mirror of
https://github.com/Llewellynvdm/php-ml.git
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4af8449b1c
* MultilayerPerceptron interface changes - Signature closer to other algorithms - New predict method - Remove desired error - Move maxIterations to constructor * MLP tests for multiple hidden layers and multi-class * Update all MLP-related tests * coding style fixes * Backpropagation included in multilayer-perceptron
130 lines
4.0 KiB
PHP
130 lines
4.0 KiB
PHP
<?php
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declare(strict_types=1);
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namespace tests\Phpml\Classification;
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use Phpml\Classification\MLPClassifier;
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use Phpml\NeuralNetwork\Training\Backpropagation;
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use Phpml\NeuralNetwork\Node\Neuron;
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use PHPUnit\Framework\TestCase;
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class MLPClassifierTest extends TestCase
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{
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public function testMLPClassifierLayersInitialization()
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{
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$mlp = new MLPClassifier(2, [2], [0, 1]);
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$this->assertCount(3, $mlp->getLayers());
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$layers = $mlp->getLayers();
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// input layer
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$this->assertCount(3, $layers[0]->getNodes());
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$this->assertNotContainsOnly(Neuron::class, $layers[0]->getNodes());
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// hidden layer
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$this->assertCount(3, $layers[1]->getNodes());
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$this->assertNotContainsOnly(Neuron::class, $layers[1]->getNodes());
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// output layer
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$this->assertCount(2, $layers[2]->getNodes());
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$this->assertContainsOnly(Neuron::class, $layers[2]->getNodes());
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}
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public function testSynapsesGeneration()
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{
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$mlp = new MLPClassifier(2, [2], [0, 1]);
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$layers = $mlp->getLayers();
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foreach ($layers[1]->getNodes() as $node) {
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if ($node instanceof Neuron) {
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$synapses = $node->getSynapses();
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$this->assertCount(3, $synapses);
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$synapsesNodes = $this->getSynapsesNodes($synapses);
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foreach ($layers[0]->getNodes() as $prevNode) {
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$this->assertContains($prevNode, $synapsesNodes);
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}
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}
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}
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}
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public function testBackpropagationLearning()
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{
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// Single layer 2 classes.
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$network = new MLPClassifier(2, [2], ['a', 'b'], 1000);
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$network->train(
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[[1, 0], [0, 1], [1, 1], [0, 0]],
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['a', 'b', 'a', 'b']
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);
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$this->assertEquals('a', $network->predict([1, 0]));
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$this->assertEquals('b', $network->predict([0, 1]));
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$this->assertEquals('a', $network->predict([1, 1]));
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$this->assertEquals('b', $network->predict([0, 0]));
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}
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public function testBackpropagationLearningMultilayer()
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{
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// Multi-layer 2 classes.
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$network = new MLPClassifier(5, [3, 2], ['a', 'b']);
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$network->train(
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[[1, 0, 0, 0, 0], [0, 1, 1, 0, 0], [1, 1, 1, 1, 1], [0, 0, 0, 0, 0]],
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['a', 'b', 'a', 'b']
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);
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$this->assertEquals('a', $network->predict([1, 0, 0, 0, 0]));
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$this->assertEquals('b', $network->predict([0, 1, 1, 0, 0]));
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$this->assertEquals('a', $network->predict([1, 1, 1, 1, 1]));
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$this->assertEquals('b', $network->predict([0, 0, 0, 0, 0]));
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}
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public function testBackpropagationLearningMulticlass()
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{
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// Multi-layer more than 2 classes.
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$network = new MLPClassifier(5, [3, 2], ['a', 'b', 4]);
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$network->train(
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[[1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 1, 0], [1, 1, 1, 1, 1], [0, 0, 0, 0, 0]],
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['a', 'b', 'a', 'a', 4]
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);
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$this->assertEquals('a', $network->predict([1, 0, 0, 0, 0]));
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$this->assertEquals('b', $network->predict([0, 1, 0, 0, 0]));
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$this->assertEquals('a', $network->predict([0, 0, 1, 1, 0]));
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$this->assertEquals('a', $network->predict([1, 1, 1, 1, 1]));
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$this->assertEquals(4, $network->predict([0, 0, 0, 0, 0]));
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}
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/**
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* @expectedException \Phpml\Exception\InvalidArgumentException
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*/
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public function testThrowExceptionOnInvalidLayersNumber()
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{
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new MLPClassifier(2, [], [0, 1]);
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}
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/**
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* @expectedException \Phpml\Exception\InvalidArgumentException
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*/
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public function testThrowExceptionOnInvalidClassesNumber()
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{
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new MLPClassifier(2, [2], [0]);
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}
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/**
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* @param array $synapses
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*
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* @return array
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*/
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private function getSynapsesNodes(array $synapses): array
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{
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$nodes = [];
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foreach ($synapses as $synapse) {
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$nodes[] = $synapse->getNode();
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}
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return $nodes;
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}
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}
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