## Types in MathNet.Numerics.LinearRegression

Type WeightedRegression

Namespace MathNet.Numerics.LinearRegression

### Public Static Functions

#### doubleGaussianKernel(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>(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.