First Commit

vendor/php-ai/php-ml/src/DimensionReduction/PCA.php

@@ -0,0 +1,131 @@
+<?php
+
+declare(strict_types=1);
+
+namespace Phpml\DimensionReduction;
+
+use Phpml\Exception\InvalidArgumentException;
+use Phpml\Exception\InvalidOperationException;
+use Phpml\Math\Statistic\Covariance;
+use Phpml\Math\Statistic\Mean;
+
+class PCA extends EigenTransformerBase
+{
+    /**
+     * Temporary storage for mean values for each dimension in given data
+     *
+     * @var array
+     */
+    protected $means = [];
+
+    /**
+     * @var bool
+     */
+    protected $fit = false;
+
+    /**
+     * PCA (Principal Component Analysis) used to explain given
+     * data with lower number of dimensions. This analysis transforms the
+     * data to a lower dimensional version of it by conserving a proportion of total variance
+     * within the data. It is a lossy data compression technique.<br>
+     *
+     * @param float $totalVariance Total explained variance to be preserved
+     * @param int   $numFeatures   Number of features to be preserved
+     *
+     * @throws InvalidArgumentException
+     */
+    public function __construct(?float $totalVariance = null, ?int $numFeatures = null)
+    {
+        if ($totalVariance !== null && ($totalVariance < 0.1 || $totalVariance > 0.99)) {
+            throw new InvalidArgumentException('Total variance can be a value between 0.1 and 0.99');
+        }
+
+        if ($numFeatures !== null && $numFeatures <= 0) {
+            throw new InvalidArgumentException('Number of features to be preserved should be greater than 0');
+        }
+
+        if (($totalVariance !== null) === ($numFeatures !== null)) {
+            throw new InvalidArgumentException('Either totalVariance or numFeatures should be specified in order to run the algorithm');
+        }
+
+        if ($numFeatures !== null) {
+            $this->numFeatures = $numFeatures;
+        }
+
+        if ($totalVariance !== null) {
+            $this->totalVariance = $totalVariance;
+        }
+    }
+
+    /**
+     * Takes a data and returns a lower dimensional version
+     * of this data while preserving $totalVariance or $numFeatures. <br>
+     * $data is an n-by-m matrix and returned array is
+     * n-by-k matrix where k <= m
+     */
+    public function fit(array $data): array
+    {
+        $n = count($data[0]);
+
+        $data = $this->normalize($data, $n);
+
+        $covMatrix = Covariance::covarianceMatrix($data, array_fill(0, $n, 0));
+
+        $this->eigenDecomposition($covMatrix);
+
+        $this->fit = true;
+
+        return $this->reduce($data);
+    }
+
+    /**
+     * Transforms the given sample to a lower dimensional vector by using
+     * the eigenVectors obtained in the last run of <code>fit</code>.
+     *
+     * @throws InvalidOperationException
+     */
+    public function transform(array $sample): array
+    {
+        if (!$this->fit) {
+            throw new InvalidOperationException('PCA has not been fitted with respect to original dataset, please run PCA::fit() first');
+        }
+
+        if (!is_array($sample[0])) {
+            $sample = [$sample];
+        }
+
+        $sample = $this->normalize($sample, count($sample[0]));
+
+        return $this->reduce($sample);
+    }
+
+    protected function calculateMeans(array $data, int $n): void
+    {
+        // Calculate means for each dimension
+        $this->means = [];
+        for ($i = 0; $i < $n; ++$i) {
+            $column = array_column($data, $i);
+            $this->means[] = Mean::arithmetic($column);
+        }
+    }
+
+    /**
+     * Normalization of the data includes subtracting mean from
+     * each dimension therefore dimensions will be centered to zero
+     */
+    protected function normalize(array $data, int $n): array
+    {
+        if (count($this->means) === 0) {
+            $this->calculateMeans($data, $n);
+        }
+
+        // Normalize data
+        foreach (array_keys($data) as $i) {
+            for ($k = 0; $k < $n; ++$k) {
+                $data[$i][$k] -= $this->means[$k];
+            }
+        }
+
+        return $data;
+    }
+}