本文整理汇总了Python中shogun.Kernel.CombinedKernel.set_subkernel_weights方法的典型用法代码示例。如果您正苦于以下问题：Python CombinedKernel.set_subkernel_weights方法的具体用法？Python CombinedKernel.set_subkernel_weights怎么用？Python CombinedKernel.set_subkernel_weights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.Kernel.CombinedKernel的用法示例。
在下文中一共展示了CombinedKernel.set_subkernel_weights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: get_weighted_spectrum_kernel
# 需要导入模块: from shogun.Kernel import CombinedKernel [as 别名]
# 或者: from shogun.Kernel.CombinedKernel import set_subkernel_weights [as 别名]
def get_weighted_spectrum_kernel(subfeats_list, options):
"""build weighted spectrum kernel with non-redundant k-mer list (removing reverse complement)
Arguments:
subfeats_list -- list of sub-feature objects
options -- object containing option data
Return:
CombinedFeatures of StringWord(Ulong)Features, CombinedKernel of CommWord(Ulong)StringKernel
"""
kmerlen = options.kmerlen
kmerlen2 = options.kmerlen2
subkernels = 0
kernel = CombinedKernel()
feats = CombinedFeatures()
for subfeats in subfeats_list:
feats.append_feature_obj(subfeats)
for k in xrange(kmerlen, kmerlen2+1):
if k <= 8:
subkernel = CommWordStringKernel(10, False)
else:
subkernel = CommUlongStringKernel(10, False)
kernel.append_kernel(subkernel)
subkernels+=1
kernel.init(feats, feats)
kernel.set_subkernel_weights(numpy.array([1/float(subkernels)]*subkernels, numpy.dtype('float64')))
return kernel示例2: StratifiedCrossValidationSplitting
# 需要导入模块: from shogun.Kernel import CombinedKernel [as 别名]
# 或者: from shogun.Kernel.CombinedKernel import set_subkernel_weights [as 别名]
c2.build_values(-4.0, 4.0, R_EXP);
param_tree_root.append_child(c2)
splitting_strategy = StratifiedCrossValidationSplitting(labels, 50)
evaluation_criterium = ContingencyTableEvaluation(ACCURACY)
cross_validation = CrossValidation(classifier, feats_train, labels, splitting_strategy, evaluation_criterium)
model_selection = GridSearchModelSelection(param_tree_root, cross_validation)
best_parameters = model_selection.select_model(True)
print "Best parameters: ",
best_parameters.print_tree()
best_parameters.apply_to_machine(classifier)
classifier.train()
w=kernel.get_subkernel_weights()
kernel.set_subkernel_weights(w)
# Plot ROC curve
subplot(111)
ROC_evaluation=ROCEvaluation()
ROC_evaluation.evaluate(classifier.apply(feats_train),Labels(trainlab))
roc = ROC_evaluation.get_ROC()
plot(roc[0], roc[1])
fill_between(roc[0],roc[1],0,alpha=0.1)
grid(True)
xlabel('FPR')
ylabel('TPR')
title('Train ROC (Width=%.3f, C1=%.3f, C2=%.3f) ROC curve = %.3f' % (10, classifier.get_C1(), classifier.get_C2(), ROC_evaluation.get_auROC()),size=10)
savefig("data/iri/mkl.png")
"""
subplot(222)