本文整理汇总了Python中shogun.Classifier.SVMLight.get_runtime方法的典型用法代码示例。如果您正苦于以下问题：Python SVMLight.get_runtime方法的具体用法？Python SVMLight.get_runtime怎么用？Python SVMLight.get_runtime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.get_runtime方法的1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: _inner_train
# 需要导入模块: from shogun.Classifier import SVMLight [as 别名]
# 或者: from shogun.Classifier.SVMLight import get_runtime [as 别名]
def _inner_train(self, train_data, param):
"""
perform inner training by processing the tree
"""
data_keys = []
# top-down processing of taxonomy
classifiers = []
classifier_at_node = {}
root = param.taxonomy.data
grey_nodes = [root]
while len(grey_nodes)>0:
node = grey_nodes.pop(0) # pop first item
# enqueue children
if node.children != None:
grey_nodes.extend(node.children)
#####################################################
# init data structures
#####################################################
# get data below current node
data = [train_data[key] for key in node.get_data_keys()]
data_keys.append(node.get_data_keys())
print "data at current level"
for instance_set in data:
print instance_set[0].dataset
# initialize containers
examples = []
labels = []
# concatenate data
for instance_set in data:
print "train split_set:", instance_set[0].dataset.organism
for inst in instance_set:
examples.append(inst.example)
labels.append(inst.label)
# create shogun data objects
k = shogun_factory.create_kernel(examples, param)
lab = shogun_factory.create_labels(labels)
#####################################################
# train weak learners
#####################################################
cost = param.cost
# set up svm
svm = SVMLight(cost, k, lab)
if param.flags["normalize_cost"]:
# set class-specific Cs
norm_c_pos = param.cost / float(len([l for l in labels if l==1]))
norm_c_neg = param.cost / float(len([l for l in labels if l==-1]))
svm.set_C(norm_c_neg, norm_c_pos)
print "using cost: negative class=%f, positive class=%f" % (norm_c_neg, norm_c_pos)
# enable output
svm.io.enable_progress()
svm.io.set_loglevel(shogun.Classifier.MSG_INFO)
# train
svm.train()
# append svm object
classifiers.append(svm)
classifier_at_node[node.name] = svm
# save some information
self.additional_information[node.name + " svm obj"] = svm.get_objective()
self.additional_information[node.name + " svm num sv"] = svm.get_num_support_vectors()
self.additional_information[node.name + " runtime"] = svm.get_runtime()
return (classifiers, classifier_at_node)