def init_svm(task_type, kernel, labels):
"""A factory for creating the right svm type"""
C=1
epsilon=1e-5
if task_type == 'Binary Classification':
svm = LibSVM(C, kernel, labels)
elif task_type == 'Multi Class Classification':
svm = LibSVMMultiClass(C, kernel, labels)
elif task_type == 'Regression':
tube_epsilon=1e-2
svm=LibSVR(C, epsilon, kernel, labels)
svm.set_tube_epsilon(tube_epsilon)
else:
print(task_type + ' unknown!')
return svm
def classifier_libsvmmulticlass_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 LibSVMMultiClass feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) kernel=GaussianKernel(feats_train, feats_train, width) labels=Labels(label_train_multiclass) svm=LibSVMMultiClass(C, kernel, labels) svm.set_epsilon(epsilon) svm.train() kernel.init(feats_train, feats_test) out = svm.apply().get_labels() predictions = svm.apply() return predictions, svm, predictions.get_labels()
def classifier_libsvmmulticlass_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 LibSVMMultiClass
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
kernel = GaussianKernel(feats_train, feats_train, width)
labels = Labels(label_train_multiclass)
svm = LibSVMMultiClass(C, kernel, labels)
svm.set_epsilon(epsilon)
svm.train()
kernel.init(feats_train, feats_test)
out = svm.classify().get_labels()
predictions = svm.classify()
return predictions, svm, predictions.get_labels()
def libsvm_multiclass (): print 'LibSVMMultiClass' from shogun.Features import RealFeatures, Labels from shogun.Kernel import GaussianKernel from shogun.Classifier import LibSVMMultiClass 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_multiclass) svm=LibSVMMultiClass(C, kernel, labels) svm.set_epsilon(epsilon) svm.train() kernel.init(feats_train, feats_test) svm.classify().get_labels()
