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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.
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Inherited from Inherited from |
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K = property(fget= _accessKohonen)
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Inherited from |
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Inherited from |
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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
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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.
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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.
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Translate a feature id into a coordinate/index in input space. This is not meaningful in the context of SOMs.
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Provide access to the Kohonen layer. With some care. |
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