本文整理汇总了Python中shogun.Classifier.SVMLight.classify方法的典型用法代码示例。如果您正苦于以下问题：Python SVMLight.classify方法的具体用法？Python SVMLight.classify怎么用？Python SVMLight.classify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.SVMLight的用法示例。
在下文中一共展示了SVMLight.classify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: svm_light
# 需要导入模块: from shogun.Classifier import SVMLight [as 别名]
# 或者: from shogun.Classifier.SVMLight import classify [as 别名]
def svm_light ():
print 'SVMLight'
from shogun.Features import StringCharFeatures, Labels, DNA
from shogun.Kernel import WeightedDegreeStringKernel
try:
from shogun.Classifier import SVMLight
except ImportError:
print 'No support for SVMLight available.'
return
feats_train=StringCharFeatures(DNA)
feats_train.set_features(fm_train_dna)
feats_test=StringCharFeatures(DNA)
feats_test.set_features(fm_test_dna)
degree=20
kernel=WeightedDegreeStringKernel(feats_train, feats_train, degree)
C=1.2
epsilon=1e-5
num_threads=1
labels=Labels(label_train_dna)
svm=SVMLight(C, kernel, labels)
svm.set_epsilon(epsilon)
svm.parallel.set_num_threads(num_threads)
svm.train()
kernel.init(feats_train, feats_test)
svm.classify().get_labels()示例2: 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 classify [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, 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)
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()
return out,kernel示例3: do_batch_linadd
# 需要导入模块: from shogun.Classifier import SVMLight [as 别名]
# 或者: from shogun.Classifier.SVMLight import classify [as 别名]
def do_batch_linadd ():
print 'SVMlight batch'
from shogun.Features import StringCharFeatures, Labels, DNA
from shogun.Kernel import WeightedDegreeStringKernel
try:
from shogun.Classifier import SVMLight
except ImportError:
print 'No support for SVMLight available.'
return
feats_train=StringCharFeatures(DNA)
feats_train.set_features(fm_train_dna)
feats_test=StringCharFeatures(DNA)
feats_test.set_features(fm_test_dna)
degree=20
kernel=WeightedDegreeStringKernel(feats_train, feats_train, degree)
C=1
epsilon=1e-5
num_threads=2
labels=Labels(label_train_dna)
svm=SVMLight(C, kernel, labels)
svm.set_epsilon(epsilon)
svm.parallel.set_num_threads(num_threads)
svm.train()
kernel.init(feats_train, feats_test)
#print 'SVMLight Objective: %f num_sv: %d' % \
# (svm.get_objective(), svm.get_num_support_vectors())
svm.set_batch_computation_enabled(False)
svm.set_linadd_enabled(False)
svm.classify().get_labels()
svm.set_batch_computation_enabled(True)
svm.classify().get_labels()示例4: classifier_svmlight_batch_linadd_modular
# 需要导入模块: from shogun.Classifier import SVMLight [as 别名]
# 或者: from shogun.Classifier.SVMLight import classify [as 别名]
def classifier_svmlight_batch_linadd_modular(fm_train_dna, fm_test_dna,
label_train_dna, degree, C, epsilon, num_threads):
from shogun.Features import StringCharFeatures, Labels, DNA
from shogun.Kernel import WeightedDegreeStringKernel, MSG_DEBUG
try:
from shogun.Classifier import SVMLight
except ImportError:
print 'No support for SVMLight available.'
return
feats_train=StringCharFeatures(DNA)
#feats_train.io.set_loglevel(MSG_DEBUG)
feats_train.set_features(fm_train_dna)
feats_test=StringCharFeatures(DNA)
feats_test.set_features(fm_test_dna)
degree=20
kernel=WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels=Labels(label_train_dna)
svm=SVMLight(C, kernel, labels)
svm.set_epsilon(epsilon)
svm.parallel.set_num_threads(num_threads)
svm.train()
kernel.init(feats_train, feats_test)
#print 'SVMLight Objective: %f num_sv: %d' % \
# (svm.get_objective(), svm.get_num_support_vectors())
svm.set_batch_computation_enabled(False)
svm.set_linadd_enabled(False)
svm.classify().get_labels()
svm.set_batch_computation_enabled(True)
labels = svm.classify().get_labels()
return labels, svm示例5:
# 需要导入模块: from shogun.Classifier import SVMLight [as 别名]
# 或者: from shogun.Classifier.SVMLight import classify [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()
示例6: int
# 需要导入模块: from shogun.Classifier import SVMLight [as 别名]
# 或者: from shogun.Classifier.SVMLight import classify [as 别名]
sv_id = int(old_svm.get_support_vectors()[j])
alpha = old_svm.get_alpha(j)
inner_sum = inner_sum + alpha * kv.kernel(sv_id, idx)
inner.append(inner_sum)
#general case
p[idx] = B * tmp_lab[idx] * inner_sum - 1.0
################
#checking inner term
presvm.set_bias(0.0)
tmp_out = presvm.classify(feat).get_labels()
for i in xrange(len(examples)):
#print inner[i], tmp_out[i]
assert(abs(inner[i]-tmp_out[i])<= 0.001)
svm = SVMLight(1.0, wdk, lab)
svm.set_linear_term(p)
Math_init_random(1)
svm.train()
###############
#compare to LibSVM