[B! dataStructure] gologo13ã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

gologo13 id:gologo13

dataStructureã«é–¢ã™ã‚‹gologo13ã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (16)

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ã€Œé«˜é€Ÿæ–‡å—åˆ—è§£æžã®ä¸–ç•Œã€ã‚’èªã‚€å‰ã«çŸ¥ã£ã¦ãŠãã¨è‰¯ã„ã“ã¨ - EchizenBlog-Zwei
ã€Œé«˜é€Ÿæ–‡å—åˆ—è§£æžã®ä¸–ç•Œã€ã¨ã„ã†å¤§å¤‰ã™ã°ã‚‰ã—ã„æœ¬ãŒç™ºå£²ã•ã‚ŒãŸã€‚ã‚ã‚Šã¨æ•·å±…ãŒé«˜ã„æœ¬ã§ã¯ã‚ã‚‹ã®ã§èªã‚€å‰ã«çŸ¥ã£ã¦ãŠãã¨ã‚ˆã•ãã†ãªã“ã¨ã‚’æ›¸ã„ã¦ãŠãã€‚ ã€Œé«˜é€Ÿæ–‡å—åˆ—è§£æžã€ã¨ã¯ æœ¬æ›¸ã§ã„ã†é«˜é€Ÿæ–‡å—åˆ—è§£æžã¨ã„ã†ã®ã¯ä¸»ã«2ã¤ã®ã“ã¨ã‚’æŒ‡ã—ã¦ã„ã‚‹ã€‚ã²ã¨ã¤ã¯ãƒ‡ãƒ¼ã‚¿ã‚’åœ§ç¸®ã—ã¦å°ã•ãã—ã¦ãƒ‡ã‚£ã‚¹ã‚¯ã‚ˆã‚Šãƒ¡ãƒ¢ãƒªã€ãƒ¡ãƒ¢ãƒªã‚ˆã‚Šã‚ãƒ£ãƒƒã‚·ãƒ¥ã¨ã„ã†ã‚ˆã†ã«ã‚ˆã‚Šé«˜é€Ÿãªè¨˜æ†¶è£…ç½®ã§æ‰±ã„ã¾ã—ã‚‡ã†ã€ã¨ã„ã†è©±ã€‚ã‚‚ã†ã²ã¨ã¤ã¯ãƒ‡ãƒ¼ã‚¿æ§‹é€ ã‚’å·¥å¤«ã™ã‚‹ã“ã¨ã§è¤‡é›‘ãªæ“ä½œã‚‚ãã“ãã“é«˜é€Ÿã«æ‰±ãˆã¾ã™ã‚ˆã€ã¨ã„ã†è©±ã€‚ã¤ã¾ã‚Šã€Œåœ§ç¸®ã€ã®è©±ã¨ã€ŒåŠ¹çŽ‡çš„ãªãƒ‡ãƒ¼ã‚¿æ§‹é€ ã€ã®è©±ãŒã‚ã‚‹ã¨è€ƒãˆã¦ãŠãã¨è‰¯ã„ã€‚ ã‚ãƒ¼ãƒ¯ãƒ¼ãƒ‰ã¯3ã¤ ã‚ªãƒ“ã«ã‚‚æ›¸ã„ã¦ã‚ã‚‹ã‘ã‚Œã©ã€æœ¬æ›¸ãŒä¸»ã«æ‰±ã†ã®ã¯ã€ŒBWTã€ã€Œç°¡æ½”ãƒ‡ãƒ¼ã‚¿æ§‹é€ ã€ã€Œã‚¦ã‚§ãƒ¼ãƒ–ãƒ¬ãƒƒãƒˆæœ¨ã€ã®3ã¤ã€‚å…·ä½“çš„ã«ã¯ã€ŒBWTã€ãŒã€Œåœ§ç¸®ã€ã«é–¢ã‚ã£ã¦ã„ã¦ã€Œã‚¦ã‚§ãƒ¼ãƒ–ãƒ¬ãƒƒãƒˆæœ¨ã€ãŒã€ŒåŠ¹çŽ‡çš„ãªãƒ‡ãƒ¼ã‚¿æ§‹é€ ã€ã«é–¢ã‚ã£ã¦ã„ã‚‹ã€‚ã€Œç°¡æ½”ãƒ‡ãƒ¼ã‚¿æ§‹é€ ã€ã¯åŸºæœ¬çš„ãªé“å…·ã¨ã—ã¦æœ¬æ›¸ã®è‰²ã€…ãªã¨ã“ã‚ã§å‡ºã¦
gologo13 2013/01/09
suffixarray

NLP

programming

algorithm

dataStructure
ãƒªãƒ³ã‚¯
http://cnode3.slideboom.com/presentations/655201/%E3%81%82%E3%81%AA%E3%81%9F%E3%81%AE%E7%9F%A5%E3%82%89%E3%81%AA%E3%81%84%E3%83%8F%E3%83%83%E3%82%B7%E3%83%A5%E3%83%86%E3%83%BC%E3%83%96%E3%83%AB%E3%81%AE%E4%B8%96%E7%95%8C/download
gologo13 2012/12/15
OpenAddresing ã¨CloseAddressing ã®é•ã„ã«ã¤ã„ã¦ã‚ˆãã‚ã‹ã£ãŸ

hash

dataStructure
ãƒªãƒ³ã‚¯
Centroid Path Decompositionã‚’ä½¿ã£ãŸãƒˆãƒ©ã‚¤ã§ãƒ€ãƒ–ãƒ«é…åˆ—ã¨å‹è² ã—ã¦ã¿ãŸ - Preferred Networks Research & Development
é‡£ã‚Šã‚¿ã‚¤ãƒˆãƒ«ã‚’ä»˜ã‘ãŸã‹ã£ãŸã®ã§ã™ãŒã•ã£ã±ã‚Šæ€ã„ã¤ã‹ãªã„ã®ã§ã‚‚ã†è«¦ã‚ã¾ã—ãŸã€‚å¾³æ°¸ã§ã™ã€‚ ä»Šå›žã¯Centroid Path Decompositionï¼ˆä»¥ä¸‹CPDï¼‰ã«ã¤ã„ã¦ã®è©±ã‚’æ›¸ãã¾ã™ã€‚ç›´è¨³ã™ã‚‹ã¨é‡å¿ƒãƒ‘ã‚¹åˆ†è§£ã¨ãªã‚‹ã§ã—ã‚‡ã†ã‹ã€‚Trieã‚’å®Ÿç¾ã™ã‚‹ãŸã‚ã®ãƒ†ã‚¯ãƒ‹ãƒƒã‚¯ï¼ˆæ™®é€šã®ãƒ„ãƒªãƒ¼ã«ã‚‚ä½¿ãˆã‚‹ã®ã‹ãªï¼Ÿãªã‹ãªã‹é›£ã—ãã†ã§ã™ãŒâ€¦ï¼‰ã®ä¸€ã¤ã§ã™ã€‚CPDã¯ä¸€å¹´å‰ã®å¼Šç¤¾å²¡é‡ŽåŽŸã®è¨˜äº‹ã«å‡ºã¦ãã¾ã™ãŒã€ç§ã®ã‚ˆã†ãªç´ äººã«ã¯ã‚ã‚Œã ã‘ã§ã¯ç†è§£ã§ããªã„éƒ¨åˆ†ãŒã‚ã£ãŸã®ã§ã€ä»Šå›žã¯ã¡ã‚‡ã£ã¨è«–æ–‡ã‚’èªã‚“ã§ã¿ã¾ã—ãŸã€‚ Trieã®å®Ÿè£…ã«ã‚ˆãã‚ã‚‹å•é¡Œ Trieã‚’å®Ÿç¾ã™ã‚‹ãŸã‚ã®ãƒ‡ãƒ¼ã‚¿æ§‹é€ ã¨ã„ã†ã¨ãƒ€ãƒ–ãƒ«é…åˆ—ã¨ã‹ãŒæŒ™ã’ã‚‰ã‚Œã¾ã™ãŒã€ã“ã†ã„ã£ãŸé«˜é€Ÿãªãƒ‡ãƒ¼ã‚¿æ§‹é€ ã«ã‚‚ã€ãƒ©ãƒ³ãƒ€ãƒ ã‚¢ã‚¯ã‚»ã‚¹ãŒå¤šã„ã¨ã„ã†å¼±ç‚¹ãŒã‚ã‚Šã¾ã™ã€‚ãƒ¡ãƒ¢ãƒªã¯HDDãªã©ã¨æ¯”ã¹ã‚‹ã¨ãƒ©ãƒ³ãƒ€ãƒ ã‚¢ã‚¯ã‚»ã‚¹ã«è€ãˆã†ã‚‹è¨˜æ†¶åª’ä½“ã§ã™ãŒã€ã¨ã¯è¨€ãˆãƒ©ãƒ³ãƒ€ãƒ ã‚¢ã‚¯ã‚»ã‚¹ã¯é¿ã‘ã‚‰ã‚Œã‚‹ã«è¶Šã—ãŸã“ã¨ã¯ã‚ã‚Šã¾ã›ã‚“ã€‚CPDã‚’ä½¿ã†ã“
gologo13 2012/09/02
dataStructure

TRIE

nlp

trie
ãƒªãƒ³ã‚¯
Count-Min Sketch ã®ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã‚’å…¬é–‹ã—ã¾ã—ãŸ
2012-02-17 Count-Min Sketch ã®ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã‚’å…¬é–‹ã—ã¾ã—ãŸ written by Susumu Yata. ã¯ã˜ã‚ã« å…ˆæ—¥ groonga ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã§ã®åˆ©ç”¨ã‚’ç›®çš„ã¨ã—ã¦é–‹ç™ºã—ã¦ã„ã‚‹ãƒ©ã‚¤ãƒ–ãƒ©ãƒª Madoka ã‚’å…¬é–‹ã—ã¾ã—ãŸï¼ŽMadoka ã¯ Count-Min Sketch ã¨ã„ã†æ‰‹æ³•ã‚’ãƒ©ã‚¤ãƒ–ãƒ©ãƒªåŒ–ã—ãŸã‚‚ã®ã§ã‚ã‚Šï¼Œæ–‡æ›¸é›†åˆã«å«ã¾ã‚Œã‚‹ã‚ãƒ¼ãƒ¯ãƒ¼ãƒ‰ã®é »åº¦ã‚’æ±‚ã‚ã‚‹ï¼Œã‚¯ã‚¨ãƒªã®é »åº¦ã‚’æ±‚ã‚ã‚‹ï¼Œãªã©ã®ç”¨é€”ã«ä½¿ã†ã“ã¨ãŒã§ãã¾ã™ï¼Ž s-yata/madoka - GitHub Documentation - Madoka ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã®ä½¿ã„æ–¹ã«ã¤ã„ã¦ã¯ãƒ‰ã‚ãƒ¥ãƒ¡ãƒ³ãƒˆã«æ›¸ã„ã¦ã‚ã‚‹ã®ã§ï¼Œã“ã¡ã‚‰ã¯ Count-Min Sketch ã¨ Madoka ã®ç‰¹å¾´ã‚’ã¾ã¨ã‚ãŸå†…å®¹ã«ãªã£ã¦ã„ã¾ã™ï¼Ž Count-Min Sketch é »åº¦ã‚’æ±‚ã‚ã‚‹ã“ã¨ãŒç›®çš„ã§ã‚ã‚Œã°ï¼Œãƒãƒƒã‚·ãƒ¥è¡¨ã«ã‚ˆã‚‹é€£æƒ³é…åˆ—ã‚’ä½¿ã†ã®ãŒï¼ŒãŠãã‚‰
gologo13 2012/02/25
dataStructure
ãƒªãƒ³ã‚¯
Bloom Filter ã®å‹•ä½œç¢ºèªã‚¹ã‚¯ãƒªãƒ—ãƒˆã‚’ Perl ã§æ›¸ã„ã¦ã¿ã¾ã—ãŸ
Bloom Filter ã®å‹•ä½œç¢ºèªã‚¹ã‚¯ãƒªãƒ—ãƒˆã‚’ Perl ã§æ›¸ã„ã¦ã¿ã¾ã—ãŸ 2011-04-28-3 [Algorithm][Programming] ç„¡æ–™ã§èªã‚ã‚‹ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ã®æ•™ç§‘æ›¸ã€ŒMining of Massive Datasetsã€[2011-03-31-3]ã‚’èªã‚“ã§ã¦æ°—ã«ãªã£ãŸ Bloom Filterï¼ˆãƒ–ãƒ«ãƒ¼ãƒ ãƒ•ã‚£ãƒ«ã‚¿ï¼‰ã€‚ è¥¿å°¾ã•ã‚“ãŒå‹•ä½œç¢ºèªã‚¹ã‚¯ãƒªãƒ—ãƒˆã‚’ Python ã§å®Ÿè£…ã—ã¦ã„ã¾ã—ãŸã€‚ - Bloom filterã®ã‚·ãƒ³ãƒ—ãƒ«ãªå®Ÿè£… (è¥¿å°¾æ³°å’Œã®ã¯ã¦ãªãƒ€ã‚¤ã‚¢ãƒªãƒ¼) http://d.hatena.ne.jp/nishiohirokazu/20080213/1202912207 è¥¿å°¾ã•ã‚“ã«ã‚ˆã‚‹åˆ†ã‹ã‚Šã‚„ã™ãã¦ç´ æ™´ã‚‰ã—ã™ãŽã‚‹è§£èª¬ã‚’å¼•ç”¨ã€‚ Bloom filterã¯æŒ‡å®šã•ã‚ŒãŸã‚‚ã®ãŒãƒªã‚¹ãƒˆã«å«ã¾ã‚Œã‚‹ãªã‚‰ã°Trueã€å«ã¾ã‚Œãªã„ãªã‚‰ã°Falseã‚’è¿”ã™ã‚ˆã†ãªãƒ‡ãƒ¼ã‚¿æ§‹é€ ã§ã‚ã‚‹ã€‚ã‚‚ã¡ã‚ã‚“ã€
gologo13 2011/12/26
dataStructure

perl
ãƒªãƒ³ã‚¯
ç°¡æ½”ãƒˆãƒ©ã‚¤ã®å®Ÿè£…ã«å«ã¾ã‚Œã‚‹ç°¡æ½”ãƒ“ãƒƒãƒˆãƒ™ã‚¯ãƒˆãƒ«ã®æ€§èƒ½æ¯”è¼ƒ - ã‚„ãŸï¼ ã¯ã¦ãªæ—¥è¨˜
ã¯ã˜ã‚ã« å…ˆæ—¥ï¼ˆ12/10ï¼‰DSIRNLP ã¨ã„ã†å‹‰å¼·ä¼šã§ç´¹ä»‹ã•ã‚Œã¦ã„ãŸï¼Œç°¡æ½”ãƒˆãƒ©ã‚¤ã®å®Ÿè£…ã«å«ã¾ã‚Œã‚‹ç°¡æ½”ãƒ“ãƒƒãƒˆãƒ™ã‚¯ãƒˆãƒ«ã®å®Ÿé¨“çµæžœãŒäºˆæƒ³ã¨ã‹ã‘é›¢ã‚Œã¦ã„ãŸã®ã§ï¼Œè‡ªèº«ã§ã‚‚èª¿ã¹ã¦ã¿ã‚‹ã“ã¨ã«ã—ã¾ã—ãŸï¼Ž partake.in DSIRNLP#2ã§ç™ºè¡¨ã—ã¾ã—ãŸã€Œä½œã‚ã†ï¼ç°¡æ½”ãƒ“ãƒƒãƒˆãƒ™ã‚¯ãƒˆãƒ«ã€ - EchizenBlog-Zwei å®Ÿé¨“è¨å®š æ¯”è¼ƒã—ãŸå®Ÿè£…ã¯å…ƒã®å®Ÿé¨“ã¨åŒã˜ã§ã™ï¼Ž ux-trie: http://code.google.com/p/ux-trie/ rx: http://code.google.com/p/mozc/ marisa-trie: http://code.google.com/p/marisa-trie/ å®Ÿé¨“ç’°å¢ƒã® CPU ã¯ Intel(R) Core(TM) i7-2640M CPU @ 2.80GHz stepping 07 ã§ã™ï¼Žç‰©ç†ãƒ¡ãƒ¢ãƒªã®å®¹é‡ã¯ååˆ†ã«ã‚ã‚Šï¼Œãƒ‡ã‚£ã‚¹ã‚¯ I/
gologo13 2011/12/17
trie

dataStructure
ãƒªãƒ³ã‚¯
ã¯ã¦ãªã‚°ãƒ«ãƒ¼ãƒ—ã®çµ‚äº†æ—¥ã‚’2020å¹´1æœˆ31æ—¥(é‡‘)ã«æ±ºå®šã—ã¾ã—ãŸ - ã¯ã¦ãªã®å‘ŠçŸ¥
ã¯ã¦ãªã‚°ãƒ«ãƒ¼ãƒ—ã®çµ‚äº†æ—¥ã‚’2020å¹´1æœˆ31æ—¥(é‡‘)ã«æ±ºå®šã—ã¾ã—ãŸ ä»¥ä¸‹ã®ã‚¨ãƒ³ãƒˆãƒªã®é€šã‚Šã€ä»Šå¹´æœ«ã‚’ç›®å‡¦ã«ã¯ã¦ãªã‚°ãƒ«ãƒ¼ãƒ—ã‚’çµ‚äº†äºˆå®šã§ã‚ã‚‹æ—¨ã‚’ãŠçŸ¥ã‚‰ã›ã—ã¦ãŠã‚Šã¾ã—ãŸã€‚ 2019å¹´æœ«ã‚’ç›®å‡¦ã«ã€ã¯ã¦ãªã‚°ãƒ«ãƒ¼ãƒ—ã®æä¾›ã‚’çµ‚äº†ã™ã‚‹äºˆå®šã§ã™ - ã¯ã¦ãªã‚°ãƒ«ãƒ¼ãƒ—æ—¥è¨˜ ã“ã®ãŸã³ã€æ£å¼ã«çµ‚äº†æ—¥ã‚’æ±ºå®šã„ãŸã—ã¾ã—ãŸã®ã§ã€ä»¥ä¸‹ã®é€šã‚Šã”ç¢ºèªãã ã•ã„ã€‚ çµ‚äº†æ—¥: 2020å¹´1æœˆ31æ—¥(é‡‘) ã‚¨ã‚¯ã‚¹ãƒãƒ¼ãƒˆå¸Œæœ›ç”³è«‹æœŸé™:2020å¹´1æœˆ31æ—¥(é‡‘) çµ‚äº†æ—¥ä»¥é™ã¯ã€ã¯ã¦ãªã‚°ãƒ«ãƒ¼ãƒ—ã®é–²è¦§ãŠã‚ˆã³æŠ•ç¨¿ã¯è¡Œãˆã¾ã›ã‚“ã€‚æ—¥è¨˜ã®ã‚¨ã‚¯ã‚¹ãƒãƒ¼ãƒˆãŒå¿…è¦ãªæ–¹ã¯ä»¥ä¸‹ã®è¨˜äº‹ã«ã—ãŸãŒã£ã¦æ‰‹ç¶šãã‚’ã—ã¦ãã ã•ã„ã€‚ ã¯ã¦ãªã‚°ãƒ«ãƒ¼ãƒ—ã«æŠ•ç¨¿ã•ã‚ŒãŸæ—¥è¨˜ãƒ‡ãƒ¼ã‚¿ã®ã‚¨ã‚¯ã‚¹ãƒãƒ¼ãƒˆã«ã¤ã„ã¦ - ã¯ã¦ãªã‚°ãƒ«ãƒ¼ãƒ—æ—¥è¨˜ ã”åˆ©ç”¨ã®ã¿ãªã•ã¾ã«ã¯ã”è¿·æƒ‘ã‚’ãŠã‹ã‘ã„ãŸã—ã¾ã™ãŒã€ã©ã†ãžã‚ˆã‚ã—ããŠé¡˜ã„ã„ãŸã—ã¾ã™ã€‚ 2020-06-25 è¿½è¨˜ ã¯ã¦ãªã‚°ãƒ«ãƒ¼ãƒ—æ—¥è¨˜ã®ã‚¨ã‚¯ã‚¹ãƒãƒ¼ãƒˆãƒ‡ãƒ¼ã‚¿ã¯2020å¹´2æœˆ28
gologo13 2011/12/06
dataStructure
ãƒªãƒ³ã‚¯
Brief Introduction to Suffix Array
Last Modified: 2000-11-14 (Since: 2000-11-14) Suffix array is a data structure designed for efficient searching of a large text. The data structure is simply an array containing all the pointers to the text suffixes sorted in lexicographical (alphabetical) order. Each suffix is a string starting at a certain poinsition in the text and ending at the end of the text. Searching a text can be performe
gologo13 2011/11/09
I understood by implementing.

dataStructure
ãƒªãƒ³ã‚¯
STLé¢¨ã«ä½¿ãˆã‚‹ãƒžãƒƒãƒ—åž‹ã‚³ãƒ³ãƒ†ãƒŠã®ç´¹ä»‹ã¨æ€§èƒ½æ¯”è¼ƒ - Preferred Networks Research & Development
æœ€è¿‘ã‚¹ãƒžãƒ¼ãƒˆãƒ•ã‚©ãƒ³ã«ä¹—ã‚Šæ›ãˆã¾ã—ãŸã€‚å¾³æ°¸ã§ã™ã€‚ C++ã¯ä¸–ã«æ•°ã‚ã‚‹ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°è¨€èªžã®ä¸ã§ã¯æ¯”è¼ƒçš„ãƒ¡ãƒ¢ãƒªã‚’é£Ÿã‚ãªã„æ–¹ã§ã™ãŒã€ãã‚Œã§ã‚‚ãƒ¡ãƒ¢ãƒªä½¿ç”¨é‡ãŒå•é¡Œã¨ãªã‚‹å ´åˆã¯ã‚ã‚Šã¾ã™ã€‚ãã®ã‚ˆã†ãªå ´åˆã®å¯¾å‡¦æ–¹æ³•ã¯ã„ãã¤ã‹æœ‰ã‚Šã¾ã™ãŒã€æ‰‹è»½ã«é¸æŠžã§ãã‚‹æ–¹æ³•ã¨ã—ã¦ã€ä»Šæ—¥ã¯STLã®mapã‚„unordered_mapã¨åŒã˜æ„Ÿã˜ã§ä½¿ãˆã‚‹ãƒ‡ãƒ¼ã‚¿æ§‹é€ ã‚’ã„ãã¤ã‹ç´¹ä»‹ã—ãŸã„æ€ã„ã¾ã™ã€‚ ä»¥ä¸‹ã€è¨ˆç®—é‡ã®è¡¨è¨˜ã‚’ã™ã‚‹éš›ã«ã¯ã€è¦ç´ æ•°ã‚’nã¨ã—ã¾ã™ã€‚ Loki::AssocVector Lokiã¯Modern C++ Designã¨ã„ã†æœ¬ã®ä½œè€…ã§ã‚ã‚‹Andrei AlexandrescuãŒé–‹ç™ºã—ãŸãƒ©ã‚¤ãƒ–ãƒ©ãƒªã§ã™ã€‚AssocVectorã¯ãã®ä¸ã®ä¸€ã¤ã¨ã—ã¦æä¾›ã•ã‚Œã¦ã„ã‚‹ã‚¯ãƒ©ã‚¹ã§ã€vector<pair<key, value> >ã¨ã„ã†åž‹ã®ãƒ™ã‚¯ã‚¿ãƒ¼ã‚’keyã§ã‚½ãƒ¼ãƒˆã—ãŸçŠ¶æ…‹ã§æŒã¤äº‹ã«ã‚ˆã‚Šã€äºŒåˆ†æŽ¢ç´¢ã«ã‚ˆã‚‹è¦ç´ ã®æŽ¢ç´¢ã‚’å¯èƒ½ã«ã—ãŸãƒ‡ãƒ¼ã‚¿æ§‹é€ ã§ã™ã€‚ã“
gologo13 2011/07/20
c++

dataStructure
ãƒªãƒ³ã‚¯
sary: a suffix array library and tools
What is sary? sary is a suffix array library and tools. It provides fast full-text search facilities for text files on the order of 10 to 100 MB using a data structure called a suffix array. It can also search specific fields in a text file by assigning index points to those fields. Table of Contents What's New Characteristics Brief Introduction to Suffix Array libsary Reference Manual Using the I
gologo13 2011/05/10
dataStructure
ãƒªãƒ³ã‚¯
Data Structure Visualization
Currently, we have visualizations for the following data structures and algorithms: Basics Stack: Array Implementation Stack: Linked List Implementation Queues: Array Implementation Queues: Linked List Implementation Lists: Array Implementation (available in java version) Lists: Linked List Implementation (available in java version) Recursion Factorial Reversing a String N-Queens Probl em Indexing
gologo13 2011/05/08
å¯è¦–åŒ–

dataStructure
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ç¬¬ 13 å›ž é †åºæœ¨
æœ¬æ—¥ã®å†…å®¹ 13-1. é †åºæœ¨ 13-2. é€£æƒ³é…åˆ— ã“ã®ãƒ‰ã‚ãƒ¥ãƒ¡ãƒ³ãƒˆã¯ http://edu.net.c.dendai.ac.jp/ ä¸Šã§å…¬é–‹ã•ã‚Œã¦ã„ã¾ã™ã€‚ 13-1. é †åºæœ¨ é †åºæœ¨ã®å®šç¾© é †åºæœ¨ã¯ã€å€¤ã®å¤§å°ã«åŸºã¥ã„ã¦å€¤ã‚’æ ¼ç´ã™ã‚‹æœ¨ã§ã‚ã‚Šã€ä½Žã„ã‚³ã‚¹ãƒˆã§å€¤ã‚’å°ã• ã„é †ã«å–ã‚Šå‡ºã™ã“ã¨ãŒå¯èƒ½ã«ãªã‚Šã¾ã™ã€‚ é †åºæœ¨ã¨ã¯ã€å„é ‚ç‚¹ã«å€¤ã‚’æŒã¤äºŒåˆ†æœ¨ã®ã†ã¡ã€æ¬¡ã®æ€§è³ªã‚’æŒã¤ã‚‚ã®ã§ã™ã€‚ å·¦ã®æžã«æŽ¥ç¶šã—ã¦ã„ã‚‹å…¨ã¦ã®é ‚ç‚¹ã®è¦ç´ ã¯ã€ã“ã®é ‚ç‚¹ã®è¦ç´ ã‚ˆã‚Šå°ã•ã„ã€‚ å³ã®æžã«æŽ¥ç¶šã—ã¦ã„ã‚‹å…¨ã¦ã®é ‚ç‚¹ã®è¦ç´ ã¯ã€ã“ã®é ‚ç‚¹ã®è¦ç´ ã‚ˆã‚Šå¤§ãã„ã€‚ ã“ã®ã‚ˆã†ãªæ€§è³ªã‚’æŒã£ã¦ã„ã‚‹æœ¨ã«å¯¾ã—ã¦æ–°ãŸãªå€¤ã‚’æ ¼ç´ã™ã‚‹ã“ã¨ã‚’è€ƒãˆã¾ã™ã€‚ å€¤ã‚’æ ¼ç´ã§ãã‚‹å ´æ‰€ã‚’æŽ¢ã™éš›ã€å„é ‚ç‚¹ã®æŒã¤å€¤ã¨æ¯”è¼ƒã—ã¦ã„ãã“ã¨ã§ã€é ‚ç‚¹ã® å³ã®æžã®æ–¹é¢ã‹å·¦ã®æžã®æ–¹é¢ã‹é¸ã¶ã“ã¨ãŒã§ãã¾ã™ã€‚ ã™ã‚‹ã¨ã€æœ¨ã®æ·±ã•åˆ†ã ã‘å€¤ã‚’æ¯”è¼ƒã™ã‚‹ã“ã¨ã§æŒ¿å…¥å¯èƒ½ãªå ´æ‰€ã‚’æœã—å‡ºã›ã‚‹ã“ã¨ ã«ãªã‚Šã¾ã™ã€‚ æœ¨ã®æ·±ã•ã¯ã€
gologo13 2011/04/28
é †åºæœ¨ã€é€£æƒ³é…åˆ—ã®é †åºæœ¨ã«ã‚ˆã‚‹å®Ÿè£…ã€‚åˆã‚ã¦ã“ã‚Œã‚‰ã®å®Ÿè£…ãŒã‚ã‹ã£ãŸ ~/snippets/C++/Tree/ ã‚’å‚ç…§ / ã¦ã‹ã€ã“ã‚Œå¦éƒ¨ï¼’å¹´ã®è¬›ç¾©ã‹ã‚ˆ(^^;

algorithm

dataStructure
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Succinct Data Structures: Cramming 80,000 words into a Javascript file.
I know how to make and sell software online, and I can share my tips with you. Em ail | Twitter | LinkedIn | Comics | All articles Let's continue our short tour of data structures for storing words. Today, we will over-optimize John Resig's Word Game. Along the way, we shall learn about a little-known branch of computer science, called succinct data structures. John wants to load a large dictionary
gologo13 2011/04/14
dataStructure

trie
ãƒªãƒ³ã‚¯
ã‚¢ãƒ¬ã‚´ãƒªã‚ºãƒ ã¨ãƒ‡ãƒ¼ã‚¿æ§‹é€ å…¥é–€ â€” KYOTO-U OPENCOURSEWARE
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gologo13 2011/03/22
sicp

algorithm

dataStructure
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rxã‚’èªã‚“ã ãƒ¡ãƒ¢ - å°„æ’ƒã—ã¤ã¤å‰è»¢ æ”¹
rxã¯Googleæ—¥æœ¬èªžå…¥åŠ›ï¼ˆMozcï¼‰ã§ã‚‚ä½¿ã‚ã‚Œã¦ã„ã‚‹Trieã®ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã§ã‚ã‚‹ã€‚LOUDSã¨ã„ã†ãƒ„ãƒªãƒ¼ã‚’è¡¨ã™ç°¡æ½”ãƒ‡ãƒ¼ã‚¿æ§‹é€ ã‚’ä½¿ã£ã¦Trieã‚’å®Ÿç¾ã—ã¦ã„ã‚‹ã€‚ç¬¬2å›žè‡ªç„¶è¨€èªžå‡¦ç†å‹‰å¼·ä¼š@æ±äº¬ã«è¡Œã£ã¦ãã¦ã€ãã†ã„ãˆã°rxã¯èªã‚“ã§ãªã‹ã£ãŸãªã¨æ€ã£ãŸã®ã§ã€ã¡ã‚‡ã£ã¨ã ã‘èªã‚“ã§ã¿ãŸã€‚ã‚ãã¾ã§ã‚‚ãƒ¡ãƒ¢æ›¸ãã§ã‚ã‚‹ã€‚çµµãŒã‚ã‚‹ã¨100å€ãã‚‰ã„ã‚ã‹ã‚Šã‚„ã™ããªã‚‹æ°—ãŒã™ã‚‹ã‘ã©ãƒ¡ãƒ¢ãªã®ã§çµµã¯æãã¾ã›ã‚“ã€‚ èªã‚“ã ã®ã¯ç¾åœ¨å…¬é–‹ã•ã‚Œã¦ã„ã‚‹æœ€æ–°ç‰ˆã§ã‚ã‚‹rx-1.0rc4ã€‚ rxã¨ã„ã†ã®ã¨rbxã¨ã„ã†2ã¤ã®æ©Ÿèƒ½ãŒã‚ã‚‹ã€‚rxãŒå®Ÿç¾ã—ã¦ã„ã‚‹ã®ã¯ã‚ãã¾ã§ã‚‚trieã§ã€å€¤çš„ãªã‚‚ã®ã¨ã—ã¦ã¯ãƒŽãƒ¼ãƒ‰IDã—ã‹å–ã‚Œãªã„ã€‚rbxã¯ãƒŽãƒ¼ãƒ‰IDã®ã‚ˆã†ãªæ•´æ•°ã‹ã‚‰ã€ä»»æ„ã®é•·ã•ã®ãƒã‚¤ãƒˆåˆ—ã‚’å¼•ã£å¼µã£ã¦ãã‚‹ãŸã‚ã®ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã€‚æ™®é€šã®é…åˆ—ã¯å½“ãŸã‚Šå‰ã ãŒè¦ç´ ãŒå›ºå®šé•·ã§ã‚ã‚‹ã®ã§ã€è¦ç´ ãŒå¯å¤‰é•·ã§ã‚ã‚‹ã¨ã„ã†ã¨ã“ã‚ãŒrbxã®å¤§ããªç‰¹å¾´ã§ã‚ã‚‹ã€‚ rxã§ã®ã‚¨ãƒƒã‚¸ã«å¯¾ã™ã‚‹æ–‡å—å‰²ã‚Šå½“
gologo13 2010/09/27
dataStructure

rx

tx

loud

lib
ãƒªãƒ³ã‚¯
HAMT(Hash Array Mapped Trie) - sileã®ãƒ–ãƒã‚°
ã€ŽIdeal Hash Treesã€*1ã¨ã„ã†è«–æ–‡ã‚’(å¿…è¦ãªã¨ã“ã‚ã ã‘ã€ã ã„ãŸã„)èªã¿çµ‚ã‚ã£ãŸã®ã§ã€ãã®ãƒ¡ãƒ¢ç‰ã€‚ æ¦‚è¦ AMT(Array Mapped Trie)ã¨ã„ã†åŸºç›¤çš„ãªãƒ‡ãƒ¼ã‚¿æ§‹é€ ã‚’ä½¿ã£ã¦ã€ideal(nearly ideal)ãªHash Treesã‚’ä½œã‚ã†ã€ã¨ã„ã†ã‚ˆã†ãªè©±ã€‚ AMTã®å¿œç”¨ä¾‹ã¨ã—ã¦ã€ä»¥ä¸‹ã®ã‚ˆã†ãªã‚‚ã®ãŒèª¬æ˜Žã•ã‚Œã¦ã„ã‚‹ã€‚ Hash Array Mapped Trie(HAMT) ãƒãƒƒã‚·ãƒ¥ãƒžãƒƒãƒ— å„ç¨®æ“ä½œãŒO(1) ãƒãƒƒã‚·ãƒ¥ãƒ†ãƒ¼ãƒ–ãƒ«ã®åˆæœŸã‚µã‚¤ã‚ºã‚’(ã‚ã¾ã‚Š)æ°—ã«ã™ã‚‹å¿…è¦ãŒãªã„ è¦ç´ ãŒå¢—ãˆãŸå ´åˆã®ãƒªã‚µã‚¤ã‚ºã®ã‚³ã‚¹ãƒˆãŒå°ã•ã„*2 ãƒªã‚µã‚¤ã‚ºä¸è¦ãªå®Ÿè£…ã‚‚å¯èƒ½ã ãŒãã®å ´åˆã¯O(log N)ã«ã€‚â€» Nã¯è¦ç´ æ•°ã€‚ä»Šå›žã®å®Ÿè£…ã¯ã“ã£ã¡ã€‚ æˆåŠŸæ¤œç´¢æ™‚ã€ã‚ãƒ¼ã®æ¯”è¼ƒã¯ä¸€å›žã—ã‹ç”Ÿã˜ãªã„ ãŸã ã—ã€ã‚ãƒ¼ã®ãƒãƒƒã‚·ãƒ¥å€¤ã®è¨ˆç®—å‡¦ç†ã¯(ç•°ãªã‚‹ãƒãƒƒã‚·ãƒ¥é–¢æ•°ã§)è¤‡æ•°å›žè¡Œã‚ã‚Œã‚‹ã“ã¨ãŒã‚ã‚‹ã€‚ Clojureã®çµ„ã¿è¾¼ã¿ã®ãƒ
gologo13 2010/09/26
trie

nlp

dataStructure
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