public function testBackpropagationForXORLearning()
{
$network = new MultilayerPerceptron([2, 2, 1]);
$training = new Backpropagation($network);
$training->train([[1, 0], [0, 1], [1, 1], [0, 0]], [[1], [1], [0], [0]], $desiredError = 0.3, 40000);
$this->assertEquals(0, $network->setInput([1, 1])->getOutput()[0], '', $desiredError);
$this->assertEquals(0, $network->setInput([0, 0])->getOutput()[0], '', $desiredError);
$this->assertEquals(1, $network->setInput([1, 0])->getOutput()[0], '', $desiredError);
$this->assertEquals(1, $network->setInput([0, 1])->getOutput()[0], '', $desiredError);
}
public function testSynapsesGeneration()
{
$mlp = new MultilayerPerceptron([2, 2, 1]);
$layers = $mlp->getLayers();
foreach ($layers[1]->getNodes() as $node) {
if ($node instanceof Neuron) {
$synapses = $node->getSynapses();
$this->assertCount(3, $synapses);
$synapsesNodes = $this->getSynapsesNodes($synapses);
foreach ($layers[0]->getNodes() as $prevNode) {
$this->assertContains($prevNode, $synapsesNodes);
}
}
}
}
/**
* @param array $sample
*
* @return array
*/
protected function predictSample(array $sample)
{
return $this->perceptron->setInput($sample)->getOutput();
}
