CODEWITHSUNDEEP
pythonnumpyscikit-learnhidden-markov-models
Jay Hack
Jay Hack

Reputation: 139

scikit-learn GaussianHMM ValueError: input must be a square array

I am working with scikit-learn's GaussianHMM and am getting the following ValueError when I try to fit it to some observations. here is code that demonstrates the error:

>>> 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
>>>

How might I remedy this? It seems that I am giving it valid inputs. Thanks!

Upvotes: 4

Views: 3476

Answers (2)

unutbu
unutbu

Reputation: 879103

According to the docs, gmm.fit(obs) expects obs to be a list of array-like objects:

obs : list
    List of array-like observation sequences (shape (n_i, n_features)).

Therefore, try:

import numpy as np
from sklearn.hmm import GaussianHMM
arr = np.matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
gmm = GaussianHMM()
print(gmm.fit([arr]))

Hidden markov models (HMMs) are no longer supported by sklearn.

Upvotes: 3

alko
alko

Reputation: 48287

You have to fit with a list, see official examples:

>>> 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

Upvotes: 3

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