NAME

       mlpack_preprocess_scale - scale data

SYNOPSIS

        mlpack_preprocess_scale -i string [-r double] [-m unknown] [-f bool] [-e int] [-b int] [-a string] [-s int] [-V bool] [-o string] [-M unknown] [-h -v]

DESCRIPTION

       This  utility  takes  a  dataset and performs feature scaling using one of the six scaler methods namely:
       'max_abs_scaler',  'mean_normalization',   ’min_max_scaler'   ,'standard_scaler',   'pca_whitening'   and
       'zca_whitening'.  The  function takes a matrix as '--input_file (-i)' and a scaling method type which you
       can specify using '--scaler_method (-a)' parameter; the default is standard scaler, and outputs a  matrix
       with scaled feature.

       The output scaled feature matrix may be saved with the '--output_file (-o)' output parameters.

       The  model  to  scale features can be saved using '--output_model_file (-M)' and later can be loaded back
       using'--input_model_file (-m)'.

       So, a simple example where we want to scale the dataset 'X.csv' into ’X_scaled.csv' with  standard_scaler
       as scaler_method, we could run

       $ mlpack_preprocess_scale --input_file X.csv --output_file X_scaled.csv --scaler_method standard_scaler

       A  simple  example  where  we  want  to  whiten  the  dataset  'X.csv'  into ’X_whitened.csv' with PCA as
       whitening_method and use 0.01 as regularization parameter, we could run

       $ mlpack_preprocess_scale --input_file X.csv  --output_file  X_scaled.csv  --scaler_method  pca_whitening
       --epsilon 0.01

       You  can  also retransform the scaled dataset back using'--inverse_scaling (-f)'. An example to rescale :
       'X_scaled.csv' into 'X.csv'using the saved model '--input_model_file (-m)' is:

       $   mlpack_preprocess_scale   --input_file    X_scaled.csv    --output_file    X.csv    --inverse_scaling
       --input_model_file saved.bin

       Another simple example where we want to scale the dataset 'X.csv' into ’X_scaled.csv' with min_max_scaler
       as scaler method, where scaling range is 1 to 3 instead of default 0 to 1. We could run

       $ mlpack_preprocess_scale --input_file X.csv --output_file  X_scaled.csv  --scaler_method  min_max_scaler
       --min_value 1 --max_value 3

REQUIRED INPUT OPTIONS

       --input_file (-i) [string]
              Matrix containing data.

OPTIONAL INPUT OPTIONS

       --epsilon (-r) [double]
              regularization  Parameter  for  pcawhitening, or zcawhitening, should be between -1 to 1.  Default
              value 1e-06.

       --help (-h) [bool]
              Default help info.

       --info [string]
              Print help on a specific option. Default value ''.

       --input_model_file (-m) [unknown]
              Input Scaling model.

       --inverse_scaling (-f) [bool]
              Inverse Scaling to get original dataset

       --max_value (-e) [int]
              Ending value of range for min_max_scaler.  Default value 1.

       --min_value (-b) [int]
              Starting value of range for min_max_scaler.  Default value 0.

       --scaler_method (-a) [string]
              method to use for scaling, the default is standard_scaler. Default value 'standard_scaler'.

       --seed (-s) [int]
              Random seed (0 for std::time(NULL)). Default value 0.

       --verbose (-v) [bool]
              Display informational messages and the full list of parameters and timers at the end of execution.

       --version (-V) [bool]
              Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

       --output_file (-o) [string]
              Matrix to save scaled data to.

       --output_model_file (-M) [unknown]
              Output scaling model.

ADDITIONAL INFORMATION

       For further information, including relevant papers, citations,  and  theory,  consult  the  documentation
       found at http://www.mlpack.org or included with your distribution of mlpack.