kshape.py 文件源码

def _kshape(x, k, initial_clustering=None):
    """
    >>> from numpy.random import seed; seed(0)
    >>> _kshape(np.array([[1,2,3,4], [0,1,2,3], [-1,1,-1,1], [1,2,2,3]]), 2)
    (array([0, 0, 1, 0]), array([[-1.2244258 , -0.35015476,  0.52411628,  1.05046429],
           [-0.8660254 ,  0.8660254 , -0.8660254 ,  0.8660254 ]]))
    """
    m = x.shape[0]

    if initial_clustering is not None:
        assert len(initial_clustering) == m, "Initial assigment does not match column length"
        idx = initial_clustering
    else:
        idx = randint(0, k, size=m)

    print(idx)

    centroids = np.zeros((k,x.shape[1]))
    distances = np.empty((m, k))

    for _ in range(100):
        old_idx = idx
        for j in range(k):
            centroids[j] = _extract_shape(idx, x, j, centroids[j])

        for i in range(m):
             for j in range(k):
                 distances[i,j] = 1 - max(_ncc_c(x[i], centroids[j]))
        idx = distances.argmin(1)
        if np.array_equal(old_idx, idx):
            break

    print(idx)

    return idx, centroids