def test_normaliselabels():
np.random.seed(22)
labels = np.zeros(120, np.uint8)
labels[40:] += 1
labels[65:] += 1
reorder = np.argsort(np.random.rand(len(labels)))
labels = labels[reorder]
labels2, names = normaliselabels(labels)
for new_n, old_n in enumerate(names):
assert np.all((labels == old_n) == (labels2 == new_n))
def test_normaliselabels():
np.random.seed(22)
labels = np.zeros(120, np.uint8)
labels[40:] += 1
labels[65:] += 1
reorder = np.argsort(np.random.rand(len(labels)))
labels = labels[reorder]
labels2,names = normaliselabels(labels)
for new_n,old_n in enumerate(names):
assert np.all( (labels == old_n) == (labels2 == new_n) )
def test_normaliselabels_multi():
np.random.seed(30)
r = np.random.random
for v in range(10):
labels = []
p = np.array([.24, .5, .1, .44])
for i in range(100):
cur = [j for j in range(4) if r() < p[j]]
if not cur: cur = [0]
labels.append(cur)
nlabels, names = normaliselabels(labels, True)
assert len(labels) == len(nlabels)
assert len(nlabels[0]) == max(list(map(max, labels))) + 1
assert nlabels.sum() == sum(map(len, labels))
def test_normaliselabels_multi():
np.random.seed(30)
r = np.random.random
for v in xrange(10):
labels = []
p = np.array([.24,.5,.1,.44])
for i in xrange(100):
cur = [j for j in xrange(4) if r() < p[j]]
if not cur: cur = [0]
labels.append(cur)
nlabels, names = normaliselabels(labels, True)
assert len(labels) == len(nlabels)
assert len(nlabels[0]) == max(map(max,labels))+1
assert nlabels.sum() == sum(map(len,labels))
