# `pp_haar` — Haar _Group_: **Augmentation** · _Binding_: `n4m.sklearn.Haar` · _C ABI_: `n4m_pp_haar_*` ## Description Single-level Haar DWT coefficient transform. ### Parameters _No constructor parameters._ ## Explanations ### Bibliographic source _Standard spectroscopic operator — see the nirs4all preprocessing / augmentation handbook and the cited literature within the binding docstring._ ### Mathematical principle Single-level Haar DWT coefficient transform. ### Implementation C ABI `n4m_pp_haar_*` in libn4m (create / apply / destroy lifecycle), wrapped by `n4m.sklearn.Haar`. The same numerical kernel backs every language binding. ### Usage ```python from n4m.sklearn import Haar op = Haar() 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)