normalize function

Normalize approximations set(s).

Normalization is done by subtracting the min.value for each dimension and dividing by the difference max.value - min.value for each dimension Certain EMOA indicators require all elements to be strictly positive. Hence, an optional offset is added to each element which defaults to zero.

normalize(x, obj.cols, min.value = NULL, max.value = NULL, offset = NULL)

Arguments

• x: [matrix | data.frame]

  Either a numeric matrix (each column corresponds to a point) or a data.frame with columns at least obj.cols.

• obj.cols: [character(>= 2)]

  Column names of the objective functions.

• min.value: [numeric]

  Vector of minimal values of length nrow(x). Only relevant if x is a matrix. Default is the row-wise minimum of x.

• max.value: [numeric]

  Vector of maximal values of length nrow(x). Only relevant if x is a matrix. Default is the row-wise maximum of x.

• offset: [numeric]

  Numeric constant added to each normalized element. Useful to make all objectives strictly positive, e.g., located in $[1,2]$.

Returns

[matrix | data.frame]

Note

In case a data.frame is passed and a prob column exists, normalization is performed for each unique element of the prob column independently (if existent).

See Also

Other EMOA performance assessment tools: approximateNadirPoint(), approximateRefPoints(), approximateRefSets(), computeDominanceRanking(), emoaIndEps(), makeEMOAIndicator(), niceCellFormater(), plotDistribution(), plotFront(), plotScatter2d(), plotScatter3d(), toLatex()