本文整理汇总了Python中mvpa.datasets.base.Dataset.sa["regressor"]方法的典型用法代码示例。如果您正苦于以下问题：Python Dataset.sa["regressor"]方法的具体用法？Python Dataset.sa["regressor"]怎么用？Python Dataset.sa["regressor"]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mvpa.datasets.base.Dataset的用法示例。
在下文中一共展示了Dataset.sa["regressor"]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: _call
# 需要导入模块: from mvpa.datasets.base import Dataset [as 别名]
# 或者: from mvpa.datasets.base.Dataset import sa["regressor"] [as 别名]
def _call(self, dataset):
# just for the beauty of it
X = self._design
# precompute transformation is not yet done
if self._inv_design is None:
self._inv_ip = (X.T * X).I
self._inv_design = self._inv_ip * X.T
# get parameter estimations for all features at once
# (betas x features)
betas = self._inv_design * dataset.samples
# charge state
self.ca.pe = pe = betas.T.A
# if betas and no z-stats are desired return them right away
if not self._voi == "pe" or self.ca.is_enabled("zstat"):
# compute residuals
residuals = X * betas
residuals -= dataset.samples
# estimates of the parameter variance and compute zstats
# assumption of mean(E) == 0 and equal variance
# XXX next lines ignore off-diagonal elements and hence covariance
# between regressors. The humble being writing these lines asks the
# god of statistics for forgives, because it knows not what it does
diag_ip = np.diag(self._inv_ip)
# (features x betas)
beta_vars = np.array([r.var() * diag_ip for r in residuals.T])
# (parameter x feature)
zstat = pe / np.sqrt(beta_vars)
# charge state
self.ca.zstat = zstat
if self._voi == "pe":
# return as (beta x feature)
result = Dataset(pe.T)
elif self._voi == "zstat":
# return as (zstat x feature)
result = Dataset(zstat.T)
else:
# we shall never get to this point
raise ValueError, "Unknown variable of interest '%s'" % str(self._voi)
result.sa["regressor"] = np.arange(len(result))
return result