59 < dashwood_> atal error: 'boost/math/special_functions/gamma.hpp' file not found
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14:15 < zoq> What command did you used to get the new error?
14:21 < dashwood_> the same one
14:21 < dashwood_> turns out boost is installed in some other directory if i use brew install
14:22 < dashwood_> boost goes into /usr/local/Cellar/boost/1.63.0/
14:24 < dashwood_> should i move it?
14:28 < dashwood_> could you give me the command you gave before?
14:30 < rcurtin> topology: sorry for the slow answer, I think there is an error in the thesis, so I would go with the ICML version
14:31 < rcurtin> to me, it seems like the approximation the thesis version calculates is | f(p_q) - f^*(p_q) | < \epsilon * |S_r|
14:31 < rcurtin> not | f(p_q) - f^*(p_q) | < \epsilon
14:33 < zoq> dashwood_: Don't move the folder: 'g++ test.cpp -std=c++11 -I/Users/marcus/src/mlpack/build/include -L/Users/marcus/src/mlpack/build/lib -lmlpack -larmadillo' is the one I used to build your code.
14:36 < dashwood_> 'boost/math/special_functions/gamma.hpp' file not found
14:36 < dashwood_> this is the error i get
14:36 < rcurtin> maybe add -I/usr/local/Cellar/boost/1.63.0/ ? but I would imagine that would already be on the include search path
14:39 < zoq> rcurtin: About to answer another mail: "What resources can you advice to brush up *advanced/modern* C++? (e.g., templates, && in functions)"
14:39 < zoq> I would go with "Modern C++ Design, Generic Programming and Design Patterns Applied" by Andrei Alexandrescu, it's still my favorite. Do you have anything to add?
14:45 < rcurtin> I'm trying to think of the name...
14:45 < rcurtin> Scott someone wrote a few books on the STL
14:45 < zoq> Scott Meyers?
14:46 < rcurtin> I want to say Scott Manley but that's the wrong guy, he just publishes videos about Kerbal Space Program on youtube
14:46 < rcurtin> yes!
14:46 < rcurtin> Scott Meyers
14:46 < zoq> the effective series I guess
14:46 < rcurtin> Effective C++ and Effective STL are good books
14:46 < rcurtin> yeah
14:47 < zoq> yeah, I agree "Effective C++" is also nice
14:47 < zoq> thanks!
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14:50 < rcurtin> I guess, maybe we should add those books as suggestions to gsoc.html?
14:52 < zoq> Might be helpful to dive into some cool aspects.
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14:54 < rcurtin> sure, I will update the page
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15:39 < mikeling> hi rcurtin , I got "for declaration does not refer into a class, class template or class template partial"if I define class like https://pastebin.mozilla.org/8981310
15:40 < mikeling> I still fell confuse after I google it
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16:01 < travis-ci> mlpack/mlpack#1985 (master - 5215c3d : Marcus Edel): The build is still failing.
16:01 < travis-ci> Change view : https://github.com/mlpack/mlpack/compare/805b760e3a73...5215c3df3ec8
16:01 < travis-ci> Build details : https://travis-ci.org/mlpack/mlpack/builds/208625339
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16:05 < rcurtin> mikeling: is there a forward definition of that method in decision_tree.hpp?
16:06 < rcurtin> make sure it matches what you've got implemented in decision_tree_impl.hpp
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16:32 -benchmark:#mlpack- MLP_BACKWARD (--input_size=50000 --hidden_size=5000 --output_size=100) | None 2.82 (old) => 2.83 (new) => 0.01 (diff) |
16:32 -benchmark:#mlpack- MLP_FORWARD (--input_size=50000 --hidden_size=5000 --output_size=100) | None 0.43 (old) => 0.43 (new) => 0.00 (diff) |
16:32 -benchmark:#mlpack- Benchmarks 2 of 2 passed.
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16:43 < topology> rcurtin: what do you think would be an appropriate deliverable for a proposal?
16:43 < topology> is implementing the simple KDE algorithm first during the summer enough? we can then build on it using Bill's paper and other approaches.
16:43 < topology> or is too little work for a gsoc project?
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18:16 < rcurtin> topology: I think that the simple KDE algorithm would not be enough for a full summer; because mlpack already has this nice dual tree algorithms infrastructure, honestly I think it could be implemented in a week or two
18:18 < topology> rcurtin: i thought as much
18:20 < topology> i think i should begin reading Bill's paper for ideas now. what do you think?
18:21 < rcurtin> that could be a good idea, yeah, but I am not sure that he presents the algorithms in a tree-independent way, so you may have to do some "translation"
18:22 < rcurtin> another idea might be to implement the simple KDE as a test to make sure you get the dual-tree algorithm abstractions that are in mlpack
18:22 < rcurtin> alex gray's paper from 2003 SDM might be good to look at too, he lays out a complex algorithm to be used with kd-trees
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18:45 < topology_> that sounds like a good idea. i do theoretically understand the dual-tree abstraction as explained in your thesis.
18:45 < topology_> But the actual code can sometimes be a little obtuse. so i think you are right, it would be a good idea to implement the simple KDE first.
18:46 < topology_> i will begin working on it as soon as i finish going through Bill March's and Alex Gray's papers. (i want to make sure i have enough to put up a strong proposal first)
18:46 < topology_> does it sound good?
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18:53 < rcurtin> yes, of course, that seems reasonable to me
18:54 < rcurtin> remember that the overarching goal in the end would be to provide a KDE program users can use that is both fast and accurate
18:54 < rcurtin> there are many papers out there about doing KDE fast (many of them using single-tree or dual-tree algorithms), so you should be familiar with the literature and which ones you'll aim to implement
18:55 < rcurtin> "ones" or "one" depending, some of them are quite complex
18:55 < rcurtin> but definitely the proposal should at least have the basic dual-tree (and single-tree) KDE as a starting point
19:05 < topology> i understand that. i will read the papers and get back to you for clarifications and further discussions :)
19:12 < rcurtin> sure, sounds good
19:13 < sicko> rcurtin, Hello, just wanted to say thanks. :)
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19:16 < rcurtin> sicko: sure, I am not sure what I did, but you are welcome :)
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20:00 < kris1> arunreddy: Good work on momentum+sgd pr. Though i just gave it glance .....:)
20:03 < arunreddy> krsi1: Thanks :)
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20:46 < rcurtin> sicko: sure, I am not sure what I did, but you are welcome :)
20:46 < rcurtin> oops, wrong terminal to hit "up-enter" in
21:51 < kris1> zoq: layer->Parameters() return 21*1 matrix so basically a row vector.
21:51 < kris1> shoudn't it return matrix that should be equal to input_size*output_size
21:52 < kris1> okay sorry i think i got it
21:54 < kris1> is the input layer model.Model()[0] ?
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23:34 < zoq> kris1: model.Model()[0] is the input/first layer, right.
--- Log closed Wed Mar 08 00:00:33 2017