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Fits one exponent per feature by maximum likelihood.

public static PowerTransformer Fit(ReadOnlySpan<double> samples, int featureCount, PowerTransformerOptions options = null)

Parameters — samples is the matrix, row-major, featureCount values per row; the span is read, never modified. options chooses the family and whether to standardise after; null takes the reference's defaults, Yeo-Johnson with standardisation.

Returns — a fitted PowerTransformer.

Exceptions — ArgumentOutOfRangeException when featureCount is not positive. ArgumentException when samples holds no row, a partial one, or a non-finite value; or when the family is PowerMethod.BoxCox and a value is not strictly positive.

Example — the two families on the same positive column, which agree on the shape and not on the number.

using Lodestar.Preprocessing;

double[] income = [22.0, 25.0, 28.0, 31.0, 35.0, 42.0, 55.0, 78.0, 120.0, 260.0];

PowerTransformer yeoJohnson = PowerTransformer.Fit(income, 1);

PowerTransformer boxCox = PowerTransformer.Fit(
    income, 1, new PowerTransformerOptions { Method = PowerMethod.BoxCox });

double byDefault = Math.Round(yeoJohnson.Lambdas[0], 4);   // => -0.8072
double byBoxCox = Math.Round(boxCox.Lambdas[0], 4);        // => -0.7802

Remarks — the exponent is searched, not solved. Box-Cox searches [-2, 2] and Yeo-Johnson a range derived from the column, both by Brent's method on the negative log-likelihood, stopping at 1.48e-8 on the argument — scipy.optimize.fminbound's own tolerance, which is what the reference calls. There is no closed form, and the objective is flat enough near its optimum that a tighter stop would buy nothing; see PowerTransformer for the curvature arithmetic behind the 1e-5 conformance tolerance.

A negative exponent is normal, not a symptom. It means the column is right-skewed enough that a reciprocal-like power straightens it, which is what the income column above is.

Applies to — net10.0, netstandard2.0.

See also — PowerTransformer.Transform, PowerTransformerOptions, PowerMethod.