{"id":14195,"date":"2025-11-28T10:37:32","date_gmt":"2025-11-28T01:37:32","guid":{"rendered":"https:\/\/blogs.mathworks.com\/japan-community\/?p=14195"},"modified":"2025-11-28T10:38:04","modified_gmt":"2025-11-28T01:38:04","slug":"creating-engineering-problems-with-matlab-copilot","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/japan-community\/2025\/11\/28\/creating-engineering-problems-with-matlab-copilot\/","title":{"rendered":"MATLAB Copilot\u306b\u76f8\u8ac7\u3057\u306a\u304c\u3089\u5de5\u5b66\u554f\u984c\u3092\u8a2d\u8a08\u3057\u3066\u307f\u305f"},"content":{"rendered":"
\n
\u3053\u3093\u306b\u3061\u306f\u3002\u79c1\u306f MathWorks Korea \u306e <\/span>Angelo<\/span><\/a> \u3068\u7533\u3057\u307e\u3059\u3002\u666e\u6bb5\u306f\u5927\u5b66\u6559\u6388\u3001\u7814\u7a76\u8005\u3001\u5b66\u751f\u304c\u6388\u696d\u3084\u7814\u7a76\u3067 MATLAB\u3068Simulink \u3092\u3088\u308a\u3088\u304f\u4f7f\u3048\u308b\u3088\u3046\u306b\u6280\u8853\u652f\u63f4\u3059\u308b\u696d\u52d9\u3092\u62c5\u5f53\u3057\u3066\u3044\u307e\u3059\u3002\u4eca\u65e5\u306f\u3001\u5b66\u751f\u306e\u8a55\u4fa1\u904e\u7a0b\u3067\u6559\u80b2\u8005\u306b\u5f79\u7acb\u3064\u5185\u5bb9\u3092\u3054\u7d39\u4ecb\u3057\u307e\u3059\u3002\u7279\u306b\u3001\u6700\u8fd1\u306e\u6559\u80b2\u5206\u91ce\u3067\u5b9f\u7528\u7684\u306a\u30c4\u30fc\u30eb\u3068\u3057\u3066\u4f4d\u7f6e\u3065\u3051\u3089\u308c\u3066\u3044\u308b\u751f\u6210 AI \u3092\u5229\u7528\u3057\u3066\u3001MATLAB \u30b3\u30fc\u30c9\u3092\u6d3b\u7528\u3057\u305f\u5de5\u5b66\u554f\u984c\u3092\u8a2d\u8a08\u3059\u308b\u65b9\u6cd5\u3092\u5b66\u3073\u307e\u3057\u3087\u3046\u3002 <\/span><\/div>\n

\u8a55\u4fa1\u306e\u305f\u3081\u306e\u554f\u984c\u3084\u8ab2\u984c\u3092\u4f5c\u308b\u904e\u7a0b\u306e\u96e3\u3057\u3055<\/span><\/h2>\n
\u6388\u696d\u3092\u8a2d\u8a08\u3059\u308b\u969b\u3001\u6982\u5ff5\u8aac\u660e\u3068\u540c\u69d8\u306b\u91cd\u8981\u306a\u306e\u306f\u307e\u3055\u306b<\/span>\u300c\u8a55\u4fa1\u300d<\/span>\u3067\u3059\u3002\u305d\u306e\u4e0a\u3001\u8a55\u4fa1\u7528\u306e\u554f\u984c\u3084\u8ab2\u984c\u3092\u4f5c\u308b\u306e\u306f\u601d\u3063\u305f\u3088\u308a\u7c21\u5358\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u3002\u6642\u9593\u304c\u304b\u304b\u308a\u3001\u8a66\u884c\u932f\u8aa4\u3082\u591a\u3044\u3067\u3059\u3002\u307b\u3068\u3093\u3069\u306e\u6559\u80b2\u8005\u306f\u3001\u3069\u306e\u5b66\u751f\u3092\u5bfe\u8c61\u3068\u3059\u308b\u306e\u304b\u3001\u3069\u306e\u30c8\u30d4\u30c3\u30af\u3092\u6271\u3046\u306e\u304b\u3001\u3069\u306e\u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3059\u308b\u306e\u304b\u306b\u3064\u3044\u3066\u306e\u5927\u307e\u304b\u306a\u30a4\u30e1\u30fc\u30b8\u306f\u3042\u308a\u307e\u3059\u304c\u3001\u305d\u308c\u3092\u5b9f\u969b\u306e\u554f\u984c\u3068\u3057\u3066\u5b9f\u88c5\u3059\u308b\u904e\u7a0b\u3067\u306f\u4e88\u60f3\u4ee5\u4e0a\u306b\u60a9\u3080\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u6700\u8fd1\u3067\u306f\u3001\u751f\u6210 AI \u3092\u6d3b\u7528\u3057\u3066\u554f\u984c\u3092\u4f5c\u308d\u3046\u3068\u3059\u308b\u8a66\u307f\u304c\u591a\u304f\u306a\u3063\u3066\u304d\u307e\u3057\u305f\u304c\u3001\u7406\u60f3\u306e\u5f62\u306e\u554f\u984c\u306b\u4f5c\u308a\u4e0a\u3052\u308b\u305f\u3081\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u3092\u8003\u3048\u308b\u306e\u306f\u5bb9\u6613\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u3002 <\/span><\/div>\n
\u4e00\u65b9\u3001\u5de5\u5b66\u6559\u80b2\u3067\u306f <\/span>\u5b66\u751f\u304c\u8ad6\u7406\u7684\u306a\u6d41\u308c\u3092\u6b63\u3057\u304f\u7406\u89e3\u3057\u3066\u3044\u308b\u304b <\/span>\u3092\u8a55\u4fa1\u3059\u308b\u3053\u3068\u304c\u91cd\u8981\u3067\u3059\u3002\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u6d3b\u7528\u3057\u305f\u554f\u984c\u306f\u3001\u3053\u306e\u70b9\u3067\u5927\u304d\u306a\u5229\u70b9\u304c\u3042\u308a\u307e\u3059\u3002\u30ed\u30b8\u30c3\u30af\u304c\u3072\u3068\u3064\u3067\u3082\u629c\u3051\u308b\u3068\u3001\u6d41\u308c\u304c\u9014\u5207\u308c\u3066\u671f\u5f85\u3059\u308b\u7d50\u679c\u304c\u5f97\u3089\u308c\u306b\u304f\u3044\u305f\u3081\u3001\u5b66\u751f\u304c\u554f\u984c\u89e3\u6c7a\u30d7\u30ed\u30bb\u30b9\u3092\u3069\u308c\u3060\u3051\u6b63\u78ba\u306b\u7406\u89e3\u3057\u305f\u304b\u304c\u660e\u78ba\u306b\u793a\u3055\u308c\u307e\u3059\u3002\u3053\u306e\u3088\u3046\u306a\u76ee\u7684\u306e\u5834\u5408\u3001<\/span>MATLAB <\/span>\u306f\u826f\u3044\u9078\u629e\u3067\u3059\u3002\u9ad8\u6c34\u6e96\u8a00\u8a9e\u306e\u305f\u3081\u3001\u5b66\u751f\u306f\u8907\u96d1\u306a\u6587\u6cd5\u306b\u3068\u3089\u308f\u308c\u305a\u3001\u554f\u984c\u306e\u672c\u8cea\u3068\u8ad6\u7406\u306b\u96c6\u4e2d\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002 <\/span><\/div>\n
\u4eca\u65e5\u7d39\u4ecb\u3059\u308b\u65b9\u6cd5\u306f\u3001\u5358\u306b\u6c7a\u307e\u3063\u305f\u30d7\u30ed\u30f3\u30d7\u30c8\u3092\u66f8\u304f\u65b9\u6cd5\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/span>MATLAB Copilot <\/span>\u3092\u5229\u7528\u3059\u308c\u3070\u3001\u751f\u6210 AI \u3068<\/span>\u300c\u76f8\u8ac7\u300d<\/span>\u3059\u308b\u3088\u3046\u306b\u4f1a\u8a71\u3057\u306a\u304c\u3089\u3001\u6559\u80b2\u8005\u304c\u982d\u306e\u4e2d\u306b\u63cf\u3044\u305f\u5927\u307e\u304b\u306a\u30a4\u30e1\u30fc\u30b8\u3092\u307e\u3059\u307e\u3059\u5177\u4f53\u5316\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u305d\u306e\u904e\u7a0b\u3067\u3001\u3088\u304f\u6574\u7406\u3055\u308c\u305f MATLAB \u30b3\u30fc\u30c9\u3092\u542b\u3080\u554f\u984c\u307e\u3067\u81ea\u52d5\u7684\u306b\u4f5c\u6210\u3055\u308c\u308b\u306e\u3067\u3001\u8a66\u884c\u932f\u8aa4\u3092\u6e1b\u3089\u3057\u3001\u306f\u308b\u304b\u306b\u52b9\u7387\u7684\u306b\u554f\u984c\u3092\u8a2d\u8a08\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u4e0b\u306e\u56f3\u304b\u3089\u308f\u304b\u308b\u3088\u3046\u306b\u3001AI \u304c\u7686\u3055\u3093\u306b\u8cea\u554f\u3057\u3001\u7686\u3055\u3093\u306f\u305d\u306e\u8cea\u554f\u306b\u7b54\u3048\u306a\u304c\u3089\u4e00\u7dd2\u306b\u30a4\u30e1\u30fc\u30b8\u3092\u63cf\u3044\u3066\u3044\u304d\u307e\u3059\u3002 <\/span><\/div>\n
\"\"<\/img><\/div>\n

MATLAB Copilot \u3068\u306f\uff1f<\/span><\/h2>\n
MATLAB Copilot<\/span><\/a> \u306f\u3001MathWorks \u304c MATLAB R2025a \u3068\u540c\u6642\u306b\u516c\u958b\u3057\u305f<\/span>\u751f\u6210 AI \u30d9\u30fc\u30b9\u306e\u30c4\u30fc\u30eb<\/span>\u3067\u3059\u3002\u3053\u306e\u88fd\u54c1\u306f MATLAB \u30ef\u30fc\u30af\u30d5\u30ed\u30fc\u3092\u30b5\u30dd\u30fc\u30c8\u3057\u3001MATLAB \u95a2\u9023\u60c5\u5831\u306b\u57fa\u3065\u3044\u305f\u56de\u7b54\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002\u4eca\u65e5\u306f <\/span>Copilot Chat \u6a5f\u80fd<\/span><\/a>\u3092\u4f7f\u3063\u3066\u554f\u984c\u8a2d\u8a08\u306b\u53d6\u308a\u7d44\u3093\u3067\u307f\u307e\u3057\u3087\u3046\u3002 <\/span>\u6700\u8fd1\u3001MATLAB Copilot\u306fGPT-5 mini\u30a8\u30f3\u30b8\u30f3\u3092\u642d\u8f09\u3059\u308b\u3053\u3068\u306b\u306a\u308a<\/span><\/a>\u3001Copilot Chat \u306f\u3055\u3089\u306b\u62e1\u5f35\u3055\u308c\u305f\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u30a6\u30a3\u30f3\u30c9\u30a6\u3092\u6301\u3064\u3088\u3046\u306b\u306a\u308a\u3001\u9577\u3044\u4f1a\u8a71\u304c\u53ef\u80fd\u306b\u306a\u308a\u307e\u3057\u305f\u3002<\/span><\/div>\n

MATLAB Copilot \u3068\u554f\u984c\u4f5c\u6210\u306e\u76f8\u8ac7\u3092\u3059\u308b<\/span><\/h2>\n
\u79c1\u304c\u7d39\u4ecb\u3057\u305f\u3044\u30a2\u30d7\u30ed\u30fc\u30c1\u306f\u3001\u5358\u7d14\u306a\u300c\u30d7\u30ed\u30f3\u30d7\u30c8\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u300d\u3068\u306f\u5c11\u3057\u7570\u306a\u308a\u307e\u3059\u3002 \u6838\u5fc3\u306f\u300c\u76f8\u8ac7\u300d\u3067\u3059\u3002\u982d\u306e\u4e2d\u306b\u3042\u308b\u30e6\u30cb\u30fc\u30af\u306a\u30a2\u30a4\u30c7\u30a2\u3092 Copilot \u3068\u8a71\u3057\u5408\u3044\u3001\u5177\u4f53\u5316\u3059\u308b\u904e\u7a0b\u3067\u3059\u3002MATLAB \u3092\u8d77\u52d5\u3057\u3001\u4e0b\u306e\u56f3\u306b\u793a\u3059 Copilot \u30c1\u30e3\u30c3\u30c8\u30dc\u30bf\u30f3\u3092\u62bc\u3059\u3068\u3001MATLAB Copilot Chat \u3092\u958b\u59cb\u3067\u304d\u307e\u3059\u3002<\/span><\/div>\n
\"\"<\/img><\/div>\n
\u4eca\u65e5\u306e\u5185\u5bb9\u3067\u306f\u3001\u4ee5\u4e0b\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u304c\u9375\u3068\u306a\u308a\u307e\u3059\u3002 <\/span><\/div>\n
\u300c\u3042\u306a\u305f\u306f AI \u306e\u5c02\u9580\u5bb6\u3067\u3059\u3002\u79c1\u306f\u3042\u306a\u305f\u306e\u52a9\u8a00\u3092\u304a\u9858\u3044\u3057\u305f\u3044\u3067\u3059\u3002MathWorks (mathworks.com)\u306e\u4e8b\u4f8b\u3092\u53c2\u8003\u306b\u3001\u5b66\u751f\u304c\u5b66\u7fd2\u306b\u4f7f\u3048\u308b\u6559\u6750\u3068\u6f14\u7fd2\u3092\u4f5c\u308d\u3046\u3068\u601d\u3044\u307e\u3059\u3002 MathWorks \u306e\u4f8b\u306f\u3001\u3055\u307e\u3056\u307e\u306a\u30c9\u30e1\u30a4\u30f3\u3068\u30e6\u30fc\u30b9\u30b1\u30fc\u30b9\u3092\u30ab\u30d0\u30fc\u3057\u3066\u3044\u307e\u3059\u3002 AI \u3092\u6d3b\u7528\u3057\u3066\u3053\u306e\u3088\u3046\u306a\u4ed5\u4e8b\u3092\u3059\u308b\u306b\u306f\u3069\u3046\u3059\u308c\u3070\u3044\u3044\u3067\u3059\u304b\uff1f \u6f14\u7fd2\u4f5c\u6210\u306e\u65b9\u5411\u6027\u3092\u6c7a\u3081\u308b\u305f\u3081\u306b\u3001\u5fc5\u8981\u306a\u8cea\u554f\u3092\u3057\u3066\u304f\u3060\u3055\u3044\u3002 \u4e00\u3064\u305a\u3064\u8cea\u554f\u3092\u3057\u3066\u3001\u8003\u3048\u304c\u5341\u5206\u306b\u6574\u7406\u3067\u304d\u305f\u3068\u611f\u3058\u305f\u3089\u3001\u554f\u984c\u3092\u4f5c\u6210\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u300d<\/span><\/div>\n
\u5192\u982d\u3067<\/span>\u300c\u3042\u306a\u305f\u306f AI \u5c02\u9580\u5bb6\u3067\u3059\u300d<\/span>\u304b\u3089\u59cb\u3081\u3066\u3044\u308b\u306e\u306f\u3001\u591a\u304f\u306e LLM \u304c\u8907\u6570\u306e\u5c02\u9580\u30e2\u30b8\u30e5\u30fc\u30eb\u306e\u7d71\u5408\u4f53\u3067\u69cb\u6210\u3055\u308c\u3066\u3044\u308b\u305f\u3081\u3067\u3059\u3002GPT \u306e\u5185\u90e8\u3067 AI \u95a2\u9023\u30e2\u30b8\u30e5\u30fc\u30eb\u3092\u4f7f\u3063\u3066\u307b\u3057\u3044\u3068\u660e\u793a\u7684\u306b\u8981\u8acb\u3057\u3066\u3044\u308b\u306e\u3067\u3059\u3002<\/span><\/div>\n
\u4eca\u56de\u3001\u79c1\u306f\u6a5f\u68b0\u5b66\u7fd2\u306e Support Vector Machine (SVM) \u3068\u3044\u3046\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u5229\u7528\u3057\u305f\u57fa\u790e\u554f\u984c\u3092\u5b66\u90e8\uff12\u3001\uff13\u5e74\u751f\u5bfe\u8c61\u306b\u4f5c\u6210\u3057\u305f\u3044\u3068\u601d\u3044\u307e\u3059\u3002 \u5b9f\u969b\u306e\u30c7\u30fc\u30bf\u3088\u308a\u306f\u7c21\u5358\u306b\u5206\u985e\u3067\u304d\u308b\u4eba\u5de5\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u6d3b\u7528\u3057\u3066\u307f\u3088\u3046\u3068\u3044\u3046\u306e\u304c\u76ee\u6a19\u3067\u3059\u304c\u3001\u305d\u308c\u4ee5\u5916\u306b\u306f\u5177\u4f53\u7684\u306a\u8a08\u753b\u306f\u306a\u3044\u72b6\u6cc1\u3060\u3068\u4eee\u5b9a\u3057\u306a\u304c\u3089\u3001MATLAB Copilot \u306e\u6848\u5185\u306b\u5f93\u3063\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/span><\/div>\n
\u4e0b\u306e\u56f3\u306e\u3088\u3046\u306b\u3001\u307e\u305a\u6700\u521d\u306b\u8cea\u554f\u3055\u308c\u305f\u306e\u306f\u5bfe\u8c61\u5b66\u751f\u306b\u95a2\u3057\u3066\u3067\u3059\u3002 \u79c1\u306f\u5b66\u90e8\uff12\u3001\uff13\u5e74\u751f\u306e MATLAB \u306b\u3064\u3044\u3066\u306e\u77e5\u8b58\u3092\u6301\u3063\u3066\u3044\u308b\u521d\u5fc3\u8005\u3060\u3068\u7b54\u3048\u3066\u307f\u307e\u3059\u3002<\/span><\/div>\n
\"\"<\/img><\/div>\n
\u6b21\u306e\u8cea\u554f\u3067\u306f\u3001\u3088\u308a\u5177\u4f53\u7684\u306a\u30c8\u30d4\u30c3\u30af\u306b\u3064\u3044\u3066\u805e\u304b\u308c\u307e\u3057\u305f\u3002\u3053\u308c\u306b\u5bfe\u3057\u3066\u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u306e SVM \u3092\u4e2d\u5fc3\u306b\u3001\u30c7\u30fc\u30bf\u5206\u6790\u3068\u53ef\u8996\u5316\u3001\u305d\u3057\u3066 MATLAB \u304b\u3089\u63d0\u4f9b\u3055\u308c\u308b\u95a2\u6570\u3092\u3046\u307e\u304f\u6d3b\u7528\u3059\u308b\u3053\u3068\u3092\u76ee\u6a19\u306b\u3057\u305f\u3044\u3068\u56de\u7b54\u3057\u307e\u3059\u3002<\/span><\/div>\n
\"\"<\/img><\/div>\n
\u3053\u306e\u56de\u7b54\u306e\u5f8c\u3001\u300e\u79c1\u306f1\u3064\u306e\u554f\u984c\u3060\u3051\u3092\u4f5c\u308d\u3046\u3068\u3057\u300120\u5206\u307b\u3069\u3067\u89e3\u3051\u308b\u554f\u984c\u306b\u3057\u305f\u3044\u300f\u3068\u8ffd\u52a0\u3057\u307e\u3057\u305f\u3002\u6b21\u306b\u3001MATLAB Copilot \u306f\u751f\u5f92\u306b\u5177\u4f53\u7684\u306b\u3069\u3093\u306a\u6d3b\u52d5\u3092\u3055\u305b\u305f\u3044\u304b\u306b\u3064\u3044\u3066\u805e\u3044\u3066\u304d\u307e\u3057\u305f\u3002\u3053\u3053\u307e\u3067\u898b\u3066\u304d\u3066\u6c17\u4ed8\u304b\u308c\u305f\u304b\u3068\u601d\u3044\u307e\u3059\u304c\u3001MATLAB Copilot \u306f\u3055\u307e\u3056\u307e\u306a\u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u63d0\u793a\u3057\u3066\u304d\u3066\u304f\u308c\u307e\u3059\u3002\u6700\u521d\u306b\u8003\u3048\u3089\u308c\u306a\u304b\u3063\u305f\u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u3055\u3089\u306b\u691c\u8a0e\u3057\u3066\u307f\u308b\u3053\u3068\u306b\u3082\u5f79\u7acb\u3061\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u3001\u5358\u7d14\u306a\u5206\u985e\u5668\u3092\u5b66\u7fd2\u3057\u3066\u30c6\u30b9\u30c8\u3059\u308b\u3053\u3068\u3092\u76ee\u6307\u3057\u3066\u3044\u308b\u3068\u56de\u7b54\u3057\u307e\u3059\u3002<\/span><\/div>\n
\"\"<\/img><\/div>\n
\u6b21\u306b\u3001\u3069\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u3046\u304b\u805e\u3044\u3066\u304d\u307e\u3057\u305f\u3002\u3053\u3053\u3067\u306f\uff0c\u3067\u304d\u308b\u9650\u308a\u306b\u5358\u7d14\u306a\u554f\u984c\u3092\u4f5c\u6210\u3057\u3088\u3046\u3068\u3057\u3066\u3044\u308b\u306e\u3067\u3001\u4eba\u5de5\u7684\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u6d3b\u7528\u3057\u3066\u307f\u308b\u3053\u3068\u306b\u3057\u307e\u3059\u3002<\/span><\/div>\n
\"\"<\/img><\/div>\n
\u3044\u3044\u3067\u3059\u306d\u3002<\/span><\/div>\n
\u3053\u308c\u3067\u3001MATLAB Copilot \u306f\u5341\u5206\u306a\u60c5\u5831\u3092\u53ce\u96c6\u3057\u305f\u3068\u5224\u65ad\u3057\u305f\u307f\u305f\u3044\u3067\u3059\u3002\u305d\u306e\u7d50\u679c\u3001\u8ab2\u984c\u3068\u914d\u70b9\u3001\u305d\u3057\u3066 MATLAB \u30b3\u30fc\u30c9\u3092\u751f\u6210\u3057\u307e\u3057\u305f\u3002\u307e\u305a\u306f\u3001\u4e0b\u56f3\u306e\u8d64\u3044\u77e2\u5370\u3067\u793a\u3057\u3066\u3044\u308b\u30dc\u30bf\u30f3\u3092\u62bc\u3057\u3066\u30b3\u30fc\u30c9\u3092\u30b3\u30d4\u30fc\u3057\u3001MATLAB \u3067\u5b9f\u884c\u3057\u3066\u307f\u3066\u671f\u5f85\u3069\u304a\u308a\u306b\u52d5\u4f5c\u3059\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/span><\/div>\n
\"\"<\/img><\/div>\n
\u5b9f\u969b\u306b\u751f\u6210\u3055\u308c\u305f\u30b3\u30fc\u30c9\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3057\u305f\u3002 <\/span><\/div>\n
\n
% synthetic_svm_exercise.m<\/span><\/span><\/div>\n
rng(1); <\/span>% \u518d\u73fe\u6027<\/span><\/span><\/div>\n
nPerClass = 100;<\/span><\/span><\/div>\n
 <\/div>\n
% \u30af\u30e9\u30b91\uff1a\u5e73\u5747[1,1]\u3001\u5171\u5206\u65630.3 * I<\/span><\/span><\/div>\n
mu1 = [1 1];<\/span><\/span><\/div>\n
Sigma1 = 0.3 * eye(2);<\/span><\/span><\/div>\n
X1 = mvnrnd(mu1, Sigma1, nPerClass);<\/span><\/span><\/div>\n
 <\/div>\n
% \u30af\u30e9\u30b92\uff1a\u5e73\u5747[3,2]\u3001\u5171\u5206\u65630.5 * I<\/span><\/span><\/div>\n
mu2 = [3 2];<\/span><\/span><\/div>\n
Sigma2 = 0.5 * eye(2);<\/span><\/span><\/div>\n
X2 = mvnrnd(mu2, Sigma2, nPerClass);<\/span><\/span><\/div>\n
 <\/div>\n
% \u30c7\u30fc\u30bf\u3068\u30e9\u30d9\u30eb<\/span><\/span><\/div>\n
X = [X1; X2];<\/span><\/span><\/div>\n
Y = [zeros(nPerClass,1); ones(nPerClass,1)]; <\/span>% 0\/1 \u30e9\u30d9\u30eb<\/span><\/span><\/div>\n
 <\/div>\n
% \u53ef\u8996\u5316: \u6563\u5e03\u56f3<\/span><\/span><\/div>\n
figure(<\/span>‘Name’<\/span>,<\/span>‘Scatter’<\/span>);<\/span><\/span><\/div>\n
gscatter(X(:,1), X(:,2), Y, <\/span>‘rb’<\/span>, <\/span>‘o^’<\/span>);<\/span><\/span><\/div>\n
xlabel(<\/span>‘Feature 1’<\/span>); ylabel(<\/span>‘Feature 2’<\/span>);<\/span><\/span><\/div>\n
title(<\/span>‘Class scatter plot’<\/span>);<\/span><\/span><\/div>\n
legend(<\/span>‘Class 0’<\/span>,<\/span>‘Class 1’<\/span>,<\/span>‘Location’<\/span>,<\/span>‘best’<\/span>);<\/span><\/span><\/div>\n
 <\/div>\n
% \u53ef\u8996\u5316: \u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\uff08\u5404\u7279\u5fb4\u91cf\u3054\u3068\u306b\u30af\u30e9\u30b9\u6bd4\u8f03\uff09<\/span><\/span><\/div>\n
figure(<\/span>‘Name’<\/span>,<\/span>‘Histograms’<\/span>);<\/span><\/span><\/div>\n
subplot(2,1,1);<\/span><\/span><\/div>\n
histogram(X(Y==0,1), <\/span>‘FaceColor’<\/span>,<\/span>‘r’<\/span>, <\/span>‘FaceAlpha’<\/span>,0.5); hold <\/span>on<\/span>;<\/span><\/span><\/div>\n
histogram(X(Y==1,1), <\/span>‘FaceColor’<\/span>,<\/span>‘b’<\/span>, <\/span>‘FaceAlpha’<\/span>,0.5); hold <\/span>off<\/span>;<\/span><\/span><\/div>\n
xlabel(<\/span>‘Feature 1’<\/span>); ylabel(<\/span>‘Count’<\/span>); title(<\/span>‘Histogram of Feature 1’<\/span>);<\/span><\/span><\/div>\n
legend(<\/span>‘Class 0’<\/span>,<\/span>‘Class 1’<\/span>);<\/span><\/span><\/div>\n
 <\/div>\n
subplot(2,1,2);<\/span><\/span><\/div>\n
histogram(X(Y==0,2), <\/span>‘FaceColor’<\/span>,<\/span>‘r’<\/span>, <\/span>‘FaceAlpha’<\/span>,0.5); hold <\/span>on<\/span>;<\/span><\/span><\/div>\n
histogram(X(Y==1,2), <\/span>‘FaceColor’<\/span>,<\/span>‘b’<\/span>, <\/span>‘FaceAlpha’<\/span>,0.5); hold <\/span>off<\/span>;<\/span><\/span><\/div>\n
xlabel(<\/span>‘Feature 2’<\/span>); ylabel(<\/span>‘Count’<\/span>); title(<\/span>‘Histogram of Feature 2’<\/span>);<\/span><\/span><\/div>\n
legend(<\/span>‘Class 0’<\/span>,<\/span>‘Class 1’<\/span>);<\/span><\/span><\/div>\n
 <\/div>\n
% \u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\/\u691c\u8a3c\u5206\u5272<\/span><\/span><\/div>\n
cv = cvpartition(Y, <\/span>‘HoldOut’<\/span>, 0.3);<\/span><\/span><\/div>\n
Xtrain = X(training(cv), :);<\/span><\/span><\/div>\n
Ytrain = Y(training(cv), :);<\/span><\/span><\/div>\n
Xtest = X(test(cv), :);<\/span><\/span><\/div>\n
Ytest = Y(test(cv), :);<\/span><\/span><\/div>\n
 <\/div>\n
% \u6a19\u6e96\u5316\uff08\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u306e\u5e73\u5747\u3068\u6a19\u6e96\u504f\u5dee\u3067\uff09<\/span><\/span><\/div>\n
mu = mean(Xtrain);<\/span><\/span><\/div>\n
sigma = std(Xtrain);<\/span><\/span><\/div>\n
XtrainZ = (Xtrain – mu) .\/ sigma;<\/span><\/span><\/div>\n
XtestZ = (Xtest – mu) .\/ sigma;<\/span><\/span><\/div>\n
 <\/div>\n
% SVM \u5b66\u7fd2\uff08\u7dda\u5f62\u30ab\u30fc\u30cd\u30eb\uff09<\/span><\/span><\/div>\n
SVMModel = fitcsvm(XtrainZ, Ytrain, <\/span>‘KernelFunction’<\/span>,<\/span>‘linear’<\/span>, <\/span>‘Standardize’<\/span>,false);<\/span><\/span><\/div>\n
 <\/div>\n
% \u4e88\u6e2c\u3068\u8a55\u4fa1<\/span><\/span><\/div>\n
Ypred = predict(SVMModel, XtestZ);<\/span><\/span><\/div>\n
C = confusionmat(Ytest, Ypred);<\/span><\/span><\/div>\n
acc = mean(Ypred == Ytest);<\/span><\/span><\/div>\n
 <\/div>\n
fprintf(<\/span>‘Confusion Matrix:\\n’<\/span>);<\/span><\/span><\/div>\n
disp(C);<\/span><\/span><\/div>\n
fprintf(<\/span>‘Accuracy: %.2f%%\\n’<\/span>, acc*100);<\/span><\/span><\/div>\n
 <\/div>\n
% \u6df7\u540c\u884c\u5217\u306e\u53ef\u8996\u5316<\/span><\/span><\/div>\n
figure(<\/span>‘Name’<\/span>,<\/span>‘ConfusionMatrix’<\/span>);<\/span><\/span><\/div>\n
confusionchart(C, {<\/span>‘Class 0’<\/span>,<\/span>‘Class 1’<\/span>});<\/span><\/span><\/div>\n
title(sprintf(<\/span>‘SVM Confusion Matrix (Accuracy %.1f%%)’<\/span>, acc*100));<\/span><\/span><\/div>\n
 <\/div>\n
% \u30aa\u30d7\u30b7\u30e7\u30f3: 2D \u6c7a\u5b9a\u5883\u754c\u306e\u63cf\u753b<\/span><\/span><\/div>\n
d = 0.02;<\/span><\/span><\/div>\n
[x1Grid, x2Grid] = meshgrid(min(XtrainZ(:,1))-1:d:max(XtrainZ(:,1))+1, <\/span>…<\/span><\/span><\/div>\n
min(XtrainZ(:,2))-1:d:max(XtrainZ(:,2))+1);<\/span><\/span><\/div>\n
xGrid = [x1Grid(:), x2Grid(:)];<\/span><\/span><\/div>\n
[~, score] = predict(SVMModel, xGrid);<\/span><\/span><\/div>\n
scoreGrid = reshape(score(:,2), size(x1Grid)); <\/span>% \u6b63\u30af\u30e9\u30b9\u306e\u30b9\u30b3\u30a2<\/span><\/span><\/div>\n
 <\/div>\n
figure(<\/span>‘Name’<\/span>,<\/span>‘Decision Boundary’<\/span>);<\/span><\/span><\/div>\n
contourf(x1Grid, x2Grid, scoreGrid, [-Inf 0 Inf], <\/span>‘LineColor’<\/span>,<\/span>‘k’<\/span>, <\/span>‘LineWidth’<\/span>,1);<\/span><\/span><\/div>\n
alpha(0.2); hold <\/span>on<\/span>;<\/span><\/span><\/div>\n
gscatter(XtrainZ(:,1), XtrainZ(:,2), Ytrain, <\/span>‘rb’<\/span>, <\/span>‘o^’<\/span>);<\/span><\/span><\/div>\n
xlabel(<\/span>‘Feature 1 (z)’<\/span>); ylabel(<\/span>‘Feature 2 (z)’<\/span>);<\/span><\/span><\/div>\n
title(<\/span>‘Decision boundary and training data’<\/span>);<\/span><\/span><\/div>\n
legend(<\/span>‘Decision regions’<\/span>,<\/span>‘Class 0’<\/span>,<\/span>‘Class 1’<\/span>,<\/span>‘Location’<\/span>,<\/span>‘best’<\/span>); <\/span><\/span><\/div>\n<\/div>\n
\u6700\u7d42\u7684\u306b\u4e0b\u8a18\u306e4\u3064\u306e\u56f3\u304c\u51fa\u529b\u3055\u308c\u307e\u3057\u305f\u306d\u3002\u4eba\u5de5\u30c7\u30fc\u30bf\u3092\u5229\u7528\u3057\u3066\u3001\u7c21\u5358\u306b\u5206\u96e2\u3055\u308c\u308b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u8981\u6c42\u901a\u308a\u306b\u6d3b\u7528\u3055\u308c\u305f\u3088\u3046\u3067\u3059\u3002\u30b3\u30fc\u30c9\u3092\u898b\u308b\u3068\u3001MATLAB \u3067\u63d0\u4f9b\u3055\u308c\u3066\u3044\u308b <\/span>fitcsvm<\/span> \u95a2\u6570\u304c\u30ea\u30af\u30a8\u30b9\u30c8\u901a\u308a\u306b\u6d3b\u7528\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u306d\u3002\u6c42\u3081\u3066\u3044\u305f\u3082\u306e\u306b\u9650\u308a\u306a\u304f\u8fd1\u3065\u3044\u305f\u3088\u3046\u3067\u3059\u3002<\/span><\/div>\n
\"\"<\/img><\/div>\n
\u3055\u3066\u79c1\u306f\u3001\u3053\u306e\u3088\u3046\u306a\u8ab2\u984c\u3068\u914d\u70b9\u306e\u5f62\u5f0f\u3088\u308a\u3082\u3001\u554f\u984c\u306e\u8aac\u660e\u3068\u5b66\u7fd2\u8005\u304c\u5165\u529b\u3067\u304d\u308b\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u3001\u305d\u3057\u3066\u6b63\u89e3\u3067\u4f7f\u3048\u308b\u30ea\u30d5\u30a1\u30ec\u30f3\u30b9\u30b3\u30fc\u30c9\u306e\u3088\u3046\u306a\u5f62\u5f0f\u3092\u6c42\u3081\u3066\u3044\u307e\u3057\u305f\u3002\u56e0\u307f\u306b\u3001MATLAB Copilot \u306f\u57fa\u672c\u7684\u306b MATLAB \u306b\u3064\u3044\u3066\u306e\u8cea\u554f\u3060\u3051\u3092\u53d7\u3051\u53d6\u308b\u306e\u3067\u3001\u3059\u3079\u3066\u306e\u30ea\u30af\u30a8\u30b9\u30c8\u306b\u5bfe\u3057\u3066\u7b54\u3048\u3089\u308c\u308b\u308f\u3051\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u3002\u6642\u3005\u3001MATLAB Copilot \u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u7b54\u3048\u307e\u3059\u3002\uff08\u30b9\u30de\u30fc\u30c8\u3060\u3068\u306f\u601d\u3044\u307e\u3059\u304c\u2026\uff09<\/span><\/div>\n
\u300e\u305d\u306e\u30ea\u30af\u30a8\u30b9\u30c8\u306b\u306f\u5fdc\u7b54\u3067\u304d\u307e\u305b\u3093\u3002MATLAB \u306b\u95a2\u3059\u308b\u305d\u306e\u4ed6\u306e\u8cea\u554f\u306b\u306f\u559c\u3093\u3067\u304a\u7b54\u3048\u3057\u307e\u3059\u3002\u300f<\/span><\/div>\n
\u3057\u305f\u304c\u3063\u3066\u3001\u51fa\u529b\u3055\u308c\u305f MATLAB \u30b3\u30fc\u30c9\u306b\u57fa\u3065\u3044\u3066\u3001<\/span>MATLAB Grader<\/span><\/a> \u3067\u6d3b\u7528\u3059\u308b\u554f\u984c\u306e\u8aac\u660e\u3001\u5b66\u7fd2\u8005\u7528\u306e\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u3001\u304a\u3088\u3073\u30ea\u30d5\u30a1\u30ec\u30f3\u30b9\u30b3\u30fc\u30c9\u304c\u5fc5\u8981\u3067\u3042\u308b\u3053\u3068\u3092\u4f1d\u3048\u307e\u3059\u3002MATLAB Grader \u306f MathWorks \u304c\u63d0\u4f9b\u3059\u308b MATLAB \u30b3\u30fc\u30c9\u306e\u8ab2\u984c\u8a55\u4fa1\u306e\u305f\u3081\u306e\u554f\u984c\u51fa\u984c\u304a\u3088\u3073\u81ea\u52d5\u63a1\u70b9\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0\u3067\u3042\u308b\u305f\u3081\u3001MATLAB Copilot \u306f\u559c\u3093\u3067\u304a\u624b\u4f1d\u3044\u3057\u307e\u3059\u3002<\/span><\/div>\n
\"\"<\/img><\/div>\n
\u5b66\u7fd2\u8005\u7528\u306e\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u4f5c\u6210\u3055\u308c\u307e\u3057\u305f\u3002\u3053\u3053\u304b\u3089\uff0c\u96e3\u6613\u5ea6\u8abf\u6574\u306e\u305f\u3081\u306b\u30b3\u30fc\u30c9\u306e\u4e00\u90e8\u3092\u7a7a\u6b04\u306a\u3069\u306b\u7f6e\u63db\u3057\u3066\u7a74\u57cb\u3081\u5f62\u5f0f\u306b\u3059\u308b\u3053\u3068\u3082\u53ef\u80fd\u3067\u3059\u3002<\/span><\/div>\n
\n
% student_solution.m<\/span><\/span><\/div>\n
rng(1); <\/span>% \u518d\u73fe\u6027<\/span><\/span><\/div>\n
nPerClass = 100;<\/span><\/span><\/div>\n
 <\/div>\n
% — BEGIN: \u30c7\u30fc\u30bf\u751f\u6210\uff08\u7de8\u96c6\u4e0d\u8981\uff09 —<\/span><\/span><\/div>\n
% \u30af\u30e9\u30b91: \u5e73\u5747 [1,1], \u5171\u5206\u6563 0.3*I<\/span><\/span><\/div>\n
mu1 = [1 1];<\/span><\/span><\/div>\n
Sigma1 = 0.3 * eye(2);<\/span><\/span><\/div>\n
X1 = mvnrnd(mu1, Sigma1, nPerClass);<\/span><\/span><\/div>\n
 <\/div>\n
% \u30af\u30e9\u30b92: \u5e73\u5747 [3,2], \u5171\u5206\u6563 0.5*I<\/span><\/span><\/div>\n
mu2 = [3 2];<\/span><\/span><\/div>\n
Sigma2 = 0.5 * eye(2);<\/span><\/span><\/div>\n
X2 = mvnrnd(mu2, Sigma2, nPerClass);<\/span><\/span><\/div>\n
 <\/div>\n
% \u30c7\u30fc\u30bf\u3068\u30e9\u30d9\u30eb<\/span><\/span><\/div>\n
X = [X1; X2];<\/span><\/span><\/div>\n
Y = [zeros(nPerClass,1); ones(nPerClass,1)]; <\/span>% 0\/1\u30e9\u30d9\u30eb<\/span><\/span><\/div>\n
% — END: \u30c7\u30fc\u30bf\u751f\u6210 —<\/span><\/span><\/div>\n
 <\/div>\n
% \u53ef\u8996\u5316\uff08\u4efb\u610f\uff09: \u6563\u5e03\u56f3 \/ \u30d2\u30b9\u30c8\u30b0\u30e9\u30e0<\/span><\/span><\/div>\n
% (\u7de8\u96c6\u53ef) \u4f8b: gscatter(X(:,1), X(:,2), Y);<\/span><\/span><\/div>\n
 <\/div>\n
% — BEGIN: \u30c7\u30fc\u30bf\u5206\u5272\u30fb\u524d\u51e6\u7406\u30fb\u5b66\u7fd2\uff08\u3053\u3053\u3092\u5b8c\u6210\u3055\u305b\u308b\uff09 —<\/span><\/span><\/div>\n
% 1) \u8a13\u7df4\/\u691c\u8a3c\u5206\u5272\uff08HoldOut = 0.3 \u3092\u4f7f\u7528\uff09<\/span><\/span><\/div>\n
% 2) \u8a13\u7df4\u30c7\u30fc\u30bf\u306e\u5e73\u5747\u30fb\u6a19\u6e96\u504f\u5dee\u3067\u6a19\u6e96\u5316\uff08z\u30b9\u30b3\u30a2\uff09<\/span><\/span><\/div>\n
% 3) \u7dda\u5f62SVM\u3092 fitcsvm \u3067\u5b66\u7fd2\uff08’KernelFunction’,’linear’\u3001’Standardize’,false\uff09<\/span><\/span><\/div>\n
% 4) \u691c\u8a3c\u30c7\u30fc\u30bf\u3092\u4e88\u6e2c\u3057\u3066\u6df7\u540c\u884c\u5217 C \u3068\u7cbe\u5ea6 acc \u3092\u8a08\u7b97<\/span><\/span><\/div>\n
%<\/span><\/span><\/div>\n
% \u3053\u3053\u306b\u30b3\u30fc\u30c9\u3092\u8ffd\u52a0\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/span><\/div>\n
% — END: \u30c7\u30fc\u30bf\u5206\u5272\u30fb\u524d\u51e6\u7406\u30fb\u5b66\u7fd2 —<\/span><\/span><\/div>\n
% \u30d2\u30f3\u30c8\uff08\u77ed\u304f\uff09<\/span><\/span><\/div>\n
% <\/span><\/span><\/div>\n
% cvpartition(Y,’HoldOut’,0.3)\u3001training(cv)\u3001test(cv) \u3092\u4f7f\u3046\u3002<\/span><\/span><\/div>\n
% \u6a19\u6e96\u5316\u306f mu = mean(Xtrain); sigma = std(Xtrain); \u3067\u884c\u3044\u3001\u305d\u308c\u3092\u4f7f\u3063\u3066\u8a13\u7df4\u30fb\u691c\u8a3c\u53cc\u65b9\u3092\u5909\u63db\u3059\u308b\u3002<\/span><\/span><\/div>\n
% predict \u306e\u8fd4\u308a\u5024\u3067 confusionmat \u3068 mean(Ypred == Ytest) \u3092\u8a08\u7b97\u3059\u308b\u3002<\/span><\/span><\/div>\n<\/div>\n
\u4ee5\u4e0b\u306f\u3001MATLAB Copilot \u304c\u4f5c\u6210\u3057\u305f\u63a1\u70b9\u7528\u306e\u6a21\u7bc4\u89e3\u7b54\u3067\u3059\u3002\u30b5\u30dd\u30fc\u30c8\u30d9\u30af\u30bf\u30fc\u30de\u30b7\u30f3\u3092\u5b66\u7fd2\u3057\u3001\u5b66\u7fd2\u6e08\u307f\u30e2\u30c7\u30eb\u3092\u4f7f\u3063\u3066\u4e88\u6e2c\u3068\u8a55\u4fa1\u3092\u884c\u3046\u6a19\u6e96\u7684\u306a\u624b\u9806\u306b\u5fe0\u5b9f\u306b\u5f93\u3063\u3066\u3044\u308b\u3053\u3068\u304c\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/span><\/div>\n
\n
% reference_solution.m<\/span><\/span><\/div>\n
rng(1);<\/span><\/span><\/div>\n
nPerClass = 100;<\/span><\/span><\/div>\n
 <\/div>\n
mu1 = [1 1];<\/span><\/span><\/div>\n
Sigma1 = 0.3 * eye(2);<\/span><\/span><\/div>\n
X1 = mvnrnd(mu1, Sigma1, nPerClass);<\/span><\/span><\/div>\n
 <\/div>\n
mu2 = [3 2];<\/span><\/span><\/div>\n
Sigma2 = 0.5 * eye(2);<\/span><\/span><\/div>\n
X2 = mvnrnd(mu2, Sigma2, nPerClass);<\/span><\/span><\/div>\n
 <\/div>\n
X = [X1; X2];<\/span><\/span><\/div>\n
Y = [zeros(nPerClass,1); ones(nPerClass,1)];<\/span><\/span><\/div>\n
 <\/div>\n
% \u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\/\u691c\u8a3c\u5206\u5272<\/span><\/span><\/div>\n
cv = cvpartition(Y, <\/span>‘HoldOut’<\/span>, 0.3);<\/span><\/span><\/div>\n
Xtrain = X(training(cv), :);<\/span><\/span><\/div>\n
Ytrain = Y(training(cv), :);<\/span><\/span><\/div>\n
Xtest = X(test(cv), :);<\/span><\/span><\/div>\n
Ytest = Y(test(cv), :);<\/span><\/span><\/div>\n
 <\/div>\n
% \u6a19\u6e96\u5316\uff08\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u306e\u7d71\u8a08\u91cf\u3092\u4f7f\u7528\uff09<\/span><\/span><\/div>\n
mu = mean(Xtrain, 1);<\/span><\/span><\/div>\n
sigma = std(Xtrain, 0, 1);<\/span><\/span><\/div>\n
XtrainZ = (Xtrain – mu) .\/ sigma;<\/span><\/span><\/div>\n
XtestZ = (Xtest – mu) .\/ sigma;<\/span><\/span><\/div>\n
 <\/div>\n
% \u7dda\u5f62SVM\u5b66\u7fd2<\/span><\/span><\/div>\n
SVMModel = fitcsvm(XtrainZ, Ytrain, <\/span>‘KernelFunction’<\/span>,<\/span>‘linear’<\/span>, <\/span>‘Standardize’<\/span>,false);<\/span><\/span><\/div>\n
 <\/div>\n
% \u4e88\u6e2c\u3068\u8a55\u4fa1<\/span><\/span><\/div>\n
Ypred = predict(SVMModel, XtestZ);<\/span><\/span><\/div>\n
C = confusionmat(Ytest, Ypred);<\/span><\/span><\/div>\n
acc = mean(Ypred == Ytest);<\/span><\/span><\/div>\n
 <\/div>\n
% \uff08\u4efb\u610f\uff09\u53ef\u8996\u5316\u30b3\u30fc\u30c9\uff08\u8a55\u4fa1\u306b\u306f\u4e0d\u8981\uff09<\/span><\/span><\/div>\n
% figure; gscatter(XtrainZ(:,1), XtrainZ(:,2), Ytrain, ‘rb’, ‘o^’);<\/span><\/span><\/div>\n
% figure; confusionchart(C, {‘Class 0′,’Class 1’}); <\/span><\/span><\/div>\n<\/div>\n
\u3061\u306a\u307f\u306b\u3001MATLAB Copilot \u3067\u306f <\/span>assert<\/span><\/a> \u6587\u306a\u3069\u3092\u5229\u7528\u3057\u305f\u30b3\u30fc\u30c9\u30d9\u30fc\u30b9\u306e\u65b9\u6cd5\u3067\u81ea\u52d5\u30c6\u30b9\u30c8\u3067\u304d\u308b\u30b3\u30fc\u30c9\u3082\u751f\u6210\u3067\u304d\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u63a1\u70b9\u306e\u81ea\u52d5\u5316\u304c\u53ef\u80fd\u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/div>\n
\"\"<\/img><\/div>\n
\u3082\u3057 MATLAB Grader \u3078\u306e\u7d71\u5408\u3092\u610f\u8b58\u3057\u3066\u304a\u3051\u3070\u3001\u63a1\u70b9\u7528\u306e <\/span>assessVariableEqual<\/span><\/a> \u306e\u3088\u3046\u306a\u95a2\u6570\u3092\u5229\u7528\u3057\u305f\u30c6\u30b9\u30c8\u30b1\u30fc\u30b9\u751f\u6210\u3092\u4f9d\u983c\u3059\u308b\u3053\u3068\u3082\u53ef\u80fd\u3067\u3059\u3002<\/span><\/div>\n
\"\"<\/img><\/div>\n

\u307e\u3068\u3081<\/span><\/h2>\n
\u6559\u80b2\u8005\u306b\u3068\u3063\u3066\u3001MATLAB Copilot \u306f\u5358\u306a\u308b\u30b3\u30fc\u30c9\u751f\u6210\u30c4\u30fc\u30eb\u3067\u306f\u306a\u304f\u3001\u30a2\u30a4\u30c7\u30a2\u3092\u5177\u4f53\u5316\u3057\u3001\u8a55\u4fa1\u554f\u984c\u3092\u52b9\u7387\u7684\u306b\u8a2d\u8a08\u3059\u308b\u305f\u3081\u306e\u983c\u308c\u308b\u5b58\u5728\u3067\u3059\u3002<\/span><\/div>\n
\u7279\u306b\u3001\u554f\u984c\u5236\u4f5c\u904e\u7a0b\u3067\u6700\u3082\u6642\u9593\u3092\u6d88\u8cbb\u3059\u308b <\/span>\u30a2\u30a4\u30c7\u30a2\u306e\u6574\u7406 \u2192 \u30b3\u30fc\u30c9\u306e\u5b9f\u88c5 \u2192 \u53ef\u8996\u5316 \u2192 \u8a55\u4fa1\u30ed\u30b8\u30c3\u30af\u8a2d\u8a08<\/span> \u3092\u3001MATLAB Copilot \u3068\u306e\u5bfe\u8a71\u3067\u8fc5\u901f\u306b\u5b8c\u4e86\u3067\u304d\u308b\u70b9\u306f\u5927\u304d\u306a\u5229\u70b9\u3067\u3059\u3002<\/span><\/div>\n
\u4eca\u56de\u306e\u4f8b\u3067\u793a\u3057\u305f\u3088\u3046\u306b\u3001MATLAB Copilot \u306f<\/span><\/div>\n