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Multivariate Pattern Analysis in Python |
>>> from mvpa.misc.data_generators import noisy_2d_fx
>>> from mvpa.mappers.pca import PCAMapper
>>> from mvpa.mappers.svd import SVDMapper
>>> from mvpa.mappers.ica import ICAMapper
>>> from mvpa import cfg
>>>
>>> import pylab as P
>>> import numpy as N
>>> center = [10, 20]
>>> axis_range = 7
>>>
>>> def plotProjDir(mproj):
>>> p = mproj + N.array(center).T
>>>
>>> P.plot([center[0], p[0,0]], [center[1], p[0,1]], hold=True)
>>> P.plot([center[0], p[1,0]], [center[1], p[1,1]], hold=True)
>>>
>>>
>>>
>>> mappers = {
>>> 'PCA': PCAMapper(),
>>> 'SVD': SVDMapper(),
>>> 'ICA': ICAMapper(),
>>> }
>>> datasets = [
>>> noisy_2d_fx(100, lambda x: x, [lambda x: x],
>>> center, noise_std=.5),
>>> noisy_2d_fx(50, lambda x: x, [lambda x: x, lambda x: -x],
>>> center, noise_std=.5),
>>> noisy_2d_fx(50, lambda x: x, [lambda x: x, lambda x: 0],
>>> center, noise_std=.5),
>>> ]
>>>
>>> ndatasets = len(datasets)
>>> nmappers = len(mappers.keys())
>>>
>>> P.figure(figsize=(8,8))
>>> fig = 1
>>>
>>> for ds in datasets:
>>> for mname, mapper in mappers.iteritems():
>>> mapper.train(ds)
>>>
>>> dproj = mapper.forward(ds.samples)
>>> mproj = mapper.proj
>>> print mproj
>>>
>>> P.subplot(ndatasets, nmappers, fig)
>>> if fig <= 3:
>>> P.title(mname)
>>> P.axis('equal')
>>>
>>> P.scatter(ds.samples[:, 0],
>>> ds.samples[:, 1],
>>> s=30, c=(ds.labels) * 200)
>>> plotProjDir(mproj)
>>> fig += 1
>>>
>>>
>>> if cfg.getboolean('examples', 'interactive', True):
>>> P.show()
>>>
Output of the example:
See also
The full source code of this example is included in the PyMVPA source distribution (doc/examples/projections.py).