train($samples, $targets); $this->assertEquals(0, $classifier->predict([0.1, 0.2])); $this->assertEquals(0, $classifier->predict([1.1, 0.2])); $this->assertEquals(1, $classifier->predict([0.1, 0.99])); $this->assertEquals(1, $classifier->predict([1.1, 0.8])); // Then: vertical test $samples = [[0, 0], [1, 0], [0, 1], [1, 1]]; $targets = [0, 1, 0, 1]; $classifier = new DecisionStump(); $classifier->train($samples, $targets); $this->assertEquals(0, $classifier->predict([0.1, 0.2])); $this->assertEquals(0, $classifier->predict([0.1, 1.1])); $this->assertEquals(1, $classifier->predict([1.0, 0.99])); $this->assertEquals(1, $classifier->predict([1.1, 0.1])); return $classifier; } public function testSaveAndRestore() { // Instantinate new Percetron trained for OR problem $samples = [[0, 0], [1, 0], [0, 1], [1, 1]]; $targets = [0, 1, 1, 1]; $classifier = new DecisionStump(); $classifier->train($samples, $targets); $testSamples = [[0, 1], [1, 1], [0.2, 0.1]]; $predicted = $classifier->predict($testSamples); $filename = 'dstump-test-'.rand(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)); } }