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Editing: MinMaxNormalizer.php

<?php

namespace Rubix\ML\Transformers;

use Rubix\ML\DataType;
use Rubix\ML\Persistable;
use Rubix\ML\Datasets\Dataset;
use Rubix\ML\Traits\AutotrackRevisions;
use Rubix\ML\Specifications\SamplesAreCompatibleWithTransformer;
use Rubix\ML\Exceptions\InvalidArgumentException;
use Rubix\ML\Exceptions\RuntimeException;

use function min;
use function max;

use const Rubix\ML\EPSILON;

/**
 * Min Max Normalizer
 *
 * The *Min Max* Normalizer scales the input features to a value between
 * a user-specified range (default 0 to 1).
 *
 * @category    Machine Learning
 * @package     Rubix/ML
 * @author      Andrew DalPino
 */
class MinMaxNormalizer implements Transformer, Stateful, Elastic, Reversible, Persistable
{
    use AutotrackRevisions;

/**
     * The minimum value of the transformed features.
     *
     * @var float
     */
    protected float $min;

/**
     * The maximum value of the transformed features.
     *
     * @var float
     */
    protected float $max;

/**
     * The computed minimums of the fitted data.
     *
     * @var (int|float)[]|null
     */
    protected ?array $minimums = null;

/**
     * The computed maximums of the fitted data.
     *
     * @var (int|float)[]|null
     */
    protected ?array $maximums = null;

/**
     * The scale coefficients of each feature.
     *
     * @var float[]|null
     */
    protected ?array $scales = null;

/**
     * @param float $min
     * @param float $max
     * @throws InvalidArgumentException
     */
    public function __construct(float $min = 0.0, float $max = 1.0)
    {
        if ($min > $max) {
            throw new InvalidArgumentException('Minimum cannot be greater'
                . ' than maximum.');
        }

$this->min = $min;
        $this->max = $max;
    }

/**
     * Return the data types that this transformer is compatible with.
     *
     * @internal
     *
     * @return list<DataType>
     */
    public function compatibility() : array
    {
        return DataType::all();
    }

/**
     * Is the transformer fitted?
     *
     * @return bool
     */
    public function fitted() : bool
    {
        return $this->minimums and $this->maximums;
    }

/**
     * Return the minmums of each feature column.
     *
     * @return (int|float)[]|null
     */
    public function minimums() : ?array
    {
        return $this->minimums;
    }

/**
     * Return the maximums of each feature column.
     *
     * @return (int|float)[]|null
     */
    public function maximums() : ?array
    {
        return $this->maximums;
    }

/**
     * Fit the transformer to a dataset.
     *
     * @param Dataset $dataset
     */
    public function fit(Dataset $dataset) : void
    {
        SamplesAreCompatibleWithTransformer::with($dataset, $this)->check();

$this->minimums = $this->maximums = $this->scales = [];

foreach ($dataset->featureTypes() as $column => $type) {
            if ($type->isContinuous()) {
                $values = $dataset->feature($column);

/** @var int|float $min */
                $min = min($values);

/** @var int|float $max */
                $max = max($values);

$scale = ($this->max - $this->min) / (($max - $min) ?: EPSILON);

$this->minimums[$column] = $min;
                $this->maximums[$column] = $max;
                $this->scales[$column] = $scale;
            }
        }
    }

/**
     * Update the fitting of the transformer.
     *
     * @param Dataset $dataset
     */
    public function update(Dataset $dataset) : void
    {
        if (!isset($this->minimums, $this->maximums, $this->scales)) {
            $this->fit($dataset);

return;
        }

SamplesAreCompatibleWithTransformer::with($dataset, $this)->check();

foreach ($this->scales as $column => &$scale) {
            $values = $dataset->feature($column);

/** @var int|float $min */
            $min = min($this->minimums[$column], ...$values);

/** @var int|float $max */
            $max = max($this->maximums[$column], ...$values);

$scale = ($this->max - $this->min) / (($max - $min) ?: EPSILON);

$this->minimums[$column] = $min;
            $this->maximums[$column] = $max;
        }
    }

/**
     * Transform the dataset in place.
     *
     * @param list<list<mixed>> $samples
     * @throws RuntimeException
     */
    public function transform(array &$samples) : void
    {
        if (!isset($this->minimums, $this->scales)) {
            throw new RuntimeException('Transformer has not been fitted.');
        }

foreach ($samples as &$sample) {
            foreach ($this->scales as $column => $scale) {
                $value = &$sample[$column];

$min = $this->minimums[$column];

$value *= $scale;

$value += $this->min - $min * $scale;
            }
        }
    }

/**
     * Perform the reverse transformation to the samples.
     *
     * @param list<list<mixed>> $samples
     * @throws RuntimeException
     */
    public function reverseTransform(array &$samples) : void
    {
        if (!isset($this->minimums, $this->scales)) {
            throw new RuntimeException('Transformer has not been fitted.');
        }

foreach ($samples as &$sample) {
            foreach ($this->scales as $column => $scale) {
                $value = &$sample[$column];

$min = $this->minimums[$column];

$value -= $this->min - $min * $scale;

$value /= $scale;
            }
        }
    }

/**
     * Return the string representation of the object.
     *
     * @internal
     *
     * @return string
     */
    public function __toString() : string
    {
        return "Min Max Normalizer (min: {$this->min}, max: {$this->max})";
    }
}

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