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# scramble

Clase: qrandset

Scramble quasi-random point set

## Sintaxis

```ps = scramble(p,type) ps = scramble(p,'clear') ps = scramble(p) ```

## Description

`ps = scramble(p,type)` returns a scrambled copy `ps` of the point set `p` of the `qrandset` class, created using the scramble type specified in the character vector or string scalar `type`. Point sets from different subclasses of `qrandset` support different scramble types, as indicated in the following table.

SubclassScramble Types
`haltonset`

`'RR2'` — A permutation of the radical inverse coefficients derived by applying a reverse-radix operation to all of the possible coefficient values. The scramble is described in [1].

`sobolset`

`'MatousekAffineOwen'` — A random linear scramble combined with a random digital shift. The scramble is described in [2].

`ps = scramble(p,'clear')` removes all scramble settings from `p` and returns the result in `ps`.

`ps = scramble(p)` removes all scramble settings from `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.

## Examples

Use `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```

Use `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```

Use `net` to 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```

## References

[1] 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.

[2] Matousek, J. “On the L2-Discrepancy for Anchored Boxes.” Journal of Complexity. Vol. 14, No. 4, 1998, pp. 527–556.