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A robust distance kernel that measures samples consisting of a mix of categorical and continuous data types while also handling missing (NaN) values. When comparing continuous data, the Gower metric is equivalent to the normalized Manhattan distance and when comparing categorical data it is equivalent to the Hamming distance.

Note: The Gower metric expects all continuous variables to have a standardized range. The default range works for values that have been normalized between 0 and 1.

Data Type Compatibility: Continuous, Categorical


# Param Default Type Description
1 range 1.0 float The standardized range of the continuous feature columns. Ex. [0, 1] has a range of 1, [-1, 1] has a range of 2, and so forth.


use Rubix\ML\Kernels\Distance\Gower;

$kernel = new Gower(2.0);


  • J. C. Gower. (1971). A General Coefficient of Similarity and Some of Its Properties.