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))