Covariance data handled incorrectly in unSupervisedLearning

[joshua-cogliati-inl]

---

Issue Description

What did you expect to see happen?

That cov(x,y) == cov(y,x), but instead they are very different numbers.

What did you see instead?

cov(x,y) and cov(y,x), are very different numbers.

Do you have a suggested fix for the development team?

Please attach the input file(s) that generate this error. The simpler the input, the faster we can find the issue.

Note that you have to add a print statement to see both covariance's: framework/PostProcessors/TemporalDataMiningPostProcessor/Clustering.GaussianMixture framework/PostProcessors/DataMiningPostProcessor/Clustering.GaussianMixture

---

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 task?
• [ ] 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 release branch? If yes, is there any issue tagged with release (create if needed)?
• [ ] 5. If the issue is being closed without a pull request, has an explanation of why it is being closed been provided?

[joshua-cogliati-inl]

Note that this was partially fixed for covariance type full, but not fixed for type diag. See XXX comment in framework/unSupervisedLearning.py

            #if covariance type == full, the shape is (n_components, n_features,
 n_features)
            if len(covariance.shape) == 3:
              covariance[:,row,col] = covariance[:,row,col] * rowSigma * colSigm
a
            else:
              #XXX if covariance type == diag, this will be wrong.
              covariance[row,col] = covariance[row,col] * rowSigma * colSigma