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.