scikit-learn GaussianHMM ValueError：输入必须是方阵

我正在使用 scikit-learn 的 GaussianHMM，当我尝试将其拟合到一些观察结果时，出现以下 ValueError 。这是演示错误的代码：

>>> from sklearn.hmm import GaussianHMM
>>> arr = np.matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> arr
matrix([[1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]])
>>> gmm = GaussianHMM ()
>>> gmm.fit (arr)
/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/lib/function_base.py:2005: RuntimeWarning: invalid value encountered in divide
  return (dot(X, X.T.conj()) / fact).squeeze()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Library/Python/2.7/site-packages/sklearn/hmm.py", line 427, in fit
    framelogprob = self._compute_log_likelihood(seq)
  File "/Library/Python/2.7/site-packages/sklearn/hmm.py", line 737, in _compute_log_likelihood
    obs, self._means_, self._covars_, self._covariance_type)
  File "/Library/Python/2.7/site-packages/sklearn/mixture/gmm.py", line 58, in log_multivariate_normal_density
    X, means, covars)
  File "/Library/Python/2.7/site-packages/sklearn/mixture/gmm.py", line 564, in _log_multivariate_normal_density_diag
    + np.dot(X ** 2, (1.0 / covars).T))
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/matrixlib/defmatrix.py", line 343, in __pow__
    return matrix_power(self, other)
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/matrixlib/defmatrix.py", line 160, in matrix_power
    raise ValueError("input must be a square array")
ValueError: input must be a square array
>>> 

我该如何补救？看来我正在给它有效的输入。谢谢！

你必须符合一个列表，请参阅官方例子 http://scikit-learn.org/stable/auto_examples/applications/plot_hmm_stock_analysis.html:

>>> gmm.fit([arr])
GaussianHMM(algorithm='viterbi', covariance_type='diag', covars_prior=0.01,
      covars_weight=1,
      init_params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ',
      means_prior=None, means_weight=0, n_components=1, n_iter=10,
      params='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ',
      random_state=None, startprob=None, startprob_prior=1.0, thresh=0.01,
      transmat=None, transmat_prior=1.0)
>>> gmm.n_features
3
>>> gmm.n_components
1