Feature Scaling

8 Mac 2023

Library "Feature_Scaling"
FS: This library helps you scale your data to certain ranges or standarize, normalize, unit scale or min-max scale your data in your prefered way. Mostly used for normalization purposes.

minmaxscale(source, min, max, length)
  minmaxscale: Min-max normalization scales your data to set minimum and maximum range
  Parameters:
    source
    min
    max
    length
  Returns: res: Data scaled to the set minimum and maximum range

meanscale(source, length)
  meanscale: Mean normalization of your data
  Parameters:
    source
    length
  Returns: res: Mean normalization result of the source

standarize(source, length, biased)
  standarize: Standarization of your data
  Parameters:
    source
    length
    biased
  Returns: res: Standarized data

unitlength(source, length)
  unitlength: Scales your data into overall unit length
  Parameters:
    source
    length
  Returns: res: Your data scaled to the unit length

23 Mac 2023

Nota Keluaran

v2

Updated: Fixed Descriptions
minmaxscale(source, min, max, length)
  minmaxscale Min-max normalization scales your data to set minimum and maximum range
  Parameters:
    source: Source data you want to use
    min: Minimum value you want
    max: Maximum value you want
    length: Length of the data you want taken into account
  Returns: res Data scaled to the set minimum and maximum range

meanscale(source, length)
  meanscale Mean normalization of your data
  Parameters:
    source: Source data you want to use
    length: Length of the data you want taken into account
  Returns: res Mean normalization result of the source

standarize(source, length, biased)
  standarize Standarization of your data
  Parameters:
    source: Source data you want to use
    length: Length of the data you want taken into account
    biased: Whether to do biased calculation while taking standard deviation, default is true
  Returns: res Standarized data

unitlength(source, length)
  unitlength Scales your data into overall unit length
  Parameters:
    source: Source data you want to use
    length: Length of the data you want taken into account
  Returns: res Your data scaled to the unit length