# real-valued toy data set. The underlying library used for the SVR training is # SVM^light. The SVR is trained with regularization parameter C=1 and a gaussian # kernel with width=2.1. The the label of both the train and the test data are # fetched via svr.classify().get_labels(). # # For more details on the SVM^light see # T. Joachims. Making large-scale SVM learning practical. In Advances in Kernel # Methods -- Support Vector Learning, pages 169-184. MIT Press, Cambridge, MA USA, 1999. ########################################################################### # svm light based support vector regression ########################################################################### from numpy import array from numpy.random import seed, rand from tools.load import LoadMatrix lm=LoadMatrix() traindat = lm.load_numbers('../data/fm_train_real.dat') testdat = lm.load_numbers('../data/fm_test_real.dat') label_traindat = lm.load_labels('../data/label_train_twoclass.dat') parameter_list = [[traindat,testdat,label_traindat,1.2,1,1e-5,1e-2,1],[traindat,testdat,label_traindat,2.3,0.5,1e-5,1e-6,1]] def regression_svrlight_modular(fm_train=traindat,fm_test=testdat,label_train=label_traindat, \ width=1.2,C=1,epsilon=1e-5,tube_epsilon=1e-2,num_threads=3): from shogun.Features import RegressionLabels, RealFeatures from shogun.Kernel import GaussianKernel try: from shogun.Regression import SVRLight