def kernel_combined_custom_poly_modular(train_fname=traindat, test_fname=testdat, train_label_fname=label_traindat): from modshogun import CombinedFeatures, RealFeatures, BinaryLabels from modshogun import CombinedKernel, PolyKernel, CustomKernel from modshogun import LibSVM, CSVFile kernel = CombinedKernel() feats_train = CombinedFeatures() tfeats = RealFeatures(CSVFile(train_fname)) tkernel = PolyKernel(10, 3) tkernel.init(tfeats, tfeats) K = tkernel.get_kernel_matrix() kernel.append_kernel(CustomKernel(K)) subkfeats_train = RealFeatures(CSVFile(train_fname)) feats_train.append_feature_obj(subkfeats_train) subkernel = PolyKernel(10, 2) kernel.append_kernel(subkernel) kernel.init(feats_train, feats_train) labels = BinaryLabels(CSVFile(train_label_fname)) svm = LibSVM(1.0, kernel, labels) svm.train() kernel = CombinedKernel() feats_pred = CombinedFeatures() pfeats = RealFeatures(CSVFile(test_fname)) tkernel = PolyKernel(10, 3) tkernel.init(tfeats, pfeats) K = tkernel.get_kernel_matrix() kernel.append_kernel(CustomKernel(K)) subkfeats_test = RealFeatures(CSVFile(test_fname)) feats_pred.append_feature_obj(subkfeats_test) subkernel = PolyKernel(10, 2) kernel.append_kernel(subkernel) kernel.init(feats_train, feats_pred) svm.set_kernel(kernel) svm.apply() km_train = kernel.get_kernel_matrix() return km_train, kernel
def kernel_combined_custom_poly_modular (train_fname = traindat,test_fname = testdat,train_label_fname=label_traindat): from modshogun import CombinedFeatures, RealFeatures, BinaryLabels from modshogun import CombinedKernel, PolyKernel, CustomKernel from modshogun import LibSVM, CSVFile kernel = CombinedKernel() feats_train = CombinedFeatures() tfeats = RealFeatures(CSVFile(train_fname)) tkernel = PolyKernel(10,3) tkernel.init(tfeats, tfeats) K = tkernel.get_kernel_matrix() kernel.append_kernel(CustomKernel(K)) subkfeats_train = RealFeatures(CSVFile(train_fname)) feats_train.append_feature_obj(subkfeats_train) subkernel = PolyKernel(10,2) kernel.append_kernel(subkernel) kernel.init(feats_train, feats_train) labels = BinaryLabels(CSVFile(train_label_fname)) svm = LibSVM(1.0, kernel, labels) svm.train() kernel = CombinedKernel() feats_pred = CombinedFeatures() pfeats = RealFeatures(CSVFile(test_fname)) tkernel = PolyKernel(10,3) tkernel.init(tfeats, pfeats) K = tkernel.get_kernel_matrix() kernel.append_kernel(CustomKernel(K)) subkfeats_test = RealFeatures(CSVFile(test_fname)) feats_pred.append_feature_obj(subkfeats_test) subkernel = PolyKernel(10, 2) kernel.append_kernel(subkernel) kernel.init(feats_train, feats_pred) svm.set_kernel(kernel) svm.apply() km_train=kernel.get_kernel_matrix() return km_train,kernel
C=0.017 epsilon=1e-5 tube_epsilon=1e-2 svm=LibSVM() svm.set_C(C, C) svm.set_epsilon(epsilon) svm.set_tube_epsilon(tube_epsilon) for i in range(3): data_train=random.rand(num_feats, num_vec) data_test=random.rand(num_feats, num_vec) feats_train=RealFeatures(data_train) feats_test=RealFeatures(data_test) labels=Labels(random.rand(num_vec).round()*2-1) svm.set_kernel(LinearKernel(size_cache, scale)) svm.set_labels(labels) kernel=svm.get_kernel() print("kernel cache size: %s" % (kernel.get_cache_size())) kernel.init(feats_test, feats_test) svm.train() kernel.init(feats_train, feats_test) print(svm.apply().get_labels()) #kernel.remove_lhs_and_rhs() #import pdb #pdb.set_trace()