Multivariate Pattern Analysis in Python

# mvpa2.mappers.svd.ProjectionMapper

class `mvpa2.mappers.svd.``ProjectionMapper`(demean=True, **kwargs)

  Linear mapping between multidimensional spaces.

  This class cannot be used directly. Sub-classes have to implement the `_train()` method, which has to compute the projection matrix `_proj` and optionally offset vectors `_offset_in` and `_offset_out` (if initialized with demean=True, which is default) given a dataset (see `_train()` docstring for more information).

  Once the projection matrix is available, this class provides functionality to perform forward and backwards linear mapping of data, the latter by default using pseudo-inverse (but could be altered in subclasses, like hermitian (conjugate) transpose in case of SVD). Additionally, `ProjectionMapper` supports optional selection of arbitrary component (i.e. columns of the projection matrix) of the projection.

  Forward and back-projection matrices (a.k.a. projection and reconstruction) are available via the `proj` and `recon` properties.

  Notes

  Available conditional attributes:

  - `calling_time+`: None
  - `raw_results`: None
  - `trained_dataset`: None
  - `trained_nsamples+`: None
  - `trained_targets+`: None
  - `training_time+`: None

  (Conditional attributes enabled by default suffixed with `+`)

  Attributes

  `auto_train` | Whether the Learner performs automatic trainingwhen called untrained.
  `descr` | Description of the object if any
  `force_train` | Whether the Learner enforces training upon everycalled.
  `is_trained` | Whether the Learner is currently trained.
  `pass_attr` | Which attributes of the dataset or self.ca to pass into result dataset upon call
  `postproc` | Node to perform post-processing of results
  `proj` | Projection matrix
  `recon` | Backprojection matrix
  `space` | Processing space name of this node

  Methods

  Initialize the ProjectionMapper

  Parameters:

  demean : bool

  > Either data should be demeaned while computing projections and applied back while doing reverse()

  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.

  pass_attr : str, list of str|tuple, optional

  > Additional attributes to pass on to an output dataset. Attributes can be taken from all three attribute collections of an input dataset (sa, fa, a – see `Dataset.get_attr()`), or from the collection of conditional attributes (ca) of a node instance. Corresponding collection name prefixes should be used to identify attributes, e.g. ‘ca.null_prob’ for the conditional attribute ‘null_prob’, or ‘fa.stats’ for the feature attribute stats. In addition to a plain attribute identifier it is possible to use a tuple to trigger more complex operations. The first tuple element is the attribute identifier, as described before. The second element is the name of the target attribute collection (sa, fa, or a). The third element is the axis number of a multidimensional array that shall be swapped with the current first axis. The fourth element is a new name that shall be used for an attribute in the output dataset. Example: (‘ca.null_prob’, ‘fa’, 1, ‘pvalues’) will take the conditional attribute ‘null_prob’ and store it as a feature attribute ‘pvalues’, while swapping the first and second axes. Simplified instructions can be given by leaving out consecutive tuple elements starting from the end.

  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

  Attributes

  `auto_train` | Whether the Learner performs automatic trainingwhen called untrained.
  `descr` | Description of the object if any
  `force_train` | Whether the Learner enforces training upon everycalled.
  `is_trained` | Whether the Learner is currently trained.
  `pass_attr` | Which attributes of the dataset or self.ca to pass into result dataset upon call
  `postproc` | Node to perform post-processing of results
  `proj` | Projection matrix
  `recon` | Backprojection matrix
  `space` | Processing space name of this node

  Methods

  `proj`

    Projection matrix

  `recon`

    Backprojection matrix