First Commit

vendor/php-ai/php-ml/src/Classification/NaiveBayes.php

@@ -0,0 +1,184 @@
+<?php
+
+declare(strict_types=1);
+
+namespace Phpml\Classification;
+
+use Phpml\Exception\InvalidArgumentException;
+use Phpml\Helper\Predictable;
+use Phpml\Helper\Trainable;
+use Phpml\Math\Statistic\Mean;
+use Phpml\Math\Statistic\StandardDeviation;
+
+class NaiveBayes implements Classifier
+{
+    use Trainable;
+    use Predictable;
+
+    public const CONTINUOS = 1;
+
+    public const NOMINAL = 2;
+
+    public const EPSILON = 1e-10;
+
+    /**
+     * @var array
+     */
+    private $std = [];
+
+    /**
+     * @var array
+     */
+    private $mean = [];
+
+    /**
+     * @var array
+     */
+    private $discreteProb = [];
+
+    /**
+     * @var array
+     */
+    private $dataType = [];
+
+    /**
+     * @var array
+     */
+    private $p = [];
+
+    /**
+     * @var int
+     */
+    private $sampleCount = 0;
+
+    /**
+     * @var int
+     */
+    private $featureCount = 0;
+
+    /**
+     * @var array
+     */
+    private $labels = [];
+
+    public function train(array $samples, array $targets): void
+    {
+        $this->samples = array_merge($this->samples, $samples);
+        $this->targets = array_merge($this->targets, $targets);
+        $this->sampleCount = count($this->samples);
+        $this->featureCount = count($this->samples[0]);
+
+        $this->labels = array_map('strval', array_flip(array_flip($this->targets)));
+        foreach ($this->labels as $label) {
+            $samples = $this->getSamplesByLabel($label);
+            $this->p[$label] = count($samples) / $this->sampleCount;
+            $this->calculateStatistics($label, $samples);
+        }
+    }
+
+    /**
+     * @return mixed
+     */
+    protected function predictSample(array $sample)
+    {
+        // Use NaiveBayes assumption for each label using:
+        //	P(label|features) = P(label) * P(feature0|label) * P(feature1|label) .... P(featureN|label)
+        // Then compare probability for each class to determine which label is most likely
+        $predictions = [];
+        foreach ($this->labels as $label) {
+            $p = $this->p[$label];
+            for ($i = 0; $i < $this->featureCount; ++$i) {
+                $Plf = $this->sampleProbability($sample, $i, $label);
+                $p += $Plf;
+            }
+
+            $predictions[$label] = $p;
+        }
+
+        arsort($predictions, SORT_NUMERIC);
+        reset($predictions);
+
+        return key($predictions);
+    }
+
+    /**
+     * Calculates vital statistics for each label & feature. Stores these
+     * values in private array in order to avoid repeated calculation
+     */
+    private function calculateStatistics(string $label, array $samples): void
+    {
+        $this->std[$label] = array_fill(0, $this->featureCount, 0);
+        $this->mean[$label] = array_fill(0, $this->featureCount, 0);
+        $this->dataType[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
+        $this->discreteProb[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
+        for ($i = 0; $i < $this->featureCount; ++$i) {
+            // Get the values of nth column in the samples array
+            // Mean::arithmetic is called twice, can be optimized
+            $values = array_column($samples, $i);
+            $numValues = count($values);
+            // if the values contain non-numeric data,
+            // then it should be treated as nominal/categorical/discrete column
+            if ($values !== array_filter($values, 'is_numeric')) {
+                $this->dataType[$label][$i] = self::NOMINAL;
+                $this->discreteProb[$label][$i] = array_count_values($values);
+                $db = &$this->discreteProb[$label][$i];
+                $db = array_map(function ($el) use ($numValues) {
+                    return $el / $numValues;
+                }, $db);
+            } else {
+                $this->mean[$label][$i] = Mean::arithmetic($values);
+                // Add epsilon in order to avoid zero stdev
+                $this->std[$label][$i] = 1e-10 + StandardDeviation::population($values, false);
+            }
+        }
+    }
+
+    /**
+     * Calculates the probability P(label|sample_n)
+     */
+    private function sampleProbability(array $sample, int $feature, string $label): float
+    {
+        if (!isset($sample[$feature])) {
+            throw new InvalidArgumentException('Missing feature. All samples must have equal number of features');
+        }
+
+        $value = $sample[$feature];
+        if ($this->dataType[$label][$feature] == self::NOMINAL) {
+            if (!isset($this->discreteProb[$label][$feature][$value]) ||
+                $this->discreteProb[$label][$feature][$value] == 0) {
+                return self::EPSILON;
+            }
+
+            return $this->discreteProb[$label][$feature][$value];
+        }
+
+        $std = $this->std[$label][$feature];
+        $mean = $this->mean[$label][$feature];
+        // Calculate the probability density by use of normal/Gaussian distribution
+        // Ref: https://en.wikipedia.org/wiki/Normal_distribution
+        //
+        // In order to avoid numerical errors because of small or zero values,
+        // some libraries adopt taking log of calculations such as
+        // scikit-learn did.
+        // (See : https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py)
+        $pdf = -0.5 * log(2.0 * M_PI * $std * $std);
+        $pdf -= 0.5 * (($value - $mean) ** 2) / ($std * $std);
+
+        return $pdf;
+    }
+
+    /**
+     * Return samples belonging to specific label
+     */
+    private function getSamplesByLabel(string $label): array
+    {
+        $samples = [];
+        for ($i = 0; $i < $this->sampleCount; ++$i) {
+            if ($this->targets[$i] == $label) {
+                $samples[] = $this->samples[$i];
+            }
+        }
+
+        return $samples;
+    }
+}