implement activation function for neural network

src/Phpml/NeuralNetwork/ActivationFunction.php

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+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\NeuralNetwork;
+
+interface ActivationFunction
+{
+    /**
+     * @param float|int $value
+     *
+     * @return float
+     */
+    public function compute($value): float;
+}

src/Phpml/NeuralNetwork/ActivationFunction/BinaryStep.php

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+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\NeuralNetwork\ActivationFunction;
+
+use Phpml\NeuralNetwork\ActivationFunction;
+
+class BinaryStep implements ActivationFunction
+{
+    /**
+     * @param float|int $value
+     *
+     * @return float
+     */
+    public function compute($value): float
+    {
+        return $value >= 0 ? 1.0 : 0.0;
+    }
+}

src/Phpml/NeuralNetwork/ActivationFunction/Gaussian.php

@@ -0,0 +1,20 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\NeuralNetwork\ActivationFunction;
+
+use Phpml\NeuralNetwork\ActivationFunction;
+
+class Gaussian implements ActivationFunction
+{
+    /**
+     * @param float|int $value
+     *
+     * @return float
+     */
+    public function compute($value): float
+    {
+        return exp(-pow($value, 2));
+    }
+}

src/Phpml/NeuralNetwork/ActivationFunction/HyperbolicTangent.php

@@ -0,0 +1,33 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\NeuralNetwork\ActivationFunction;
+
+use Phpml\NeuralNetwork\ActivationFunction;
+
+class HyperbolicTangent implements ActivationFunction
+{
+    /**
+     * @var float
+     */
+    private $beta;
+
+    /**
+     * @param float $beta
+     */
+    public function __construct($beta = 1.0)
+    {
+        $this->beta = $beta;
+    }
+
+    /**
+     * @param float|int $value
+     *
+     * @return float
+     */
+    public function compute($value): float
+    {
+        return tanh($this->beta * $value);
+    }
+}

src/Phpml/NeuralNetwork/ActivationFunction/Sigmoid.php

@@ -0,0 +1,33 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\NeuralNetwork\ActivationFunction;
+
+use Phpml\NeuralNetwork\ActivationFunction;
+
+class Sigmoid implements ActivationFunction
+{
+    /**
+     * @var float
+     */
+    private $beta;
+
+    /**
+     * @param float $beta
+     */
+    public function __construct($beta = 1.0)
+    {
+        $this->beta = $beta;
+    }
+
+    /**
+     * @param float|int $value
+     *
+     * @return float
+     */
+    public function compute($value): float
+    {
+        return 1 / (1 + exp(-$this->beta * $value));
+    }
+}

src/Phpml/NeuralNetwork/Node/Neuron.php

@@ -0,0 +1,9 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\NeuralNetwork\Node;
+
+class Neuron
+{
+}

tests/Phpml/NeuralNetwork/ActivationFunction/BinaryStepTest.php

@@ -0,0 +1,35 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace tests\Phpml\NeuralNetwork\ActivationFunction;
+
+use Phpml\NeuralNetwork\ActivationFunction\BinaryStep;
+
+class BinaryStepTest extends \PHPUnit_Framework_TestCase
+{
+    /**
+     * @param $expected
+     * @param $value
+     *
+     * @dataProvider binaryStepProvider
+     */
+    public function testBinaryStepActivationFunction($expected, $value)
+    {
+        $binaryStep = new BinaryStep();
+
+        $this->assertEquals($expected, $binaryStep->compute($value));
+    }
+
+    /**
+     * @return array
+     */
+    public function binaryStepProvider()
+    {
+        return [
+            [1, 1],
+            [1, 0],
+            [0, -0.1],
+        ];
+    }
+}

tests/Phpml/NeuralNetwork/ActivationFunction/GaussianTest.php

@@ -0,0 +1,37 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace tests\Phpml\NeuralNetwork\ActivationFunction;
+
+use Phpml\NeuralNetwork\ActivationFunction\Gaussian;
+
+class GaussianTest extends \PHPUnit_Framework_TestCase
+{
+    /**
+     * @param $expected
+     * @param $value
+     *
+     * @dataProvider gaussianProvider
+     */
+    public function testGaussianActivationFunction($expected, $value)
+    {
+        $gaussian = new Gaussian();
+
+        $this->assertEquals($expected, $gaussian->compute($value), '', 0.001);
+    }
+
+    /**
+     * @return array
+     */
+    public function gaussianProvider()
+    {
+        return [
+            [0.367, 1],
+            [1, 0],
+            [0.367, -1],
+            [0, 3],
+            [0, -3],
+        ];
+    }
+}

tests/Phpml/NeuralNetwork/ActivationFunction/HyperboliTangentTest.php

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+<?php
+
+declare (strict_types = 1);
+
+namespace tests\Phpml\NeuralNetwork\ActivationFunction;
+
+use Phpml\NeuralNetwork\ActivationFunction\HyperbolicTangent;
+
+class HyperboliTangentTest extends \PHPUnit_Framework_TestCase
+{
+    /**
+     * @param $beta
+     * @param $expected
+     * @param $value
+     *
+     * @dataProvider tanhProvider
+     */
+    public function testHyperbolicTangentActivationFunction($beta, $expected, $value)
+    {
+        $tanh = new HyperbolicTangent($beta);
+
+        $this->assertEquals($expected, $tanh->compute($value), '', 0.001);
+    }
+
+    /**
+     * @return array
+     */
+    public function tanhProvider()
+    {
+        return [
+            [1.0, 0.761, 1],
+            [1.0, 0, 0],
+            [1.0, 1, 4],
+            [1.0, -1, -4],
+            [0.5, 0.462, 1],
+            [0.3, 0, 0],
+        ];
+    }
+}

tests/Phpml/NeuralNetwork/ActivationFunction/SigmoidTest.php

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+<?php
+
+declare (strict_types = 1);
+
+namespace tests\Phpml\NeuralNetwork\ActivationFunction;
+
+use Phpml\NeuralNetwork\ActivationFunction\Sigmoid;
+
+class SigmoidTest extends \PHPUnit_Framework_TestCase
+{
+    /**
+     * @param $beta
+     * @param $expected
+     * @param $value
+     *
+     * @dataProvider sigmoidProvider
+     */
+    public function testSigmoidActivationFunction($beta, $expected, $value)
+    {
+        $sigmoid = new Sigmoid($beta);
+
+        $this->assertEquals($expected, $sigmoid->compute($value), '', 0.001);
+    }
+
+    /**
+     * @return array
+     */
+    public function sigmoidProvider()
+    {
+        return [
+            [1.0, 1, 7.25],
+            [2.0, 1, 3.75],
+            [1.0, 0.5, 0],
+            [0.5, 0.5, 0],
+            [1.0, 0, -7.25],
+            [2.0, 0, -3.75],
+        ];
+    }
+}