From ed452bb7eb9cc01519750cbb535e3807015602eb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=87=8C=E5=BB=BA=E5=8F=91?= Date: Sat, 26 Dec 2020 23:00:20 +0800 Subject: [PATCH] 1.3.0 --- .github/workflows/npm-publish.yml | 1 + README.md | 6 +- index.js | 308 ++++++++++++++++-------------- package.json | 5 +- 4 files changed, 172 insertions(+), 148 deletions(-) diff --git a/.github/workflows/npm-publish.yml b/.github/workflows/npm-publish.yml index d2c2948..dee7bb3 100644 --- a/.github/workflows/npm-publish.yml +++ b/.github/workflows/npm-publish.yml @@ -16,6 +16,7 @@ jobs: with: node-version: 12 registry-url: https://registry.npmjs.org/ + - run: npm ci - run: npm publish env: NODE_AUTH_TOKEN: ${{secrets.NPMJS_PUBLISH_KEY}} diff --git a/README.md b/README.md index 157133c..96e2e59 100644 --- a/README.md +++ b/README.md @@ -45,15 +45,15 @@ h2dict/dict/f 1k 5 使用编辑距离算法模糊搜索，默认最多显示20个最匹配的单词 ```Shell -ff stff +h2dict/dict/f -e stff ``` 使用编辑距离算法，在前1万常用单词中，模糊搜索，并显示前3个最匹配的单词 ```Shell -ff stff 1w 3 +h2dict/dict/f -e stff 1w 3 ``` 使用类SublimeText矢量算法，在前1万常用单词中，模糊搜索，并显示前3个最匹配的单词 ```Shell -fff stff 1w 3 +h2dict/dict/f -v stff 1w 3 ``` diff --git a/index.js b/index.js index e3020d8..23cc032 100755 --- a/index.js +++ b/index.js @@ -1,197 +1,217 @@ #!/usr/bin/env node -var S1 = require('./lcs.js'); -var S2 = require('./levenshtein.js'); -var S3 = require('./fts_fuzzy_match.js'); -var S4 = require('./double_metaphone.js'); +var S1 = require("./lcs.js"); +var S2 = require("./levenshtein.js"); +var S3 = require("./fts_fuzzy_match.js"); +var S4 = require("./double_metaphone.js"); + +var P = require("commander"); + +var wordlist = require("./wordlist.json"); +var wordmap = require("./wordmap.json"); -var wordlist = require('./wordlist.json'); -var wordmap = require('./wordmap.json'); function calcSimilarity(a, b) { - //Similarity(A,B)=LCS(A,B)/(LD(A,B)+LCS(A,B)) - var ld = S2.Levenshtein(a, b); - var lc = S1.LCS(a, b).length; - return lc / (ld + lc); + //Similarity(A,B)=LCS(A,B)/(LD(A,B)+LCS(A,B)) + var ld = S2.Levenshtein(a, b); + var lc = S1.LCS(a, b).length; + return lc / (ld + lc); } function getSimilars(x) { - var h = []; - for (var i = 0; i < wordlist.length; i++) { - h.push([wordlist[i], calcSimilarity(x, wordlist[i])]); - } - return h.sort(function(x,y){return y[1]-x[1];}).map(function(d){return {word:d[0], similarity:d[1]};}); + var h = []; + for (var i = 0; i < wordlist.length; i++) { + h.push([wordlist[i], calcSimilarity(x, wordlist[i])]); + } + return h + .sort(function (x, y) { + return y[1] - x[1]; + }) + .map(function (d) { + return { word: d[0], similarity: d[1] }; + }); } function getFuzzys(x) { - var h = []; - for (var i = 0; i < wordlist.length; i++) { - r = S3.fuzzyMatch(x, wordlist[i]); - if (r[0]) h.push([wordlist[i], r[1]]); - } - return h.sort(function(x,y){return y[1]-x[1];}).map(function(d){return {word:d[0], similarity:d[1]};}); + var h = []; + for (var i = 0; i < wordlist.length; i++) { + r = S3.fuzzyMatch(x, wordlist[i]); + if (r[0]) h.push([wordlist[i], r[1]]); + } + return h + .sort(function (x, y) { + return y[1] - x[1]; + }) + .map(function (d) { + return { word: d[0], similarity: d[1] }; + }); } function getPhontics(x) { - var m = S4.DoubleMetaphone(x); - var h = []; - for (var i = 0; i < wordlist.length; i++) { - var n = S4.DoubleMetaphone(wordlist[i]) - if (m[0] == n[0] || m[1] == n[1]) { - h.push(wordlist[i]); - } - } - return h; + var m = S4.DoubleMetaphone(x); + var h = []; + for (var i = 0; i < wordlist.length; i++) { + var n = S4.DoubleMetaphone(wordlist[i]); + if (m[0] == n[0] || m[1] == n[1]) { + h.push(wordlist[i]); + } + } + return h; } function isNumbers(x) { - return /^[0-9\-/]+/.test(x); + return /^[0-9\-/]+/.test(x); } function isWildCard(x) { - var result = -1; - if (x) { - result = x.search(/[\*\?\[\]\^\$]/); - } - return result !== -1; + var result = -1; + if (x) { + result = x.search(/[\*\?\[\]\^\$]/); + } + return result !== -1; } function getWildcards(x) { - var result = []; - var c = 0; - var re = new RegExp('^' + x.replace(/\*/g, '.*').replace(/\?/g, '.') + '$', "i"); - for (var i = 0; i < wordlist.length; i++) { - if (re.test(wordlist[i])) { - c = c + 1; - result.push({word:wordlist[i]}); - } - } - return result; + var result = []; + var c = 0; + var re = new RegExp( + "^" + x.replace(/\*/g, ".*").replace(/\?/g, ".") + "$", + "i" + ); + for (var i = 0; i < wordlist.length; i++) { + if (re.test(wordlist[i])) { + c = c + 1; + result.push({ word: wordlist[i] }); + } + } + return result; } function number_fmt(a) { - a = a * 100; - return a.toFixed(0)/100 + a = a * 100; + return a.toFixed(0) / 100; } function parseKWM(str) { - n = parseFloat(str); - if (str.toUpperCase().endsWith('K')) n = n * 1000; - else if (str.toUpperCase().endsWith('W')) n = n * 1000 * 10; - else if (str.toUpperCase().endsWith('M')) n = n * 1000 * 1000; - return n; + n = parseFloat(str); + if (str.toUpperCase().endsWith("K")) n = n * 1000; + else if (str.toUpperCase().endsWith("W")) n = n * 1000 * 10; + else if (str.toUpperCase().endsWith("M")) n = n * 1000 * 1000; + return n; } function isKWM(str) { - if (str.toUpperCase().endsWith('K')) return true; - else if (str.toUpperCase().endsWith('W')) return true; - else if (str.toUpperCase().endsWith('M')) return true; - else return false; + if (str.toUpperCase().endsWith("K")) return true; + else if (str.toUpperCase().endsWith("W")) return true; + else if (str.toUpperCase().endsWith("M")) return true; + else return false; } function short_explain(e) { - var a = e.join("; ") - if (a.length > 24) { - return a.substring(0, 24) + ' ......' - } - return a; + var a = e.join("; "); + if (a.length > 24) { + return a.substring(0, 24) + " ......"; + } + return a; } function show_words(words, a1, a2) { - ki = 30000; - ci = 20; - if (a1) { - if (isKWM(a1)) ki = parseKWM(a1); - else ci = parseInt(a1); - } - if (a2) { - if (isKWM(a2)) ki = parseKWM(a2); - else ci = parseInt(a2); - } - - res = {}; - s = 0; - words.forEach(function(x) { - if (s < ci && s < ki && wordmap[x.word]["i"][1] < ki) { - s++; - if (x.similarity) { - res[x.word] = [ - wordmap[x.word]["i"][1], - number_fmt(x.similarity), - short_explain(wordmap[x.word]["e"]) - ]; - } else { - res[x.word] = [ - wordmap[x.word]["i"][1], - short_explain(wordmap[x.word]["e"]) - ]; - } - } - }); - console.log(res); + ki = 30000; + ci = 20; + if (a1) { + if (isKWM(a1)) ki = parseKWM(a1); + else ci = parseInt(a1); + } + if (a2) { + if (isKWM(a2)) ki = parseKWM(a2); + else ci = parseInt(a2); + } + + res = {}; + s = 0; + words.forEach(function (x) { + if (s < ci && s < ki && wordmap[x.word]["i"][1] < ki) { + s++; + if (x.similarity) { + res[x.word] = [ + wordmap[x.word]["i"][1], + number_fmt(x.similarity), + short_explain(wordmap[x.word]["e"]), + ]; + } else { + res[x.word] = [ + wordmap[x.word]["i"][1], + short_explain(wordmap[x.word]["e"]), + ]; + } + } + }); + console.log(res); } -var args = process.argv; +P.version("h2dict 1.0.0 https://github.com/lingjf/h2dict.git") +.option("-e, --edit_distance_fuzzy", "Fuzzy search with edit distance") +.option("-v, --vector_fuzzy", "Fuzzy search with vector matches") +.parse(process.argv); -if (!args[2]) { +var args = P.args; +var tool = process.argv[1]; -} else if (args[2] == '-v') { - console.log("h2dict 1.2.0 https://github.com/lingjf/h2dict.git"); -} else if (args[2] == '-h') { +if (!args[0]) { console.log(""); console.log("h2dict/dict/f staff #查询单词staff"); console.log("h2dict/dict/f 'st?ff' #使用通配符搜索单词"); - console.log("h2dict/dict/f 'st?ff' 1w # 使用通配符搜索1万常用单词，默认最多显示20个"); - console.log("h2dict/dict/f 'st?ff' 1w 3 # 使用通配符搜索1万常用单词，并显示前3个"); + console.log("h2dict/dict/f 'st?ff' 1w # 使用通配符搜索1万常用单词，默认最多显示20个" ); + console.log("h2dict/dict/f 'st?ff' 1w 3 # 使用通配符搜索1万常用单词，并显示前3个" ); console.log("h2dict/dict/f 10 # 列举前10常用单词"); console.log("h2dict/dict/f 1k 5 # 列举1000到1005常用单词"); - console.log("ff stff # 使用编辑距离算法模糊搜索，默认最多显示20个最匹配的单词"); - console.log("ff stff 1w 3 # 使用编辑距离算法，在前1万常用单词中模糊搜索，并显示前3个最匹配的单词"); - console.log("fff stff 1w 3 # 使用类SublimeText矢量算法，在前1万常用单词中模糊搜索，并显示前3个最匹配的单词"); + console.log("h2dict/dict/f -e stff # 使用编辑距离算法模糊搜索，默认最多显示20个最匹配的单词" ); + console.log("h2dict/dict/f -e stff 1w 3 # 使用编辑距离算法，在前1万常用单词中模糊搜索，并显示前3个最匹配的单词" ); + console.log("h2dict/dict/f -v stff 1w 3 # 使用类SublimeText矢量算法，在前1万常用单词中模糊搜索，并显示前3个最匹配的单词" ); console.log(""); -} else if (args[1].endsWith("/ff")) { - show_words(getSimilars(args[2]), args[3], args[4]); -} else if (args[1].endsWith("/fff")) { - show_words(getFuzzys(args[2]), args[3], args[4]); -} else if (isWildCard(args[2])) { - show_words(getWildcards(args[2]), args[3], args[4]); -} else if (isNumbers(args[2])) { - var start = 0; - var end = 0; - - if (args[3]) { - start = parseKWM(args[2]); - end = parseKWM(args[3]) + start; - } else { - end = parseKWM(args[2]); - } + +} else { + if (tool.endsWith("/ff") || P.edit_distance_fuzzy) { + show_words(getSimilars(args[0]), args[1], args[2]); + } else if (tool.endsWith("/fff") || P.vector_fuzzy) { + show_words(getFuzzys(args[0]), args[1], args[2]); + } else if (isWildCard(args[0])) { + show_words(getWildcards(args[0]), args[1], args[2]); + } else if (isNumbers(args[0])) { + var start = 0; + var end = 0; - r2 = {}; - wordlist.forEach(function(x, i){ - if (start <= i && i < end) { - r2[x] = [ - wordmap[x]["i"][1], - short_explain(wordmap[x]["e"]) - ] + if (args[2]) { + start = parseKWM(args[1]); + end = parseKWM(args[2]) + start; + } else { + end = parseKWM(args[1]); } - }); - console.log(r2); -} else { - word = args[2]; - if (wordmap[word]) { - wout = {} - wout["解释"] = wordmap[word]["e"]; - wout["常用榜"] = wordmap[word]["i"]; - wout["音标"] = wordmap[word]["p"]; - if (wordmap[word]["s"]) wout["近义词"] = wordmap[word]["s"]; - if (wordmap[word]["f"]) wout["词家族"] = wordmap[word]["f"]; - var t1 = getSimilars(word).slice(0,8).filter(function(x){return x.similarity!=1}).map(function(x) {return x.word}); - if (t1) wout["形似字"] = t1; - var t2 = getPhontics(word).slice(0,4).filter(function(x){return x!=word}); - if (t2) wout["音似字"] = t2; - if (wordmap[word]["r"]) wout["相关字"] = wordmap[word]["r"]; - console.log(wout); + + r2 = {}; + wordlist.forEach(function (x, i) { + if (start <= i && i < end) { + r2[x] = [wordmap[x]["i"][1], short_explain(wordmap[x]["e"])]; + } + }); + console.log(r2); } else { - show_words(getSimilars(word)); + word = args[0]; + if (wordmap[word]) { + wout = {}; + wout["解释"] = wordmap[word]["e"]; + wout["常用榜"] = wordmap[word]["i"]; + wout["音标"] = wordmap[word]["p"]; + if (wordmap[word]["s"]) wout["近义词"] = wordmap[word]["s"]; + if (wordmap[word]["f"]) wout["词家族"] = wordmap[word]["f"]; + var t1 = getSimilars(word).slice(0, 8) .filter(function (x) { return x.similarity != 1; }) .map(function (x) { return x.word; }); + if (t1) wout["形似字"] = t1; + var t2 = getPhontics(word) .slice(0, 4) .filter(function (x) { return x != word; }); + if (t2) wout["音似字"] = t2; + if (wordmap[word]["r"]) wout["相关字"] = wordmap[word]["r"]; + console.log(wout); + } else { + show_words(getSimilars(word)); + } } } diff --git a/package.json b/package.json index 8ab2cd3..781a2c3 100644 --- a/package.json +++ b/package.json @@ -1,6 +1,6 @@ { "name": "h2dict", - "version": "1.2.0", + "version": "1.3.0", "repository": "http://registry.npmjs.org/h2dict", "main": "index.js", "bin": { @@ -9,5 +9,8 @@ "f": "index.js", "ff": "index.js", "fff": "index.js" + }, + "dependencies": { + "commander": "~2.9.0" } }