All,
I am trying to implement the GaussianHMM: there s a bug when all inmputs
of the time series are equal.
[ 5. 5. 5. 5. 5. 5. 5. 5. 5. 5. 5. 5.]
C:\Python27\lib\site-packages\sklearn\mixture\gmm.py:555:
RuntimeWarning: divide by zero encountered in divide
+ np.dot(X ** 2, (1.0 / covars).T))
C:\Python27\lib\site-packages\sklearn\mixture\gmm.py:555:
RuntimeWarning: invalid value encountered in add
+ np.dot(X ** 2, (1.0 / covars).T))
File "C:\Users\dvila\My Documents\Aptana Studio 3
Workspace\Current\account.py", line 312, in calculate_gaussian_hmm
model.fit([data])
File "C:\Python27\lib\site-packages\sklearn\hmm.py", line 454, in fit
self._do_mstep(stats, self.params)
File "C:\Python27\lib\site-packages\sklearn\hmm.py", line 820, in
_do_mstep
super(GaussianHMM, self)._do_mstep(stats, params)
File "C:\Python27\lib\site-packages\sklearn\hmm.py", line 598, in
_do_mstep
np.maximum(self.startprob_prior - 1.0 + stats['start'], 1e-20))
File "C:\Python27\lib\site-packages\sklearn\hmm.py", line 485, in
_set_startprob
raise ValueError('startprob must sum to 1.0')
ValueError: startprob must sum to 1.0
Thanks for your help.
Didier Vila, PhD | Risk | CapQuest Group Ltd | Fleet 27 | Rye Close |
Fleet | Hampshire | GU51 2QQ | Fax: 0871 574 2992 | Email:
[email protected] <mailto:[email protected]>
This e-mail is intended solely for the addressee, is strictly confidential and
may also be legally privileged. If you are not the addressee please do not
read, print, re-transmit, store or act in reliance on it or any attachments.
Instead, please email it back to the sender and then immediately permanently
delete it. E-mail communications cannot be guaranteed to be secure or error
free, as information could be intercepted, corrupted, amended, lost, destroyed,
arrive late or incomplete, or contain viruses. We do not accept liability for
any such matters or their consequences. Anyone who communicates with us by
e-mail is taken to accept the risks in doing so. Opinions, conclusions and
other information in this e-mail and any attachments are solely those of the
author and do not represent those of CapQuest Group Limited or any of its
subsidiaries unless otherwise stated. CapQuest Group Limited (registered number
4936030), CapQuest Debt Recovery Limited (registered number 3772278), CapQuest
Investments Limited (registered number 5245825), CapQuest Asset Management
Limited (registered number 5245829) and CapQuest Mortgage Servicing Limited
(registered number 05821008) are all limited companies registered in England
and Wales with their registered offices at Fleet 27, Rye Close, Fleet,
Hampshire, GU51 2QQ. Each company is a separate and independent legal entity.
None of the companies have any liability for each other's acts or omissions.
This communication is from the company named in the sender's details above.------------------------------------------------------------------------------
Everyone hates slow websites. So do we.
Make your web apps faster with AppDynamics
Download AppDynamics Lite for free today:
http://p.sf.net/sfu/appdyn_sfd2d_oct
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
