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/
< jeffin>
Zoq : regarding confusion matrix #PR , is it ok to convert continuous data into discrete like applying greatest integer function* and then proceeding
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< jeffin>
Or may be only provide functionality for discrete data, but then should i just describe it in documention that user has to provide discrete data or should i have a check for the input
< jeffin>
It would have time penality if i run a check
< jeffin>
So may be just specify the user about how to use the api *
< zoq>
Shine__: Hello, not at all.
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< Shine__>
zoq: Thanks. I am interested in applying for the project of Reinforcement Learning. I also wanted to ask if there is any other communication channel other than irc. Like slack or gitter?
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< Shine__>
I am not to familiar with irc
< Shine__>
too*
< zoq>
jeff: I would go with discrete data only, about the check, we only have to check eT once, so I don't think this will be slow.
< jeffin>
Ok done
< zoq>
Shine__: We are old school, so IRC, mailing list, github, but we could think about gitter or something similar if the majority think this is a good idea.
< Shine__>
zoq: Oh i forgot about the mailing list. I will subscribe to it right away
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< Suryo>
zoq: hi! Have you been able to take a look at PR#86, the one on constrained particle swarm optimization? Kindly let me know. Thank you.
< Suryo>
Also, how do you guys remain connected to this IRC channel without any breaks in the connection? :)
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< whoKnows>
I generated a PR earlier but know it is showing me "continuous-integration/travis-ci/pr" error during the checks, what it is and how to remove it?
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< Suryo>
whoKnows: hi! The travis-ci helps you check if mlpack is passing all the unit tests for each of the methods
< Suryo>
Under the tests directory, there are multiple tests written corresponding to each method in the library.
< Suryo>
The purpose of these is to make sure that there's some amount of testing done whenever something in the library is changed.
< Suryo>
travis runs the tests, on a virtual machine instance if I'm right, and then tells you if any changes are causing any test failures. If you're failing any tests, it's best to look at the output log of travis to find out
< Suryo>
Alternatively, what I tend to do is run "ctest --verbose" on my system before pushing any changes to my mlpack or ensmallen branches. That helps me make sure that the tests are passing.
< Suryo>
Rather, doing this tells you which tests are failing. Then you've gotta look at them and fix things appropriately.
< Suryo>
I hope that helps.
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< zoq>
Suryo: Will take a look today or tomorrow.
< whoKnows>
ok I got it thankyou
< zoq>
Suryo: I use a combination of irssi + tmux on a remote machine
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< Suryo>
zoq: thanks! It'll be great if you could give me some inputs. Also thanks for the tips for the IRC thing
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< Shine_>
The documentation, community and Help link are broken on the main README file of the mlpack repo. This may be due to recent change in the official website.
< Shine_>
Can i open an issue to address this?
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< rcurtin>
Shine_: thanks for the report. actually there is already a PR open for it (#1808)
< rcurtin>
this happened because we just deployed the new website, and a couple of links are different
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< sreenik>
rcurtin: Thanks for the response there. Have a great weekend :)
< rcurtin>
sreenik: sure, will do :)
< Karl>
Hi, I have trouble in building mlpack from source on Mac. I tried both 3.0.4 and master branch, but always end up with error in some steps. I wonder if anyone can help me on this? Thanks
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< Guest33421>
am i correct in saying that Linear layers in a NN do not learn because there is nothing to adjust?
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< zoq>
Guest33421: Do you mean besides the weights?