.\" Text automatically generated by txt2man
.TH mlpack_preprocess_scale 1 "12 December 2020" "mlpack-3.4.2" "User Commands"
.SH NAME
\fBmlpack_preprocess_scale \fP- scale data
.SH SYNOPSIS
.nf
.fam C
 \fBmlpack_preprocess_scale\fP \fB-i\fP \fIstring\fP [\fB-r\fP \fIdouble\fP] [\fB-m\fP \fIunknown\fP] [\fB-f\fP \fIbool\fP] [\fB-e\fP \fIint\fP] [\fB-b\fP \fIint\fP] [\fB-a\fP \fIstring\fP] [\fB-s\fP \fIint\fP] [\fB-V\fP \fIbool\fP] [\fB-o\fP \fIstring\fP] [\fB-M\fP \fIunknown\fP] [\fB-h\fP \fB-v\fP] 
.fam T
.fi
.fam T
.fi
.SH DESCRIPTION


This utility takes a dataset and performs feature scaling using one of the six
scaler methods namely: 'max_abs_scaler', 'mean_normalization',
\(cqmin_max_scaler' ,'standard_scaler', 'pca_whitening' and 'zca_whitening'. The
function takes a matrix as '\fB--input_file\fP (\fB-i\fP)' and a scaling method type which
you can specify using '\fB--scaler_method\fP (\fB-a\fP)' parameter; the default is
standard scaler, and outputs a matrix with scaled feature.
.PP
The output scaled feature matrix may be saved with the '\fB--output_file\fP (\fB-o\fP)'
output parameters.
.PP
The model to scale features can be saved using '\fB--output_model_file\fP (\fB-M\fP)' and
later can be loaded back using'\fB--input_model_file\fP (\fB-m\fP)'.
.PP
So, a simple example where we want to scale the dataset 'X.csv' into
\(cqX_scaled.csv' with standard_scaler as scaler_method, we could run 
.PP
$ \fBmlpack_preprocess_scale\fP \fB--input_file\fP X.csv \fB--output_file\fP X_scaled.csv
\fB--scaler_method\fP standard_scaler
.PP
A simple example where we want to whiten the dataset 'X.csv' into
\(cqX_whitened.csv' with PCA as whitening_method and use 0.01 as regularization
parameter, we could run 
.PP
$ \fBmlpack_preprocess_scale\fP \fB--input_file\fP X.csv \fB--output_file\fP X_scaled.csv
\fB--scaler_method\fP pca_whitening \fB--epsilon\fP 0.01
.PP
You can also retransform the scaled dataset back using'\fB--inverse_scaling\fP
(\fB-f\fP)'. An example to rescale : 'X_scaled.csv' into 'X.csv'using the saved
model '\fB--input_model_file\fP (\fB-m\fP)' is:
.PP
$ \fBmlpack_preprocess_scale\fP \fB--input_file\fP X_scaled.csv \fB--output_file\fP X.csv
\fB--inverse_scaling\fP \fB--input_model_file\fP saved.bin
.PP
Another simple example where we want to scale the dataset 'X.csv' into
\(cqX_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 
.PP
$ \fBmlpack_preprocess_scale\fP \fB--input_file\fP X.csv \fB--output_file\fP X_scaled.csv
\fB--scaler_method\fP min_max_scaler \fB--min_value\fP 1 \fB--max_value\fP 3
.RE
.PP

.SH REQUIRED INPUT OPTIONS 

.TP
.B
\fB--input_file\fP (\fB-i\fP) [\fIstring\fP]
Matrix containing data.  
.SH OPTIONAL INPUT OPTIONS 

.TP
.B
\fB--epsilon\fP (\fB-r\fP) [\fIdouble\fP]
regularization Parameter for pcawhitening, or zcawhitening, should be between \fB-1\fP to 1.  Default value 1e-06. 
.TP
.B
\fB--help\fP (\fB-h\fP) [\fIbool\fP]
Default help info. 
.TP
.B
\fB--info\fP [\fIstring\fP]
Print help on a specific option. Default value ''. 
.TP
.B
\fB--input_model_file\fP (\fB-m\fP) [\fIunknown\fP]
Input Scaling model. 
.TP
.B
\fB--inverse_scaling\fP (\fB-f\fP) [\fIbool\fP]
Inverse Scaling to get original dataset 
.TP
.B
\fB--max_value\fP (\fB-e\fP) [\fIint\fP]
Ending value of range for min_max_scaler.  Default value 1. 
.TP
.B
\fB--min_value\fP (\fB-b\fP) [\fIint\fP]
Starting value of range for min_max_scaler.  Default value 0. 
.TP
.B
\fB--scaler_method\fP (\fB-a\fP) [\fIstring\fP]
method to use for scaling, the default is standard_scaler. Default value 'standard_scaler'. 
.TP
.B
\fB--seed\fP (\fB-s\fP) [\fIint\fP]
Random seed (0 for \fBstd::time\fP(NULL)). Default value 0. 
.TP
.B
\fB--verbose\fP (\fB-v\fP) [\fIbool\fP]
Display informational messages and the full list of parameters and timers at the end of execution. 
.TP
.B
\fB--version\fP (\fB-V\fP) [\fIbool\fP]
Display the version of mlpack.  
.SH OPTIONAL OUTPUT OPTIONS 

.TP
.B
\fB--output_file\fP (\fB-o\fP) [\fIstring\fP]
Matrix to save scaled data to. 
.TP
.B
\fB--output_model_file\fP (\fB-M\fP) [\fIunknown\fP]
Output scaling model.
.SH 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.