mvpa2.mappers.wavelet.WaveletTransformationMapper

Inheritance diagram of WaveletTransformationMapper
class mvpa2.mappers.wavelet.WaveletTransformationMapper(dim=1, wavelet='sym4', mode=None, maxlevel=None)

Convert signal into wavelet representaion

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.
  • trained_dataset: The dataset it has been trained on
  • trained_nsamples+: Number of samples it has been trained on
  • trained_targets+: Set of unique targets (or any other space) it has been trained on (if present in the dataset trained on)
  • training_time+: Time (in seconds) it took to train the learner

(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 every call.
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
space Processing space name of this node

Methods

__call__(ds)
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_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

Initialize _WaveletMapper mapper

Parameters:

dim : int or tuple of int

dimensions to work across (for now just scalar value, ie 1D transformation) is supported

wavelet : str

one from the families available withing pywt package

mode : str

periodization mode

maxlevel : int or None

number of levels to use. If None - automatically selected by pywt

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

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 every call.
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
space Processing space name of this node

Methods

__call__(ds)
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_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