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
Informacje o Wersji
⋅
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