Lodestar.Preprocessing 0.1.0. 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.

Standardises a row-major sample matrix with the fitted statistics.

public double[] Transform(ReadOnlySpan<double> samples)

Parameters — samples is the matrix to transform, row-major, with FeatureCount values per row. It need not be the matrix the scaler was fitted on.

Returns — a new array of the same length. The input is never written to.

Exceptions — ArgumentException when samples holds no row, or a partial one.

Example — fit on training rows, apply to unseen ones.

using Lodestar.Preprocessing;

double[] training = [1.0, 10.0, 2.0, 10.0, 4.0, 10.0];
StandardScaler scaler = StandardScaler.Fit(training, featureCount: 2);

// A row the scaler never saw, standardised with the training statistics.
double[] unseen = scaler.Transform([3.0, 10.0]);

double centred = unseen[0];   // => 0.5345224838248487
double constant = unseen[1];  // => 0

Remarks — the statistics come from the fit and are not recomputed here, which is the whole point of the two-step shape: a validation set standardised with its own mean has been told something about itself that the training set did not know.

What the two steps do is decided by the options, not by which statistics exist. With WithMean = false the scaler still carries a Mean — see StandardScaler's table — and this method still does not subtract it.

A feature whose Scale was forced to 1 comes through centred but unscaled. That is the intended outcome: its spread was below what the variance computation could resolve, so there is nothing to divide by that would not be noise.

Applies to — net10.0, netstandard2.0.

See also — StandardScaler.Fit, StandardScaler.InverseTransform.