SDBOX Bounding box classifier

   P=SDBOX(DATA)

 INPUT
   DATA    data set

 OUTPUT
   P       bounding box pipeline

 OPTIONS
  'bounds',[min max] - define minimum and maximum (scalar) bounds
                       applicable to all features.
  'min',M  - define minimum value (M may be vector with min per feature)
  'max',M2 - define minimum value (M may be vector with min per feature)
  'reject',P - reject P-percentile lowest and highest values

 DESCRIPTION
 SDBOX defines a bounding box classifier for the entire data set DATA. By
 default minimum and maximum values for each feature are
 used. Alternatively, we may provide percentile to be rejected using
 'reject',P option. If scalar P is provided, P/2 percentile is rejected
 from bottom and P/2 from the top distribution of each feature. P may be
 2-component vector directly defining low and high-percentile.
 The 'bounds' option allows user to set specific min and max scalar values
 used for all features. This is meaningful when all features have
 identical bounds.  Alternatively, min and max bounds per-feature may be
 set using 'min' and 'max' options, resp.
 SDBOX uses 'inside' and 'outside' decisions by default.

 EXAMPLE:
 Protect data in a PCA subspace in all directions:
  >> p=sdpca*sdbox; pbox=data*p
 Make sure data is between 0 and 5000:
  >> p=sdbox(data,'bounds',[0 5000]);

 SEE ALSO
 SDPCA, SDRELAB