mvpa2.mappers.som.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 +)

Attributes

K	Provide access to the Kohonen layer.
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
space	Processing space name of this node

Methods

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

Attributes

K	Provide access to the Kohonen layer.
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
space	Processing space name of this node

Methods

K

Provide access to the Kohonen layer.

With some care.