2012/9/13 Dimitrios Pritsos <[email protected]>: > There is a Great difference in the performance of SVM.fit() method > (OneClassSVM in particular) depending on the input. When the input is a > Sparse Matrix the Training is Extremely slow for a very small amount of > data i.e. 180x1000 matrix where 1000 are the features size and 180 are > the samples. On the other hand an Array input of the same size is quite > fast, even faster than the Libsvm Python API as I can recall. > > Is that normal or I ve encountered some short of a bug?
No, that's not normal. Good you tell us... 1. How slow is slow? 2. Is it equally slow when you use the deprecated sklearn.svm.sparse.OneClassSVM? 3, How sparse is your data? I.e., how many zeros are there in X? -- Lars Buitinck Scientific programmer, ILPS University of Amsterdam ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
