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                ## Pipeline > 在機器學習中,通常運行一系列算法來處理和學習數據集。例如: * 將每個文檔的文本拆分為標記。 * 將每個文檔的單詞轉換為數字特征向量。 * 使用特征向量和標簽學習預測模型。 PHP-ML表示像Pipeline這樣的工作流程,它包含變換器序列和估計器。 ### 構造函數參數 `$transformers`(array | Transformer []) - 實現Transformer接口的對象序列 `$estimator`(Estimator) - 可以訓練和預測的估算器 ``` use Phpml\Classification\SVC; use Phpml\FeatureExtraction\TfIdfTransformer; use Phpml\Pipeline; $transformers = [ new TfIdfTransformer(), ]; $estimator = new SVC(); $pipeline = new Pipeline($transformers, $estimator); ``` ***** ### 例 首先,我們的管道替換缺失值,然后標準化樣本,最后訓練SVC估計。這樣制備的管道重復預測樣品的每個轉化步驟。 ``` use Phpml\Classification\SVC; use Phpml\Pipeline; use Phpml\Preprocessing\Imputer; use Phpml\Preprocessing\Normalizer; use Phpml\Preprocessing\Imputer\Strategy\MostFrequentStrategy; $transformers = [ new Imputer(null, new MostFrequentStrategy()), new Normalizer(), ]; $estimator = new SVC(); $samples = [ [1, -1, 2], [2, 0, null], [null, 1, -1], ]; $targets = [ 4, 1, 4, ]; $pipeline = new Pipeline($transformers, $estimator); $pipeline->train($samples, $targets); $predicted = $pipeline->predict([[0, 0, 0]]); // $predicted == 4 ```
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