本文整理汇总了Python中mvpa2.datasets.Dataset.fa["null_prob"]方法的典型用法代码示例。如果您正苦于以下问题：Python Dataset.fa["null_prob"]方法的具体用法？Python Dataset.fa["null_prob"]怎么用？Python Dataset.fa["null_prob"]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mvpa2.datasets.Dataset的用法示例。
在下文中一共展示了Dataset.fa["null_prob"]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: _fill_in_scattered_results
# 需要导入模块: from mvpa2.datasets import Dataset [as 别名]
# 或者: from mvpa2.datasets.Dataset import fa["null_prob"] [as 别名]
def _fill_in_scattered_results(sl, dataset, roi_ids, results):
"""this requires the searchlight conditional attribute 'roi_feature_ids'
to be enabled"""
import numpy as np
from mvpa2.datasets import Dataset
resmap = None
probmap = None
for resblock in results:
for res in resblock:
if resmap is None:
# prepare the result container
resmap = np.zeros((len(res), dataset.nfeatures), dtype=res.samples.dtype)
if "null_prob" in res.fa:
# initialize the prob map also with zeroes, as p=0 can never
# happen as an empirical result
probmap = np.zeros((dataset.nfeatures,) + res.fa.null_prob.shape[1:], dtype=res.samples.dtype)
observ_counter = np.zeros(dataset.nfeatures, dtype=int)
# project the result onto all features -- love broadcasting!
resmap[:, res.a.roi_feature_ids] += res.samples
if not probmap is None:
probmap[res.a.roi_feature_ids] += res.fa.null_prob
# increment observation counter for all relevant features
observ_counter[res.a.roi_feature_ids] += 1
# when all results have been added up average them according to the number
# of observations
observ_mask = observ_counter > 0
resmap[:, observ_mask] /= observ_counter[observ_mask]
result_ds = Dataset(resmap, fa={"observations": observ_counter})
if not probmap is None:
# transpose to make broadcasting work -- creates a view, so in-place
# modification still does the job
probmap.T[:, observ_mask] /= observ_counter[observ_mask]
result_ds.fa["null_prob"] = probmap.squeeze()
if "mapper" in dataset.a:
import copy
result_ds.a["mapper"] = copy.copy(dataset.a.mapper)
return result_ds