Multivariate Pattern Analysis in Python

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# mvpa2.algorithms.hyperalignment.zscore

`mvpa2.algorithms.hyperalignment.``zscore`(ds, **kwargs)

  In-place Z-scoring of a `Dataset` or `ndarray`.

  This function behaves identical to `ZScoreMapper`. The only difference is that the actual Z-scoring is done in-place – potentially causing a significant reduction of memory demands.

  Parameters:

  ds : Dataset or ndarray

  > The data that will be Z-scored in-place.

  params : None or tuple(mean, std) or dict

  > Fixed Z-Scoring parameters (mean, standard deviation). If provided, no parameters are estimated from the data. It is possible to specify individual parameters for each chunk by passing a dictionary with the chunk ids as keys and the parameter tuples as values. If None, parameters will be estimated from the training data.

  param_est : None or tuple(attrname, attrvalues)

  > Limits the choice of samples used for automatic parameter estimation to a specific subset identified by a set of a given sample attribute values. The tuple should have the name of that sample attribute as the first element, and a sequence of attribute values as the second element. If None, all samples will be used for parameter estimation.

  chunks_attr : str or None

  > If provided, it specifies the name of a samples attribute in the training data, unique values of which will be used to identify chunks of samples, and to perform individual Z-scoring within them.

  dtype : Numpy dtype, optional

  > Target dtype that is used for upcasting, in case integer data is to be Z-scored.

  enable_ca : None or list of str

  > Names of the conditional attributes which should be enabled in addition to the default ones

  disable_ca : None or list of str

  > Names of the conditional attributes which should be disabled

  auto_train : bool

  > Flag whether the learner will automatically train itself on the input dataset when called untrained.

  force_train : bool

  > Flag whether the learner will enforce training on the input dataset upon every call.

  space: str, optional :

  > Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

  postproc : Node instance, optional

  > Node to perform post-processing of results. This node is applied in `__call__()` to perform a final processing step on the to be result dataset. If None, nothing is done.

  descr : str

  > Description of the instance

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