train($samples, $labels); $this->assertEquals('b', $classifier->predict([3, 2])); $this->assertEquals('b', $classifier->predict([5, 1])); $this->assertEquals('b', $classifier->predict([4, 3])); $this->assertEquals('b', $classifier->predict([4, -5])); $this->assertEquals('a', $classifier->predict([2, 3])); $this->assertEquals('a', $classifier->predict([1, 2])); $this->assertEquals('a', $classifier->predict([1, 5])); $this->assertEquals('a', $classifier->predict([3, 10])); } public function testPredictArrayOfSamples() { $trainSamples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]]; $trainLabels = ['a', 'a', 'a', 'b', 'b', 'b']; $testSamples = [[3, 2], [5, 1], [4, 3], [4, -5], [2, 3], [1, 2], [1, 5], [3, 10]]; $testLabels = ['b', 'b', 'b', 'b', 'a', 'a', 'a', 'a']; $classifier = new KNearestNeighbors(); $classifier->train($trainSamples, $trainLabels); $predicted = $classifier->predict($testSamples); $this->assertEquals($testLabels, $predicted); } public function testAccuracyOnIrisDataset() { $dataset = new RandomSplit(new Iris(), $testSize = 0.5, $seed = 123); $classifier = new KNearestNeighbors($k = 4); $classifier->train($dataset->getTrainSamples(), $dataset->getTrainLabels()); $predicted = $classifier->predict($dataset->getTestSamples()); $score = Accuracy::score($dataset->getTestLabels(), $predicted); $this->assertEquals(0.96, $score); } }