Hi,

The 'gradient' function currently only allows you to estimate the

gradient of discrete data (i.e. data in a matrix). I think it would make

sense if the 'gradient' function was also defined for function handles,

such that you could do something like this:

f = @sin;

df_dx = gradient(f, 0); # calculates the gradient at x = 0

Is this something there's interest in? The attached patch implements

this using a simple central difference scheme. For multi-dimensional

functions the API is like this:

f = @(x,y) sin(x).*cos(x);

[dx, dy] = gradient(f, rand(7,2)); # calculate the gradient in 7

random points

Thoughts?

Søren