基于PHP-ML库实现机器学习
基于语言学习基于语言学习，根据语言编码实现学习
实例require_once 'vendor/autoload.php';use Phpml\Classification\KNearestNeighbors; use Phpml\Dataset\CsvDataset;use Phpml\Dataset\ArrayDataset;use Phpml\FeatureExtraction\TokenCountVectorizer;use Phpml\Tokenization\WordTokenizer;use Phpml\CrossValidation\StratifiedRandomSplit;use Phpml\FeatureExtraction\TfIdfTransformer;use Phpml\Metric\Accuracy;use Phpml\Classification\SVC;use Phpml\Regression\SVR;use Phpml\SupportVectorMachine\Kernel;$dataset = new CsvDataset('languages.csv', 1);$vectorizer = new TokenCountVectorizer(new WordTokenizer());$tfIdfTransformer = new TfIdfTransformer();$testample=['我是中国人'];$samples = [];foreach ($dataset->getSamples() as $sample) { $samples[] = $sample[0];}$vectorizer->fit($samples);$vectorizer->transform($samples);$vectorizer->fit($testample);$vectorizer->transform($testample);$tfIdfTransformer->fit($samples);$tfIdfTransformer->transform($samples);$dataset = new ArrayDataset($samples, $dataset->getTargets());$randomSplit = new StratifiedRandomSplit($dataset, 0.1);$classifier = new SVC(Kernel::RBF, 10000);$classifier->train($randomSplit->getTrainSamples(), $randomSplit->getTrainLabels());$testpredictedLabels = $classifier->predict($testample);print_r($testpredictedLabels);// return Array ( [0] => zh )exit;
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