Data Structure & Algorithms Implementation

TZ boy

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Salaam kwa wote,
napenda kuuliza kama kuna mtu kwa hapa TANZANIA yuko interest na Data Structure & Algorithms Implementation in any language tuweze kushilikiana na kupeana mawazo njia na material mbalimbali maana ikiwa lengo ni kwenda mbele na kujua mengi kusoma peke yako haya mambo sometimes unaweza tumia energy kubwa bure, wakati umoja ni nguvu. nimefatilia sana ka hapa bongo sijapata mtu ambaye yuko interest na mambo haya kama uko interest hata PM nicheck please...
nawakilisha!.
 
LIFO, FIFO. ILA BADO SIJAJUA REal application ya aligarithm and data structure. Nahic ATM
 
Jitu jeusi said:
LIFO, FIFO. ILA BADO SIJAJUA REal application ya aligarithm and data structure. Nahic ATM
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Kama huna idea mkuu hua ni vizuri kutojibu maana unaweza potosha watu humu wakaenda kukomaa na vitu ambavyo si sahihi. Japo umebahatisha kugusia stack kidogo.
 
NullPointer said:
Kama huna idea mkuu hua ni vizuri kutojibu maana unaweza potosha watu humu wakaenda kukomaa na vitu ambavyo si sahihi. Japo umebahatisha kugusia stack kidogo.
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mkuu samahani kidogo unaweza nipa notes za ukweli nipate maarifa tosha
 
big gift said:
mkuu samahani kidogo unaweza nipa notes za ukweli nipate maarifa tosha
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Notes za Data Structures na Algorithms?
Mimi sijasomea sehemu moja, nimesoma sources nyingi tofauti tofauti nyingine hata sikumbuki, kwa kuanzia lakini ni vizuri ukapitia hiki kitabu cha Data Structures kinapatikana kwenye hii link bure kabisa Data Structures and Algorithm Analysis hicho ndicho nakumbuka nilitumia chuoni, chagua kama ni Java au C++ latest version komaa nacho, nimepitia naona hakuna kitu cha kuruka pale, soma chote tu coz kila point ni ya muhimu, sehemu nyingine unaweza usizielewe kama hauko vizuri kihesabu lakini zisikukatishe tamaa, unavozidi kwenda utazidi kuzielewa mbele kwa mbele. Ila kuanzia I.3 - Analysis of algorithms, hakikisha pale huvuki kama hujaelewa concept.

Ukielewa hicho kitabu vizuri nina uhakika utakua unaweza kupewa tatizo ukafikiria datastructure ipi inafaa kulisolve efficiently. Ukimaliza kipo kingine kwa ajili ya Algorithms kitabu famous sana "Introduction to Algorithms by Thomas H.Cormen" chenyewe ni advanced kwa hiyo hakikisha cha kwanza kimekaa kichwani vizuri la si hivyo hiki cha pili unaweza usipate kitu. Mambo yakikusumbua unaona huelewi waweza ingia Youtube ukaandika kilichokushinda nina uhakika utakutana na video ya mtu anaelekeza au ukipigwa chenga waweza uliza humu tutakujibu.

Mwisho nakushauri ukisoma kitu fulani hakikisha unacode kwenye language unayotumia, run it hakikisha kweli its operational sio kuangalia code alafu ukasema umeelewa ukaendelea mbele, unless una photographic memory na very high iq.
 
Data structures and Algorithms za level ipi? undergrad, postgrad?
 
htuc said:
Data structures and Algorithms za level ipi? undergrad, postgrad?
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Data structures na algorithms zina level?
Heap ni heap tu hata mtu awe na masters au PhD ataisoma heap ileile tu, Hashtable ni ileile doesn't mean mtu wa masters anaandika hash function iliyotoka mars. Same goes for algorithms, kama ni binary search itarun vilevile kwa log(n) hata ikiandikwa na mtu wa PhD.

Hakuna undergraduate wala Postgraduate when it comes to this. Wote wanasoma yaleyale tu, Masters kama ya computer science wakiwa wanafanya research, watatumia hizihizi fundamental structures kwenye kusolve their problems, hakuna nyingine zilizofichwa unless atengeneze a new data structure au a come up with a new algorithm yeye mwenyewe.
 

nashukuri sana kamanda.
 
Sorry Unaweza nielezea in simple way the concept of BIG O NOTATION i find hard tu understand na inanipa wakati mgumu kuendelea na binary Search problems!!
 
TZ boy said:
Sorry Unaweza nielezea in simple way the concept of BIG O NOTATION i find hard tu understand na inanipa wakati mgumu kuendelea na binary Search problems!!
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Big O kwenye ku~measure algorithm complexity in very simple terms ni upper limit ya function. Unapokua na function jaribu kuifikiria what happens ikiwa ina~approach infinity. Nikupe mfano

f(n) = n^3 + 2n + 1 ikiwa inaelekea infinity hizi namba ambazo zina power ndogo e.g 2n zitakua irrelevant, n^3 itakua imetawala, kwa hiyo hapo tunasema hiyo function ni O(n^3).

f(n) = n*(n-1) = n^2 - n, moja kwa moja unaona hapa namba zikiwa kubwa n itakua irrelevant, imagine hata 10Million, ukiisquare 10M ukatoa 10million inakua irrelevant kwa kua ni 10M square ni kubwa sana. Hapo tunasema hiyo function running time ni Big-O(n^2)

Sasa Big O iko vipi ukilinganisha na Theta au Omega, Kuna algorithms ambazo zinakua na runtime tofauti kwenye cases tofauti, nikupe mfano Quicksort algorithm, kwenye worst case pale ambapo list yako inakua sorted in reverse, ukirun itatumia O(n^2) lakini in best case inakua Big-Omega(nlogn) na Big-O(nlogn) kwa hiyo tunasema running time yake ni Theta(nlogn). Ila usichanganye kua moja inatumika kwenye best case na nyingine kwenye worst case, hii hua ni misconception kwa watu wengi, kinachomatter ni kuangalia bounds tu za function. Mfano wa Binary search, yenyewe kwa input size yoyote ile inarun kwa log(n) kwa hiyo both Big-Omega na Big-O ni log(n) na ndio maana tunasema ni Theta(log(n))

Kwa kifupi Big-Omega ni kinyume cha Big-O, wakati Big-O imefocus kwenye upper bound, Big-Omega imefocus kwenye lower bound ya function. Big-O na Big-Omega zikiwa sawa tunatumia Theta
 
TZ boy said:
Sorry Unaweza nielezea in simple way the concept of BIG O NOTATION i find hard tu understand na inanipa wakati mgumu kuendelea na binary Search problems!!
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Mkuu nadhani Graph kaelezea vizuri sana hapo juu, unless kama hujamuelewa naweza elezea zaidi.
 
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