def libsvm(): print 'LibSVM' from shogun.Features import RealFeatures, Labels from shogun.Kernel import GaussianKernel from shogun.Evaluation import PerformanceMeasures from shogun.Classifier import LibSVM feats_train = RealFeatures(fm_train_real) feats_test = RealFeatures(fm_test_real) width = 2.1 kernel = GaussianKernel(feats_train, feats_train, width) C = 1 epsilon = 1e-5 labels = Labels(label_train_twoclass) svm = LibSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train() #kernel.init(feats_train, feats_test) output = svm.classify(feats_test) #.get_labels() #output_vector = output.get_labels() out = svm.classify().get_labels() testerr = mean(sign(out) != testlab) print testerr
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 shogun.Features import RealFeatures, MulticlassLabels from shogun.Kernel import GaussianKernel from shogun.Classifier 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 libsvm (): print 'LibSVM' from shogun.Features import RealFeatures, Labels from shogun.Kernel import GaussianKernel from shogun.Evaluation import PerformanceMeasures from shogun.Classifier import LibSVM feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) width=2.1 kernel=GaussianKernel(feats_train, feats_train, width) C=1 epsilon=1e-5 labels=Labels(label_train_twoclass) svm=LibSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train() #kernel.init(feats_train, feats_test) output = svm.classify(feats_test)#.get_labels() #output_vector = output.get_labels() out=svm.classify().get_labels() testerr=mean(sign(out)!=testlab) print testerr
def classifier_libsvm_modular(fm_train_real=traindat, fm_test_real=testdat, label_train_twoclass=label_traindat, width=2.1, C=1, epsilon=1e-5): from shogun.Features import RealFeatures, Labels from shogun.Kernel import GaussianKernel from shogun.Classifier import LibSVM feats_train = RealFeatures(fm_train_real) feats_test = RealFeatures(fm_test_real) kernel = GaussianKernel(feats_train, feats_train, width) labels = Labels(label_train_twoclass) svm = LibSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train() kernel.init(feats_train, feats_test) labels = svm.classify().get_labels() supportvectors = sv_idx = svm.get_support_vectors() alphas = svm.get_alphas() predictions = svm.classify() 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 shogun.Features import RealFeatures, Labels from shogun.Kernel import GaussianKernel from shogun.Classifier import LibSVM, KernelMulticlassMachine, ONE_VS_REST_STRATEGY feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) kernel=GaussianKernel(feats_train, feats_train, width) labels=Labels(label_train_multiclass) classifier = LibSVM(C, kernel, labels) classifier.set_epsilon(epsilon) mc_classifier = KernelMulticlassMachine(ONE_VS_REST_STRATEGY,kernel,classifier,labels) mc_classifier.train() kernel.init(feats_train, feats_test) out = mc_classifier.apply().get_labels() return out
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 shogun.Features import RealFeatures, MulticlassLabels from shogun.Kernel import GaussianKernel from shogun.Classifier 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 (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,width=2.1,C=1,epsilon=1e-5): from shogun.Features import RealFeatures, Labels from shogun.Kernel import GaussianKernel from shogun.Classifier import LibSVM feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) kernel=GaussianKernel(feats_train, feats_train, width) labels=Labels(label_train_twoclass) svm=LibSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train() kernel.init(feats_train, feats_test) labels = svm.apply().get_labels() supportvectors = sv_idx=svm.get_support_vectors() alphas=svm.get_alphas() predictions = svm.apply() return predictions, svm, predictions.get_labels()
def libsvm (): print 'LibSVM' from shogun.Features import RealFeatures, Labels from shogun.Kernel import GaussianKernel from shogun.Classifier import LibSVM feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) width=2.1 kernel=GaussianKernel(feats_train, feats_train, width) C=1 epsilon=1e-5 labels=Labels(label_train_twoclass) svm=LibSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train() kernel.init(feats_train, feats_test) svm.classify().get_labels() sv_idx=svm.get_support_vectors() alphas=svm.get_alphas()
from shogun.Classifier import LibSVM from shogun.Features import RealFeatures, Labels from shogun.Kernel 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 xrange(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())
from shogun.Features import RealFeatures, Labels from shogun.Kernel 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 xrange(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())