{"id":30210,"date":"2024-02-03T05:43:58","date_gmt":"2023-12-29T05:45:42","guid":{"rendered":"https:\/\/www.silicloud.com\/zh\/blog\/%e4%bd%bf%e7%94%a8php%e8%bf%9b%e8%a1%8c%e9%b8%a2%e5%b0%be%e8%8a%b1%e5%88%86%e7%b1%bb%ef%bc%88%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%ef%bc%89%e3%80%90php-ml%e3%80%91%e3%80%82\/"},"modified":"2025-08-12T18:06:22","modified_gmt":"2025-08-12T10:06:22","slug":"%e4%bd%bf%e7%94%a8php%e8%bf%9b%e8%a1%8c%e9%b8%a2%e5%b0%be%e8%8a%b1%e5%88%86%e7%b1%bb%ef%bc%88%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%ef%bc%89%e3%80%90php-ml%e3%80%91%e3%80%82","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/zh\/blog\/%e4%bd%bf%e7%94%a8php%e8%bf%9b%e8%a1%8c%e9%b8%a2%e5%b0%be%e8%8a%b1%e5%88%86%e7%b1%bb%ef%bc%88%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%ef%bc%89%e3%80%90php-ml%e3%80%91%e3%80%82\/","title":{"rendered":"\u4f7f\u7528PHP\u8fdb\u884c\u9e22\u5c3e\u82b1\u5206\u7c7b\uff08\u673a\u5668\u5b66\u4e60\uff09\u3010PHP-ML\u3011"},"content":{"rendered":"

\u5951\u673a<\/p>\n

\u666e\u6bb5\u306fPHP\u3067\u958b\u767a\u3092\u3057\u3066\u3044\u308b\u306e\u3067\u3059\u304c\u3001\u4ee5\u524d\u304b\u3089\u6a5f\u68b0\u5b66\u7fd2\u306b\u8208\u5473\u304c\u3042\u308aPython\u306e\u52c9\u5f37\u3092\u3057\u3066\u307f\u305f\u308a\u3057\u3066\u3044\u307e\u3057\u305f\u3002
\n\u6a5f\u68b0\u5b66\u7fd2\u30fbAI=Python\u3068\u3044\u3046\u8003\u3048\u304c\u3042\u3063\u305f\u306e\u3067\u3059\u304c\u3001\u3075\u3068PHP\u3067\u6a5f\u68b0\u5b66\u7fd2\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u304c\u3042\u308b\u3068\u3044\u3046\u3053\u3068\u3092\u805e\u304d\u4f7f\u3063\u3066\u898b\u308b\u3053\u3068\u306b\u3057\u307e\u3057\u305f\u3002<\/p>\n

\u5b89\u88c5\u56fe\u4e66\u9986<\/p>\n

\u4f7f\u7528PHP-ML\u8fd9\u4e2a\u5e93\u3002
\n\u4f7f\u7528composer\u8fdb\u884c\u5b89\u88c5\u3002<\/p>\n

composer require php-ai\/php-ml\r\n<\/code><\/pre>\n
\u5b9e\u8df5<\/p>\n
\u6211\u73b0\u5728\u7acb\u5373\u5f00\u59cb\u5199\u4ee3\u7801\u3002
\n\u4e8b\u5148\u51c6\u5907\u4e86\u4e00\u4e9b\u793a\u4f8b\u6570\u636e\u3002
\n\u8fd9\u6b21\u6211\u4eec\u6765\u5c1d\u8bd5\u505a\u4e00\u4e2a\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u7ecf\u5178\u6848\u4f8b\uff0c\u9e22\u5c3e\u82b1\u7684\u5206\u7c7b\u3002
\n\u6211\u4eec\u5c06\u4f7f\u7528k\u8fd1\u90bb\u6cd5\u6765\u8fdb\u884c\u5b66\u4e60\u3002
\n\u5173\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u65b9\u6cd5\uff0c\u4ee5\u4e0b\u7684\u6587\u7ae0\u5e94\u8be5\u4f1a\u6709\u6240\u5e2e\u52a9\u3002
\n\u673a\u5668\u5b66\u4e60\u768413\u79cd\u65b9\u6cd5 \u30fc\u521d\u5b66\u8005\u548c\u4e2d\u7ea7\u8005\u7684\u89e3\u91ca\uff01<\/p>\n
\u6570\u636e\u51c6\u5907<\/p>\n
\u5373\u4f7f\u8bf4\u6570\u636e\u5df2\u7ecf\u51c6\u5907\u597d\u4e86\uff0c\u4e5f\u4e0d\u80fd\u76f4\u63a5\u4f7f\u7528\u3002
\n\u56e0\u6b64\uff0c\u4e3a\u4e86\u8bad\u7ec3\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u52a0\u5de5\u3002\uff08\u636e\u8bf4\u8fd9\u662f\u673a\u5668\u5b66\u4e60\u4e2d\u6700\u56f0\u96be\u7684\u90e8\u5206\uff09<\/p>\n
\/\/ \u5b66\u7fd2\u30c7\u30fc\u30bf120,\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf30(8:2)<\/span>\r\n\/\/ \u5b66\u7fd2\u3055\u305b\u308b\u305f\u3081\u306e\u30c7\u30fc\u30bf\u3068\u5b66\u7fd2\u3055\u305b\u305f\u7269\u306e\u30c6\u30b9\u30c8\u3092\u3059\u308b\u305f\u3081\u306e\u30c7\u30fc\u30bf\u306b\u5206\u3051\u308b<\/span>\r\nconst<\/span> LERNING_NUM<\/span> =<\/span> 120<\/span>;<\/span>\r\nconst<\/span> TEST_NUM<\/span> =<\/span> 30<\/span>;<\/span>\r\n\r\n\/\/ \u30c7\u30fc\u30bf\u53d6\u5f97<\/span>\r\n$dataset<\/span> =<\/span> new<\/span> IrisDataset<\/span>();<\/span>\r\n$samples<\/span> =<\/span> $dataset<\/span>-><\/span>getSamples<\/span>();<\/span>\r\n$targets<\/span> =<\/span> $dataset<\/span>-><\/span>getTargets<\/span>();<\/span>\r\n\r\n\/\/ \u30c7\u30fc\u30bf\u3092\u52a0\u5de5<\/span>\r\n\/\/ \u30e9\u30d9\u30eb\u3068\u6570\u5024\u30c7\u30fc\u30bf\u304c\u5225\u3005\u306e\u914d\u5217\u306b\u306a\u3063\u3066\u3044\u308b\u306e\u3067\u3001\u540c\u3058\u914d\u5217\u306b\u3059\u308b<\/span>\r\nfor<\/span> (<\/span>$i<\/span>=<\/span>0<\/span>;<\/span> $i<\/span> <<\/span> count<\/span>(<\/span>$samples<\/span>);<\/span> $i<\/span>++<\/span>)<\/span> {<\/span> \r\n    $processedData<\/span>[]<\/span> =<\/span> [<\/span>\r\n        'label'<\/span> =><\/span> $targets<\/span>[<\/span>$i<\/span>],<\/span>\r\n        $columns<\/span>[<\/span>0<\/span>]<\/span> =><\/span> (<\/span>float<\/span>)<\/span>$samples<\/span>[<\/span>$i<\/span>][<\/span>0<\/span>],<\/span>\r\n        $columns<\/span>[<\/span>1<\/span>]<\/span> =><\/span> (<\/span>float<\/span>)<\/span>$samples<\/span>[<\/span>$i<\/span>][<\/span>1<\/span>],<\/span>\r\n        $columns<\/span>[<\/span>2<\/span>]<\/span> =><\/span> (<\/span>float<\/span>)<\/span>$samples<\/span>[<\/span>$i<\/span>][<\/span>2<\/span>],<\/span>\r\n        $columns<\/span>[<\/span>3<\/span>]<\/span> =><\/span> (<\/span>float<\/span>)<\/span>$samples<\/span>[<\/span>$i<\/span>][<\/span>3<\/span>],<\/span>\r\n    ];<\/span>\r\n}<\/span>\r\n\/\/ \u540c\u3058\u7a2e\u985e\u306e\u30c7\u30fc\u30bf\u3067\u56fa\u307e\u3063\u3066\u3044\u308b\u306e\u3067\u30b7\u30e3\u30c3\u30d5\u30eb<\/span>\r\nshuffle<\/span>(<\/span>$processedData<\/span>);<\/span>\r\n\r\n\/\/ \u5b66\u7fd2\u7528\u30c7\u30fc\u30bf\u3068\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u5206\u5272<\/span>\r\n$splitDatas<\/span> =<\/span> array_chunk<\/span>(<\/span>$processedData<\/span>,<\/span> LERNING_NUM<\/span>);<\/span>\r\n\r\n\/\/ \u5b66\u7fd2\u7528\u30c7\u30fc\u30bf<\/span>\r\nfor<\/span> (<\/span>$i<\/span>=<\/span>0<\/span>;<\/span> $i<\/span> <<\/span> LERNING_NUM<\/span>;<\/span> $i<\/span>++<\/span>)<\/span> {<\/span>\r\n    $learningSamples<\/span>[]<\/span> =<\/span> [<\/span>\r\n        $splitDatas<\/span>[<\/span>0<\/span>][<\/span>$i<\/span>][<\/span>'sepal_length'<\/span>],<\/span>\r\n        $splitDatas<\/span>[<\/span>0<\/span>][<\/span>$i<\/span>][<\/span>'sepal_width'<\/span>],<\/span>\r\n        $splitDatas<\/span>[<\/span>0<\/span>][<\/span>$i<\/span>][<\/span>'petal_length'<\/span>],<\/span>\r\n        $splitDatas<\/span>[<\/span>0<\/span>][<\/span>$i<\/span>][<\/span>'petal_width'<\/span>],<\/span>\r\n    ];<\/span>\r\n    $learningTargets<\/span>[]<\/span> =<\/span> $splitDatas<\/span>[<\/span>0<\/span>][<\/span>$i<\/span>][<\/span>'label'<\/span>];<\/span>\r\n}<\/span>\r\n\r\n\/\/ \u30c6\u30b9\u30c8\u7528\u30c7\u30fc\u30bf<\/span>\r\nfor<\/span> (<\/span>$i<\/span>=<\/span>0<\/span>;<\/span> $i<\/span> <<\/span> TEST_NUM<\/span>;<\/span> $i<\/span>++<\/span>)<\/span> {<\/span>\r\n    $testSamples<\/span>[]<\/span> =<\/span> [<\/span>\r\n        $splitDatas<\/span>[<\/span>1<\/span>][<\/span>$i<\/span>][<\/span>'sepal_length'<\/span>],<\/span>\r\n        $splitDatas<\/span>[<\/span>1<\/span>][<\/span>$i<\/span>][<\/span>'sepal_width'<\/span>],<\/span>\r\n        $splitDatas<\/span>[<\/span>1<\/span>][<\/span>$i<\/span>][<\/span>'petal_length'<\/span>],<\/span>\r\n        $splitDatas<\/span>[<\/span>1<\/span>][<\/span>$i<\/span>][<\/span>'petal_width'<\/span>],<\/span>\r\n    ];<\/span>\r\n    $testTargets<\/span>[]<\/span> =<\/span> $splitDatas<\/span>[<\/span>1<\/span>][<\/span>$i<\/span>][<\/span>'label'<\/span>];<\/span>\r\n    ;<\/span>\r\n}<\/span>\r\n<\/code><\/pre>\n
\u5b66\u4e60<\/p>\n
\u53ea\u9700\u8981\u8fd9\u6bb5\u4ee3\u7801\u5c31\u53ef\u4ee5\u8fdb\u884c\u5b66\u4e60\u3002
\n\u53ea\u9700\u5c06\u4e4b\u524d\u5904\u7406\u8fc7\u7684\u6570\u636e\u4f20\u9012\u8fc7\u53bb\u3002\uff08\u975e\u5e38\u65b9\u4fbf\uff09<\/p>\n
$classifier<\/span> =<\/span> new<\/span> KNearestNeighbors<\/span>();<\/span>\r\n$classifier<\/span>-><\/span>train<\/span>(<\/span>$learningSamples<\/span>,<\/span> $learningTargets<\/span>);<\/span>\r\n<\/code><\/pre>\n
\u8003\u8bd5<\/p>\n
\u6211\u4eec\u5c06\u6d4b\u8bd5\u5176\u662f\u5426\u80fd\u591f\u8fdb\u884c\u6709\u6548\u7684\u5206\u7c7b\u3002<\/p>\n
$results<\/span> =<\/span> $classifier<\/span>-><\/span>predict<\/span>(<\/span>$testSamples<\/span>);<\/span>\r\n<\/code><\/pre>\n
\u4e92\u76f8\u6838\u5bf9\u7b54\u6848<\/p>\n
\u6211\u4f1a\u4ed4\u7ec6\u68c0\u67e5\u5e76\u4e88\u4ee5\u56de\u7b54\u3002<\/p>\n
$correctResult<\/span> =<\/span> 0<\/span>;<\/span>\r\n\/\/ \u6b63\u7b54\u6570\u62bd\u51fa<\/span>\r\nforeach<\/span> (<\/span>$results<\/span> as<\/span> $key<\/span> =><\/span> $result<\/span>)<\/span> {<\/span>\r\n    \/\/ \u6b63\u7b54\u6570<\/span>\r\n    if<\/span> (<\/span>$result<\/span> ===<\/span> $testTargets<\/span>[<\/span>$key<\/span>])<\/span> {<\/span>\r\n        $correctResult<\/span>++<\/span>;<\/span>\r\n    }<\/span>\r\n}<\/span>\r\n \r\necho<\/span> '\u6b63\u7b54\u7387\u306f'<\/span> .<\/span> (<\/span>$correctResult<\/span>\/<\/span>TEST_NUM<\/span>)<\/span>*<\/span>100<\/span> .<\/span> '%\u3067\u3059\u3002'<\/span> .<\/span> \"<\/span>\\n<\/span>\"<\/span>;<\/span>\r\n<\/code><\/pre>\n
\u8f93\u51fa\u7ed3\u679c<\/p>\n
\u6b63\u7b54\u7387\u306f96.666666666667%\u3067\u3059\u3002\r\n<\/code><\/pre>\n
\u771f\u4e0d\u9519\u5462\u3002
\n\u987a\u4fbf\u8bf4\u4e00\u4e0b\uff0c\u6bcf\u6b21\u6267\u884c\u7ed3\u679c\u90fd\u4f1a\u7a0d\u5fae\u53d8\u5316\u3002<\/p>\n
\/\/ 2\u56de\u76ee\r\n\u6b63\u7b54\u7387\u306f90%\u3067\u3059\u3002\r\n\/\/ 3\u56de\u76ee\r\n\u6b63\u7b54\u738793.333333333333%\u3067\u3059\u3002\r\n.\r\n.\r\n<\/code><\/pre>\n
\u6211\u4eec\u8fd0\u884c\u5927\u7ea610\u6b21\uff0c\u6709\u8d85\u8fc790%\u7684\u7ed3\u679c\uff0c\u53ef\u4ee5\u8ba4\u4e3a\u5f88\u597d\u4e86\u3002<\/p>\n
\u7ebf\u8863<\/p>\n
\u6574\u4e2a\u4ee3\u7801\u7684\u6837\u5b50\u662f\u8fd9\u6837\u7684\u3002<\/p>\n
<?php<\/span>\r\nrequire_once<\/span> __DIR__<\/span> .<\/span> '\/vendor\/autoload.php'<\/span>;<\/span>\r\n\r\nuse<\/span> Phpml\\Dataset\\Demo\\IrisDataset<\/span>;<\/span>\r\nuse<\/span> Phpml\\Classification\\KNearestNeighbors<\/span>;<\/span>\r\n\r\n\/\/ \u5b66\u7fd2\u30c7\u30fc\u30bf120,\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf30(8:2)<\/span>\r\nconst<\/span> LERNING_NUM<\/span> =<\/span> 120<\/span>;<\/span>\r\nconst<\/span> TEST_NUM<\/span> =<\/span> 30<\/span>;<\/span>\r\n\r\n\/\/ \u30c7\u30fc\u30bf\u53d6\u5f97<\/span>\r\n$dataset<\/span> =<\/span> new<\/span> IrisDataset<\/span>();<\/span>\r\n$samples<\/span> =<\/span> $dataset<\/span>-><\/span>getSamples<\/span>();<\/span>\r\n$targets<\/span> =<\/span> $dataset<\/span>-><\/span>getTargets<\/span>();<\/span>\r\n\r\n\/\/ \u30c7\u30fc\u30bf\u3092\u52a0\u5de5<\/span>\r\nfor<\/span> (<\/span>$i<\/span>=<\/span>0<\/span>;<\/span> $i<\/span> <<\/span> count<\/span>(<\/span>$samples<\/span>);<\/span> $i<\/span>++<\/span>)<\/span> {<\/span> \r\n    $processedData<\/span>[]<\/span> =<\/span> [<\/span>\r\n        'label'<\/span> =><\/span> $targets<\/span>[<\/span>$i<\/span>],<\/span>\r\n        $columns<\/span>[<\/span>0<\/span>]<\/span> =><\/span> (<\/span>float<\/span>)<\/span>$samples<\/span>[<\/span>$i<\/span>][<\/span>0<\/span>],<\/span>\r\n        $columns<\/span>[<\/span>1<\/span>]<\/span> =><\/span> (<\/span>float<\/span>)<\/span>$samples<\/span>[<\/span>$i<\/span>][<\/span>1<\/span>],<\/span>\r\n        $columns<\/span>[<\/span>2<\/span>]<\/span> =><\/span> (<\/span>float<\/span>)<\/span>$samples<\/span>[<\/span>$i<\/span>][<\/span>2<\/span>],<\/span>\r\n        $columns<\/span>[<\/span>3<\/span>]<\/span> =><\/span> (<\/span>float<\/span>)<\/span>$samples<\/span>[<\/span>$i<\/span>][<\/span>3<\/span>],<\/span>\r\n    ];<\/span>\r\n}<\/span>\r\n\/\/ \u30c7\u30fc\u30bf\u3092\u30b7\u30e3\u30c3\u30d5\u30eb<\/span>\r\nshuffle<\/span>(<\/span>$processedData<\/span>);<\/span>\r\n\r\n\/\/ \u5b66\u7fd2\u7528\u30c7\u30fc\u30bf\u3068\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u5206\u5272<\/span>\r\n$splitDatas<\/span> =<\/span> array_chunk<\/span>(<\/span>$processedData<\/span>,<\/span> LERNING_NUM<\/span>);<\/span>\r\n\r\n\/\/ \u5b66\u7fd2\u7528\u30c7\u30fc\u30bf<\/span>\r\nfor<\/span> (<\/span>$i<\/span>=<\/span>0<\/span>;<\/span> $i<\/span> <<\/span> LERNING_NUM<\/span>;<\/span> $i<\/span>++<\/span>)<\/span> {<\/span>\r\n    $learningSamples<\/span>[]<\/span> =<\/span> [<\/span>\r\n        $splitDatas<\/span>[<\/span>0<\/span>][<\/span>$i<\/span>][<\/span>'sepal_length'<\/span>],<\/span>\r\n        $splitDatas<\/span>[<\/span>0<\/span>][<\/span>$i<\/span>][<\/span>'sepal_width'<\/span>],<\/span>\r\n        $splitDatas<\/span>[<\/span>0<\/span>][<\/span>$i<\/span>][<\/span>'petal_length'<\/span>],<\/span>\r\n        $splitDatas<\/span>[<\/span>0<\/span>][<\/span>$i<\/span>][<\/span>'petal_width'<\/span>],<\/span>\r\n    ];<\/span>\r\n    $learningTargets<\/span>[]<\/span> =<\/span> $splitDatas<\/span>[<\/span>0<\/span>][<\/span>$i<\/span>][<\/span>'label'<\/span>];<\/span>\r\n}<\/span>\r\n\r\n\/\/ \u30c6\u30b9\u30c8\u7528\u30c7\u30fc\u30bf<\/span>\r\nfor<\/span> (<\/span>$i<\/span>=<\/span>0<\/span>;<\/span> $i<\/span> <<\/span> TEST_NUM<\/span>;<\/span> $i<\/span>++<\/span>)<\/span> {<\/span>\r\n    $testSamples<\/span>[]<\/span> =<\/span> [<\/span>\r\n        $splitDatas<\/span>[<\/span>1<\/span>][<\/span>$i<\/span>][<\/span>'sepal_length'<\/span>],<\/span>\r\n        $splitDatas<\/span>[<\/span>1<\/span>][<\/span>$i<\/span>][<\/span>'sepal_width'<\/span>],<\/span>\r\n        $splitDatas<\/span>[<\/span>1<\/span>][<\/span>$i<\/span>][<\/span>'petal_length'<\/span>],<\/span>\r\n        $splitDatas<\/span>[<\/span>1<\/span>][<\/span>$i<\/span>][<\/span>'petal_width'<\/span>],<\/span>\r\n    ];<\/span>\r\n    $testTargets<\/span>[]<\/span> =<\/span> $splitDatas<\/span>[<\/span>1<\/span>][<\/span>$i<\/span>][<\/span>'label'<\/span>];<\/span>\r\n    ;<\/span>\r\n}<\/span>\r\n \r\n\/\/ \u5b66\u7fd2\u3055\u305b\u308b<\/span>\r\n$classifier<\/span> =<\/span> new<\/span> KNearestNeighbors<\/span>();<\/span>\r\n$classifier<\/span>-><\/span>train<\/span>(<\/span>$learningSamples<\/span>,<\/span> $learningTargets<\/span>);<\/span>\r\n\r\n\/\/ \u30c6\u30b9\u30c8<\/span>\r\n$results<\/span> =<\/span> $classifier<\/span>-><\/span>predict<\/span>(<\/span>$testSamples<\/span>);<\/span>\r\n\r\n$correctResult<\/span> =<\/span> 0<\/span>;<\/span>\r\n\/\/ \u6b63\u7b54\u7387\u62bd\u51fa<\/span>\r\nforeach<\/span> (<\/span>$results<\/span> as<\/span> $key<\/span> =><\/span> $result<\/span>)<\/span> {<\/span>\r\n    \/\/ \u6b63\u7b54\u6570<\/span>\r\n    if<\/span> (<\/span>$result<\/span> ===<\/span> $testTargets<\/span>[<\/span>$key<\/span>])<\/span> {<\/span>\r\n        $correctResult<\/span>++<\/span>;<\/span>\r\n    }<\/span>\r\n}<\/span>\r\n\r\necho<\/span> '\u6b63\u7b54\u7387\u306f'<\/span> .<\/span> (<\/span>$correctResult<\/span>\/<\/span>30<\/span>)<\/span>*<\/span>100<\/span> .<\/span> '%\u3067\u3059\u3002'<\/span> .<\/span> \"<\/span>\\n<\/span>\"<\/span>;<\/span>\r\n<\/code><\/pre>\n
\u6700\u540e<\/p>\n
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