phpml基于 PHP-ML 库实现机器学习
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_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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