Distance Class |
Metrics to measure the distance between two structures.
Inheritance HierarchyNamespace: Altaxo.Calc
Assembly: AltaxoCore (in AltaxoCore.dll) Version: 4.8.3179.0 (4.8.3179.0)
SyntaxC#
public static class Distance
The Distance type exposes the following members.
Methods| Name | Description | |
|---|---|---|
  | Canberra(Double, Double) | Canberra Distance, a weighted version of the L1-norm of the difference. |
| Canberra(Single, Single) | Canberra Distance, a weighted version of the L1-norm of the difference. |
| Chebyshev(Double, Double) | Chebyshev Distance, i.e. the Infinity-norm of the difference. |
| Chebyshev(Single, Single) | Chebyshev Distance, i.e. the Infinity-norm of the difference. |
| ChebyshevT(VectorT, VectorT) | Chebyshev Distance, i.e. the Infinity-norm of the difference. |
| Cosine(Double, Double) | Cosine Distance, representing the angular distance while ignoring the scale. |
| Cosine(Single, Single) | Cosine Distance, representing the angular distance while ignoring the scale. |
| Euclidean(Double, Double) | Euclidean Distance, i.e. the L2-norm of the difference. |
| Euclidean(Single, Single) | Euclidean Distance, i.e. the L2-norm of the difference. |
| EuclideanT(VectorT, VectorT) | Euclidean Distance, i.e. the L2-norm of the difference. |
| Hamming(Double, Double) | Hamming Distance, i.e. the number of positions that have different values in the vectors. |
| Hamming(Single, Single) | Hamming Distance, i.e. the number of positions that have different values in the vectors. |
| Jaccard(Double, Double) | Jaccard distance, i.e. 1 - the Jaccard index. |
| Jaccard(Single, Single) | Jaccard distance, i.e. 1 - the Jaccard index. |
| MAE(Double, Double) | Mean-Absolute Error (MAE), i.e. the normalized L1-norm (Manhattan) of the difference. |
| MAE(Single, Single) | Mean-Absolute Error (MAE), i.e. the normalized L1-norm (Manhattan) of the difference. |
| MAET(VectorT, VectorT) | Mean-Absolute Error (MAE), i.e. the normalized L1-norm (Manhattan) of the difference. |
| Manhattan(Double, Double) | Manhattan Distance, i.e. the L1-norm of the difference. |
| Manhattan(Single, Single) | Manhattan Distance, i.e. the L1-norm of the difference. |
| ManhattanT(VectorT, VectorT) | Manhattan Distance, i.e. the L1-norm of the difference. |
| Minkowski(Double, Double, Double) | Minkowski Distance, i.e. the generalized p-norm of the difference. |
| Minkowski(Double, Single, Single) | Minkowski Distance, i.e. the generalized p-norm of the difference. |
| MinkowskiT(Double, VectorT, VectorT) | Minkowski Distance, i.e. the generalized p-norm of the difference. |
| MSE(Double, Double) | Mean-Squared Error (MSE), i.e. the normalized squared L2-norm (Euclidean) of the difference. |
| MSE(Single, Single) | Mean-Squared Error (MSE), i.e. the normalized squared L2-norm (Euclidean) of the difference. |
| MSET(VectorT, VectorT) | Mean-Squared Error (MSE), i.e. the normalized squared L2-norm (Euclidean) of the difference. |
| Pearson | Pearson's distance, i.e. 1 - the person correlation coefficient. |
| SAD(Double, Double) | Sum of Absolute Difference (SAD), i.e. the L1-norm (Manhattan) of the difference. |
| SAD(Single, Single) | Sum of Absolute Difference (SAD), i.e. the L1-norm (Manhattan) of the difference. |
| SADT(VectorT, VectorT) | Sum of Absolute Difference (SAD), i.e. the L1-norm (Manhattan) of the difference. |
| SSD(Double, Double) | Sum of Squared Difference (SSD), i.e. the squared L2-norm (Euclidean) of the difference. |
| SSD(Single, Single) | Sum of Squared Difference (SSD), i.e. the squared L2-norm (Euclidean) of the difference. |
| SSDT(VectorT, VectorT) | Sum of Squared Difference (SSD), i.e. the squared L2-norm (Euclidean) of the difference. |
See Also