src.environment.problem.SOO.COCO_BBOB.kan.spline

Module Contents

Functions

B_batch	evaludate x on B-spline bases
coef2curve	converting B-spline coefficients to B-spline curves. Evaluate x on B-spline curves (summing up B_batch results over B-spline basis).
curve2coef	converting B-spline curves to B-spline coefficients using least squares.
extend_grid	extend grid

API

src.environment.problem.SOO.COCO_BBOB.kan.spline.B_batch(x, grid, k=0, extend=True, device='cpu')

evaludate x on B-spline bases

Args:

x : 2D torch.tensor
    inputs, shape (number of splines, number of samples)
grid : 2D torch.tensor
    grids, shape (number of splines, number of grid points)
k : int
    the piecewise polynomial order of splines.
extend : bool
    If True, k points are extended on both ends. If False, no extension (zero boundary condition). Default: True
device : str
    devicde

Returns:

spline values : 3D torch.tensor
    shape (batch, in_dim, G+k). G: the number of grid intervals, k: spline order.

Example

from kan.spline import B_batch x = torch.rand(100,2) grid = torch.linspace(-1,1,steps=11)[None, :].expand(2, 11) B_batch(x, grid, k=3).shape

src.environment.problem.SOO.COCO_BBOB.kan.spline.coef2curve(x_eval, grid, coef, k, device='cpu')

converting B-spline coefficients to B-spline curves. Evaluate x on B-spline curves (summing up B_batch results over B-spline basis).

Args:

x_eval : 2D torch.tensor
    shape (batch, in_dim)
grid : 2D torch.tensor
    shape (in_dim, G+2k). G: the number of grid intervals; k: spline order.
coef : 3D torch.tensor
    shape (in_dim, out_dim, G+k)
k : int
    the piecewise polynomial order of splines.
device : str
    devicde

Returns:

y_eval : 3D torch.tensor
    shape (number of samples, in_dim, out_dim)

src.environment.problem.SOO.COCO_BBOB.kan.spline.curve2coef(x_eval, y_eval, grid, k, lamb=1e-08)

converting B-spline curves to B-spline coefficients using least squares.

Args:

x_eval : 2D torch.tensor
    shape (in_dim, out_dim, number of samples)
y_eval : 2D torch.tensor
    shape (in_dim, out_dim, number of samples)
grid : 2D torch.tensor
    shape (in_dim, grid+2*k)
k : int
    spline order
lamb : float
    regularized least square lambda

Returns:

coef : 3D torch.tensor
    shape (in_dim, out_dim, G+k)

src.environment.problem.SOO.COCO_BBOB.kan.spline.extend_grid(grid, k_extend=0)

extend grid