{"id":5288,"date":"2023-01-05T12:23:23","date_gmt":"2023-01-05T04:23:23","guid":{"rendered":"https:\/\/badgameshow.com\/steven\/?p=5288"},"modified":"2023-01-05T12:23:23","modified_gmt":"2023-01-05T04:23:23","slug":"%e5%ad%b8%e7%bf%92%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8typescript%e6%b1%82%e7%b7%9a%e6%80%a7%e5%9b%9e%e6%ad%b8","status":"publish","type":"post","link":"https:\/\/badgameshow.com\/steven\/typescript\/%e5%ad%b8%e7%bf%92%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8typescript%e6%b1%82%e7%b7%9a%e6%80%a7%e5%9b%9e%e6%ad%b8\/","title":{"rendered":"\u5b78\u7fd2\u5982\u4f55\u4f7f\u7528TypeScript\u6c42\u7dda\u6027\u56de\u6b78"},"content":{"rendered":"

<\/p>\n

\u4f7f\u7528 TypeScript \u4f86\u6c42\u7dda\u6027\u56de\u6b78 (Array Linear Regression)<\/h1>\n

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\u4ec0\u9ebc\u662f\u7dda\u6027\u56de\u6b78\uff1f<\/h2>\n

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\u4f7f\u7528 TypeScript \u4f86\u6c42\u7dda\u6027\u56de\u6b78<\/h2>\n

TypeScript \u662f\u4e00\u7a2e JavaScript \u7684\u8d85\u96c6\uff0c\u53ef\u4ee5\u63d0\u4f9b\u66f4\u591a\u7684\u529f\u80fd\u548c\u7a69\u5b9a\u6027\u3002\u5b83\u53ef\u4ee5\u7528\u4f86\u64b0\u5beb\u66f4\u8907\u96dc\u7684\u7a0b\u5f0f\u78bc\uff0c\u4e26\u63d0\u4f9b\u66f4\u591a\u7684\u7de8\u8b6f\u6642\u9593\u6aa2\u67e5\u3002<\/p>\n

\u8981\u4f7f\u7528 TypeScript \u4f86\u6c42\u7dda\u6027\u56de\u6b78\uff0c\u9996\u5148\u9700\u8981\u5b89\u88dd TypeScript \u7de8\u8b6f\u5668\uff0c\u4e26\u5c07 TypeScript \u7a0b\u5f0f\u78bc\u7de8\u8b6f\u6210 JavaScript \u7a0b\u5f0f\u78bc\u3002<\/p>\n

<\/p>\n

\u63a5\u4e0b\u4f86\uff0c\u6211\u5011\u53ef\u4ee5\u4f7f\u7528 TypeScript \u4f86\u5be6\u73fe\u7dda\u6027\u56de\u6b78\u7684\u7b97\u6cd5\u3002\u70ba\u4e86\u7c21\u55ae\u8d77\u898b\uff0c\u6211\u5011\u5c07\u4f7f\u7528\u4e00\u500b\u7c21\u55ae\u7684\u7bc4\u4f8b\u4f86\u8aaa\u660e\uff1a<\/p>\n

\/\/ \u5b9a\u7fa9\u8cc7\u6599\nlet x = [1, 2, 3, 4, 5];\nlet y = [2, 4, 6, 8, 10];\n\n\/\/ \u8a08\u7b97 x \u548c y \u7684\u5e73\u5747\u503c\nlet x_mean = x.reduce((a, b) => a + b) \/ x.length;\nlet y_mean = y.reduce((a, b) => a + b) \/ y.length;\n\n\/\/ \u8a08\u7b97 x \u548c y \u7684\u8b8a\u7570\u6578\nlet x_variance = x.map(x => (x - x_mean) ** 2).reduce((a, b) => a + b) \/ x.length;\nlet y_variance = y.map(y => (y - y_mean) ** 2).reduce((a, b) => a + b) \/ y.length;\n\n\/\/ \u8a08\u7b97 x \u548c y \u7684\u5171\u8b8a\u7570\u6578\nlet xy_covariance = x.map((x, i) => (x - x_mean) * (y[i] - y_mean)).reduce((a, b) => a + b) \/ x.length;\n\n\/\/ \u8a08\u7b97\u659c\u7387\nlet slope = xy_covariance \/ x_variance;\n\n\/\/ \u8a08\u7b97\u622a\u8ddd\nlet intercept = y_mean - (slope * x_mean);\n\n\/\/ \u8f38\u51fa\u7d50\u679c\nconsole.log(`\u659c\u7387: {slope}`);\nconsole.log(`\u622a\u8ddd:<\/span>{intercept}`);\n<\/code><\/pre>\n
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\u7d50\u8ad6<\/h2>\n
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