## Types in MathNet.Numerics

Type Distance

Namespace MathNet.Numerics

Metrics to measure the distance between two structures.

### Public Static Functions

#### floatCanberra(Single[] a, Single[] b)

Canberra Distance, a weighted version of the L1-norm of the difference.

#### doubleCanberra(Double[] a, Double[] b)

Canberra Distance, a weighted version of the L1-norm of the difference.

#### floatChebyshev(Single[] a, Single[] b)

Chebyshev Distance, i.e. the Infinity-norm of the difference.

#### doubleChebyshev(Double[] a, Double[] b)

Chebyshev Distance, i.e. the Infinity-norm of the difference.

#### doubleChebyshev<T>(Vector<T> a, Vector<T> b)

Chebyshev Distance, i.e. the Infinity-norm of the difference.

#### floatCosine(Single[] a, Single[] b)

Cosine Distance, representing the angular distance while ignoring the scale.

#### doubleCosine(Double[] a, Double[] b)

Cosine Distance, representing the angular distance while ignoring the scale.

#### doubleEuclidean(Double[] a, Double[] b)

Euclidean Distance, i.e. the L2-norm of the difference.

#### floatEuclidean(Single[] a, Single[] b)

Euclidean Distance, i.e. the L2-norm of the difference.

#### doubleEuclidean<T>(Vector<T> a, Vector<T> b)

Euclidean Distance, i.e. the L2-norm of the difference.

#### floatHamming(Single[] a, Single[] b)

Hamming Distance, i.e. the number of positions that have different values in the vectors.

#### doubleHamming(Double[] a, Double[] b)

Hamming Distance, i.e. the number of positions that have different values in the vectors.

#### doubleJaccard(Double[] a, Double[] b)

Jaccard distance, i.e. 1 - the Jaccard index.
##### Return
###### `double`

Jaccard distance.

#### doubleJaccard(Single[] a, Single[] b)

Jaccard distance, i.e. 1 - the Jaccard index.
##### Return
###### `double`

Jaccard distance.

#### floatMAE(Single[] a, Single[] b)

Mean-Absolute Error (MAE), i.e. the normalized L1-norm (Manhattan) of the difference.

#### doubleMAE(Double[] a, Double[] b)

Mean-Absolute Error (MAE), i.e. the normalized L1-norm (Manhattan) of the difference.

#### doubleMAE<T>(Vector<T> a, Vector<T> b)

Mean-Absolute Error (MAE), i.e. the normalized L1-norm (Manhattan) of the difference.

#### doubleManhattan(Double[] a, Double[] b)

Manhattan Distance, i.e. the L1-norm of the difference.

#### floatManhattan(Single[] a, Single[] b)

Manhattan Distance, i.e. the L1-norm of the difference.

#### doubleManhattan<T>(Vector<T> a, Vector<T> b)

Manhattan Distance, i.e. the L1-norm of the difference.

#### doubleMinkowski(double p, Double[] a, Double[] b)

Minkowski Distance, i.e. the generalized p-norm of the difference.

#### floatMinkowski(double p, Single[] a, Single[] b)

Minkowski Distance, i.e. the generalized p-norm of the difference.

#### doubleMinkowski<T>(double p, Vector<T> a, Vector<T> b)

Minkowski Distance, i.e. the generalized p-norm of the difference.

#### floatMSE(Single[] a, Single[] b)

Mean-Squared Error (MSE), i.e. the normalized squared L2-norm (Euclidean) of the difference.

#### doubleMSE(Double[] a, Double[] b)

Mean-Squared Error (MSE), i.e. the normalized squared L2-norm (Euclidean) of the difference.

#### doubleMSE<T>(Vector<T> a, Vector<T> b)

Mean-Squared Error (MSE), i.e. the normalized squared L2-norm (Euclidean) of the difference.

#### doublePearson(IEnumerable<double> a, IEnumerable<double> b)

Pearson's distance, i.e. 1 - the person correlation coefficient.

Sum of Absolute Difference (SAD), i.e. the L1-norm (Manhattan) of the difference.

Sum of Absolute Difference (SAD), i.e. the L1-norm (Manhattan) of the difference.

Sum of Absolute Difference (SAD), i.e. the L1-norm (Manhattan) of the difference.

#### doubleSSD(Double[] a, Double[] b)

Sum of Squared Difference (SSD), i.e. the squared L2-norm (Euclidean) of the difference.

#### floatSSD(Single[] a, Single[] b)

Sum of Squared Difference (SSD), i.e. the squared L2-norm (Euclidean) of the difference.

#### doubleSSD<T>(Vector<T> a, Vector<T> b)

Sum of Squared Difference (SSD), i.e. the squared L2-norm (Euclidean) of the difference.