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

NeuroDebian

NITRC-listed