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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__init__(self,
kshape,
niter,
learning_rate=0.005,
iradius=None)
x.__init__(...) initializes x; see x.__class__.__doc__ for signature |
source code
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getInId(self,
outId)
Translate a feature id into a coordinate/index in input space. |
source code
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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__
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