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: >>&g

Solution 1:

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

Solution 2:

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

obs : list
    List ofarray-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]))

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