def test_mdpnodemapper():
ds = normal_feature_dataset(perlabel=10, nlabels=2, nfeatures=4)
node = mdp.nodes.PCANode()
mm = MDPNodeMapper(node, nodeargs={'stoptrain': ((), {'debug': True})})
mm.train(ds)
fds = mm.forward(ds)
if externals.versions['mdp'] >= '2.5':
assert_true(hasattr(mm.node, 'cov_mtx'))
assert_true(isinstance(fds, Dataset))
assert_equal(fds.samples.shape, ds.samples.shape)
# set projection onto first 2 components
mm.nodeargs['exec'] = ((), {'n': 2})
#should be different from above
lfds = mm.forward(ds.samples)
# output shape changes although the node still claim otherwise
assert_equal(mm.node.output_dim, 4)
assert_equal(lfds.shape[0], fds.samples.shape[0])
assert_equal(lfds.shape[1], 2)
assert_array_equal(lfds, fds.samples[:, :2])
# reverse
rfds = mm.reverse(fds)
# even smaller size works
rlfds = mm.reverse(lfds)
assert_equal(rfds.samples.shape, ds.samples.shape)
# retraining has to work on a new dataset too, since we copy the node
# internally
dsbig = normal_feature_dataset(perlabel=10, nlabels=2, nfeatures=10)
mm.train(dsbig)
