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Generiranje algoritmov za štetje k-gramov
ID Novosel, Rok (Author), ID Mihelič, Jurij (Mentor) More about this mentor... This link opens in a new window

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PID: 20.500.12556/rul/a71bed3b-777e-472d-8d23-f77a3f474982

Abstract
Generativna algoritmika je proces zasnove in implementacije generiranih algoritmov. Spada v večje področje inženiringa algoritmov, ki se ukvarja z eksperimentalno evaluacijo in pohitritvijo algoritmov. Na začetku diplomske naloge opišemo oba področja in prikažemo osnove generativne algoritmike na primeru računanja predpon. Za glavni problem, ki ga bomo rešili z generativno algoritmiko smo si izbrali štetje k-gramov. Na začetku implementiramo splošni algoritem za štetje k-gramov, s katerim bomo primerjali generirane algoritme. Sledila je zasnova in implementacija osnovnega in izboljšanega generiranega algoritma. V zadnjem delu smo opravili eksperimentalno evaluacijo vseh algoritmov in preverili do katere meje so učinkoviti generirani algoritmi. V zaključku je diskusija rezultatov, možne izboljšave in možnosti za nadaljno raziskovanje.

Language:Slovenian
Keywords:algoritmi, generiranje algoritmov, k-grami, računanje predpon, končni avtomati stanj
Work type:Bachelor thesis/paper
Organization:FRI - Faculty of Computer and Information Science
Year:2016
PID:20.500.12556/RUL-84738 This link opens in a new window
Publication date in RUL:01.09.2016
Views:1248
Downloads:283
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Secondary language

Language:English
Title:Generating algorithms for counting k-grams
Abstract:
Generative algorithmics is a process of designing and implementing generated algorithms. It belongs in a larger area of algorithm engineering, which deals with experimental evaluation and speedup of algorithms. At the beginning of the thesis we describe both areas and show the basics of generative algorithmics solving parallel prefix scan. For our main problem we choose counting k-grams. First, we implement a general algorithm for counting k-grams against which we will compare the generated algorithms. This was followed by the design and implementation of basic and enhanced generated algorithm. At the end we conducted an experimental evaluation of all the algorithms and verify the effectiveness of generated algorithms. In the last section we discussed the results, potential improvements and opportunities for further exploration.

Keywords:algorithm, generating algorithms, k-grams, prefix scan, finite state automatons

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