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

Question

I work with scikit-learn GaussianHMM and get the next ValueError when I try to fit it to some observations. here is the 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, XTconj()) / 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 can i fix this? It seems that I give him reliable data. Thank you

Answer 1

You must match the 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 

Answer 2

According to docs , gmm.fit(obs) expects obs as a list of objects similar to an array:

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

So 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])) 

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Hidden Markov Models (HMMs) are no longer supported with sklearn.