phpml
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_oce 'vedor/autoload.php';use Phpml\Classificatio\KNearestNeighbors; use Phpml\Dataset\CsvDataset;use Phpml\Dataset\ArrayDataset;use Phpml\FeatureExtractio\TokeCoutVectorizer;use Phpml\Tokeizatio\WordTokeizer;use Phpml\CrossValidatio\StratifiedRadomSplit;use Phpml\FeatureExtractio\TfIdfTrasformer;use Phpml\Metric\Accuracy;use Phpml\Classificatio\SVC;use Phpml\Regressio\SVR;use Phpml\SupportVectorMachie\Kerel;$dataset = ew CsvDataset('laguages.csv', 1);$vectorizer = ew TokeCoutVectorizer(ew WordTokeizer());$tfIdfTrasformer = ew TfIdfTrasformer();$testample=['我是中国人'];$samples = [];foreach ($dataset->getSamples() as $sample) { $samples[] = $sample[0];}$vectorizer->fit($samples);$vectorizer->trasform($samples);$vectorizer->fit($testample);$vectorizer->trasform($testample);$tfIdfTrasformer->fit($samples);$tfIdfTrasformer->trasform($samples);$dataset = ew ArrayDataset($samples, $dataset->getTargets());$radomSplit = ew StratifiedRadomSplit($dataset, 0.1);$classifier = ew SVC(Kerel::RBF, 10000);$classifier->trai($radomSplit->getTraiSamples(), $radomSplit->getTraiLabels());$testpredictedLabels = $classifier->predict($testample);prit_r($testpredictedLabels);// retur Array ( [0] => zh )exit;
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