perClass Documentation
version 5.0 (21-sep-2016)
 SDSCALE  Scaling data

 Train scaling transformation from data:

 Define scaling by multiplication and offset vectors:

 Apply non-linear scaling to a pipeline:

   DATA       Data set
   METHOD     Scaling method (default: 'standardization')
   MUL        Multiplication vector
   OFFSET     Offset vector

   PS         Scaling pipeline

 SDSCALE trains a scaling pipeline from DATA or crates manually-defined
 scaling transformation from multiplication and offset vectors.
 Scaling methods:
  'standardization' - shift data to zero mean and scale to unit variance
                      (default, synonym: 'variance')
  'centering' - mean centering shifting data to zero mean
  'range' - scale data into 0-1 interval using min and max data values
  'robust' - scale the bulk of data into 0-1 range using lower and
             upper percentiles. This is robust to outliers. Percentile
             may be set with an additional parameter (default: 0.05)

  'reg',R - for 'variance' method: add regularization parameter R to
            standard deviation to avoid NaNs on sparse data.
            Example: ps=sdscale(small_data,'reg',eps)

 Non-linear scaling methods:
  'exp',A  - exp(x*A) (default A=1.0)
  'log',A  - log(x+A) (default A=1.0)
  'log2',A - log(abs(x)+A) (default A=1.0)

sdscale is referenced in examples: