feat: Chain fit methods using result#284
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Bencher Report| Branch | feature/chain_bootstrap_iov |
| Testbed | mhovd-pgx |
Click to view all benchmark results
| Benchmark | Latency | Benchmark Result milliseconds (ms) (Result Δ%) | Upper Boundary milliseconds (ms) (Limit %) |
|---|---|---|---|
| bimodal_ke_npag | 📈 view plot 🚷 view threshold | 4,963.80 ms(+1.74%)Baseline: 4,879.07 ms | 5,360.95 ms (92.59%) |
| bimodal_ke_npod | 📈 view plot 🚷 view threshold | 1,468.40 ms(+7.95%)Baseline: 1,360.23 ms | 1,614.47 ms (90.95%) |
| bimodal_ke_postprob | 📈 view plot 🚷 view threshold | 514.98 ms(+90.63%)Baseline: 270.15 ms | 753.68 ms (68.33%) |
mhovd
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Jun 25, 2026
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Overall great additions, just not sure about the bootstrapping.
Maybe we can extract it and discuss it more? Or implement directly in Pmetrics if needed.
You can also decide on the placement of the IOV module.
| /// [`Theta::latin`], and [`Theta::from_file`]. | ||
| /// | ||
| /// See the [module-level documentation](self) for details. | ||
| pub fn bootstrap_theta<ESource: Equation, ETarget: Equation + EquationMetadataSource>( |
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I am not sure about this functionality. Preferrably this should be in a different PR to be discussed.
| // Chain: NPAG runs first, then NPOD continues from its result | ||
| let result = EstimationProblem::nonparametric(eq, data, prior, error_models)? | ||
| .fit_with(NpagConfig::new().max_cycles(10))? | ||
| .chain(NpodConfig::new().max_cycles(5))?; |
| @@ -0,0 +1,243 @@ | |||
| //! SDE-based Inter-Occasion Variability (IOV) analysis. | |||
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This module should probably be a child of the nonparametric module, due to its dependency on ´Theta`.
Alternatively, written as a trait or as a generic over the framework (P vs NP).
| /// | ||
| /// Free-function form of [`DiffusionOptimize::optimize_diffusion`]. | ||
| /// Prefer `sde.optimize_diffusion(...)` for readability. | ||
| pub fn optimize_diffusion( |
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This should be a method on SDE only
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