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A fitted non-negative matrix factorization of a sparse matrix: X ≈ W H, with every entry of both factors at or above zero. There is no unfitted state — Nmf.Fit is the only way to reach an instance — and no property has to decide what to do when it is read too early.

The non-negativity is the whole point. A truncated SVD's components are signed and orthogonal, so a "topic" may subtract a word as readily as add one; these components only ever add, which is what makes a row of H readable as a set of terms and a row of W readable as a mixture of them. TruncatedSvd is the answer when the subspace matters and the signs do not.

Property	What it holds
ComponentCount	int — how many components were fitted, the rank W and H share.
FeatureCount	int — how many columns the factorized matrix had.
Iterations	int — how many multiplicative updates ran, scikit-learn's n_iter_. Equal to MaxIterations when the tolerance is zero.
ReconstructionError	double — the beta divergence between X and W H at the end, square-rooted: scikit-learn's reconstruction_err_.
Weights	IReadOnlyList<double> — W, row-major rows × ComponentCount. Row i's mixture starts at i * ComponentCount. This is what scikit-learn's fit_transform returns.
Components	IReadOnlyList<double> — H, row-major ComponentCount × FeatureCount, scikit-learn's components_. Component c starts at c * FeatureCount.

ReconstructionError is comparable only between fits that minimised the same NmfBetaLoss: the Frobenius number is a distance and the Kullback–Leibler one is a divergence, and they are not on one scale.

There is no Transform. Projecting an unseen row onto a non-negative basis is itself a factorization — the same multiplicative loop with H held fixed — rather than the product a name borrowed from the SVD would suggest, and shipping it under that name would promise a cost it does not have.

Member	What it does
Nmf.Fit	Factorizes a sparse matrix, from an initialisation it computes or one you supply.