def test_icamapper():
# data: 40 sample feature line in 2d space (40x2; samples x features)
samples = np.vstack([np.arange(40.) for i in range(2)]).T
samples -= samples.mean()
samples += np.random.normal(size=samples.shape, scale=0.1)
ndlin = Dataset(samples)
pm = ICAMapper()
pm.train(ndlin.copy())
assert_equal(pm.proj.shape, (2, 2))
p = pm.forward(ndlin.copy())
assert_equal(p.shape, (40, 2))
# check that the mapped data can be fully recovered by 'reverse()'
assert_array_almost_equal(pm.reverse(p), ndlin)
def test_icamapper():
# data: 40 sample feature line in 2d space (40x2; samples x features)
samples = np.vstack([np.arange(40.) for i in range(2)]).T
samples -= samples.mean()
samples += np.random.normal(size=samples.shape, scale=0.1)
ndlin = Dataset(samples)
pm = ICAMapper()
try:
pm.train(ndlin.copy())
assert_equal(pm.proj.shape, (2, 2))
p = pm.forward(ndlin.copy())
assert_equal(p.shape, (40, 2))
# check that the mapped data can be fully recovered by 'reverse()'
assert_array_almost_equal(pm.reverse(p), ndlin)
except mdp.NodeException:
# do not puke if the ICA did not converge at all -- that is not our
# fault but MDP's
pass
def test_icamapper():
# data: 40 sample feature line in 2d space (40x2; samples x features)
samples = np.vstack([np.arange(40.) for i in range(2)]).T
samples -= samples.mean()
samples += np.random.normal(size=samples.shape, scale=0.1)
ndlin = Dataset(samples)
pm = ICAMapper()
pm.train(ndlin.copy())
assert_equal(pm.proj.shape, (2, 2))
p = pm.forward(ndlin.copy())
assert_equal(p.shape, (40, 2))
# check that the mapped data can be fully recovered by 'reverse()'
assert_array_almost_equal(pm.reverse(p), ndlin)
from mvpa import cfg
center = [10, 20]
axis_range = 7
##REF: Name was automagically refactored
def plot_proj_dir(p):
pl.plot([0, p[0, 0]], [0, p[0, 1]], linewidth=3, hold=True, color='y')
pl.plot([0, p[1, 0]], [0, p[1, 1]], linewidth=3, hold=True, color='k')
mappers = {
'PCA': PCAMapper(),
'SVD': SVDMapper(),
'ICA': ICAMapper(alg='CuBICA'),
}
datasets = [
noisy_2d_fx(100, lambda x: x, [lambda x: x], center, noise_std=0.5),
noisy_2d_fx(50,
lambda x: x, [lambda x: x, lambda x: -x],
center,
noise_std=0.5),
noisy_2d_fx(50,
lambda x: x, [lambda x: x, lambda x: 0],
center,
noise_std=0.5),
]
ndatasets = len(datasets)
nmappers = len(mappers.keys())
