Lodestar.Decomposition 0.1.1. This page is frozen at that release. Read the current documentation for what main says now. A link to a decision or a migration page follows main, and leaves the archive.

A fitted truncated SVD of a sparse matrix, at the rank you asked for and with nothing centred. There is no unfitted state: TruncatedSvd.Fit is the only way to reach an instance, so no property has to decide what to do when it is read too early.

FitTransform is deliberately absent. scikit-learn's returns X · Componentsᵀ for the randomized solver, while U · Σ is the other reading of the same words, and the two differ by exactly the error the randomized solver leaves behind. Call TruncatedSvd.Transform on the matrix you fitted and the answer is unambiguous.

Property	What it holds
ComponentCount	int — how many components were kept, the componentCount passed to the fit.
FeatureCount	int — how many columns the fitted matrix had, and how many every matrix projected afterwards must have.
Components	IReadOnlyList<double> — the right singular vectors, row-major ComponentCount × FeatureCount.
SingularValues	IReadOnlyList<double> — the ComponentCount singular values kept, largest first.
ExplainedVariance	IReadOnlyList<double> — the variance of each column of the projected training matrix, over n rather than n − 1.
ExplainedVarianceRatio	IReadOnlyList<double> — each of those over the input's total column variance, so the sum says whether the rank is enough.

The signs of Components are pinned, not arbitrary: each row is flipped so that its largest-magnitude entry is positive. Without that, two runs of the same fit would agree on the subspace and disagree on every number in it.

Member	What it does
TruncatedSvd.Fit	Factorizes a sparse matrix at a given rank.
TruncatedSvd.Transform	Projects rows onto the fitted components.