mvpa2.mappers.som.SimpleSOMMapper

Inheritance diagram of SimpleSOMMapper

class mvpa2.mappers.som.SimpleSOMMapper(kshape, niter, learning_rate=0.005, iradius=None, distance_metric=None, initialization_func=None)

Mapper using a self-organizing map (SOM) for dimensionality reduction.

This mapper provides a simple, but pretty fast implementation of a self-organizing map using an unsupervised training algorithm. It performs a ND -> 2D mapping, which can for, example, be used for visualization of high-dimensional data.

This SOM implementation uses squared Euclidean distance to determine the best matching Kohonen unit and a Gaussian neighborhood influence kernel.

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 +)

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:

kshape : (int, int)

Shape of the internal Kohonen layer. Currently, only 2D Kohonen layers are supported, although the length of an axis might be set to 1.

niter : int

Number of iteration during network training.

learning_rate : float

Initial learning rate, which will continuously decreased during network training.

iradius : float or None

Initial radius of the Gaussian neighborhood kernel radius, which will continuously decreased during network training. If None (default) the radius is set equal to the longest edge of the Kohonen layer.

distance_metric: callable or None :

Kernel distance metric between elements in Kohonen layer. If None then Euclidean distance is used. Otherwise it should be a callable that accepts two input arguments x and y and returns the distance d through d=distance_metric(x,y)

initialization_func: callable or None :

Initialization function to set self._K, that should take one argument with training samples and return an numpy array. If None, then values in the returned array are taken from a standard normal distribution.

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

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
K

Provide access to the Kohonen layer.

With some care.

NeuroDebian

NITRC-listed