# `pp_asls` — As L S _Group_: **Baseline correction** · _Binding_: `n4m.sklearn.AsLS` · _C ABI_: `n4m_pp_asls_*` ## Description Asymmetric Least Squares (Eilers & Boelens 2005). ### Parameters | Name | Type | Default | |------|------|---------| | `lam` | `float` | `1000000.0` | | `p` | `float` | `0.01` | | `max_iter` | `int` | `50` | | `tol` | `float` | `0.001` | ## Explanations ### Bibliographic source _Standard spectroscopic operator — see the nirs4all preprocessing / augmentation handbook and the cited literature within the binding docstring._ ### Mathematical principle Asymmetric Least Squares (Eilers & Boelens 2005). ### Implementation C ABI `n4m_pp_asls_*` in libn4m (create / apply / destroy lifecycle), wrapped by `n4m.sklearn.AsLS`. The same numerical kernel backs every language binding. ### Usage ```python from n4m.sklearn import AsLS op = AsLS() X_transformed = op.fit_transform(X) ``` ### Benchmarks Adaptive wall-clock per cell measured against [`full_matrix.csv`](../benchmarks/overview.md). Only backends that implement this method are listed; libraries without the method are omitted. **Verdict**  ·  ✓ ref / ≈ ref / ~ shape mark a reference-gate pass at strict / relaxed / qualitative tolerance  ·  ✓ bind = pls4all binding agrees with the C++ baseline  ·  ✗ divergent  ·  ⚠ error  ·  — not run. The fastest backend per column is marked 🏆. **Reference gate**: strict — numeric equivalence (`rmse_rel_tol ≤ 1e-12`). ::::{tab-set} :class: parity-tabs :::{tab-item} 1 thread :sync: threads-1
BackendParity50×250 (ms)250×50 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp✓ ref
Python · external
ref.python_numpysource
::: :::: --- _See also_: [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)