Namespaces

Types in MathNet.Numerics.LinearRegression

Type WeightedRegression

Namespace MathNet.Numerics.LinearRegression

Static Functions

Public Static Functions

double GaussianKernel(double normalizedDistance)

Obsolete: Warning: This function is here to stay but will likely be refactored and/or moved to another place. Opting out from semantic versioning.

Vector<T> Local<T>(Matrix<T> x, Vector<T> y, Vector<T> t, double radius, Func<double, T> kernel)

Locally-Weighted Linear Regression using normal equations.
Obsolete: Warning: This function is here to stay but its signature will likely change. Opting out from semantic versioning.

Matrix<T> Local<T>(Matrix<T> x, Matrix<T> y, Vector<T> t, double radius, Func<double, T> kernel)

Locally-Weighted Linear Regression using normal equations.
Obsolete: Warning: This function is here to stay but its signature will likely change. Opting out from semantic versioning.

Vector<T> Weighted<T>(Matrix<T> x, Vector<T> y, Matrix<T> w)

Weighted Linear Regression using normal equations.
Parameters
Matrix<T> x

Predictor matrix X

Vector<T> y

Response vector Y

Matrix<T> w

Weight matrix W, usually diagonal with an entry for each predictor (row).

Matrix<T> Weighted<T>(Matrix<T> x, Matrix<T> y, Matrix<T> w)

Weighted Linear Regression using normal equations.
Parameters
Matrix<T> x

Predictor matrix X

Matrix<T> y

Response matrix Y

Matrix<T> w

Weight matrix W, usually diagonal with an entry for each predictor (row).

T[] Weighted<T>(T[][] x, T[] y, T[] w, bool intercept)

T[] Weighted<T>(IEnumerable<Tuple<T[], T>> samples, T[] weights, bool intercept)

Weighted Linear Regression using normal equations.
Parameters
IEnumerable<Tuple<T[], T>> samples

List of sample vectors (predictor) together with their response.

T[] weights

List of weights, one for each sample.

bool intercept

True if an intercept should be added as first artificial predictor value. Default = false.