MLPY - Machine Learning Py - Python/NumPy based package for machine learning
*Machine Learning Py* (MLPY) is a *Python/NumPy* based package for machine learning. The package now includes: * *Support Vector Machines* (linear, gaussian, polinomial, terminated ramps) for 2-class problems * *Fisher Discriminant Analysis* for 2-class problems * *Iterative Relief* for feature weighting for 2-class problems * *Feature Ranking* methods based on Recursive Feature Elimination (rfe, onerfe, erfe, bisrfe, sqrtrfe) and Recursive Forward Selection (rfs) * *Input Data* functions * *Confidence Interval* functions Requires Python <http://www.python.org/> >= 2.4 and NumPy <http://www.scipy.org/> >= 1.0.3.* MLPY* is a project of MPBA Group <http://mpa.fbk.eu/> (mpa.fbk.eu) at Fondazione Bruno Kessler (www.fbk.eu). <http://www.fbk.eu/>* MLPY* is free software. It is licensed under the GNU General Public License (GPL) version 3 <http://www.gnu.org/licenses/gpl-3.0.html>. HomePage: mlpy.fbk.eu
isn't MLPY a new name to PyML? http://mloss.org/software/view/28/ if no, I guess you'd better add link to your software to http://mloss.org/software/ ("mloss" is "machine learning open source software") Regards, D. Davide Albanese wrote:
*Machine Learning Py* (MLPY) is a *Python/NumPy* based package for machine learning. The package now includes:
* *Support Vector Machines* (linear, gaussian, polinomial, terminated ramps) for 2-class problems * *Fisher Discriminant Analysis* for 2-class problems * *Iterative Relief* for feature weighting for 2-class problems * *Feature Ranking* methods based on Recursive Feature Elimination (rfe, onerfe, erfe, bisrfe, sqrtrfe) and Recursive Forward Selection (rfs) * *Input Data* functions * *Confidence Interval* functions
Requires Python <http://www.python.org/> >= 2.4 and NumPy <http://www.scipy.org/> >= 1.0.3.* MLPY* is a project of MPBA Group <http://mpa.fbk.eu/> (mpa.fbk.eu) at Fondazione Bruno Kessler (www.fbk.eu). <http://www.fbk.eu/>* MLPY* is free software. It is licensed under the GNU General Public License (GPL) version 3 <http://www.gnu.org/licenses/gpl-3.0.html>.
HomePage: mlpy.fbk.eu _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
No, it isn't, a new name to PyML, it is a new project. Thank you for your advice! Regards, /* da */ dmitrey ha scritto:
isn't MLPY a new name to PyML? http://mloss.org/software/view/28/
if no, I guess you'd better add link to your software to http://mloss.org/software/ ("mloss" is "machine learning open source software") Regards, D.
Davide Albanese wrote:
*Machine Learning Py* (MLPY) is a *Python/NumPy* based package for machine learning. The package now includes:
* *Support Vector Machines* (linear, gaussian, polinomial, terminated ramps) for 2-class problems * *Fisher Discriminant Analysis* for 2-class problems * *Iterative Relief* for feature weighting for 2-class problems * *Feature Ranking* methods based on Recursive Feature Elimination (rfe, onerfe, erfe, bisrfe, sqrtrfe) and Recursive Forward Selection (rfs) * *Input Data* functions * *Confidence Interval* functions
Requires Python <http://www.python.org/> >= 2.4 and NumPy <http://www.scipy.org/> >= 1.0.3.* MLPY* is a project of MPBA Group <http://mpa.fbk.eu/> (mpa.fbk.eu) at Fondazione Bruno Kessler (www.fbk.eu). <http://www.fbk.eu/>* MLPY* is free software. It is licensed under the GNU General Public License (GPL) version 3 <http://www.gnu.org/licenses/gpl-3.0.html>.
HomePage: mlpy.fbk.eu _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
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Hi, How does it compare to the elarn scikit, especially for the SVM part ? How was it implemented ? Matthieu 2008/2/14, Davide Albanese <albanese@fbk.eu>:
*Machine Learning Py* (MLPY) is a *Python/NumPy* based package for machine learning. The package now includes:
* *Support Vector Machines* (linear, gaussian, polinomial, terminated ramps) for 2-class problems * *Fisher Discriminant Analysis* for 2-class problems * *Iterative Relief* for feature weighting for 2-class problems * *Feature Ranking* methods based on Recursive Feature Elimination (rfe, onerfe, erfe, bisrfe, sqrtrfe) and Recursive Forward Selection (rfs) * *Input Data* functions * *Confidence Interval* functions
Requires Python <http://www.python.org/> >= 2.4 and NumPy <http://www.scipy.org/> >= 1.0.3.* MLPY* is a project of MPBA Group <http://mpa.fbk.eu/> (mpa.fbk.eu) at Fondazione Bruno Kessler (www.fbk.eu). <http://www.fbk.eu/>* MLPY* is free software. It is licensed under the GNU General Public License (GPL) version 3 <http://www.gnu.org/licenses/gpl-3.0.html>.
HomePage: mlpy.fbk.eu _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
-- French PhD student Website : http://matthieu-brucher.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher
Dear Matthieu, I don't know very well scikit. The Svm is implemented by Sequential Minimal Optimization (SMO). As for Terminated Ramps (TR) you can read this paper: /S. Merler and G. Jurman/* Terminated Ramp - Support Vector Machine: a nonparametric data dependent kernel* Neural Network, 19(10), 1597-1611, 2006. /* da */ Matthieu Brucher ha scritto:
Hi,
How does it compare to the elarn scikit, especially for the SVM part ? How was it implemented ?
Matthieu
2008/2/14, Davide Albanese <albanese@fbk.eu <mailto:albanese@fbk.eu>>:
*Machine Learning Py* (MLPY) is a *Python/NumPy* based package for machine learning. The package now includes:
* *Support Vector Machines* (linear, gaussian, polinomial, terminated ramps) for 2-class problems * *Fisher Discriminant Analysis* for 2-class problems * *Iterative Relief* for feature weighting for 2-class problems * *Feature Ranking* methods based on Recursive Feature Elimination (rfe, onerfe, erfe, bisrfe, sqrtrfe) and Recursive Forward Selection (rfs) * *Input Data* functions * *Confidence Interval* functions
Requires Python <http://www.python.org/> >= 2.4 and NumPy <http://www.scipy.org/> >= 1.0.3.* MLPY* is a project of MPBA Group <http://mpa.fbk.eu/> (mpa.fbk.eu) at Fondazione Bruno Kessler (www.fbk.eu). <http://www.fbk.eu/>* MLPY* is free software. It is licensed under the GNU General Public License (GPL) version 3 <http://www.gnu.org/licenses/gpl-3.0.html>.
HomePage: mlpy.fbk.eu _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org <mailto:Numpy-discussion@scipy.org> http://projects.scipy.org/mailman/listinfo/numpy-discussion
-- French PhD student Website : http://matthieu-brucher.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher
Thanks for the reference :) I should have asked in other terms : how does it compare to libsvm, which is one of the most known packages for SVMs ? Matthieu 2008/2/15, Davide Albanese <albanese@fbk.eu>:
Dear Matthieu, I don't know very well scikit. The Svm is implemented by Sequential Minimal Optimization (SMO). As for Terminated Ramps (TR) you can read this paper: /S. Merler and G. Jurman/* Terminated Ramp - Support Vector Machine: a nonparametric data dependent kernel* Neural Network, 19(10), 1597-1611, 2006.
/* da */
-- French PhD student Website : http://matthieu-brucher.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher
I don't know very well libsvm too, the core of svm-mlpy is written in C and was developed by Stefano Merler (merler@fbk.eu). I have simply wrapped it into svm() Python class. Regards, /* da */ Matthieu Brucher ha scritto:
Thanks for the reference :)
I should have asked in other terms : how does it compare to libsvm, which is one of the most known packages for SVMs ?
Matthieu
2008/2/15, Davide Albanese <albanese@fbk.eu <mailto:albanese@fbk.eu>>:
Dear Matthieu, I don't know very well scikit. The Svm is implemented by Sequential Minimal Optimization (SMO). As for Terminated Ramps (TR) you can read this paper: /S. Merler and G. Jurman/* Terminated Ramp - Support Vector Machine: a nonparametric data dependent kernel* Neural Network, 19(10), 1597-1611, 2006.
/* da */
-- French PhD student Website : http://matthieu-brucher.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher
OK, I'll try it then :) Is there an access to the underlying cost function ? (this is mainly what I need) Matthieu 2008/2/15, Davide Albanese <albanese@fbk.eu>:
I don't know very well libsvm too, the core of svm-mlpy is written in C and was developed by Stefano Merler (merler@fbk.eu). I have simply wrapped it into svm() Python class.
Regards,
/* da */
-- French PhD student Website : http://matthieu-brucher.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher
Yes: https://mlpy.fbk.eu/wiki/MlpyExamplesWithDoc * svm() Initialize the svm class. Inputs: ... cost - for cost-sensitive classification [-1.0, 1.0] Matthieu Brucher ha scritto:
OK, I'll try it then :)
Is there an access to the underlying cost function ? (this is mainly what I need)
Matthieu
2008/2/15, Davide Albanese <albanese@fbk.eu <mailto:albanese@fbk.eu>>:
I don't know very well libsvm too, the core of svm-mlpy is written in C and was developed by Stefano Merler (merler@fbk.eu <mailto:merler@fbk.eu>). I have simply wrapped it into svm() Python class.
Regards,
/* da */
-- French PhD student Website : http://matthieu-brucher.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher
Well this is an input parameter, I'd like to access the cost function directly so that I can use it to follow its gradient to the limit between the two classes. Matthieu 2008/2/15, Davide Albanese <albanese@fbk.eu>:
Yes: https://mlpy.fbk.eu/wiki/MlpyExamplesWithDoc
* svm() Initialize the svm class.
Inputs: ... cost - for cost-sensitive classification [-1.0, 1.0]
Matthieu Brucher ha scritto:
OK, I'll try it then :)
Is there an access to the underlying cost function ? (this is mainly what I need)
Matthieu
2008/2/15, Davide Albanese <albanese@fbk.eu <mailto:albanese@fbk.eu>>:
I don't know very well libsvm too, the core of svm-mlpy is written in C and was developed by Stefano Merler (merler@fbk.eu
<mailto:merler@fbk.eu>).
I have simply wrapped it into svm() Python class.
Regards,
/* da */
-- French PhD student Website : http://matthieu-brucher.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher
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-- French PhD student Website : http://matthieu-brucher.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher
Ok, sorry! The cost function is embedded into the C smo() function. I think that you cannot access it directly. Matthieu Brucher ha scritto:
Well this is an input parameter, I'd like to access the cost function directly so that I can use it to follow its gradient to the limit between the two classes.
Matthieu
2008/2/15, Davide Albanese <albanese@fbk.eu <mailto:albanese@fbk.eu>>:
Yes: https://mlpy.fbk.eu/wiki/MlpyExamplesWithDoc
* svm() Initialize the svm class.
Inputs: ... cost - for cost-sensitive classification [-1.0, 1.0]
Matthieu Brucher ha scritto:
> OK, I'll try it then :) > > Is there an access to the underlying cost function ? (this is mainly > what I need) > > Matthieu >
> 2008/2/15, Davide Albanese <albanese@fbk.eu <mailto:albanese@fbk.eu> <mailto:albanese@fbk.eu <mailto:albanese@fbk.eu>>>:
> > I don't know very well libsvm too, the core of svm-mlpy is written > in C > and was developed by Stefano Merler (merler@fbk.eu <mailto:merler@fbk.eu>
> <mailto:merler@fbk.eu <mailto:merler@fbk.eu>>).
> I have simply wrapped it into svm() Python class. > > Regards, > > /* da */ > > > > -- > French PhD student > Website : http://matthieu-brucher.developpez.com/ > Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 > LinkedIn : http://www.linkedin.com/in/matthieubrucher
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-- French PhD student Website : http://matthieu-brucher.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher
participants (3)
-
Davide Albanese -
dmitrey -
Matthieu Brucher