# Aufgabe
Manually write data to demonstrate the use of the K-means clustering algorithm in the scikit-learn library (sklearn). The random seed is 42 and n_init is 10. Calculate the output results of each data sample.


# Prompt
Your task is to generate python code to solve the following problem. The generated code must be placed between the ```python and ```, and only one code block is allowed: 
Manually write data to demonstrate the use of the K-means clustering algorithm in the scikit-learn library (sklearn). The random seed is 42 and n_init is 10. Calculate the output results of each data sample.

You need to follow the function names or class names in the test cases. The generated code should not contain any test cases: 
class Testk_means_clustering:
    def test_k_means_clustering_1(self):
        np.random.seed(0)
        data = np.array([[1, 2], [5, 8], [1.5, 1.8], [8, 8], [1, 0.6], [9, 11]])
        num_clusters = 2
        assert np.all(k_means_clustering(data, num_clusters) == np.array([1, 0, 1, 0, 1, 0]))