Scramble quasi-random point set
ps = scramble(p,
ps = scramble(p,'clear')
ps = scramble(p)
ps = scramble(p, returns
a scrambled copy
ps of the point set
created using the scramble type specified in the character vector
Point sets from different subclasses of
different scramble types, as indicated in the following table.
ps = scramble(p,'clear') removes all scramble
p and returns the result in
ps = scramble(p) removes all scramble settings
p and then adds them back in the order they
were originally applied. This typically results in a different point
set because of the randomness of the scrambling algorithms.
haltonset to generate
a 3-D Halton point set, skip the first 1000 values, and then retain
every 101st point:
p = haltonset(3,'Skip',1e3,'Leap',1e2) p = Halton point set in 3 dimensions (8.918019e+013 points) Properties: Skip : 1000 Leap : 100 ScrambleMethod : none
scramble to apply reverse-radix scrambling:
p = scramble(p,'RR2') p = Halton point set in 3 dimensions (8.918019e+013 points) Properties: Skip : 1000 Leap : 100 ScrambleMethod : RR2
generate the first four points:
X0 = net(p,4) X0 = 0.0928 0.6950 0.0029 0.6958 0.2958 0.8269 0.3013 0.6497 0.4141 0.9087 0.7883 0.2166
Use parenthesis indexing to generate every third point, up to the 11th point:
X = p(1:3:11,:) X = 0.0928 0.6950 0.0029 0.9087 0.7883 0.2166 0.3843 0.9840 0.9878 0.6831 0.7357 0.7923
 Kocis, L., and W. J. Whiten. “Computational Investigations of Low-Discrepancy Sequences.” ACM Transactions on Mathematical Software. Vol. 23, No. 2, 1997, pp. 266–294.
 Matousek, J. “On the L2-Discrepancy for Anchored Boxes.” Journal of Complexity. Vol. 14, No. 4, 1998, pp. 527–556.