verne.freenode.net changed the topic of #mlpack to: http://www.mlpack.org/ -- We don't respond instantly... but we will respond. Give it a few minutes. Or hours. -- Channel logs: http://www.mlpack.org/irc/
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< vasanth>
Hi, I wanted to know how to run test for a single module. I tried compiling it with lboost_unit_test_framework and armadillo and mlpack, but it returns failure, LD error. I also tried the -static option and setting the dyn_link flag for boost, all of which failed.
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< zoq>
vasanth: Once you build 'mlpack_test' ('make mlpack_test'), you can run a single test suite with 'bin/mlpack_test -t TestSuite'.
< zoq>
vasanth: A specific test case called 'TestCase' (BOOST_AUTO_TEST_CASE(TestCase)) could be run with 'bin/mlpack_test -t TestSuite/TestCase'.
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< Nilabhra>
anyone here who has gone through the collaborative filtering codes?
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< vasanth>
@zoq, thank you for that it works now.
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< Nilabhra>
zoq: Hi! Any idea how many students google has allowed mlpack to select?
< zoq>
Nilabhra: Google announces the number of slots after the application period has ended, so no.
< Nilabhra>
zoq: I see, well I better work on my proposal then. Thanks! :)
< kirizaki_>
I understand that we shouldn't use any inheritance
< kirizaki_>
in fuzzy logic for example: class MembershipFunction is parent class
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< kirizaki_>
from which another classes derives: Trapezoidal, Sigmoidal, etc.
< kirizaki_>
shoud I prevent this?
< kirizaki_>
should*
< rcurtin>
why not use template policy classes to achieve the same thing?
< rcurtin>
kind of like GiniImpurity and InformationGain in src/mlpack/methods/hoeffding_trees/
< rcurtin>
(I'm in a meeting right now so I can't respond much more in depth right now)
< kirizaki_>
ok, thanks ;)
< kirizaki_>
it's good enough
< wasiq>
rcurtin, Hey i had spoken to you a few days back about the Frank-Wilde implementation, and one of the things i read in the paper were the different variants of FW.
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< wasiq>
such as the approximation,line search for step size,fully corrective,away step variants. Will all these variants be present in our final implementation?
< rcurtin>
sure, variants are nice, but it's up to you what you include in your proposal
< rcurtin>
if you don't think it's reasonable to include all of those variants inside of the timeline you have, then you can omit them, that's not a problem
< rcurtin>
probably, if the optimizer is coded well, it should be easy to write the variants
< wasiq>
rcurtin, yeah thats true, the variants have a few trivial differences. In that case the implementation would be the bareback version of FW right?
< rcurtin>
definitely we would want the standard algorithm, yes
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< wasiq>
Great. Also,I looked through the sgd optimizers code to get an idea of what my implementation should look like, im now running tests to get a feel of the parameters of an optimizer.
< wasiq>
Is there anything else i should look into?
< rcurtin>
you might take a look at some of the mlpack methods that use the optimizers, like logistic_regression or softmax_regression or nca
< rcurtin>
there are some more I think, and I think the ANN code also uses the optimizer interface too
< wasiq>
yup,i was looking through the website for this..
< wasiq>
I'll get back to you with a couple of problems. :) thanks.
< rcurtin>
sure, sounds good
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< ankur>
hi any mentors here
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< zoq>
ankur: There are always mentors online, but "We don't respond instantly... but we will respond. Give it a few minutes. Or hours." :)
< rcurtin>
ankur: I saw that, I haven't had a chance to figure out why my system has warnings and yours doesn't yet
< rcurtin>
I'm using a Chromebook 2 (which is ARM), maybe that has something to do with it
< rcurtin>
with gcc 5,2,0 on a 32-bit armv7l system
< ankur>
ok that may be the case.
< rcurtin>
I guess it could have to do with the Armadillo version too... my chromebook is using Armadillo 5.200.2
< rcurtin>
but I would guess maybe you used some Armadillo 6 version?
< ankur>
yes I used the latest version
< rcurtin>
hm, maybe try with Armadillo 5.200.2 and see what you get?
< ankur>
Actually I want to do something as a proof of concept before my GSoC application for "Essential Deep Learning Modules"
< ankur>
what should I do?
< ankur>
I will try with Armadillo 5.200.2 today
< rcurtin>
what do you mean by your question? I'm not sure how to answer it
< rcurtin>
you can easily write some proof of concept code
< ankur>
its not related to the issue.
< ankur>
its just I wanted to know should I write some sample code before I apply for GSoC
< ankur>
so that It increases my chances
< rcurtin>
it's not required that you write sample code to apply for mlpack
< rcurtin>
it can be helpful, but you should not feel obligated to solve bugs if you can't find anything that is directly related to what you want to do
< ankur>
ok
< ankur>
but I think solving bugs can give me greater insights of the mlpack code.
< ankur>
BTW thanks
< zoq>
ankur: You are always welcome to just poke around the library and try to fix any problems you find, or improve the speed of various parts.