Return labels in MultilayerPerceptron output (#315)

src/Classification/MLPClassifier.php

@@ -41,17 +41,16 @@ protected function predictSample(array $sample)
             }
         }
 
-        return $this->classes[$predictedClass];
+        return $predictedClass;
     }
 
     /**
      * @param mixed $target
      */
     protected function trainSample(array $sample, $target): void
     {
-
         // Feed-forward.
-        $this->setInput($sample)->getOutput();
+        $this->setInput($sample);
 
         // Back-propagate.
         $this->backpropagation->backpropagate($this->getLayers(), $this->getTargetClass($target));

src/NeuralNetwork/Layer.php

@@ -41,12 +41,9 @@ public function getNodes(): array
         return $this->nodes;
     }
 
-    /**
-     * @return Neuron
-     */
     private function createNode(string $nodeClass, ?ActivationFunction $activationFunction = null): Node
     {
-        if ($nodeClass == Neuron::class) {
+        if ($nodeClass === Neuron::class) {
             return new Neuron($activationFunction);
         }
 

src/NeuralNetwork/Network/LayeredNetwork.php

@@ -51,8 +51,6 @@ public function getOutput(): array
 
     /**
      * @param mixed $input
-     *
-     * @return $this
      */
     public function setInput($input): Network
     {

src/NeuralNetwork/Network/MultilayerPerceptron.php

@@ -69,6 +69,10 @@ public function __construct(int $inputLayerFeatures, array $hiddenLayers, array
             throw new InvalidArgumentException('Provide at least 2 different classes');
         }
 
+        if (count($classes) !== count(array_unique($classes))) {
+            throw new InvalidArgumentException('Classes must be unique');
+        }
+
         $this->classes = array_values($classes);
         $this->iterations = $iterations;
         $this->inputLayerFeatures = $inputLayerFeatures;
@@ -109,6 +113,16 @@ public function setLearningRate(float $learningRate): void
         $this->backpropagation->setLearningRate($this->learningRate);
     }
 
+    public function getOutput(): array
+    {
+        $result = [];
+        foreach ($this->getOutputLayer()->getNodes() as $i => $neuron) {
+            $result[$this->classes[$i]] = $neuron->getOutput();
+        }
+
+        return $result;
+    }
+
     /**
      * @param mixed $target
      */

src/NeuralNetwork/Node/Neuron.php

@@ -44,7 +44,7 @@ public function addSynapse(Synapse $synapse): void
     /**
      * @return Synapse[]
      */
-    public function getSynapses()
+    public function getSynapses(): array
     {
         return $this->synapses;
     }

tests/Classification/MLPClassifierTest.php

@@ -183,7 +183,7 @@ public function testSaveAndRestore(): void
         $testSamples = [[0, 0], [1, 0], [0, 1], [1, 1]];
         $predicted = $classifier->predict($testSamples);
 
-        $filename = 'perceptron-test-'.random_int(100, 999).'-'.uniqid();
+        $filename = 'perceptron-test-'.random_int(100, 999).'-'.uniqid('', false);
         $filepath = tempnam(sys_get_temp_dir(), $filename);
         $modelManager = new ModelManager();
         $modelManager->saveToFile($classifier, $filepath);
@@ -204,7 +204,7 @@ public function testSaveAndRestoreWithPartialTraining(): void
         $this->assertEquals('a', $network->predict([1, 0]));
         $this->assertEquals('b', $network->predict([0, 1]));
 
-        $filename = 'perceptron-test-'.random_int(100, 999).'-'.uniqid();
+        $filename = 'perceptron-test-'.random_int(100, 999).'-'.uniqid('', false);
         $filepath = tempnam(sys_get_temp_dir(), $filename);
         $modelManager = new ModelManager();
         $modelManager->saveToFile($network, $filepath);
@@ -245,6 +245,13 @@ public function testThrowExceptionOnInvalidClassesNumber(): void
         new MLPClassifier(2, [2], [0]);
     }
 
+    public function testOutputWithLabels(): void
+    {
+        $output = (new MLPClassifier(2, [2, 2], ['T', 'F']))->getOutput();
+
+        $this->assertEquals(['T', 'F'], array_keys($output));
+    }
+
     private function getSynapsesNodes(array $synapses): array
     {
         $nodes = [];

tests/NeuralNetwork/Network/MultilayerPerceptronTest.php

@@ -4,6 +4,7 @@
 
 namespace Phpml\Tests\NeuralNetwork\Network;
 
+use Phpml\Exception\InvalidArgumentException;
 use Phpml\NeuralNetwork\ActivationFunction;
 use Phpml\NeuralNetwork\Layer;
 use Phpml\NeuralNetwork\Network\MultilayerPerceptron;
@@ -13,6 +14,39 @@
 
 class MultilayerPerceptronTest extends TestCase
 {
+    public function testThrowExceptionWhenHiddenLayersAreEmpty(): void
+    {
+        $this->expectException(InvalidArgumentException::class);
+        $this->expectExceptionMessage('Provide at least 1 hidden layer');
+
+        $this->getMockForAbstractClass(
+            MultilayerPerceptron::class,
+            [5, [], [0, 1], 1000, null, 0.42]
+        );
+    }
+
+    public function testThrowExceptionWhenThereIsOnlyOneClass(): void
+    {
+        $this->expectException(InvalidArgumentException::class);
+        $this->expectExceptionMessage('Provide at least 2 different classes');
+
+        $this->getMockForAbstractClass(
+            MultilayerPerceptron::class,
+            [5, [3], [0], 1000, null, 0.42]
+        );
+    }
+
+    public function testThrowExceptionWhenClassesAreNotUnique(): void
+    {
+        $this->expectException(InvalidArgumentException::class);
+        $this->expectExceptionMessage('Classes must be unique');
+
+        $this->getMockForAbstractClass(
+            MultilayerPerceptron::class,
+            [5, [3], [0, 1, 2, 3, 1], 1000, null, 0.42]
+        );
+    }
+
     public function testLearningRateSetter(): void
     {
         /** @var MultilayerPerceptron $mlp */