本文整理汇总了Python中shogun.Classifier.LibSVM.set_epsilon方法的典型用法代码示例。如果您正苦于以下问题：Python LibSVM.set_epsilon方法的具体用法？Python LibSVM.set_epsilon怎么用？Python LibSVM.set_epsilon使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类shogun.Classifier.LibSVM的用法示例。
在下文中一共展示了LibSVM.set_epsilon方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: libsvm
# 需要导入模块: from shogun.Classifier import LibSVM [as 别名]
# 或者: from shogun.Classifier.LibSVM import set_epsilon [as 别名]
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示例2: classifier_multiclassmachine_modular
# 需要导入模块: from shogun.Classifier import LibSVM [as 别名]
# 或者: from shogun.Classifier.LibSVM import set_epsilon [as 别名]
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示例3: classifier_multiclassmachine_modular
# 需要导入模块: from shogun.Classifier import LibSVM [as 别名]
# 或者: from shogun.Classifier.LibSVM import set_epsilon [as 别名]
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示例4: classifier_libsvm_modular
# 需要导入模块: from shogun.Classifier import LibSVM [as 别名]
# 或者: from shogun.Classifier.LibSVM import set_epsilon [as 别名]
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()示例5: libsvm
# 需要导入模块: from shogun.Classifier import LibSVM [as 别名]
# 或者: from shogun.Classifier.LibSVM import set_epsilon [as 别名]
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()示例6: xrange
# 需要导入模块: from shogun.Classifier import LibSVM [as 别名]
# 或者: from shogun.Classifier.LibSVM import set_epsilon [as 别名]
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())