pp_gaussian — Gaussian smoothing¶
Group: Preprocessing · Binding: n4m.sklearn.Gaussian · C ABI: n4m_pp_gaussian_*
Description¶
SciPy-compatible 1-D Gaussian filter along the wavelength axis.
Parameters¶
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Explanations¶
Bibliographic source¶
Classical Gaussian-kernel convolution smoothing.
Mathematical principle¶
Convolves each spectrum with a discretised Gaussian kernel of a given standard deviation, a low-pass filter that attenuates high-frequency noise with minimal ringing compared with a boxcar average.
Implementation¶
C ABI n4m_pp_gaussian_* in libn4m (create / apply / destroy lifecycle), wrapped by n4m.sklearn.Gaussian. The same numerical kernel backs every language binding.
Usage¶
from n4m.sklearn import Gaussian
op = Gaussian()
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).
| Backend | Parity | 50×250 (ms) | 250×50 (ms) |
|---|---|---|---|
| C++ native · libn4m | |||
pls4all.cpp.blas | ✓ ref | — | — |
pls4all.cpp.blas+omp | ✓ ref | 0.09 ms | 0.16 ms |
pls4all.cpp.omp | ✓ ref | — | — |
pls4all.cpp.ref | ✓ ref | 0.09 ms | 0.15 ms |
| Python · pls4all | |||
pls4all.python | ✓ bind | 0.06 ms🏆 | 0.09 ms |
pls4all.sklearn | ✓ bind | 0.06 ms | 0.09 ms |
| Python · external | |||
nirs4all | source | 0.06 ms | 0.07 ms🏆 |
ref.python_numpy | source | — | — |
See also: methods index · interactive dashboard