/usr/share/pyshared/mdp/test/test_NearestMeanClassifier.py is in python-mdp 3.3-1.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | from _tools import *
# These tests are basically taken from the GaussianClassifier.
def testNearestMeanClassifier_train():
nclasses = 10
dim = 4
npoints = 10000
covs = []
means = []
node = mdp.nodes.NearestMeanClassifier()
for i in xrange(nclasses):
cov = utils.symrand(uniform((dim,))*dim+1)
mn = uniform((dim,))*10.
x = normal(0., 1., size=(npoints, dim))
x = mult(x, utils.sqrtm(cov)) + mn
x = utils.refcast(x, 'd')
cl = numx.ones((npoints,))*i
mn_estimate = mean(x, axis=0)
means.append(mn_estimate)
covs.append(numx.cov(x, rowvar=0))
node.train(x, cl)
try:
node.train(x, numx.ones((2,)))
assert False, 'No exception despite wrong number of labels'
except mdp.TrainingException:
pass
node.stop_training()
for i in xrange(nclasses):
lbl_idx = node.ordered_labels.index(i)
assert_array_almost_equal_diff(means[i],
node.label_means[lbl_idx],
decimal-1)
def testNearestMeanClassifier_label():
mean1 = [0., 2.]
mean2 = [0., -2.]
std_ = numx.array([1., 0.2])
npoints = 100
rot = 45
# input data: two distinct gaussians rotated by 45 deg
def distr(size): return normal(0, 1., size=(size)) * std_
x1 = distr((npoints,2)) + mean1
utils.rotate(x1, rot, units='degrees')
x2 = distr((npoints,2)) + mean2
utils.rotate(x2, rot, units='degrees')
x = numx.concatenate((x1, x2), axis=0)
# labels
cl1 = numx.ones((x1.shape[0],), dtype='i')
cl2 = 2*numx.ones((x2.shape[0],), dtype='i')
classes = numx.concatenate((cl1, cl2))
# shuffle the data
perm_idx = numx_rand.permutation(classes.shape[0])
x = numx.take(x, perm_idx, axis=0)
classes = numx.take(classes, perm_idx, axis=0)
node = mdp.nodes.NearestMeanClassifier()
node.train(x, classes)
classification = node.label(x)
assert_array_equal(classes, classification)
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