scipy.cluster.vq.kmeans2中的“矩阵不是肯定的”错误

Question

I am trying to perform kmeans clustering over 128-dimensional points (descriptors of interest points in an image). When I use scipy.cluster.vq.kmeans2 function, I sometimes get the following error:

  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

I know that this has something to do with the random initialization because on the same data and for the same k , I sometimes do not get this error.

My data is a numpy matrix with 128 columns and variable number of rows. I am not constructing the co-variance matrix, and hence do not have control over the same. Is there a way of getting rid of this error.

Answer 1

尝试将minit参数更改为'points'：

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