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NmfOptions

What Nmf.Fit is allowed to vary. Every property has an initialiser, so new NmfOptions() is scikit-learn's own default configuration and you set only what you are changing.

public sealed record NmfOptions

Properties

Property	Default	What it does
BetaLoss	Frobenius	What the factorization minimises — see NmfBetaLoss.
Initialization	NndSvda	Where the iteration starts, scikit-learn's init=None at every rank a fit accepts — see NmfInitialization. Ignored by the overload handed W₀ and H₀.
MaxIterations	200	The iteration cap, and the exact iteration count when Tolerance is zero. scikit-learn's default is the same 200.
Tolerance	1e-4	The relative improvement below which the loop stops, measured every tenth iteration. Zero disables the stop.
Seed	0	Seeds this package's generator for the initialisation's Ω when RandomMatrix is null. It reproduces a run of Lodestar, never a run of NumPy.
RandomMatrix	null	Ω itself, row-major and min(rows, columns) × (componentCount + 10): a matrix with fewer rows than columns is factored as its transpose, as the reference's initialisation factors it. Given, it replaces the draw entirely. It is not copied: writing to it changes these options, what they equal and a with copy of them, not a fit already run (#1305).

The ten in that shape is not this type's to choose. NNDSVD reaches for the randomized SVD with scikit-learn's own defaults rather than the ones TruncatedSvdOptions exposes, and ten oversamples is one of them — so the block an initialisation wants is wider than the rank by exactly that, and an Ω of any other length is refused rather than silently reshaped. See ADR 0004 for why Ω is an input at all.

Tolerance = 0 is a feature, not a way to disable a safety net. With the stop off, MaxIterations stops being a cap and becomes the number of updates that will run, which is what makes two implementations comparable: an early stop that fires one check apart leaves two correct runs disagreeing on every digit. It is also the slowest setting, since nothing can end the loop early.

MaxIterations below one is refused, and so is a negative or NaN Tolerance. A BetaLoss and an Initialization are enums, and a value outside either falls back to nothing: the loss that is not KullbackLeibler is Frobenius, and the initialisation that is not NndSvda leaves the zeros alone.

A record since #1232, like every other options type: with copies one and changes a property, and two option sets built from separate arrays holding the same Ω compare equal.

Members

member	what it does
NmfOptions.Equals	Value equality, Ω element by element.
NmfOptions.GetHashCode	A hash consistent with it.