duplicate got through here. Already on another thread
2013/5/7 James Jensen <jdjen...@eng.ucsd.edu>
> Thanks. You mentioned that I could "[add] positive to LassoCV and [pass]
> it to the Lasso estimators used in the cross-val." In the directory of
> my own installation of scikit-learn, I modified
> sklearn/linear_model/coordinate_descent.py to include "positive=False"
> to the parameter list of __init__ for the classes LassoCV, ElasticNetCV,
> and LinearModelCV, and added "self.positive=positive" in the body of the
> __init__ methods. However, calling LassoCV("positive=True", cv=20) still
> gives me the error "TypeError: __init__() got an unexpected keyword
> argument 'positive'".
>
> I appreciate your patience with me. I have been programming in Python
> for only a few months and am no expert in machine learning. I imagine
> that I'm overlooking or misunderstanding some things that are obvious to
> those with more experience.
>
> I notice that Lasso inherits from ElasticNet, and that ElasticNet
> includes the "positive" option, although some of the documentation for
> ElasticNet doesn't seem to reflect this. I imagine that this means it
> would be at least as straightforward for me to add the "positive" option
> to ElasticNetCV as to LassoCV. ElasticNetCV may be even better for my
> problem than LassoCV, since I expect many of my regressors to be
> correlated.
>
> I'm using these regularized regression methods as part of an iterative
> solver for non-negative canonical correlation. CCA can be done by
> finding w that minimizes ||Yv-Xw||^2, then scaling w by ||Xw||, then
> doing the same for v, and so on back and forth until convergence. Lasso
> and ElasticNet can be used for the minimization step. I'm realizing,
> however, that the objective function I need to minimize will require an
> additional quadratic term to enforce the orthogonality of each
> projection direction to all previous directions. These methods from
> scikit-learn could give me the first pair of canonical variables, but if
> I want to get subsequent ones (and I do) I may have to use a more
> general-purpose optimization library like scipy.optimize and define my
> own objective function.
>
>
>
> ------------------------------------------------------------------------------
> Learn Graph Databases - Download FREE O'Reilly Book
> "Graph Databases" is the definitive new guide to graph databases and
> their applications. This 200-page book is written by three acclaimed
> leaders in the field. The early access version is available now.
> Download your free book today! http://p.sf.net/sfu/neotech_d2d_may
> _______________________________________________
> Scikit-learn-general mailing list
> Scikit-learn-general@lists.sourceforge.net
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
------------------------------------------------------------------------------
Learn Graph Databases - Download FREE O'Reilly Book
"Graph Databases" is the definitive new guide to graph databases and
their applications. This 200-page book is written by three acclaimed
leaders in the field. The early access version is available now.
Download your free book today! http://p.sf.net/sfu/neotech_d2d_may
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
