Comments (6)
I expect the io_rls.py
file is missing the line Nb = np.sum(nb[:])
; however, I could be completely wrong about this.
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Dear Tsokneelsbd,
thank you for using our SIPPY.
Your issue sounds strange..
Where the error arises? in a core code or in a example file?
Then, it is systematic when running any example file?
Can you share an example file so that we can investigate the issue ..
More, a general quote from the Guide:
By using an Information Criterion (IC), the user does not need to the define the exact model
orders, but he/she has to assign a range of orders and let the IC find the best combination.
Note that, when an input-output model structure is selected, the information criteria are
implemented only for SISO systems.
This means that for MIMO systems, when using I/O models (ARX,ARMAX, etc.) IC mode is not available.
@CameronDevine: the io_rsl.py is actually missing the line Nb = np.sum(nb[:]),
but this not seems the reason for the problem...
Best regards
SIPPY Team
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I am also getting TypeError: 'int' object is not subscriptable
from sippy.
Dear @Tsokneelsbd,
sorry for the big delay in this response.
We just fixed a silly typo in the io_rls.py
file (nb instead of Nb). This should solve your problem.
Let us know if now it is running errors free.
Best Regards,
SIPPY team
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Related Issues (20)
- Please update document SS_max_order or SS_orders if a valid IC is used. HOT 1
- Why the SS_threshold is 0.0 HOT 3
- Fails to install under Windows HOT 4
- Output of the identified model Yid is not correct HOT 6
- io_opt - bad import? HOT 1
- Support new Pythons HOT 7
- Invalid license?
- BJ identification
- Box-Jenkins MIMO- Invalid Transfer functions HOT 1
- Setup script exited with Problem with the CMake installation HOT 3
- SIMO/SISO model verification with functionset.validation HOT 4
- How to install it through Anaconda? HOT 1
- ARMA(X) Model prediction is a bit ambigious HOT 2
- Some suggestions
- Forecasting with trained model
- Problem with model simulation
- a bug in the example codes? HOT 5
- Problem for simulating Linear Parameter Varying Model
- Regarding prediction and identified coefficient of input/output models. HOT 8
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