From efd76ef4c22bc487da5c17d12fcf4256ed3cc5ca Mon Sep 17 00:00:00 2001
From: Dennis Eichhorn <spl1nes.com@googlemail.com>
Date: Sat, 11 Jul 2020 20:25:42 +0200
Subject: [PATCH] start implementing basic Ai algorithms

---
 Ai/Ocr/BasicOcr.php | 151 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 151 insertions(+)
 create mode 100644 Ai/Ocr/BasicOcr.php

diff --git a/Ai/Ocr/BasicOcr.php b/Ai/Ocr/BasicOcr.php
new file mode 100644
index 000000000..f503a37e3
--- /dev/null
+++ b/Ai/Ocr/BasicOcr.php
@@ -0,0 +1,151 @@
+<?php
+/**
+ * Orange Management
+ *
+ * PHP Version 7.4
+ *
+ * @package   phpOMS\Ai\Ocr
+ * @copyright Dennis Eichhorn
+ * @license   OMS License 1.0
+ * @version   1.0.0
+ * @link      https://orange-management.org
+ */
+declare(strict_types=1);
+
+namespace phpOMS\Ai\Ocr;
+
+use phpOMS\Math\Topology\MetricsND;
+
+/**
+ * Matching a value with a set of coins
+ *
+ * @package phpOMS\Ai\Ocr
+ * @license OMS License 1.0
+ * @link    https://orange-management.org
+ * @since   1.0.0
+ */
+final class BasicOcr
+{
+    private array $Xtrain = [];
+
+    private array $ytrain = [];
+
+    public function trainWith(string $dataPath, string $labelPath) : void
+    {
+        $Xtrain = $this->readImages($dataPath);
+        $ytrain = $this->readLabels($labelPath);
+
+        $this->Xtrain = \array_merge($this->Xtrain, $this->extractFeatures($Xtrain));
+        $this->ytrain = \array_merge($this->ytrain, $this->extractFeatures($ytrain));
+    }
+
+    private function readImages(string $path) : array
+    {
+        $fp = \fopen($path, 'r');
+        $magicNumber = \unpack('l', \fread($fp, 4))[1];
+        $numberOfImages = \unpack('l', \fread($fp, 4))[1];
+        $numberOfRows = \unpack('l', \fread($fp, 4))[1];
+        $numberOfColumns = \unpack('l', \fread($fp, 4))[1];
+
+        $images = [];
+
+        for ($i = 0; $i < $numberOfImages; ++$i) {
+            $image = [];
+
+            for ($row = 0; $row < $numberOfRows; ++$row) {
+                $rows = [];
+
+                for ($col = 0; $col < $numberOfColumns; ++$col) {
+                    $rows[] = \unpack('l', \fread($fp, 1))[1]; //fread($fp, 1);
+                }
+
+                $image[] = $rows;
+            }
+
+            $images[] = $image;
+        }
+
+        return $images;
+    }
+
+    private function readLabels(string $path) : array
+    {
+        $fp = \fopen($path, 'r');
+        $magicNumber = \unpack('l', \fread($fp, 4))[1];
+        $numberOfLabels = \unpack('l', \fread($fp, 4))[1];
+
+        $labels = [];
+
+        for ($i = 0; $i < $numberOfLabels; ++$i) {
+            $labels[] = \unpack('l', \fread($fp, 1))[1]; //fread($fp, 1);
+        }
+
+        return $labels;
+    }
+
+    public function flatten(array $data) : array
+    {
+        $flat = [];
+        foreach ($data as $sublist) {
+            foreach ($sublist as $pixel) {
+                $flat[] = $pixel;
+            }
+        }
+
+        return $flat;
+    }
+
+    private function extractFeatures(array $data) : array
+    {
+        $features = [];
+        foreach ($data as $sample) {
+            $features[] = $this->flatten($sample);
+        }
+
+        return $features;
+    }
+
+    private function kNearest(array $Xtrain, array $ytrain, array $Xtest, int $k = 3) : array
+    {
+        $predictedLabels = [];
+        foreach ($Xtest as $sample) {
+            // @todo: consider to path the k-limit to the getDistances function for earlier filtering
+            $distances = $this->getDistances($Xtrain, $sample);
+            \asort($distances);
+
+            // find possible k-labels for a image
+            $kKeys = \array_keys(\array_slice($distances, 0, $k));
+            $candidateLabels = [];
+
+            foreach ($kKeys as $key) {
+                $candidateLabels[] = $ytrain[$key];
+            }
+
+            // find best match
+            $countedCandidates = \array_count_values($candidateLabels);
+            \asort($countedCandidates);
+
+            $predictedLabels[] = ['label' => \array_key_first($countedCandidates), 'prob' => \reset($countedCandidates) / $k];
+        }
+
+        return $predictedLabels;
+    }
+
+    private function getDistances(array $Xtrain, $sample) : array
+    {
+        $dist = [];
+        foreach ($Xtrain as $train) {
+            $dist[] = MetricsND::euclidean($train, $sample);
+        }
+
+        return $dist;
+    }
+
+    public function match(string $path) : array
+    {
+        // @todo: implement image reading if it isn't an mnist file
+        $Xtest = $this->readImages($path);
+
+        return $this->kNearest($this->Xtrain, $this->ytrain, $Xtest);
+    }
+}