[ml] Wednesday, 10/27/2010 @ 7:30pm: Linear Classifier Workshop w/ scikits.learn
David Faden
dfaden at gmail.com
Thu Oct 28 09:23:40 PDT 2010
Ah, cool... I think Sage is based on iPython so maybe it's best just to go
with iPython directly.
On Thu, Oct 28, 2010 at 9:19 AM, Adam Skory <askory at gmail.com> wrote:
> Sage looks pretty promising, but another way to get numpy and
> matplotlib up and running is to use iPython; starting ipython with the
> -pylab argument magically imports the good bits of numpy, scipy, and
> matplotlib.
>
> (really, iPython is so awesome I use it as my default shell...)
>
> -Skory
>
> On Thu, Oct 28, 2010 at 4:40 AM, David Faden <dfaden at gmail.com> wrote:
> > Here's a hacky way that worked for me to get started with
> scikits.learning
> > under Mac OS X:
> > 1. Install Sage <http://www.sagemath.org/>. (I dropped it in
> /Applications
> > as suggested in the docs.) This brings with it its own custom Python
> system
> > with all of the dependencies present already -- numpy, scipy, matplotlib
> and
> > associated libraries.
> > 2. Download the source for scikits.learn
> > <http://sourceforge.net/projects/scikit-learn/files/> and unpack them:
> > $ tar zxvf scikits.learn-0.5.tar.gz
> > 3. Set PYTHONPATH to point to Sage's local directory: (I think this may
> not
> > be necessary.)
> > $ export PYTHONPATH=/Applications/sage/local/lib/python/site-packages/
> > 4. Change into scikits.learn source directory and build, using the sage
> > frontend (which I guess is just a souped up Python interpreter):
> > $ cd scikits.learn-0.5
> > $ /Applications/sage/sage setup.py install
> > 5. Try it out
> > $ /Applications/sage/sage
> > Despite having the "sage:" prompt, you still have a Python interpreter
> there
> > to play with. The logistic regression example here
> > <
> http://scikit-learn.sourceforge.net/auto_examples/logistic_l1_l2_coef.html
> >
> > worked for me with no modification. (I haven't gotten a chance to go
> through
> > the actual examples for our class, but I'm hopeful that if this works so
> > will probably most other stuff.)
> > On Wed, Oct 27, 2010 at 6:12 PM, Mike Schachter <mike at mindmech.com>
> wrote:
> >>
> >> I posted the code to ml-noisebridge's sourceforge git repository. It
> >> probably needs some more work, but you can find it in the scikits.linear
> >> subdirectory of this repo:
> >>
> >> git clone
> >> git://
> ml-noisebridge.git.sourceforge.net/gitroot/ml-noisebridge/ml-noisebridge
> >>
> >>
> >>
> >> On Wed, Oct 27, 2010 at 5:06 PM, Mike Schachter <mike at mindmech.com>
> wrote:
> >>>
> >>> Two more things:
> >>>
> >>> Don't forget to install scipy:
> >>>
> >>> http://www.scipy.org/
> >>>
> >>> And by "linear classification" i actually meant "comparing
> >>> support vector machines and k-nearest neighbors"
> >>>
> >>>
> >>>
> >>>
> >>>
> >>> On Wed, Oct 27, 2010 at 12:14 PM, Mike Schachter <mike at mindmech.com>
> >>> wrote:
> >>>>
> >>>> There are some prerequisites:
> >>>>
> >>>> Python 2.5+
> >>>>
> >>>> Numpy: http://numpy.scipy.org/
> >>>>
> >>>> Matplotlib: http://matplotlib.sourceforge.net/
> >>>>
> >>>> scikits.learn: http://scikit-learn.sourceforge.net/
> >>>>
> >>>> Try to have these installed before we get started.
> >>>>
> >>>> mike
> >>>>
> >>>>
> >>>>
> >>>>
> >>>> On Tue, Oct 26, 2010 at 2:08 PM, Mike Schachter <mike at mindmech.com>
> >>>> wrote:
> >>>>>
> >>>>> Hey everyone,
> >>>>>
> >>>>> Tomorrow I'll be guiding an impromptu workshop with
> >>>>> scikits.learn. We'll use a sample dataset and try our
> >>>>> hands at classifying it with linear classifiers and perhaps
> >>>>> even support vector machines. See you there!
> >>>>>
> >>>>> http://scikit-learn.sourceforge.net/
> >>>>>
> >>>>> mike
> >>>>>
> >>>>
> >>>
> >>
> >>
> >> _______________________________________________
> >> ml mailing list
> >> ml at lists.noisebridge.net
> >> https://www.noisebridge.net/mailman/listinfo/ml
> >>
> >
> >
> > _______________________________________________
> > ml mailing list
> > ml at lists.noisebridge.net
> > https://www.noisebridge.net/mailman/listinfo/ml
> >
> >
>
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