scikit lære GaussianHMM ValueError: inngangen må være en kvadratisk matrise

stemmer
3

Jeg jobber med scikit lære oss GaussianHMM og jeg får følgende ValueError når jeg prøver å passe det til noen observasjoner. her er kode som viser feilen:

>>> 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
>>> 

Hvordan kan jeg løse dette? Det virker som jeg gir det gyldige innganger. Takk!

Publisert på 16/12/2013 klokken 16:56
bruker
På andre språk...                            


2 svar

stemmer
3

Du må passe med en liste, se offisielle eksempler :

>>> 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
Svarte 16/12/2013 kl. 17:03
kilden bruker

stemmer
3

Ifølge docs , gmm.fit(obs)forventer obså være en liste av Array-lignende objekter:

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

Derfor prøver:

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]))

Skjulte Markovmodeller (HMM) er ikke lenger støttes av sklearn.

Svarte 16/12/2013 kl. 17:04
kilden bruker

Cookies help us deliver our services. By using our services, you agree to our use of cookies. Learn more