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< tham>
zoq : I saw your discussions with nilay
< tham>
IMHO, selective search is easier to implement and understand, opencv and dlib, both of them provide the implementation of selective search
< tham>
If you prefer selective search, you can convert the implementation of those libs to something suit for mlapck
< tham>
About edgeBoxes, openCV never provide complete implementation
< tham>
The class, StructureEdgeDetection of openCV show us how to detect the edges
< tham>
but it do not tell us how to train the random forest
< tham>
I mean, structured random forest
< tham>
To understand the details, you need to study the paper and the source codes(I am stuck at it)
< tham>
For me, if you prefer to implement selective search, you can ensure there are two well known libraries like opencv and dlib could be a reference
< tham>
both of them are developed by c++, convert their codes to something mlpack could use is quite easy
< tham>
If you prefer edgeBoxes, there are two benefits I can see
< tham>
first, edgeBoxes is much faster than selective search
< tham>
second, none of the open source c++ library have ever provide a complete version of edgeBoxes
< tham>
You may able to contribute the edgeBoxes to openCV after this project finished too.
< tham>
There are many details I do not understand about edgesBoxes, it is not a big deal to convert the matlab codes to c++
< tham>
the hard part is understand the reasons behind the codes
< tham>
Before you start to implement random forest for mlpack(if we could treat Hoeffding tree as dtree, this should be easy)
< tham>
You need to find a way to convert the data of mat file to something armadillo can eat
< rcurtin>
if the hoeffding tree needs to be refactored, I can help do that
< tham>
rcurtin : it is funny :). And thanks for the help of refactored(I do not know we need to do that or not)
< rcurtin>
:)
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< Queries>
Hello!
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< mentekid>
sumedghaisas: I read yesterday when you were chatting with marcus that you implemented several SVD algorithms, I wanted to ask if they are in mlpack
< rcurtin>
mentekid: very unexpected results from my simulations
< rcurtin>
in every single case the unique() strategy was best
< mentekid>
you mean regardless of cutoff?
< rcurtin>
I tested with { default parameters, (K=10, L=10) } and cutoff { 0.0 0.001 0.1 0.3 0.5 0.7 0.9 0.99 1}
< rcurtin>
on covertype, phy, corel, and miniboone datasets
< rcurtin>
I think the corel dataset I have might be different than the one you are using
< rcurtin>
but still
< mentekid>
I think it is trolling us :P
< rcurtin>
usually the cutoff 0.3 or 0.5 gave the very fastest results but there was never a case where cutoff = 0 was faster than cutoff = 1
< rcurtin>
yeah I agree
< rcurtin>
I want to try on another system
< rcurtin>
in this case I am using Armadillo 6 with default Debian configuration (I think this is using ATLAS)
< rcurtin>
I will paste my numbers when I am at my desk but I am on the train right now so I can't
< rcurtin>
maybe it is possible that different Armadillo setups produce wildly sifferent results
< mentekid>
I have no idea what's happening... Were the previous results (ones you posted here a few days ago) from the same configuration?
< rcurtin>
no, they were from a different system
< rcurtin>
I am going to replicate what I did last night exactly on that system today
< rcurtin>
I see that the system I will be testing on today uses Armadillo with OpenBLAS
< rcurtin>
that may be the big factor
< mentekid>
I'm not sure which one I'm running
< mentekid>
so yeah it's probably the underlying system
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< rcurtin>
ldd /path/to/libarmadillo.so is how I am checking
< rcurtin>
on one system it is linked to libatlas.so and the other it is linked to libopenblas.so
< mentekid>
ah thanks. Mine is linked to libblas 3
< mentekid>
but atlas is mentioned further down :/
< rcurtin>
yeah so it is very odd then that your results are so inconsistent with mine that use atlas
< rcurtin>
when I can paste well I will get you the test scripts I used (very similar to yours)
< mentekid>
Yeah I should probably run mine again too, maybe the inconsistencies are caused by something running in the background
< rcurtin>
I believe that what I am using is a modified version of ColorHistogram.asc
< rcurtin>
the first column (the index?) is dropped, leaving 32 columns
< rcurtin>
and I only have 37749 points
< rcurtin>
to be honest I should probably throw away what I have and use the full 68040 points
< mentekid>
yeah I have 32 dimensions as well, but more points. It shouldn't be different though it's just half the dataset instead of all of it (or something like that)
< rcurtin>
that paper references the 37749x32 dataset that I have
< rcurtin>
but that and random mlpack output that's posted around the internet are all I can find
< rcurtin>
so I have no idea how I ended up with a smaller dataset; I think I got it from the lab group I worked for
< rcurtin>
and they probably got it from the lab group my advisor worked for
< rcurtin>
which was related to John Langford's lab, so probably I just have a file of unknown origin that's been passed down for generations :)
< mentekid>
I had a similar story with my "mnist" dataset which ended up being something completely unrelated to the mnist handwritten digits actual dataset
< mentekid>
at least yours has the correct dimensions :P
< rcurtin>
haha
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< rcurtin>
mentekid: okay, I got the test script going on the system with openblas; I will let you know what results I get
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< mentekid>
rcurtin: I have to leave soon (they're locking the lab and I don't have keys :P), is it good for you if we talk tomorrow?
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< rcurtin>
sure that works for me
< rcurtin>
but I think that you are already gone :)
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