Opened 9 years ago
Closed 9 years ago
#2449 closed defect (done)
Persistence of Gaussian process models is inefficient
| Reported by: | gkronber | Owned by: | gkronber |
|---|---|---|---|
| Priority: | medium | Milestone: | HeuristicLab 3.3.13 |
| Component: | Algorithms.DataAnalysis | Version: | 3.3.12 |
| Keywords: | Cc: |
Description
It is not necessary to persist the full covariance matrix for the model as it could be recalculated from the training data. Similarly to the random forest model it is only necessary to store all parameters of the model and the original training data set.
Change History (5)
comment:1 Changed 9 years ago by gkronber
- Owner set to gkronber
- Status changed from new to accepted
comment:2 Changed 9 years ago by gkronber
comment:3 Changed 9 years ago by gkronber
- Owner changed from gkronber to mkommend
- Status changed from accepted to reviewing
comment:4 Changed 9 years ago by mkommend
- Owner changed from mkommend to gkronber
- Status changed from reviewing to readytorelease
Reviewed r12819.
comment:5 Changed 9 years ago by gkronber
- Resolution set to done
- Status changed from readytorelease to closed
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r12819: improved persistence of GaussianProcessModel (Cholesky decomposed covariance matrix is not stored and recalculated lazily)