/
L1Normalizer.php
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/
L1Normalizer.php
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<?php
namespace Rubix\ML\Transformers;
use Rubix\ML\DataType;
use function array_map;
use function array_walk;
use function array_sum;
/**
* L1 Normalizer
*
* Transform each sample vector in the sample matrix such that each feature is divided
* by the L1 norm (or *magnitude*) of that vector. The resulting sample will have
* continuous features between 0 and 1.
*
* @category Machine Learning
* @package Rubix/ML
* @author Andrew DalPino
*/
class L1Normalizer implements Transformer
{
/**
* Return the data types that this transformer is compatible with.
*
* @internal
*
* @return list<\Rubix\ML\DataType>
*/
public function compatibility() : array
{
return [
DataType::continuous(),
];
}
/**
* Transform the dataset in place.
*
* @param array<mixed[]> $samples
*/
public function transform(array &$samples) : void
{
array_walk($samples, [$this, 'normalize']);
}
/**
* Normalize a sample by its L1 norm.
*
* @param list<int|float> $sample
*/
protected function normalize(array &$sample) : void
{
$norm = array_sum(array_map('abs', $sample));
if ($norm == 0) {
return;
}
foreach ($sample as &$value) {
$value /= $norm;
}
}
/**
* Return the string representation of the object.
*
* @internal
*
* @return string
*/
public function __toString() : string
{
return 'L1 Normalizer';
}
}