Python LibSVMOneClass 예제들

예제 #1

def classifier_libsvmoneclass_modular (fm_train_real=traindat,fm_test_real=testdat,width=2.1,C=1,epsilon=1e-5):
	from shogun.Features import RealFeatures, Labels
	from shogun.Kernel import GaussianKernel
	from shogun.Classifier import LibSVMOneClass

	feats_train=RealFeatures(fm_train_real)
	feats_test=RealFeatures(fm_test_real)

	kernel=GaussianKernel(feats_train, feats_train, width)

	svm=LibSVMOneClass(C, kernel)
	svm.set_epsilon(epsilon)
	svm.train()

	kernel.init(feats_train, feats_test)
	svm.apply().get_labels()

	predictions = svm.apply()
	return predictions, svm, predictions.get_labels()

예제 #2

파일: classifier_libsvmoneclass_modular.py 프로젝트: Anshul-Bansal/gsoc

def classifier_libsvmoneclass_modular (fm_train_real=traindat,fm_test_real=testdat,width=2.1,C=1,epsilon=1e-5):
	from shogun.Features import RealFeatures, Labels
	from shogun.Kernel import GaussianKernel
	from shogun.Classifier import LibSVMOneClass

	feats_train=RealFeatures(fm_train_real)
	feats_test=RealFeatures(fm_test_real)

	kernel=GaussianKernel(feats_train, feats_train, width)

	svm=LibSVMOneClass(C, kernel)
	svm.set_epsilon(epsilon)
	svm.train()

	kernel.init(feats_train, feats_test)
	svm.apply().get_labels()

	predictions = svm.apply()
	return predictions, svm, predictions.get_labels()

예제 #3

def svm_learn(kernel, options):
    svm = LibSVMOneClass()

    if options.quiet == False:
        svm.io.set_loglevel(MSG_INFO)
        svm.io.set_target_to_stderr()

    # use default value of epsilon: 1e-5
    # svm.set_epsilon(options.epsilon)
    svm.parallel.set_num_threads(1)

    svm.set_nu(options.svmNu)
    svm.set_kernel(kernel)

    svm.train()

    if options.quiet == False:
        svm.io.set_loglevel(MSG_ERROR)

    return svm

예제 #4

파일: classifier_libsvmoneclass_modular.py 프로젝트: memimo/shogun-liblinear

def libsvm_oneclass ():
	print 'LibSVMOneClass'

	from shogun.Features import RealFeatures, Labels
	from shogun.Kernel import GaussianKernel
	from shogun.Classifier import LibSVMOneClass

	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

	svm=LibSVMOneClass(C, kernel)
	svm.set_epsilon(epsilon)
	svm.train()

	kernel.init(feats_train, feats_test)
	svm.classify().get_labels()