Types in MathNet.Numerics
Public Static Functions
float Canberra(Single[] a, Single[] b)
Canberra Distance, a weighted version of the L1-norm of the difference.
double Canberra(Double[] a, Double[] b)
Canberra Distance, a weighted version of the L1-norm of the difference.
float Chebyshev(Single[] a, Single[] b)
Chebyshev Distance, i.e. the Infinity-norm of the difference.
double Chebyshev(Double[] a, Double[] b)
Chebyshev Distance, i.e. the Infinity-norm of the difference.
Chebyshev Distance, i.e. the Infinity-norm of the difference.
float Cosine(Single[] a, Single[] b)
Cosine Distance, representing the angular distance while ignoring the scale.
double Cosine(Double[] a, Double[] b)
Cosine Distance, representing the angular distance while ignoring the scale.
double Euclidean(Double[] a, Double[] b)
Euclidean Distance, i.e. the L2-norm of the difference.
float Euclidean(Single[] a, Single[] b)
Euclidean Distance, i.e. the L2-norm of the difference.
Euclidean Distance, i.e. the L2-norm of the difference.
float Hamming(Single[] a, Single[] b)
Hamming Distance, i.e. the number of positions that have different values in the vectors.
double Hamming(Double[] a, Double[] b)
Hamming Distance, i.e. the number of positions that have different values in the vectors.
double Jaccard(Double[] a, Double[] b)
Jaccard distance, i.e. 1 - the Jaccard index.
Return
double
Jaccard distance.
double Jaccard(Single[] a, Single[] b)
Jaccard distance, i.e. 1 - the Jaccard index.
Return
double
Jaccard distance.
float MAE(Single[] a, Single[] b)
Mean-Absolute Error (MAE), i.e. the normalized L1-norm (Manhattan) of the difference.
double MAE(Double[] a, Double[] b)
Mean-Absolute Error (MAE), i.e. the normalized L1-norm (Manhattan) of the difference.
Mean-Absolute Error (MAE), i.e. the normalized L1-norm (Manhattan) of the difference.
double Manhattan(Double[] a, Double[] b)
Manhattan Distance, i.e. the L1-norm of the difference.
float Manhattan(Single[] a, Single[] b)
Manhattan Distance, i.e. the L1-norm of the difference.
Manhattan Distance, i.e. the L1-norm of the difference.
double Minkowski(double p, Double[] a, Double[] b)
Minkowski Distance, i.e. the generalized p-norm of the difference.
float Minkowski(double p, Single[] a, Single[] b)
Minkowski Distance, i.e. the generalized p-norm of the difference.
double Minkowski<T>(double p, Vector<T> a, Vector<T> b)
Minkowski Distance, i.e. the generalized p-norm of the difference.
float MSE(Single[] a, Single[] b)
Mean-Squared Error (MSE), i.e. the normalized squared L2-norm (Euclidean) of the difference.
double MSE(Double[] a, Double[] b)
Mean-Squared Error (MSE), i.e. the normalized squared L2-norm (Euclidean) of the difference.
Mean-Squared Error (MSE), i.e. the normalized squared L2-norm (Euclidean) of the difference.
double Pearson(IEnumerable<double> a, IEnumerable<double> b)
Pearson's distance, i.e. 1 - the person correlation coefficient.
float SAD(Single[] a, Single[] b)
Sum of Absolute Difference (SAD), i.e. the L1-norm (Manhattan) of the difference.
double SAD(Double[] a, Double[] b)
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.
double SSD(Double[] a, Double[] b)
Sum of Squared Difference (SSD), i.e. the squared L2-norm (Euclidean) of the difference.
float SSD(Single[] a, Single[] b)
Sum of Squared Difference (SSD), i.e. the squared L2-norm (Euclidean) of the difference.
Sum of Squared Difference (SSD), i.e. the squared L2-norm (Euclidean) of the difference.