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- # tr_features and te_features contain features, Y_tr contain train labels
- K_tr = CombinedKernel()
- K_te = CombinedKernel()
- ksub = GaussianKernel(tr_features, tr_features, width)
- ksub_tr = ksub.get_kernel_matrix()
- ksub.init(tr_features, te_features)
- ksub_te = ksub.get_kernel_matrix()
- K_tr.append_kernel(CustomKernel(ksub_tr))
- K_te.append_kernel(CustomKernel(ksub_te))
- K_tr.append_kernel(CustomKernel(ksub_tr)) # if I add these lines results
- K_te.append_kernel(CustomKernel(ksub_te)) # are bad, otherwise they are fine
- mkl = MKLRegression()
- mkl.set_kernel(K_tr)
- mkl.set_mkl_norm(2.0) # I tried 1.0 too, results in same problem
- mkl.set_labels(Labels(Y_tr))
- mkl.set_C_mkl(10.0)
- mkl.set_epsilon(1e-3)
- mkl.io.enable_progress()
- mkl.train()
- #K_tr.set_subkernel_weights((1.0, 0.0))
- out_tr = mkl.apply().get_labels()
- mkl.set_kernel(K_te)
- out_te = mkl.apply().get_labels()
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