本文整理汇总了Python中shogun.Classifier.SVMLight.set_qpsize方法的典型用法代码示例。如果您正苦于以下问题：Python SVMLight.set_qpsize方法的具体用法？Python SVMLight.set_qpsize怎么用？Python SVMLight.set_qpsize使用的例子？那么, 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.SVMLight的用法示例。
在下文中一共展示了SVMLight.set_qpsize方法的2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: classifier_svmlight_linear_term_modular
# 需要导入模块: from shogun.Classifier import SVMLight [as 别名]
# 或者: from shogun.Classifier.SVMLight import set_qpsize [as 别名]
def classifier_svmlight_linear_term_modular(fm_train_dna=traindna,fm_test_dna=testdna, \
label_train_dna=label_traindna,degree=3, \
C=10,epsilon=1e-5,num_threads=1):
from shogun.Features import StringCharFeatures, BinaryLabels, DNA
from shogun.Kernel import WeightedDegreeStringKernel
from shogun.Classifier import SVMLight
feats_train=StringCharFeatures(DNA)
feats_train.set_features(fm_train_dna)
feats_test=StringCharFeatures(DNA)
feats_test.set_features(fm_test_dna)
kernel=WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels=BinaryLabels(label_train_dna)
svm=SVMLight(C, kernel, labels)
svm.set_qpsize(3)
svm.set_linear_term(-numpy.array([1,2,3,4,5,6,7,8,7,6], dtype=numpy.double));
svm.set_epsilon(epsilon)
svm.parallel.set_num_threads(num_threads)
svm.train()
kernel.init(feats_train, feats_test)
out = svm.apply().get_labels()
return out,kernel示例2:
# 需要导入模块: from shogun.Classifier import SVMLight [as 别名]
# 或者: from shogun.Classifier.SVMLight import set_qpsize [as 别名]
print 'SVMLight'
from shogun.Features import StringCharFeatures, Labels, DNA
from shogun.Kernel import WeightedDegreeStringKernel
from shogun.Classifier import SVMLight
feats_train=StringCharFeatures(DNA)
feats_train.set_features(fm_train_dna)
feats_test=StringCharFeatures(DNA)
feats_test.set_features(fm_test_dna)
kernel=WeightedDegreeStringKernel(feats_train, feats_train, degree)
C=10
epsilon=1e-5
num_threads=1
labels=Labels(label_train_dna)
svm=SVMLight(C, kernel, labels)
svm.set_qpsize(3)
svm.set_linear_term(-numpy.array([1,2,3,4,5,6,7,8,7,6], dtype=numpy.double));
svm.set_epsilon(epsilon)
svm.parallel.set_num_threads(num_threads)
svm.train()
kernel.init(feats_train, feats_test)
out = svm.classify().get_labels()