test_batch_kmeans.py 文件源码

def test_step_E(self):
        points = np.random.randn(self.n_points,self.dim)
        KM = Kmeans(self.n_components)
        KM._initialize(points)

        expected_assignements = np.zeros((self.n_points,self.n_components))
        M = dist_matrix(points,KM.means)
        for i in range(self.n_points):
            index_min = np.argmin(M[i]) #the cluster number of the ith point is index_min
            if (isinstance(index_min,np.int64)):
                expected_assignements[i][index_min] = 1
            else: #Happens when two points are equally distant from a cluster mean
                expected_assignements[i][index_min[0]] = 1

        predected_assignements = KM._step_E(points)

        assert_almost_equal(expected_assignements,predected_assignements)