pp_normalize — Normalize

Group: Preprocessing · Binding: n4m.sklearn.Normalize · C ABI: n4m_pp_normalize_*

Description

Column-wise normalisation.

Parameters

Name	Type	Default
feature_min	float	-1.0
feature_max	float	1.0

Explanations

Bibliographic source

Standard spectroscopic operator — see the nirs4all preprocessing / augmentation handbook and the cited literature within the binding docstring.

Mathematical principle

Column-wise normalisation.

Implementation

C ABI n4m_pp_normalize_* in libn4m (create / apply / destroy lifecycle), wrapped by n4m.sklearn.Normalize. The same numerical kernel backs every language binding.

Usage

from n4m.sklearn import Normalize
op = Normalize()
X_transformed = op.fit_transform(X)

Benchmarks

Adaptive wall-clock per cell measured against full_matrix.csv. 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  ·  ⇄ cross-check = documented by-design selector/RNG/model, noncanonical API/facade convention, or secondary oracle  ·  ✗ divergent  ·  ⚠ error  ·  — not run. The fastest backend per column is marked 🏆.

Reference gate: strict — numeric equivalence (rmse_rel_tol ≤ 1e-12).

1 thread

Backend	Parity	50×250 (ms)	250×50 (ms)
C++ native · libn4m
pls4all.cpp.blas	✓ ref	0.02 ms	—
pls4all.cpp.blas+omp	✓ ref	0.02 ms	0.03 ms
pls4all.cpp.omp	✓ ref	0.02 ms🏆	—
pls4all.cpp.ref	✓ ref	0.03 ms	0.03 ms
Python · pls4all
pls4all.python	✓ bind	0.02 ms	0.02 ms🏆
pls4all.sklearn	✓ bind	0.02 ms	0.02 ms
Python · external
ref.python_numpy	source	—	—

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See also: methods index · interactive dashboard