From 2e60c43993741579f5b37ea36ccaf53538f635b0 Mon Sep 17 00:00:00 2001 From: pp3avk64l <1328510073@qq.com> Date: Thu, 24 Nov 2022 21:34:47 +0800 Subject: [PATCH 01/12] Delete 'doc/1.txt' --- doc/1.txt | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 doc/1.txt diff --git a/doc/1.txt b/doc/1.txt deleted file mode 100644 index e69de29..0000000 From ee0efce9e92451ee11f38debbef6641c637cee0c Mon Sep 17 00:00:00 2001 From: pp3avk64l <1328510073@qq.com> Date: Thu, 24 Nov 2022 21:35:32 +0800 Subject: [PATCH 02/12] Delete 'model/2.txt' --- model/2.txt | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 model/2.txt diff --git a/model/2.txt b/model/2.txt deleted file mode 100644 index e69de29..0000000 From 286b4bbd610cee82c9d0e5a33772ad28bf2971f7 Mon Sep 17 00:00:00 2001 From: pp3avk64l <1328510073@qq.com> Date: Thu, 24 Nov 2022 22:04:46 +0800 Subject: [PATCH 03/12] Delete 'scr/miniprogram-2/project.private.config.json' --- scr/miniprogram-2/project.private.config.json | 7 ------- 1 file changed, 7 deletions(-) delete mode 100644 scr/miniprogram-2/project.private.config.json diff --git a/scr/miniprogram-2/project.private.config.json b/scr/miniprogram-2/project.private.config.json deleted file mode 100644 index 5108d59..0000000 --- a/scr/miniprogram-2/project.private.config.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "description": "项目私有配置文件。此文件中的内容将覆盖 project.config.json 中的相同字段。项目的改动优先同步到此文件中。详见文档:https://developers.weixin.qq.com/miniprogram/dev/devtools/projectconfig.html", - "projectname": "%E5%A4%96%E5%8D%96", - "setting": { - "compileHotReLoad": true - } -} \ No newline at end of file From fa0c96ee6b423740f43754b42abbe5ff1e875ffa Mon Sep 17 00:00:00 2001 From: pp3avk64l <1328510073@qq.com> Date: Thu, 24 Nov 2022 22:05:17 +0800 Subject: [PATCH 04/12] =?UTF-8?q?Delete=20'scr/=E6=A8=A1=E7=B3=8A=E4=B8=93?= =?UTF-8?q?=E5=AE=B6=E7=B3=BB=E7=BB=9F/README.md'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- scr/模糊专家系统/README.md | 549 ------------------------------- 1 file changed, 549 deletions(-) delete mode 100644 scr/模糊专家系统/README.md diff --git a/scr/模糊专家系统/README.md b/scr/模糊专家系统/README.md deleted file mode 100644 index d8077cd..0000000 --- a/scr/模糊专家系统/README.md +++ /dev/null @@ -1,549 +0,0 @@ -相机导购专家系统 -==================================== - -如今单反相机的高度发达为摄影爱好者带来了无限的可能,让每个人都可以在花不是那么多钱的情况下创造艺术或是记录生活。我认为,这是科技给人带来的福音。 - -然而,正是因为单反市场太过发达,初入单反坑的玩家往往被搞得晕头转向。常见的单反厂商有尼康、索尼、佳能。高端市场有哈苏、徕卡,低端市场有理光、富士等。每个品牌又有很多个产品线,各有不同,设计给不同需求的用户使用。 - -如果对单反相机的参数没有深入的研究,仅仅是听商家的吹捧,那很难在最高的性价比上买到合适自己的那款相机。往往会花冤枉钱,或发现相机的主打功能自己根本就用不上。 - -本专家系统就为了解决这个问题而创建,担任一个细心公正的相机导购师,通过询问用户更在意哪方面的内容、平时使用情景、预算、对重量的承受能力等,为用户推荐最合适的相机,让用户少花冤枉钱,用最少的钱买到最有用的相机。 - -# 数据库的采集 - -对专家系统而言,不仅要有规则数据库,相机本身各项指标的数据库也非常重要。本专家系统采集权威平台:DxOMark的数据库,来获得详细的相机各项指标数据。 - -由于没有现成的数据可供下载,我自己使用NodeJS写了采集程序来做这件事情。代码如下: - -```javascript -let request = require('request').defaults({ 'proxy': "http://127.0.0.1:1080" }); -let Queue = require('promise-queue'); -let fs = require('fs'); -let path = require('path'); -let queue = new Queue(10); //最多同时10线程采集 - -request('https://www.dxomark.com/daksensor/ajax/jsontested', //获得相机列表 - (error, response, body) => { - let cameraList = JSON.parse(body).data; - let finishedCount = 0; - cameraList.forEach(cameraMeta => { - let camera = Object.assign({}, cameraMeta); - let link = `https://www.dxomark.com${camera.link}---Specifications`; //拼合对应的规格网址 - queue.add(() => new Promise(res => { //把request放入队列,以保证同时http请求不超过10个 - let doRequest = () => { - request(link, (error, response, body) => { - if (error) { //如果失败则重试 - console.log("retrying.." + camera.name); - doRequest(); - return; - } - let specMatcherRegexp = /descriptifgauche.+?>([\s\S]+?)<\/td>[\s\S]+?descriptif_data.+?>([\s\S]+?)<\/td>/img; - let match = specMatcherRegexp.exec(body); - while (match) { //用正则表达式匹配table里的每一个项目,并添加至采集结果中 - camera[match[1]] = match[2]; - match = specMatcherRegexp.exec(body); - } - fs.writeFileSync(path.join('./scraped', camera.name + '.txt'), JSON.stringify(camera, null, 4), { encoding: "UTF8" }); - finishedCount++; - console.log(`Finished ${finishedCount}/${cameraList.length}: ${camera.name}`); - res(); - }) - }; - doRequest(); - })); - }); - }); -``` - -采集结果如图所示,总共采集到357款单反产品: -![scrape](imgs/scrape.gif) - -# 数据清洗 - -数据采集完了,但有些数据比较脏(如含有 ),有些数据是我们不需要的,有些数据用起来不方便(如分辨率是 1234 x 1234形式的字符串),属性名中有空格和大写字符,也不美观。因此,额外添加一步数据清洗。 - -数据清洗之后,希望留下以下属性: - -> 相机名称、相机图片、价格、像素数量、每秒连拍数量、重量、对焦系统质量、最大感光度、模型出厂日期、触屏存在、可录视频、有闪光灯、有蓝牙、有GPS、防水、机身材质质量 - -数据清洗的代码实现为: -```javascript -let fs = require("fs"); -let path = require("path"); - -function parseNumberFunctionFactory(keyMatcher, valueMatcher = /(\d+\.?\d*)/im, - returnValueDecider = match => +match[1]) { - return (data) => { - let key = Object.keys(data).filter(_ => keyMatcher.test(_.trim()))[0]; - if (!key) return null; - let match = data[key].toString().match(valueMatcher); - if (match) - return returnValueDecider(match); - else - return null; - }; -} - -let parseResolution = parseNumberFunctionFactory(/^resolution$/im, /(\d+)\s*x\s*(\d+)/im, match => [+match[1], +match[2]]); -let parseFrameRate = parseNumberFunctionFactory(/frame rate/im); -let parseWeight = parseNumberFunctionFactory(/weight/im); -let parseAutoFocus = parseNumberFunctionFactory(/number of autofocus points/im); -let parseISO = parseNumberFunctionFactory(/ISO latitude/im, /(\d+)\s*-\s*(\d+)/im, match => [+match[1], +match[2]]); -let parseLaunchDate = parseNumberFunctionFactory(/launchDateGraph/im, /(\d+)-(\d+)-(\d+)/im, match => new Date(match[1], match[2] - 1, match[3])); -let parseTouchScreen = parseNumberFunctionFactory(/Touch screen/im, /yes/im, match => !!match); -let parseVideo = parseNumberFunctionFactory(/^Video$/m, /yes/im, match => !!match); -let parseFlash = parseNumberFunctionFactory(/^flash$/im, /yes/im, match => !!match); -let parseWaterproof = parseNumberFunctionFactory(/^waterproof$/im, /yes/im, match => !!match); -let parseBluetooth = parseNumberFunctionFactory(/^Bluetooth$/im, /yes/im, match => !!match); -let parseGps = parseNumberFunctionFactory(/^GPS$/im, /yes/im, match => !!match); -let parseIsMetal = parseNumberFunctionFactory(/^camera material$/im, /metal/im, match => !!match); - -let files = fs.readdirSync("./scraped"); -files.forEach(file => { - let data = JSON.parse(fs.readFileSync(path.join('./scraped', file), { encoding: "utf8" })); - let resolution = parseResolution(data); - //机身材质质量 - let frameRate = parseFrameRate(data); - let weight = parseWeight(data); - let autoFocus = parseAutoFocus(data); - let iso = parseISO(data); - let launchDate = parseLaunchDate(data); - let touchScreen = parseTouchScreen(data); - let video = parseVideo(data); - let flash = parseFlash(data); - let waterproof = parseWaterproof(data); - let bluetooth = parseBluetooth(data); - let gps = parseGps(data); - let isMetal = parseIsMetal(data); - let cleanedData = { - name: data.name, - image: data.image, - brand: data.brand, - price: data.price, - pixelDepth: data.pixelDepth, - pixels: resolution ? (resolution[0] * resolution[1]) : 0, - ISO: iso, - maxISO: iso ? iso[1] : 0, - launchDate: +launchDate, - touchScreen, - video, - flash, - waterproof, - bluetooth, - gps, - isMetal, - frameRate, - resolution, - weight, - autoFocus, - }; - fs.writeFileSync(path.join('./cleaned', file), JSON.stringify(cleanedData, null, 4), { encoding: "utf8" }); -}); -``` - -清洗结果演示: -```json -{ - "name": "Nikon D5", - "image": "//cdn.dxomark.com/dakdata/xml/D5/vignette3.png", - "brand": "Nikon", - "price": 6500, - "pixelDepth": 20.8, - "pixels": 20817152, - "ISO": [ - 50, - 3280000 - ], - "maxISO": 3280000, - "launchDate": 1452009600000, - "touchScreen": true, - "video": true, - "flash": null, - "waterproof": null, - "bluetooth": null, - "gps": true, - "isMetal": true, - "frameRate": 14, - "resolution": [ - 5584, - 3728 - ], - "weight": 1225, - "autoFocus": 153 -} -``` -看起来好多了。 - -# 问题的设计 -斟酌一番后,我决定将询问用户的问题定为: - -1. 您将如何使用本相机(多选) - * 记录旅行 - * 拍摄学校或公司活动 - * 拍摄体育比赛 - * 拍摄自然风景 - * 拍摄人像 - * 拍摄天文 -2. 您看中哪些额外功能吗(多选) - * 内置闪光灯 - * 可录制视频 - * 可蓝牙传输照片 - * 可触屏 - * 内置GPS - * 防水 -3. 您是否愿意承受单反的重量 - * 没问题,3公斤的机器都扛得住 - * 在能避免负重的情况下尽可能避免负重 - * 不愿意接受重的单反,必须较为轻便 -4. 您愿意在单反上投入的经济 - * 很多,一步到位买高端设备 - * 普通,好用实用的设备 - * 经济,请推荐入门基本款 -5. 您有什么别的要求吗(多选) - * 尽量购买新的型号 - * 机身材质要好 - - -# 模糊专家系统的设计 - -一个显著的问题是:模糊专家系统一般只能用于数值的计算(如评估房价),但我这里做的,却是根据用户的输入推荐产品。用模糊专家系统如何做产品推荐呢? - -绕一个弯,不难解决这个问题:使用模糊专家系统对每一款相机产品计算出一个“推荐度”,根据推荐度排序,给用户推荐排名前三的产品。 - -具体的,我使用专家系统,计算出以下几个指标: - -> 适合拍摄旅行,适合拍摄活动,适合拍摄体育,适合拍摄风景,适合拍摄人像,适合拍摄天文,型号新,机身材质好,重量轻,价格低 - -用户的要求可以和这几个指标对号入座,来计算要求满足度。 - -对于另外一些如“有内置闪光灯”等指标,用不着用模糊专家系统,直接布尔判断来算满足度。 - -满足度根据规则加权平均,就是产品的总推荐度,排序后,前几名就是推荐给用户的相机。 - -这样做的好处,除了推荐相机之外,还可以列出相机的Pros & Cons (如:+ 适合拍摄天文 + 可录制视频 - 重量较重),只要去取满足度最高/最低的几名就可以了。 - -为实现模糊专家系统,我使用jFuzzyLogic框架,它是Java下最完整的模糊逻辑框架,支持fuzzy language (FCL),IEC 61131-7标准。 - -# 规则的建立 - -模糊专家系统在相机推荐中,最重要的工作就是:建立相机参数和相机适合拍摄的照片种类之间的联系,比如: - -> 适合拍摄体育类型相片的相机,需要有**很高**的连拍速度、**较好**的对焦系统。 - -在这个实验性的导购系统中,由我自己担任“领域专家”的角色。 - -首先,要定义术语,如: - -* 价格(美金)便宜: \$0-1000 中等: \$600-1700 贵: \$1500- - -翻译成FCL之后: - -``` -FUZZIFY price - TERM low := (0, 1) (1000, 0) ; - TERM medium := (600, 0) (800,1) (1500,1) (1700,0); - TERM high := (1500, 0) (1700, 1); -END_FUZZIFY - -FUZZIFY weight - TERM light := (0, 1) (500, 0) ; - TERM medium := (300, 0) (500,1) (700,1) (1000,0); - TERM heavy := (800, 0) (1000, 1); -END_FUZZIFY - -... -``` - -每个术语的定义如图所示: - -![fuzzify](imgs/fuzzify.gif) - -然后,要定义defuzzify规则,这里方便起见,均只定义“veryGood”、“good”、"average"、"bad"、"veryBad"。 - -``` -DEFUZZIFY travel - TERM veryBad := (0,1) (0.2,0); - TERM bad := (0,0) (0.1,1) (0.5,0); - TERM average:= (0,0) (0.5,1) (1,0); - TERM good:= (0.5,0) (0.9,1) (1,0); - TERM veryGood:= (0.8,0) (1,1); - METHOD : COG; - DEFAULT := 0.5; -END_DEFUZZIFY -``` - -最后,要定义规则,举例如下: - -* 如果 感光度范围 很高 而且 像素 高 那么 适合拍天文 高 - -* 如果 连拍速度 很高 而且 对焦系统 好 那么 适合拍体育 高 - -* 如果 重量 轻 且 有GPS 那么 适合记录旅行 高 - -* …… - -翻译成FCL之后: - -``` -RULEBLOCK travel - AND : MIN; - RULE 1 : IF weight IS light THEN travel IS good; - RULE 2 : IF video IS yes THEN travel IS good; - RULE 3 : IF gps IS yes THEN travel IS good; - RULE 4 : IF flash IS no THEN travel IS bad; - RULE 5 : IF weight IS heavy THEN travel IS veryBad; -END_RULEBLOCK - -RULEBLOCK sports - AND : MIN; - RULE 1 : IF frameRate IS high THEN sports IS veryGood; - RULE 2 : IF autoFocus IS high THEN sports IS veryGood; - RULE 3 : IF pixels IS high THEN sports IS good; - RULE 4 : IF frameRate IS low THEN sports IS veryBad; - RULE 5 : IF autoFocus IS low THEN sports IS veryBad; -END_RULEBLOCK - -RULEBLOCK astronomy - AND : MIN; - RULE 1 : IF pixels IS high THEN astronomy IS good; - RULE 2 : IF pixelDepth IS high THEN astronomy IS good; - RULE 3 : IF maxISO IS high THEN astronomy IS good; - RULE 4 : IF maxISO IS low THEN astronomy IS veryBad; - RULE 5 : IF pixels IS low THEN astronomy IS veryBad; -END_RULEBLOCK - -... -``` - -# 打分的具体实现 -数据和规则都准备就绪后,就可以开始进行模糊推理。 - -Java程序框架如下: - -```java -package ai.fuzzy; - -import com.alibaba.fastjson.JSON; -import net.sourceforge.jFuzzyLogic.FIS; -import net.sourceforge.jFuzzyLogic.FunctionBlock; -import net.sourceforge.jFuzzyLogic.plot.JFuzzyChart; -import net.sourceforge.jFuzzyLogic.rule.Variable; - -import java.io.*; -import java.nio.file.Paths; - -class CameraData { - public CameraAssessment assessment; - public String name; - public String image; - public String brand; - public Integer price; - public Integer pixelDepth; - public Integer pixels; - public Integer maxISO; - public Integer weight; - public Integer autoFocus; - public Long launchDate; - public Float frameRate; - public Integer[] resolution; - public Integer[] ISO; - public boolean touchScreen; - public boolean video; - public boolean flash; - public boolean waterproof; - public boolean bluetooth; - public boolean gps; - public boolean isMetal; -} - -class CameraAssessment { - public double travel; - public double event; - public double sports; - public double scenery; - public double portrait; - public double astronomy; - public double newModel; - public double durableBuild; - public double lightBuild; - public double lowPrice; -} - -public class Main { - public static void main(String[] args) throws IOException { - File rootFolder = new File("input"); - for (final File fileEntry : rootFolder.listFiles()) { - if (fileEntry.isFile()) { - CameraData camera = JSON.parseObject(readFileContents(fileEntry), CameraData.class); - camera.assessment = assess(camera); - writeFile(fileEntry, JSON.toJSONString(camera, true)); - } - } - } - - private static void writeFile(File fileEntry, String jsonString) throws FileNotFoundException { - ... - } - - private static String readFileContents(File fileEntry) throws IOException { - ... - } - - static CameraAssessment assess(CameraData cameraData) { - ... - } -} - -``` - -```assess```函数如下: - -```java - - static CameraAssessment assess(CameraData cameraData) { - CameraAssessment cameraAssessment = new CameraAssessment(); - String fileName = "fcl/camera.fcl"; - FIS fis = FIS.load(fileName, true); - - // Set inputs - fis.setVariable("price", cameraData.price); - fis.setVariable("pixelDepth", cameraData.pixelDepth); - fis.setVariable("pixels", cameraData.pixels); - fis.setVariable("maxISO", cameraData.maxISO); - fis.setVariable("weight", cameraData.weight); - fis.setVariable("autoFocus", cameraData.autoFocus); - fis.setVariable("launchDate", cameraData.launchDate); - fis.setVariable("frameRate", cameraData.frameRate); - fis.setVariable("touchScreen", cameraData.touchScreen ? 1 : 0); - fis.setVariable("video", cameraData.video ? 1 : 0); - fis.setVariable("flash", cameraData.flash ? 1 : 0); - fis.setVariable("waterproof", cameraData.waterproof ? 1 : 0); - fis.setVariable("bluetooth", cameraData.bluetooth ? 1 : 0); - fis.setVariable("gps", cameraData.gps ? 1 : 0); - fis.setVariable("isMetal", cameraData.isMetal ? 1 : 0); - - // Evaluate - fis.evaluate(); - - // Save results to cameraAssessment - cameraAssessment.travel = fis.getVariable("travel").defuzzify(); - cameraAssessment.event = fis.getVariable("event").defuzzify(); - cameraAssessment.sports = fis.getVariable("sports").defuzzify(); - cameraAssessment.scenery = fis.getVariable("scenery").defuzzify(); - cameraAssessment.portrait = fis.getVariable("portrait").defuzzify(); - cameraAssessment.astronomy = fis.getVariable("astronomy").defuzzify(); - cameraAssessment.newModel = fis.getVariable("newModel").defuzzify(); - cameraAssessment.durableBuild = fis.getVariable("durableBuild").defuzzify(); - cameraAssessment.lightBuild = fis.getVariable("lightBuild").defuzzify(); - cameraAssessment.lowPrice = fis.getVariable("lowPrice").defuzzify(); - - return cameraAssessment; - } -``` - -调用后,模糊专家系统就会给每个相机做评测,并作出如下输出: - -```json -"assessment": { - "astronomy": 0.07206054514320881, - "durableBuild": 0.1999999999999999, - "event": 0.1999999999999999, - "lightBuild": 0.1999999999999999, - "lowPrice": 0.1999999999999999, - "newModel": 0.20107756449438624, - "portrait": 0.2280106572609703, - "scenery": 0.2280106572609703, - "sports": 0.07875190169689873, - "travel": 0.16167332382310975, - }, -``` - -分别是相机对于每一项情景的适合度,在0-1之间。 - -接下来,需要把用户输入和适合度整合,得出总评分。 - -这里,总评分直接通过用户输入和适合度做内积的方法获得,评分的同时,也记录分数变动记录,这样给用户推荐就可以说明推荐原因。 - -```javascript -function evaluate(item, tags) { - let score = 0, changes = []; - tags.forEach(tag => { - let reverse = false; - let normalizedTag = tag; - if (tag.startsWith("!")) { - //允许使用!开头,表示相反。如:!lowPrice时,lowPrice原本加分现在变成减分 - reverse = true; - normalizedTag = tag.substr(1); - } - let scoreChange = 0; - if (item.assessment[normalizedTag]) { // 如果是专家系统assess出来的结果,一个占20分 - scoreChange = (reverse ? -1 : 1) * (item.assessment[normalizedTag] * 20) * (weight[normalizedTag] || 1); - } else { // 如果不是,那么是“防水”等基本要求,一个占3分 - scoreChange = (reverse ? -1 : 1) * (item[normalizedTag] ? 1 : -1) * 3 * (weight[normalizedTag] || 1); - } - if (scoreChange) { - score += scoreChange; - changes.push([scoreChange, tag]); // 记录评分变化,之后好出pros & cons - } - }); - return { score, changes }; -} -``` - -系统核心算法大致就完成了。 - -# 参数调整 -参数调整方面,花了不少心思。 - -原来的专家系统规则中,用了比较多的OR,导致多款相机某个指标打分非常相近,没有区分度。因此,我后来重写了FCL,尽量避免使用OR。为了拉开差距,我还把原来的“good”、"average"、"bad"改成了“veryGood”、“good”、"average"、"bad"、"veryBad",以说明某些规格的重要性大于其他规格。 - -由于RULES的good、bad不平衡,会导致一些assessment普遍偏低,另一些普遍偏高,因此,在后续处理中,我把这些assessment进行了normalization,让他们的平均值归一到0。 - -又出现了新的问题:推荐的相机,基本都是“价格低”、“型号新”,即这两个特性给相机总体加分太多,掩盖了别的优点,因此,我人工指定了指标的权重,以削弱价格和型号新旧对总体评分的影响,彰显“适合拍的类型”在推荐中的占比。 - -但还有很多问题,其中最主要的是:数据区分度不明显,高端相机的数据都差不多;指标内部有较强的关联性(合适拍天文的,一般也合适拍风景)。数据内部关联性不好解决。 - -经过一番调整,系统可以产生尚可的结果。 - -# 前端与运行效果 - -为了获得更好的效果,我为专家系统做了一个用户友好的前端界面,可以引导用户输入信息、以用户友好的方式给出推荐结果。前端是使用React + antd做的,基于Web。代码省略,界面长这样: - -![ui](imgs/ui.png) - -以上用户选择会被转换为tags: - -```json -["travel", "scenery", "video", "gps", "!lowPrice", "durableBuild"] -``` - -然后,这些tags会被送到之前设计好的系统中进行运算(内积rank),获得结果。 - -![ui2](imgs/ui2.png) - -可以看出,本系统可以根据用户的需要推荐相机(Pros和Cons都与用户第一步选择的“需求”有关)。 - -并能以一种易于理解的方式说清楚每一项“需求”是如何影响最终打分的。 - -# 结论 - -本导购系统只是一个试水,效果还过得去。 - -在进行导购系统制作的过程中,我充分体验了制作一个专家系统需要的每一个步骤。从采集数据,到模糊集定义,到模糊集推理,到整合结果并输出给用户看。 - -这个专家系统与普通的估测房价/工资/小费的模糊专家系统不同,必须对模糊专家系统defuzzify之后的结果进行进一步的整合,并给用户做出推荐。这个整合算法,应该也能算是广义专家系统的一部分吧。 - -效果比预期的稍微差一些,我认为原因主要在: - -1. 原始数据不够完整(DxOMark上仍然很多型号缺很多数据) -2. 数据区分度不高、内部关联性太强(高端相机的指标都差不多;合适拍天文的,一般也合适拍风景) - -进一步完善数据库、调整参数、或换一种性价比的模型之后,有望提升整体效果。 - -但无论如何,本专家系统至少做到了往正确的方向响应用户的输入,有理有据地为用户推荐符合要求的相机。 - -这种专家系统是有价值的,在进一步的优化后,说不定能产生一些商业效益。 - -现在我仅仅对机身做了推荐,如果加上镜头的组合,那么规则更加复杂、要考虑的因素更加多。 - -但按照规则解决这些复杂的问题,或许就是专家系统真正意义所在。真的解决之后,专家系统就可以和一个专业的人类导购师一样,耐心服务每一位顾客,让每个人都把钱花在刀刃上。 \ No newline at end of file From 11ae31feaed6fa04409a14fdd6ac0c088b3b7125 Mon Sep 17 00:00:00 2001 From: pp3avk64l <1328510073@qq.com> Date: Thu, 24 Nov 2022 22:06:26 +0800 Subject: [PATCH 05/12] =?UTF-8?q?Delete=20'scr/=E6=A8=A1=E7=B3=8A=E4=B8=93?= =?UTF-8?q?=E5=AE=B6=E7=B3=BB=E7=BB=9F/.gitignore'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- scr/模糊专家系统/.gitignore | 2 -- 1 file changed, 2 deletions(-) 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