Create MLP Regressor draft

src/Phpml/Regression/MLPRegressor.php

@@ -0,0 +1,81 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\Regression;
+
+
+use Phpml\Helper\Predictable;
+use Phpml\NeuralNetwork\ActivationFunction;
+use Phpml\NeuralNetwork\Network\MultilayerPerceptron;
+use Phpml\NeuralNetwork\Training\Backpropagation;
+
+class MLPRegressor implements Regression
+{
+    use Predictable;
+
+    /**
+     * @var MultilayerPerceptron
+     */
+    private $perceptron;
+
+    /**
+     * @var array
+     */
+    private $hiddenLayers;
+
+    /**
+     * @var float
+     */
+    private $desiredError;
+
+    /**
+     * @var int
+     */
+    private $maxIterations;
+
+    /**
+     * @var ActivationFunction
+     */
+    private $activationFunction;
+
+    /**
+     * @param array $hiddenLayers
+     * @param float $desiredError
+     * @param int $maxIterations
+     * @param ActivationFunction $activationFunction
+     */
+    public function __construct(array $hiddenLayers = [100], float $desiredError, int $maxIterations, ActivationFunction $activationFunction = null)
+    {
+        $this->hiddenLayers = $hiddenLayers;
+        $this->desiredError = $desiredError;
+        $this->maxIterations = $maxIterations;
+        $this->activationFunction = $activationFunction;
+    }
+
+
+    /**
+     * @param array $samples
+     * @param array $targets
+     */
+    public function train(array $samples, array $targets)
+    {
+        $layers = [count($samples[0])] + $this->hiddenLayers + [count($targets[0])];
+
+        $this->perceptron = new MultilayerPerceptron($layers, $this->activationFunction);
+
+        $trainer = new Backpropagation($this->perceptron);
+        $trainer->train($samples, $targets, $this->desiredError, $this->maxIterations);
+    }
+
+    /**
+     * @param array $sample
+     *
+     * @return array
+     */
+    protected function predictSample(array $sample)
+    {
+        return $this->perceptron->setInput($sample)->getOutput();
+    }
+
+}