train($samples, $targets); $this->assertEquals(0, $classifier->predict([0.1, 0.2])); $this->assertEquals(0, $classifier->predict([0.1, 0.99])); $this->assertEquals(1, $classifier->predict([1.1, 0.8])); // OR problem $samples = [[0, 0], [0.1, 0.2], [0.2, 0.1], [1, 0], [0, 1], [1, 1]]; $targets = [0, 0, 0, 1, 1, 1]; $classifier = new AdaBoost(); $classifier->train($samples, $targets); $this->assertEquals(0, $classifier->predict([0.1, 0.2])); $this->assertEquals(1, $classifier->predict([0.1, 0.99])); $this->assertEquals(1, $classifier->predict([1.1, 0.8])); // XOR problem $samples = [[0.1, 0.2], [1., 1.], [0.9, 0.8], [0., 1.], [1., 0.], [0.2, 0.8]]; $targets = [0, 0, 0, 1, 1, 1]; $classifier = new AdaBoost(5); $classifier->train($samples, $targets); $this->assertEquals(0, $classifier->predict([0.1, 0.1])); $this->assertEquals(1, $classifier->predict([0, 0.999])); $this->assertEquals(0, $classifier->predict([1.1, 0.8])); return $classifier; } public function testSaveAndRestore(): void { // Instantinate new Percetron trained for OR problem $samples = [[0, 0], [1, 0], [0, 1], [1, 1]]; $targets = [0, 1, 1, 1]; $classifier = new AdaBoost(); $classifier->train($samples, $targets); $testSamples = [[0, 1], [1, 1], [0.2, 0.1]]; $predicted = $classifier->predict($testSamples); $filename = 'adaboost-test-'.random_int(100, 999).'-'.uniqid(); $filepath = tempnam(sys_get_temp_dir(), $filename); $modelManager = new ModelManager(); $modelManager->saveToFile($classifier, $filepath); $restoredClassifier = $modelManager->restoreFromFile($filepath); $this->assertEquals($classifier, $restoredClassifier); $this->assertEquals($predicted, $restoredClassifier->predict($testSamples)); } }