pymordemos.vkoga

Demo of the VKOGA algorithm for function approximation.

Module Contents

pymordemos.vkoga.main(training_points_sampling: Choices('random uniform') = Option('random', help='Method for sampling the training points'), num_training_points: int = Option(40, help='Number of training points in the weak greedy algorithm.'), greedy_criterion: Choices('fp f p') = Option('fp', help='Selection criterion for the greedy algorithm.'), max_centers: int = Option(20, help='Maximum number of selected centers in the greedy algorithm.'), tol: float = Option(1e-06, help='Tolerance for the weak greedy algorithm.'), reg: float = Option(1e-12, help='Regularization parameter for the kernel interpolation.'), length_scale: float = Option(1.0, help='The length scale parameter of the kernel. Only used when `kernel = diagonal`.'))

Approximates a function with 2d output from training data using VKOGA.