def classifier_multiclassmachine_modular(fm_train_real=traindat, fm_test_real=testdat, label_train_multiclass=label_traindat, width=2.1, C=1, epsilon=1e-5): from modshogun import RealFeatures, MulticlassLabels from modshogun import GaussianKernel from modshogun import LibSVM, KernelMulticlassMachine, MulticlassOneVsRestStrategy feats_train = RealFeatures(fm_train_real) feats_test = RealFeatures(fm_test_real) kernel = GaussianKernel(feats_train, feats_train, width) labels = MulticlassLabels(label_train_multiclass) classifier = LibSVM() classifier.set_epsilon(epsilon) #print labels.get_labels() mc_classifier = KernelMulticlassMachine(MulticlassOneVsRestStrategy(), kernel, classifier, labels) mc_classifier.train() kernel.init(feats_train, feats_test) out = mc_classifier.apply().get_labels() return out
def classifier_libsvm_modular (train_fname=traindat,test_fname=testdat,label_fname=label_traindat,width=2.1,C=1,epsilon=1e-5): from modshogun import RealFeatures, BinaryLabels from modshogun import GaussianKernel, LibSVM, CSVFile feats_train=RealFeatures(CSVFile(train_fname)) feats_test=RealFeatures(CSVFile(test_fname)) labels=BinaryLabels(CSVFile(label_fname)) kernel=GaussianKernel(feats_train, feats_train, width) svm=LibSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train() supportvectors = sv_idx=svm.get_support_vectors() alphas=svm.get_alphas() predictions = svm.apply(feats_test) #print predictions.get_labels() return predictions, svm, predictions.get_labels()
def classifier_multiclassmachine_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5): from modshogun import RealFeatures, MulticlassLabels from modshogun import GaussianKernel from modshogun import LibSVM, KernelMulticlassMachine, MulticlassOneVsRestStrategy feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) kernel=GaussianKernel(feats_train, feats_train, width) labels=MulticlassLabels(label_train_multiclass) classifier = LibSVM() classifier.set_epsilon(epsilon) #print labels.get_labels() mc_classifier = KernelMulticlassMachine(MulticlassOneVsRestStrategy(),kernel,classifier,labels) mc_classifier.train() kernel.init(feats_train, feats_test) out = mc_classifier.apply().get_labels() return out
def classifier_libsvm_modular(train_fname=traindat, test_fname=testdat, label_fname=label_traindat, width=2.1, C=1, epsilon=1e-5): from modshogun import RealFeatures, BinaryLabels from modshogun import GaussianKernel, LibSVM, CSVFile feats_train = RealFeatures(CSVFile(train_fname)) feats_test = RealFeatures(CSVFile(test_fname)) labels = BinaryLabels(CSVFile(label_fname)) kernel = GaussianKernel(feats_train, feats_train, width) svm = LibSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train() supportvectors = sv_idx = svm.get_support_vectors() alphas = svm.get_alphas() predictions = svm.apply(feats_test) #print predictions.get_labels() return predictions, svm, predictions.get_labels()
from modshogun import RealFeatures, Labels from modshogun import LinearKernel num_feats=23 num_vec=42 scale=2.1 size_cache=10 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()))