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/
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< jonpsy[m]>
@zoq Updates on the remote machine?
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< zoq>
jonpsy[m]: Commented on the gdoc let me know if you run into any issues.
< jonpsy[m]>
okay it looks like the entire thing is empty?
< jonpsy[m]>
it looks like i need sudo access to install things
< jonpsy[m]>
can i ask you to setup ensmallen, mlpack and xeus cling?
< jonpsy[m]>
* do you mind to setup ensmallen, mlpack and xeus cling?
< jonpsy[m]>
.......aand armadillo :)
< zoq>
You can also use docker on the machine, that way you have root as well.
< zoq>
docker pull mlpack/jenkins-amd64-debian
< zoq>
That one has everything you need I think
< zoq>
besides xeus cling
< swaingotnochill[>
zoq I have some doubts regarding the linear regression implementation.
< swaingotnochill[>
I trained the model on trainSet, trainLabels
< swaingotnochill[>
Then I predicted the responses on testSet as output
< swaingotnochill[>
So, I am confused in Compute Error function. Does, it automatically uses the outputs of testSet to calculate error with testLabels.
< swaingotnochill[>
(edited) ... with testLabels. => ... with testLabels?
< swaingotnochill[>
Also, is there any way to convert arma:: rowvec into normal vector?
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< jonpsy[m]>
<zoq "That one has everything you need"> I guess that way I won't need ssh, no..?
< zoq>
swaingotnochill[: Not sure I get the question, you have to pass the ground-truth labels and the predicted responses to the compute error function to get the L2 squared error, internally the compute error function uses the trained linear regression model.
< jonpsy[m]>
<swaingotnochill[ "Also, is there any way to conver"> See "arma::conv_to<>::from() "
< zoq>
with normal vector you mean arma::vec?
< zoq>
Right arma::conv_to<arma::vec>::from(...); should do the trick.
< jonpsy[m]>
<jonpsy[m] "I guess that way I won't need ss"> Abt this?
< swaingotnochill[>
I think the ComputeError takes a matrix and responses as input
< swaingotnochill[>
By normal vector means, will I able to use .begin(), .end() operators , coz I was first trying to calculate MSE
< zoq>
So std::vector? It does work for std::vector as well.
< jonpsy[m]>
I think rowvec has begin end
< zoq>
Yes, Mat, Col, and Row have iterators as well
< rcurtin[m]>
swaingotnochill: if you're trying to compute MSE, I might suggest writing it as a single operation using Armadillo functions... `arma::mean((vec1 - vec2) ^ 2)` I think should work
< zoq>
swaingotnochill[: Ahh, I see what you mean, in this case you just pass the testData, the trained model will do the prediction internnaly and compare it with the passed ground-truth.
< rcurtin[m]>
in general, the more high-level operations you can use, the better job Armadillo will do of producing efficient code
< swaingotnochill[>
zoq yaah...ComputeError calculates L2 loss by its own
< swaingotnochill[>
<rcurtin[m] "swaingotnochill: if you're tryin"> Yaah..Will try using armadillo functions...
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< jeffin143[m]>
Currently we support only numeric data type
< jeffin143[m]>
We need to find out a way to , take input as string
< jeffin143[m]>
I mean how do we store that matrix in the binding ???
< zoq>
Right, that is for the executable/python binding.
< zoq>
Do you think you can do the encoding in pure python as well?
< jeffin143[m]>
zoq: I didn't get you ??
< zoq>
So we don't have to rely on the preprocess_one_hot_encoding.
< zoq>
I was just suggesting to drop the one_hot_encoding in the notebook, since categorical data is not supported
< zoq>
at least for the python API
< DavidportlouisDa>
ok
< zoq>
C++ should work, @jeffin143[m] correct me if I'm wrong.
< jeffin143[m]>
> I was just suggesting to drop the one_hot_encoding in the notebook, since categorical data is not supported
< jeffin143[m]>
Got it ,
< jeffin143[m]>
Yeah you can one hot encode a particular column , or multiple column
< jeffin143[m]>
So you can first create a mapping of the string type and then one hot encode it
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< jeffin143[m]>
But like zoq suggest you can ditch the preprocess one hot encoding for string data type
< zoq>
Not sure there are plans to support that?
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< jeffin143[m]>
I mean , we can't store it in mat ?? Right
< zoq>
Good question, since arma::Mat can't store std::string, there are some thoughts around using xtensor wondering if that is one way to fix this.
< rcurtin[m]>
jeffin143: in a binding you can use `PARAM_MATRIX_WITH_INFO`, which will give you a `DatasetInfo` along with the `arma::mat`
< rcurtin[m]>
the automatic binding implementation for each language then defines what a user can actually pass in that type of situation
< jeffin143[m]>
Ok
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< rcurtin[m]>
it looks like maybe CMake 3.20 broke the CI build? that's frustrating... can anyone reproduce it locally?
< heisenbuugGopiMT>
What do I have to do to reproduce it? Can you elaborate a bit if that's possible...
< rcurtin[m]>
I am not sure, but just looking at the output, it seems like just using CMake 3.20 at all to try and compile mlpack causes `make` to fail at the next step
< heisenbuugGopiMT>
Okay, I am trying...
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< heisenbuugGopiMT>
Also after making my latest commit, this test is failing...
< heisenbuugGopiMT>
This seems a bit unrelated to my changes maybe...
< heisenbuugGopiMT>
Regarding the parser I think `load_save_test` is the test that matters...
< rcurtin[m]>
it's possible that test does use `data::Load()` somewhere, but at least personally I think it's probably best to not bother to investigate it until `load_save_test` is all passing
< heisenbuugGopiMT>
okay, thank you...
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< heisenbuugGopiMT>
Hey @ryan:ratml.org I am getting an error when I am trying to build it...