Hi Jake,
i organized things to be a little better/neater/nicer/cleaner
can you take from https://github.com/smarttypes/graphreduce
$ python graphreduce_tests.py
will reproduce the error
On Fri, Dec 9, 2011 at 5:54 PM, Jacob VanderPlas
wrote:
> Thanks Timmy,
> If you could also include in th
Hi Jacob,
Thank you so much !
On 10 December 2011 00:46, Jacob VanderPlas wrote:
> Bala,
> That's great - we could certainly use your expertise!
>
>
> The best way to start is to set-up an account on github:
> http://www.github.com
> You can then browse the list of known issues at
> https://gi
Thanks Timmy,
If you could also include in the gist a minimal python script which
reproduces the error from the included data, that would be very helpful.
Jake
Timmy Wilson wrote:
> Thanks Jake
>
> here are the files -- https://gist.github.com/1453617
>
> here's the code creating the input data
Thanks Jake
here are the files -- https://gist.github.com/1453617
here's the code creating the input data:
https://github.com/smarttypes/SmartTypes/blob/master/smarttypes/model/twitter_user.py
- def get_adjacency_matrix(self, distance=10):
On Fri, Dec 9, 2011 at 5:17 PM, Jacob VanderPlas
w
If you have a github account, you could create a gist:
https://gist.github.com/
This results in a sort of temporary git repository you can push files to
as with any git repository.
If you push your data file and python code you use to process and plot
it, I can take a look and see if I can figure
This was rejected because attachments were > 40K
What's the preferred way to pass random/tmp files?
-- Forwarded message --
From: Timmy Wilson
Date: Fri, Dec 9, 2011 at 8:57 AM
Subject: Re: [Scikit-learn-general] RuntimeError: Factor is exactly singular
To: scikit-learn-general@
Is this essentially agent based modeling?
Not exactly the same, but i think this paper touches on the essence of
the method:
"
Their internal states are governed by finite state machines, but their
external relationships are governed by a simulated physics that
includes Brownian motion, viscosity
Bala,
That's great - we could certainly use your expertise!
The best way to start is to set-up an account on github:
http://www.github.com
You can then browse the list of known issues at
https://github.com/scikit-learn/scikit-learn/issues
If there is anything there that you'd like to address, yo
Hi Bala. Welcome on the list!
Hi list. I met Bala at Scipy India. He is very enthousiastic and motivated,
looking to learn by contributing. It would be great if we direct his way easy
tasks to get him started.
Cheers,
Gael
- Original message -
> Hi all :)
>
> My name is Bala Subrahma
2011/12/9 Bala Subrahmanyam Varanasi :
> Hi all :)
>
> My name is Bala Subrahmanyam Varanasi.
Welcome to the mailing list!
> Initially, I would like to start contributing by working on improving the
> documentation part. Then I'll look forward to develop the code.
This is indeed a very good way
Hi all :)
My name is Bala Subrahmanyam Varanasi. I'm pursuing final year of B.Tech in
Information Technology at Vishnu Institute of Technology (http://vishnuit.in)
in India. Though I'm very new, I'm very passionate in the fields of Natural
Language Processing, Machine Learning and Artificial Intel
Hi All !
I plan on also working on scikit learn during this time, but from
France. I'd like to implement the HAP, the hierarchical version of the
affinity propagation during that time.
On 8 December 2011 15:27, Mathieu Blondel wrote:
> Hi everyone,
>
> I've edited the wiki with ideas for the two
2011/12/9 Fernando Perez :
> On Fri, Dec 9, 2011 at 1:37 AM, Olivier Grisel
> wrote:
>> I was thinking: it would be great if we could get a ssh access to a
>> small linux cluster (e.g. 10 nodes) with IPython / numpy / scipy
>> installed on it at Berkeley for the duration of the sprint so as to be
On Fri, Dec 9, 2011 at 1:37 AM, Olivier Grisel wrote:
> I was thinking: it would be great if we could get a ssh access to a
> small linux cluster (e.g. 10 nodes) with IPython / numpy / scipy
> installed on it at Berkeley for the duration of the sprint so as to be
> able to quickly test implementat
I first ran into energy-based learning when studying neural nets.
Recently i found a few promising papers/examples that focus on energy
based graph embedding.
I'm curious what the community thinks of this brand of learning?
The guys @ Gephi published a nice overview of force/energy
Ok as nobody expressed hard concerns w.r.t. dropping support for
python 2.5, I think we can make that decision official now and close
this vote.
So let's officially support python 2.6, scipy 0.7.2 and numpy 1.3.0
from now on. I will try to find the time to setup a jenkins build +
tests reflecting
2011/12/9 Olivier Grisel :
> 2011/12/9 Fernando Perez :
>> On Thu, Dec 8, 2011 at 2:35 AM, Olivier Grisel
>> wrote:
>>> Ok I have edited the wiki page to add scope, motivation and use case
>>> information for this sprint.
>>>
>>> Please feel free to edit and comment:
>>>
>>> http://wiki.ipython.
2011/12/9 Fernando Perez :
> On Thu, Dec 8, 2011 at 2:35 AM, Olivier Grisel
> wrote:
>> Ok I have edited the wiki page to add scope, motivation and use case
>> information for this sprint.
>>
>> Please feel free to edit and comment:
>>
>> http://wiki.ipython.org/PyCon12Sprint
>>
>> In particular
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