scipy.cluster.vq.kmeans2

Question

我正在尝试在128维点（图像中的兴趣点的描述符）上执行kmeans聚类。 当我使用scipy.cluster.vq.kmeans2函数时，我有时会收到以下错误：

  File "main.py", line 21, in level_routine
current.centroids, current.labels = cluster.vq.kmeans2( current.descriptors, k)
  File "/usr/lib/python2.7/dist-packages/scipy/cluster/vq.py", line 706, in kmeans2
    clusters = init(data, k)
  File "/usr/lib/python2.7/dist-packages/scipy/cluster/vq.py", line 593, in _krandinit
    return init_rankn(data)
  File "/usr/lib/python2.7/dist-packages/scipy/cluster/vq.py", line 586, in init_rankn
    x = np.dot(x, np.linalg.cholesky(cov).T) + mu
  File "/usr/lib/python2.7/dist-packages/numpy/linalg/linalg.py", line 603, in cholesky
    return wrap(gufunc(a, signature=signature, extobj=extobj).astype(result_t))
  File "/usr/lib/python2.7/dist-packages/numpy/linalg/linalg.py", line 93, in _raise_linalgerror_nonposdef
    raise LinAlgError("Matrix is not positive definite")
numpy.linalg.linalg.LinAlgError: Matrix is not positive definite

我知道这与随机初始化有关，因为在同一data和同一k上，我有时不会收到此错误。

我的data是一个numpy矩阵，有128列和可变行数。我没有构建协方差矩阵，因此无法控制它。有没有办法摆脱这个错误。

Answer 1

尝试将minit参数更改为＆＃39; points＆＃39;：

kmeans2(obs,k,minit='points')