the point equivalence classes are easy enough to define. points are either in a size-1 class (the center point, can be ignored), a size-4 class (axes, both vert/horiz. and diagonal), or a size-8 class (all other points on the board).
for each tuple of points in the equivalence class, normalize whatever measure you're using for the importance of those points (varies according to what you've implemented, of course) to sum to 1. then calculate the mean and standard deviation of the values of the points in the tuple. sum the standard deviation over all of your tuples, and you should have a reasonable full-board measure of the symmetry of your algorithm that can be compared with other people's measures. not a rigorous statistical measure of anything by any means, but it ought to be fair enough for what you're suggesting. i think that a more rigorous (or just overblown and more painful to calculate) measure would be the sum over tuples of [width of 95% confidence interval using "tuple-sized-1" degrees-of-freedom student's t distribution] after normalizing each tuple to sum to 1. s. ____________________________________________________________________________________ Looking for earth-friendly autos? Browse Top Cars by "Green Rating" at Yahoo! Autos' Green Center. http://autos.yahoo.com/green_center/ _______________________________________________ computer-go mailing list [email protected] http://www.computer-go.org/mailman/listinfo/computer-go/
