skcriteria.preprocessing.distance module

Warning

This module is deprecated.

Normalization through the distance to distance function.

This entire module is deprecated.

skcriteria.preprocessing.distance.cenit_distance(matrix, objectives)[source]

Calculate a scores with respect to an ideal and anti-ideal alternative.

For every criterion \(f\) of this multicriteria problem we define a membership function \(x_j\) mapping the values of \(f_j\) to the interval [0, 1].

The result score \(x_{aj}\) is close to the ideal value \(f_{j}^*\), which is the best performance in criterion , and far from the anti-ideal value \(f_{j^*}\), which is the worst performance in criterion \(j\). Both ideal and anti-ideal, are achieved by at least one of the alternatives under consideration.

\[x_{aj} = \frac{f_j(a) - f_{j^*}}{f_{j}^* - f_{j^*}}\]

Deprecated since version 0.8: Use skcriteria.preprocessing.scalers.matrix_scale_by_cenit_distance instead

class skcriteria.preprocessing.distance.CenitDistance(*args, **kwargs)[source]

Bases: CenitDistanceMatrixScaler

Relative scores with respect to an ideal and anti-ideal alternative.

For every criterion \(f\) of this multicriteria problem we define a membership function \(x_j\) mapping the values of \(f_j\) to the interval [0, 1].

The result score \(x_{aj}\) is close to the ideal value \(f_{j}^*\), which is the best performance in criterion , and far from the anti-ideal value \(f_{j^*}\), which is the worst performance in criterion \(j\). Both ideal and anti-ideal, are achieved by at least one of the alternatives under consideration.

\[x_{aj} = \frac{f_j(a) - f_{j^*}}{f_{j}^* - f_{j^*}}\]

Deprecated since version 0.8: Use skcriteria.preprocessing.scalers.CenitDistanceMatrixScaler instead

References

[Diakoulaki et al., 1995]