Extending 'gradient' to handle function handles

[Søren Hauberg]

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
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