Markov chaining Checkout DataStructures project from SVN - - PowerPoint PPT Presentation

▶
markov chaining
SMART_READER_LITE
LIVE PREVIEW

Markov chaining Checkout DataStructures project from SVN - - PowerPoint PPT Presentation

Oct 24, 2023 395 likes •602 views

Data-structure-palooza Fixed-length queues Markov chaining Checkout DataStructures project from SVN Understanding the engineering trade-offs when storing data Boil down data types (e.g., lists) to their essential operations Choosing a

slide-1
SLIDE 1

Data-structure-palooza Fixed-length queues Markov chaining

Checkout DataStructures project from SVN

slide-2
SLIDE 2
slide-3
SLIDE 3

Understanding the engineering trade-offs when storing data

slide-4
SLIDE 4

 Boil down data types (e.g., lists) to their

essential operations

 Choosing a data structure for a project then

becomes:

  • Identify the operations needed
  • Identify the abstract data type that most efficiently

supports those operations

 Goal: that you understand several basic

abstract data types and when to use them

slide-5
SLIDE 5

 Array List  Linked List  Stack  Queue  Set  Map

Implementations for all of these are provided by the Java Collections Framework in the java.util package.

slide-6
SLIDE 6

Op Operati ations

  • ns

Prov

  • vide

ided Array List Efficie cienc ncy Linke nked d List Efficie cienc ncy Random access O(1) O(n) Add/remove item O(n) O(1)

slide-7
SLIDE 7

 A last-in, first-out (LIFO) data structure  Real-world stacks

  • Plate dispensers in the cafeteria
  • Pancakes!

 Some uses:

  • Tracking paths through a maze
  • Providing “unlimited undo” in an application

Op Operati ations

  • ns

Prov

  • vide

ided Efficie cienc ncy Push item O(1) Pop item O(1)

Implemented by Stack, LinkedList, and ArrayDeque in Java

slide-8
SLIDE 8

 A first-in, first-out (FIFO) data structure  Real-world queues

  • Waiting line at the BMV
  • Character on Star Trek TNG

 Some uses:

  • Scheduling access to shared resource (e.g., printer)

Op Operati ations

  • ns

Prov

  • vide

ided Efficie cienc ncy Enqueue item O(1) Dequeue item O(1)

Implemented by LinkedList and ArrayDeque in Java

slide-9
SLIDE 9

 Unorder

rdered ed collections wi without t duplic icate ates

 Real-world sets

  • Students
  • Collectibles

 Some uses:

  • Quickly checking if an item is in a collection

Op Operati ations

  • ns

HashS hSet et Tr TreeSet Add/remove item O(1) O(lg n) Contains? O(1) O(lg n)

Can hog space Sorts items! Q1

slide-10
SLIDE 10

 Associate keys with va

values es

 Real-world “maps”

  • Dictionary
  • Phone book

 Some uses:

  • Associating student ID with transcript
  • Associating name with high scores

Op Operati ations

  • ns

HashMap hMap Tr TreeMap Insert key-value pair O(1) O(lg n) Look up value for key O(1) O(lg n)

Can hog space Sorts items by key! Q2-4

slide-11
SLIDE 11

Demonstration

slide-12
SLIDE 12

 Curt’s section teams:

  • 11,caijy,filhobc
  • 12,hirtjd,spurrme
  • 13,luok,shanx

 Curt’s section individuals:

  • addantnb, chena1, cornetcl, eckertzs, elswicwj,

hopwoocp, lyonska, nelsonca, taos, wilsonam

Team am URL (Individuals use individual repositories) http://svn.csse.rose-hulman.edu/repos/csse220-201130-markov-teamXX

slide-13
SLIDE 13

 Delvin’s section

teams:

  • 21, solorzaa, whitemrj
  • 22, hazelrtj, tilleraj
  • 23, haydr, lawrener
  • 24, myersem, rybickcb
  • 25, mehrinla, vassardm
  • 26, cooperdl,fengk

 Delvin’s section

Individuals:

  • amesen
  • finnelhn
  • oliverr
  • senatwj
  • zhenw

Team am URL (Individuals use individual repositories) http://svn.csse.rose-hulman.edu/repos/csse220-201130-markov-teamXX

slide-14
SLIDE 14

 Input: a text file

the skunk jumped over the stump the stump jumped over the skunk the skunk said the stump stunk and the stump said the skunk stunk

 Output: a randomly

generated list of words that is “like” the original input in a well-defined way

slide-15
SLIDE 15

 Gather statistics on word patterns by building

an appropriate data structure

 Use the data structure to generate random

text that follows the discovered patterns

slide-16
SLIDE 16

 Input: a text file

the e skunk nk jumped mped ove ver the stump the e stump p jumped mped ove ver the skunk nk th the e skunk nk said th the stu tump mp stu tunk k and d the stump mp said the skunk nk stunk nk Prefix Suffix ffixes NONWORD the the skunk (4), stump (4) skunk jumped, said, stunk, the jumped

  • ver (2)
  • ver

the (2) stump jumped, said, stunk, the said the (2) stunk and, NONWORD and the

slide-17
SLIDE 17

 Input: a text file

the e skunk nk jumped mped ove ver the stump the e stump p jumped mped ove ver the skunk nk th the e skunk nk said th the stu tump mp stu tunk k and d the stump mp said the skunk nk stunk nk Prefi fix Suffixe fixes NW NW the NW the skunk the skunk jumped, said, the, stunk skunk jumped

  • ver

jumped over the

  • ver the

stump, skunk the stump the, jumped, stunk, said …

slide-18
SLIDE 18

 n=1:

the skunk the skunk jumped over the skunk stunk the skunk stunk

 n=2:

the skunk said the stump stunk and the stump jumped over the skunk jumped

  • ver the skunk stunk

 Note: it’s also

possible to hit the max before you hit the last nonword.

slide-19
SLIDE 19

 For the prefixes?  For the set of suffixes?  To relate them?

Prefi fix Suffixe fixes NW NW the NW the skunk the skunk jumped, said, the, stunk skunk jumped

  • ver

jumped over the

  • ver the

stump, skunk the stump the, jumped, stunk, said …

slide-20
SLIDE 20

 FixedLengthQueue: a specialized data

structure, useful for Markov problem

 Implement FLQ in the next 25 minutes or so  When you finish, read the (long) Markov

description and start working on it

 We will only do mi

miles estone

  • ne 1

(so no text justification)

Check out FixedLengthQueue from your Markov team or individual repo