2012/2/15 Alexandre Gramfort <[email protected]>: >> Fabian: any idea on how to do that? > > use : > > > libsvm.set_verbosity_wrap(1) > libsvm_sparse.set_verbosity_wrap(1)
Thanks. > in svm/base.py > > maybe we could add a verbose flag to SVM estimators. Unfortunately we cannot make it a per-estimator parameter as I initially suggested as this flag is a runtime, per-process singleton: libsvm does not allow to pass a function pointer for handling info printing per svm_train call... Now that dense and sparse input models have been merged into a single classes (SVC, SVR...) we could make it a bit user-friendly with a global function such as: sklearn.svm.enable_libsvm_stdout(True) WDYT? -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Virtualization & Cloud Management Using Capacity Planning Cloud computing makes use of virtualization - but cloud computing also focuses on allowing computing to be delivered as a service. http://www.accelacomm.com/jaw/sfnl/114/51521223/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
