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Fits a scaler on a row-major sample matrix.

public static RobustScaler Fit(ReadOnlySpan<double> samples, int featureCount, RobustScalerOptions options = null)
public static RobustScaler Fit(CsrMatrix samples, RobustScalerOptions options = null)

Parameters — samples is the sample matrix, row-major: featureCount values per row. featureCount is how many values each row carries. options chooses which steps to apply and between which percentiles; null applies both, at the quartiles.

Returns — a fitted RobustScaler.

Exceptions — ArgumentNullException when the sparse overload is given no matrix. ArgumentOutOfRangeException when featureCount is not positive, the percentile range is not 0 ≤ lower ≤ upper ≤ 100, or centring is asked of a sparse matrix. ArgumentException when samples holds no row, a partial one, or a non-finite value.

Example — the percentile interpolates between two values when it falls between them.

using Lodestar.Preprocessing;

// Six values: the lower quartile sits at h = (6 - 1) * 0.25 = 1.25, between 1 and 2.
double[] six = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0];

RobustScaler scaler = RobustScaler.Fit(six, featureCount: 1);

double centre = scaler.Centre![0];  // => 2.5
double scale = scaler.Scale![0];    // => 2.5

Remarks — the percentile is numpy.percentile's linear interpolation (Hyndman and Fan's type 7): at h = (n − 1)·q/100 the value is x[⌊h⌋] + (h − ⌊h⌋)·(x[⌊h⌋+1] − x[⌊h⌋]) over the sorted column. Neither quantile already in this repository is that convention — SplitConformal.Quantile takes the conformal order statistic ⌈(n+1)(1−α)⌉ and Lodestar.Metrics averages a weighted percentile — so it is written here rather than shared, and this page says so instead of leaving a reader to assume.

Five values put the quartiles on an index (h = 1 and h = 3) and six put them between two, which is the pair worth reading twice.

The interpercentile range is floored to 1 below 10·eps, the rule MinMaxScaler.Fit carries.

The sparse overload takes a CsrMatrix and refuses centring, as StandardScaler's does and for the same reason. The percentiles still read the whole column, the absent zeros included — which is why a mostly-zero column's quartiles are usually zero and its range is floored to 1.

Applies to — net10.0, netstandard2.0.

See also — RobustScaler, RobustScalerOptions.