NonlinearLeastSquares Properties

Gets the Least-Squares optimization algorithm used to perform the actual learning.

Gets or sets a value indicating whether standard errors should be computed in the next iteration.

Gets or sets the model function, mapping inputs to outputs given a suitable parameter vector.

Gets or sets a function that computes the gradient of the Function in respect to the current parameters.

Gets the number of variables (free parameters) in the non-linear model specified in Function.

Gets or sets the vector of initial values to be used at the beginning of the optimization. Setting a suitable set of initial values can be important to achieve good convergence or avoid poor local minimas.

Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running.