Hi Ronnie.
I don't really understand the comment about the kernel approximation.
Each of the algorithms has a reference to the paper it is from.
As Robert said, the others are textbook algorithms. But even these
have often References, mostly in the documentation, though, as for
example naive Bayes:
http://scikit-learn.org/dev/modules/naive_bayes.html
In general "Elements of Statistical Learning" is a textbook we reference
often.
It is available online for free. Another standard reference is Chris
Bishop's
"Pattern Recognition and Machine Learning".
If you want to start contributing, I can talk you through some
of the issues. I try to hang out in IRC this week. I can also just sent
you a list and we can discuss on the issue tracker.
Cheers,
Andy
On 08/29/2012 12:59 AM, Robert Layton wrote:
On 29 August 2012 08:47, Ronnie Ghose <[email protected]
<mailto:[email protected]>> wrote:
Honestly, I'm not sure if i'm wrong but for example:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/kernel_approximation.py
I'm reasonably sure kernel approximation is from a paper?
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cluster/_k_means.pyx
K means clustering
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/neighbors/classification.py
This
doesn't seem to ...?
I'm looking at ones that do not look like
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py
,
which has a nice list at the bottom.
Please do tell me if i'm completely wrong as that is likely.
As a sidenote, where would you suggest I begin working on the
project - I was thinking of fixing issues listed in the Github
Issues tab.
Thanks,
Ronnie
On 28 August 2012 18:21, Robert Layton <[email protected]
<mailto:[email protected]>> wrote:
On 29 August 2012 08:17, Ronnie Ghose <[email protected]
<mailto:[email protected]>> wrote:
Where do you guys get your algorithms from? Some
algorithms have different variations and I personally
don't really see any sources.
Thanks,
Ronnie
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Hi Ronnie,
Could you give some examples? The algorithms I've coded have
references to the original paper, and most algorithms should
have them too.
- Robert
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Hi Ronnie,
Thanks for that.
The k-nearest neighbours is a fairly obvious algorithm I don't believe
it is generally /cited/ in academic papers anymore. k-means is a bit
similar (that does have a reference in the kmeans.py file), and Naive
Bayes less so.
Most of the files do have (sometimes poor) references in the "notes"
section of the specific classes, but some of the ones you pointed to
do not. Thanks for pointing that out!
It's hard to say exactly where to start. We generally point new
contributors to the easyfix bugs in the bug tracker:
https://github.com/scikit-learn/scikit-learn/issues?direction=desc&labels=EasyFix&page=1&sort=created&state=open
If you aren't a very strong coder, you could learn a lot by helping us
review pull requests (this is true even if you are a strong coder).
If you really aren't a strong coder, start with the documentation and
examples - do all the examples work? Do the tutorials make sense?
If you *are* a strong coder, this issue
(https://github.com/scikit-learn/scikit-learn/issues/339) may be a
good spot to start -- some of the tests are slow, which makes building
annoying. We have improved significantly and have a test suite which
is very useful: http://jenkins-scikit-learn.github.com/scikit-learn-speed/
Thanks!
- Robert
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