| | 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.
|
Methods