test_clustering.py 文件源码

def test_cluster(self):
        data = np.random.uniform(size=(10, 5))
        data = list(data)
        d2 = np.random.uniform(size=(10, 5)) + ([5] * 5)
        data.extend(list(d2))
        d2 = np.random.uniform(size=(10, 5)) + ([-5] * 5)
        data.extend(list(d2))
        data = np.array(data)

        k = Kmeans()
        clusters = []
        for i in range(10):
            clusters.append(k.cluster(data, 3))
        c1, l1, ss = min(clusters, key=lambda d: d[2])
        c2, d = kmeans(data, 3)
        same = False
        for a in itertools.permutations(c2):
            if np.allclose(c1, a):
                same = True
                break
        self.assertTrue(same)