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Package mvpa :: Package mappers :: Module som :: Class SimpleSOMMapper

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Class SimpleSOMMapper

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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.

Instance Methods

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__init__(self, kshape, niter, learning_rate=0.005, iradius=None)
x.__init__(...) initializes x; see help(type(x)) for signature

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train(self, ds)
Perform network training.

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_computeInfluenceKernel(self, iter, dqd)
Compute the neighborhood kernel for some iteration.

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_getBMU(self, sample)
Returns the ID of the best matching unit.

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forward(self, data)
Map data from the IN dataspace into OUT space.

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reverse(self, data)
Reverse map data from OUT space into the IN space.

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getInSize(self)
Returns the size of the entity in input space

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getOutSize(self)
Returns the size of the entity in output space

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selectOut(self, outIds)
Limit the OUT space to a certain set of features.

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getInId(self, outId)
Translate a feature id into a coordinate/index in input space.

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isValidOutId(self, outId)
Validate feature id in OUT space.

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__repr__(self)
repr(x)

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_accessKohonen(self)
Provide access to the Kohonen layer.

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Inherited from base.Mapper: __call__, getMetric, getNeighbor, getNeighborIn, getNeighbors, isValidInId, setMetric

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __str__, __subclasshook__

Class Variables

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K = property(fget= _accessKohonen)

Inherited from base.Mapper: metric, nfeatures

Properties

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Inherited from object: __class__

Method Details

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init(self, kshape, niter, learning_rate=0.005, iradius=None)
(Constructor)

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x.__init__(...) initializes x; see help(type(x)) for signature

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, |, 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.

Overrides: object.__init__

train(self, ds)

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Perform network training.

Overrides: base.Mapper.train

_computeInfluenceKernel(self, iter, dqd)

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Compute the neighborhood kernel for some iteration.

Parameters:

iter, int - The iteration for which to compute the kernel.
dqd, array, (nrows, x, ncolumns) - This is one quadrant of Euclidean distances between Kohonen unit locations.

_getBMU(self, sample)

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Returns the ID of the best matching unit.

'best' is determined as minimal squared Euclidean distance between any units weight vector and some given target sample

Parameters:

sample, array - Target sample.

Returns:

tuple: (row, column)

forward(self, data)

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Map data from the IN dataspace into OUT space.

Mapping is performs by simple determining the best matching Kohonen unit for each data sample.

Overrides: base.Mapper.forward

reverse(self, data)

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Reverse map data from OUT space into the IN space.

Overrides: base.Mapper.reverse

getInSize(self)

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Returns the size of the entity in input space

Overrides: base.Mapper.getInSize

getOutSize(self)

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Returns the size of the entity in output space

Overrides: base.Mapper.getOutSize

selectOut(self, outIds)

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Limit the OUT space to a certain set of features.

This is currently not implemented. Moreover, although it is technically possible to implement this functionality, it is unsure whether it is meaningful in the context of SOMs.

Parameters: