Opened 4 months ago
Last modified 5 days ago
#2722 accepted feature request
Evaluator for symbolic regression models for learning a variance model
Reported by: | gkronber | Owned by: | gkronber |
---|---|---|---|
Priority: | medium | Milestone: | HeuristicLab 3.3.x Backlog |
Component: | Problems.DataAnalysis.Symbolic.Regression | Version: | 3.3.14 |
Keywords: | Cc: |
Description
Change History (7)
comment:1 Changed 4 months ago by gkronber
- Status changed from new to accepted
comment:2 Changed 4 months ago by gkronber
comment:3 Changed 2 months ago by gkronber
- Owner changed from gkronber to mkommend
- Status changed from accepted to reviewing
comment:4 Changed 12 days ago by gkronber
Linear scaling needs to be turned off.
comment:5 Changed 12 days ago by gkronber
The objective is to maximize the following log likelihood that the observed residuals stem from a zero-mean normal distribution with std. dev. s = f(x).
var l2pi = Math.Log(2.0 * Math.PI); var ll = -0.5 * boundedEstimatedValues.Zip(targetValues, (s, t) => +l2pi + Math.Log(s * s) + (t * t) / (s * s) ).Sum(); return ll;
This is problematic as the sign of s has no effect on the result (same for the sign of t). It would probably be better to model the variance s^{2} directly. However, in this case negative values for s^{2} are not possible and must be prevented. On option would be to produce a model for log(s^{2}) instead.
comment:6 Changed 11 days ago by gkronber
- Owner changed from mkommend to gkronber
- Status changed from reviewing to assigned
comment:7 Changed 5 days ago by gkronber
- Milestone changed from HeuristicLab 3.3.15 to HeuristicLab 3.3.x Backlog
- Status changed from assigned to accepted
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r14528: added an evaluator for symbolic regression models which calculates the likelihood that variable values are sampled from a zero mean Gaussian distribution where the variance is given by the model. This can be used to learn input-dependent variances.