Hi, I'm using sklearn for training a linear Support Vector Machine, but I cant match dual_coef_ with coef_. Here goes the code:
X = np.array([[0, 0], [1, 1]]) y = np.array([0, 1]) clf = svm.SVC(kernel = "linear") clf.fit(X, y) this gives me a value for clf.dual_coef_ = [[ 1., -1.]] and clf.coef_ = [[ 1., 1.]]. How do I get from the dual_coef_ to the normal vector coef_ (w)? I understand from http://scikit-learn.org/stable/modules/svm.html#svc that the dual_coef_ are y_i * alpha_i. In that sense I would only need to multiply by X and sum in each coordinate in order to get w, but this gives me [1, -1]. This is just a test. I'm planning to use different kernels, and I understand coef_ only works for linear kernels. Thanks in advance, Guillermo. ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://p.sf.net/sfu/appdyn_d2d_feb _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
