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
insteadReferences