This document describes the ML.NORMALIZER function, which lets you normalize an array of numerical expressions using a given p-norm.

You can use this function with models that support manual feature preprocessing. For more information, see the following documents:

• End-to-end user journeys for ML models
• Contribution analysis user journey

Syntax

ML.NORMALIZER(array_expression [, p])

Arguments

ML.NORMALIZER takes the following arguments:

• array_expression: an array of numerical expressions to normalize.
• p: a FLOAT64 value that specifies the degree of p-norm. This can be 0.0, any value greater than or equal to 1.0, or CAST('+INF' AS FLOAT64). The default value is 2.

Output

ML.NORMALIZER returns an array of FLOAT64 values that represent the normalized numerical expressions.

Example

The following example normalizes a set of numerical expressions using a p-norm of 2:

SELECT ML.NORMALIZER([4.0, 1.0, 2.0, 2.0, 0.0]) AS output;

The output looks similar to the following:

+--------+
| output |
+--------+
| 0.8    |
| 0.2    |
| 0.4    |
| 0.4    |
| 0.0    |
+--------+

What's next

• For information about feature preprocessing, see Feature preprocessing overview.