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An ARMA to be trained on solar generation data that has significant periods of zero generation (night).
What did you see instead?
Throws an error that says the 25th and 75th percentiles are equal and the operation can't be done.
Do you have a suggested fix for the development team?
The Freedman-Diaconis rule that is used doesn't allow the 25th and 75th bin to be equal, so I used a different method, which worked for the equal bins. (Aaron should remember what the method is called)
Implemented in the _computeNumberOfBins function in framework/SupervisedLearning/ARMA.py:
xxx = np.log2(len(data)) + 1
return max(int(np.ceil(xxx)), 20)
Please attach the input file(s) that generate this error. The simpler the input, the faster we can find the issue.
For Change Control Board: Issue Review
This review should occur before any development is performed as a response to this issue.
1. Is it tagged with a type: defect or improvement?
2. Is it tagged with a priority: critical, normal or minor?
3. If it will impact requirements or requirements tests, is it tagged with requirements?
4. If it is a defect, can it cause wrong results for users? If so an email needs to be sent to the users.
5. Is a rationale provided? (Such as explaining why the improvement is needed or why current code is wrong.)
For Change Control Board: Issue Closure
This review should occur when the issue is imminently going to be closed.
1. If the issue is a defect, is the defect fixed?
2. If the issue is a defect, is the defect tested for in the regression test system? (If not explain why not.)
3. If the issue can impact users, has an email to the users group been written (the email should specify if the defect impacts stable or master)?
4. If the issue is a defect, does it impact the latest stable branch? If yes, is there any issue tagged with stable (create if needed)?
5. If the issue is being closed without a merge request, has an explanation of why it is being closed been provided?
The text was updated successfully, but these errors were encountered:
Issue Description
What did you expect to see happen?
An ARMA to be trained on solar generation data that has significant periods of zero generation (night).
What did you see instead?
Throws an error that says the 25th and 75th percentiles are equal and the operation can't be done.
Do you have a suggested fix for the development team?
The Freedman-Diaconis rule that is used doesn't allow the 25th and 75th bin to be equal, so I used a different method, which worked for the equal bins. (Aaron should remember what the method is called)
Implemented in the _computeNumberOfBins function in framework/SupervisedLearning/ARMA.py:
xxx = np.log2(len(data)) + 1
return max(int(np.ceil(xxx)), 20)
Please attach the input file(s) that generate this error. The simpler the input, the faster we can find the issue.
For Change Control Board: Issue Review
This review should occur before any development is performed as a response to this issue.
For Change Control Board: Issue Closure
This review should occur when the issue is imminently going to be closed.
The text was updated successfully, but these errors were encountered: