Multivariate Pattern Analysis in Python

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# mvpa2.mappers.shape.AddAxisMapper

class `mvpa2.mappers.shape.``AddAxisMapper`(pos, **kwargs)

  Add an axis to a dataset at an arbitrary position.

  This mapper can be useful when there is need for aggregating multiple datasets, where it is often necessary or at least useful to have a dedicated aggregation axis. An axis can be added at any position

  When adding an axis that causes the current sample (1st) or feature axis (2nd) to shift the corresponding attribute collections are modified to accomodate the change. This typically means also adding an axis at the corresponding position of the attribute arrays. A special case is, however, prepending an axis to the dataset, i.e. shifting both sample and feature axis towards the back. In this case all feature attibutes are duplicated to match the new number of features (formaly the number of samples).

  Notes

  Available conditional attributes:

  - `calling_time+`: Time (in seconds) it took to call the node
  - `raw_results`: Computed results before invoking postproc. Stored only if postproc is not None.
  - `training_time+`: Time (in seconds) it took to train the learner

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

  Examples

      >>> from mvpa2.datasets.base import Dataset
      >>> from mvpa2.mappers.shape import AddAxisMapper
      >>> ds = Dataset(np.arange(24).reshape(2,3,4))
      >>> am = AddAxisMapper(pos=1)
      >>> print am(ds).shape
      (2, 1, 3, 4)

  Methods

  `forward`(data) | Map data from input to output space.
  `forward1`(data) | Wrapper method to map single samples.
  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_space`() | Query the processing space name of this node.
  `reset`() |
  `reverse`(data) | Reverse-map data from output back into input space.
  `reverse1`(data) | Wrapper method to map single samples.
  `set_postproc`(node) | Assigns a post-processing node Set to `None` to disable postprocessing.
  `set_space`(name) | Set the processing space name of this node.
  `train`(ds) | The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
  `untrain`() | Reverts changes in the state of this node caused by previous training

  Parameters:

  pos : int

  > Axis index to which the new axis is prepended. Negative indices are supported as well, but the new axis will be placed behind the given index. For example, a position of `-1` will cause an axis to be added behind the last axis. If `pos` is larger than the number of existing axes additional new axes will be created match the value of `pos`.

  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

  Methods

  `forward`(data) | Map data from input to output space.
  `forward1`(data) | Wrapper method to map single samples.
  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_space`() | Query the processing space name of this node.
  `reset`() |
  `reverse`(data) | Reverse-map data from output back into input space.
  `reverse1`(data) | Wrapper method to map single samples.
  `set_postproc`(node) | Assigns a post-processing node Set to `None` to disable postprocessing.
  `set_space`(name) | Set the processing space name of this node.
  `train`(ds) | The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
  `untrain`() | Reverts changes in the state of this node caused by previous training

  `is_trained` = True

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