Python LibSVM.set_epsilon方法代碼示例

本文整理匯總了Python中shogun.Classifier.LibSVM.set_epsilon方法的典型用法代碼示例。如果您正苦於以下問題：Python LibSVM.set_epsilon方法的具體用法？Python LibSVM.set_epsilon怎麽用？Python LibSVM.set_epsilon使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類shogun.Classifier.LibSVM的用法示例。

在下文中一共展示了LibSVM.set_epsilon方法的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: libsvm

# 需要導入模塊: from shogun.Classifier import LibSVM [as 別名]
# 或者: from shogun.Classifier.LibSVM import set_epsilon [as 別名]
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

開發者ID:BrainTech，項目名稱:openbci，代碼行數:30，代碼來源:svm.py

示例2: classifier_multiclassmachine_modular

# 需要導入模塊: from shogun.Classifier import LibSVM [as 別名]
# 或者: from shogun.Classifier.LibSVM import set_epsilon [as 別名]
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

開發者ID:ashish-sadh，項目名稱:shogun，代碼行數:21，代碼來源:classifier_multiclassmachine_modular.py

示例3: classifier_multiclassmachine_modular

# 需要導入模塊: from shogun.Classifier import LibSVM [as 別名]
# 或者: from shogun.Classifier.LibSVM import set_epsilon [as 別名]
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

開發者ID:lgatto，項目名稱:shogun，代碼行數:22，代碼來源:classifier_multiclassmachine_modular.py

示例4: classifier_libsvm_modular

# 需要導入模塊: from shogun.Classifier import LibSVM [as 別名]
# 或者: from shogun.Classifier.LibSVM import set_epsilon [as 別名]
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()

開發者ID:harshitsyal，項目名稱:shogun，代碼行數:23，代碼來源:classifier_libsvm_modular.py

示例5: libsvm

# 需要導入模塊: from shogun.Classifier import LibSVM [as 別名]
# 或者: from shogun.Classifier.LibSVM import set_epsilon [as 別名]
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()

開發者ID:memimo，項目名稱:shogun-liblinear，代碼行數:26，代碼來源:classifier_libsvm_modular.py

示例6: xrange

# 需要導入模塊: from shogun.Classifier import LibSVM [as 別名]
# 或者: from shogun.Classifier.LibSVM import set_epsilon [as 別名]
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())

開發者ID:42MachineLearning，項目名稱:shogun，代碼行數:32，代碼來源:test_svm_kernel_multiple.py

注：本文中的shogun.Classifier.LibSVM.set_epsilon方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。