/usr/share/pyshared/mvpa2/mappers/lle.py is in python-mvpa2 2.1.0-1.
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# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
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"""Local Linear Embedding Data mapper.
This is a wrapper class around the corresponding MDP nodes LLE and HLLE
(since MDP 2.4).
"""
__docformat__ = 'restructuredtext'
from mvpa2.base import externals
import numpy as np
from mvpa2.mappers.mdp_adaptor import MDPNodeMapper
if externals.exists('mdp ge 2.4', raise_=True):
import mdp
class LLEMapper(MDPNodeMapper):
"""Locally linear embbeding Mapper.
This mapper performs dimensionality reduction. It wraps two algorithms
provided by the Modular Data Processing (MDP) framework.
Locally linear embedding (LLE) approximates the input data with a
low-dimensional surface and reduces its dimensionality by learning a
mapping to the surface.
This wrapper class provides access to two different LLE algorithms (i.e.
the corresponding MDP processing nodes). 1) An algorithm outlined in *An
Introduction to Locally Linear Embedding* by L. Saul and S. Roweis, using
improvements suggested in *Locally Linear Embedding for Classification* by
D. deRidder and R.pl.W. Duin (aka `LLENode`) and 2) Hessian Locally Linear
Embedding analysis based on algorithm outlined in *Hessian Eigenmaps: new
locally linear embedding techniques for high-dimensional data* by C. Grimes
and D. Donoho, 2003.
For more information see the MDP website at
http://mdp-toolkit.sourceforge.net
Notes
-----
This mapper only provides forward-mapping functionality -- no reverse
mapping is available.
"""
def __init__(self, k, alg='LLE', nodeargs=None, **kwargs):
"""
Parameters
----------
k : int
Number of nearest neighbors to be used by the algorithm.
algorithm : {'LLE', 'HLLE'}
Either use the standard LLE algorithm or Hessian Linear Local
Embedding (HLLE).
nodeargs : None or dict
Arguments passed to the MDP node in various stages of its lifetime.
See the baseclass for more details.
**kwargs
Additional constructor arguments for the MDP node.
"""
if alg == 'LLE':
node = mdp.nodes.LLENode(k, **kwargs)
elif alg == 'HLLE':
node = mdp.nodes.HLLENode(k, **kwargs)
else:
raise ValueError("Unkown algorithm '%s' for LLEMapper.")
MDPNodeMapper.__init__(self, node, nodeargs=nodeargs)
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