I think this library might apply zero mean, unit variance scaling before using libsvm. Try applying the ``StandardScaler`` to your data before using SVC.
On 01/20/2015 11:29 AM, Timothy Vivian-Griffiths wrote: > Hi Andy, > > Firstly, the dimensions that I gave were wrong, you're right. The inputs are > correct but the target vector shape is (7763,) so there are that many samples > with 125 features (that is in the smaller dataset I am using, the other has > over 30,000 features but I haven't tried that one in R yet). > > I am using the svm model from the e1071 library in R. The documentation > states that this uses libsvm as well. And I have tried to set as many > parameters to be the same as possible (those that I can remember are: kernel, > cost, gamma, cache_size, shrinking and tolerance. Come to think of it, I > haven't tried setting the random_state for either of them, so I'll give that > a go when I can, but I don't know if that will be the same across software > anyway. > > But, I am definitely loading in the same data and the R version is not giving > only 0s (for C=1, kernel='rbf'). I will also compare the performance with > some other kernels and parameters when I can as well. > > Tim > > >> On 01/19/2015 10:43 AM, Timothy Vivian-Griffiths wrote: >>> I have used this same dataset and parameters in Rs implementation of an >>> SVM, and it is not outputting all 0s, so I don't think that it's a >>> particular problem with the data. . >> This seems odd. What implementation are you using in R? >> Scikit-learn uses libsvm, which is more or less the reference >> implementation for kernel SVMs. >> Maybe the R package you are using parametrizes the SVM in a different way. >> >> Btw, you said: >> >> for interest the inputs matrix had shape (7763, 125) and the target >> vector (125,): >> >> That can not be. The input needs to be (n_samples, n_features) and the >> target (n_samples,) >> Do you only have 125 samples and 7763 features? >> That is very few samples for an RBF-SVM .... >> > > ------------------------------------------------------------------------------ > New Year. New Location. New Benefits. New Data Center in Ashburn, VA. > GigeNET is offering a free month of service with a new server in Ashburn. > Choose from 2 high performing configs, both with 100TB of bandwidth. > Higher redundancy.Lower latency.Increased capacity.Completely compliant. > http://p.sf.net/sfu/gigenet > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ New Year. New Location. New Benefits. New Data Center in Ashburn, VA. GigeNET is offering a free month of service with a new server in Ashburn. Choose from 2 high performing configs, both with 100TB of bandwidth. Higher redundancy.Lower latency.Increased capacity.Completely compliant. http://p.sf.net/sfu/gigenet _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
