本文整理匯總了Python中mvpa2.base.node.ChainNode.append方法的典型用法代碼示例。如果您正苦於以下問題：Python ChainNode.append方法的具體用法？Python ChainNode.append怎麽用？Python ChainNode.append使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類mvpa2.base.node.ChainNode的用法示例。
在下文中一共展示了ChainNode.append方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: get_crossvalidation_instance
# 需要導入模塊: from mvpa2.base.node import ChainNode [as 別名]
# 或者: from mvpa2.base.node.ChainNode import append [as 別名]
def get_crossvalidation_instance(learner, partitioner, errorfx,
sampling_repetitions=1,
learner_space='targets',
balance_training=None,
permutations=0,
avg_datafold_results=True,
prob_tail='left'):
from mvpa2.base.node import ChainNode
from mvpa2.measures.base import CrossValidation
if not balance_training is None:
# balance training data
try:
amount = int(balance_training)
except ValueError:
try:
amount = float(balance_training)
except ValueError:
amount = balance_training
from mvpa2.generators.resampling import Balancer
balancer = Balancer(amount=amount, attr=learner_space,
count=sampling_repetitions,
limit={partitioner.get_space(): 1},
apply_selection=True,
include_offlimit=True)
else:
balancer = None
# set learner space
learner.set_space(learner_space)
# setup generator for data folding -- put in a chain node for easy
# amending
gennode = ChainNode([partitioner], space=partitioner.get_space())
if avg_datafold_results:
from mvpa2.mappers.fx import mean_sample
postproc = mean_sample()
else:
postproc = None
if not balancer is None:
# enable balancing step for each partitioning step
gennode.append(balancer)
if permutations > 0:
from mvpa2.generators.base import Repeater
from mvpa2.generators.permutation import AttributePermutator
from mvpa2.clfs.stats import MCNullDist
# how often do we want to shuffle the data
repeater = Repeater(count=permutations)
# permute the training part of a dataset exactly ONCE
permutator = AttributePermutator(
learner_space,
limit={partitioner.get_space(): 1},
count=1)
# CV with null-distribution estimation that permutes the training data for
# each fold independently
perm_gen_node = copy.deepcopy(gennode)
perm_gen_node.append(permutator)
null_cv = CrossValidation(learner,
perm_gen_node,
postproc=postproc,
errorfx=errorfx)
# Monte Carlo distribution estimator
distr_est = MCNullDist(repeater,
tail=prob_tail,
measure=null_cv,
enable_ca=['dist_samples'])
# pass the p-values as feature attributes on to the results
pass_attr = [('ca.null_prob', 'fa', 1)]
else:
distr_est = None
pass_attr = None
# final CV node
cv = CrossValidation(learner,
gennode,
errorfx=errorfx,
null_dist=distr_est,
postproc=postproc,
enable_ca=['stats', 'null_prob'],
pass_attr=pass_attr)
return cv