代写COMP3311程序、代做Python程序设计

代写COMP3311程序、代做Python程序设计

COMP3311 21T1 - Assignment 2

SQL, Python, SQLite

Database Systems

Last updated: Monday 29th Mar 05:47pm (most recent updates are in [..])

Due : Friday 16th April 17:00

Aims

This assignment aims to Give you practice in

use of SQL in SQLite (i.e., sqlite3)

writing scripts in Python that interact with a database

populating a RDBMS with a larger dataset, and analysing the data; making test data for testing database applications;

Your task is to complete the functionality of some command-line tools via a combination of database code and Python code.

Summary

Submission: Login to Course Web Site > Assignments > Assignment 2 > Assignment 2 Specification > Make Submission > upload required files > [Submit]

Required Files (total 3 files): msearch, toprank, shortest

You may also submit the assignment via the give command from CSE machines: give cs3311 a2 msearch toprank shortest

Deadline: Friday 16 April 2021 @ 17:00

Late Penalty: Late submissions will have marks deducted from the maximum achievable mark at the rate of 2% of the total mark per hour that they are late (i.e.,

48% per day).

This assignment contributes 20 marks toward your total mark for this course.

Downloads: a2.tgz, a2.zip

Note that a2.tgz and a2.zip contain the same material.

Each archive contains the IMDB database dump a2.db, plus a sample Python code file called sample.

What To Do Now:

read this specification Carefully and completely

login to a CSE linux machine

create a directory for this assignment

unpack the supplied zip file into this directory

get familiar with the schema and data by exploring and querying the provided database using the command: sqlite3 a2.db

familiarise (read the code) yourself with the provided sample Python code file called sample

try out the sample code by running: ./sample YEAR where YEAR is a number representing a year, e.g., 1989 make sure sample is executable (by chmod u+x sample) and run it

complete the assignment tasks using sample as a reference template

submit all these files (the 'Required Files') via WebCMS3 or give as described above

Details of the above steps are further elaborated below. You can edit and run the Python files on any CSE machines.

Introduction

A successful movie (including TV show) not only entertains audience, but also enables film companies to gain tremendous profit. A lot of factors (such as good

directors, Experienced actors, etc) are important for creating good movies. Nevertheless, famous directors and actors usually bring an attractive box-office income,

but they do not necessarily guarantee a highly rated imdb score. This assignment is based on an IMDB dataset to build several small Python commands to show

interesting results.

The dataset itself contains around 5000 movies, spanning across 100 years in 66 countries. There are more than 2000 movie directors, and thousands of

actors/actresses. It also contains the IMDB rating score, numbers of votes and various facebook likes. To give you a feel for the kind of data that you are dealing with,

a (unordered) glimpse of the dataset is included below:

cs3311@wagner:~/sqlite/a2$ sqlite3 a2.db

SQLite version 3.27.2 2019-02-25 16:06:06

Enter ".help" for usage hints.

sqlite>

sqlite> select * from movie limit 10;

1|Avatar|2009|PG-13|178|English|USA|760505847|237000000|735

2|Pirates of the Caribbean: At World's End|2007|PG-13|169|English|USA|309404152|300000000|555

3|Spectre|2015|PG-13|148|English|UK|200074175|245000000|1776

4|The Dark Knight Rises|2012|PG-13|164|English|USA|448130642|250000000|1078

6|John Carter|2012|PG-13|132|English|USA|73058679|263700000|2217

7|Spider-Man 3|2007|PG-13|156|English|USA|336530303|258000000|625

8|Tangled|2010|PG|100|English|USA|200807262|260000000|922

9|Avengers: Age of Ultron|2015|PG-13|141|English|USA|458991599|250000000|1410

10|Harry Potter And the Half-Blood Prince|2009|PG|153|English|UK|301956980|250000000|1117

11|Batman v Superman: Dawn of Justice|2016|PG-13|183|English|USA|330249062|250000000|2180

sqlite>

sqlite> .headers on

sqlite>

sqlite> select * from movie limit 10;

id|title|year|content_rating|duration|lang|country|gross|budget|director_id

1|Avatar|2009|PG-13|178|English|USA|760505847|237000000|735

2|Pirates of the Caribbean: At World's End|2007|PG-13|169|English|USA|309404152|300000000|555

3|Spectre|2015|PG-13|148|English|UK|200074175|245000000|1776

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4|The Dark Knight Rises|2012|PG-13|164|English|USA|448130642|250000000|1078

6|John Carter|2012|PG-13|132|English|USA|73058679|263700000|2217

7|Spider-Man 3|2007|PG-13|156|English|USA|336530303|258000000|625

8|Tangled|2010|PG|100|English|USA|200807262|260000000|922

9|Avengers: Age of Ultron|2015|PG-13|141|English|USA|458991599|250000000|1410

10|Harry Potter and the Half-Blood Prince|2009|PG|153|English|UK|301956980|250000000|1117

11|Batman v Superman: Dawn of Justice|2016|PG-13|183|English|USA|330249062|250000000|2180

sqlite>

sqlite> select * from Rating limit 10;

movie_id|num_critic_for_reviews|num_user_for_reviews|num_voted_users|movie_facebook_likes|cast_total_facebook_likes|imdb_

1|723|3054|886204|33000|4834|7.9

2|302|1238|471220|0|48350|7.1

3|602|994|275868|85000|11700|6.8

4|813|2701|1144337|164000|106759|8.5

6|462|738|212204|24000|1873|6.6

7|392|1902|383056|0|46055|6.2

8|324|387|294810|29000|2036|7.8

9|635|1117|462669|118000|92000|7.5

10|375|973|321795|10000|58753|7.5

11|673|3018|371639|197000|24450|6.9

sqlite>

sqlite> .mode column

sqlite>

sqlite> select * from director limit 10;

id name facebook_likes

---------- ---------- --------------

1 JK Youn 2

2 David S. W 42

3 James Fraw 21

4 Kar-Wai Wo 0

5 Brian Tren 53

6 Perry Lang 17

7 Jeff Burr 155

8 Al Sillima 0

9 Morten Tyl 77

10 Hue Rhodes 0

sqlite>

sqlite> select * from actor limit 10;

id name facebook_likes

---------- ----------------- --------------

1 Maureen McCormick 458

2 Andrew Fiscella 137000

3 Brittany Daniel 861

4 Michael Smiley 177

5 Najarra Townsend 538

6 Gustaf Skarsgrd 908

7 Laila Haley 1000

8 Isaac C. Singleto 312

9 Veronica Ferres 30000

10 Chris 'Wonder' Sc 0

sqlite>

sqlite> select * from acting limit 10;

movie_id actor_id

---------- ----------

407 2024

3699 1841

3016 11

2846 195

3421 738

3645 1186

2430 211

4823 1299

1737 786

2282 866

sqlite>

sqlite> select * from genre limit 10;

movie_id genre

---------- ----------

407 Adventure

407 Comedy

407 Family

3699 Drama

3699 Horror

3699 Mystery

3699 Sci-Fi

3699 Thriller

3016 Drama

2846 Comedy

sqlite>

sqlite> select * from keyword limit 10;

movie_id keyword

---------- ----------

407 dog

407 parole

407 parole off

407 prison

2021/4/9 COMP3311 21T1 - Assignment 2

https://www.cse.unsw.edu.au/~cs3311/21T1/assignments/a2/index.html 3/7

407 puppy

3699 alien

3699 bunker

3699 car crash

3699 kidnapping

3699 minimal ca

sqlite>

sqlite> .width 10 30

sqlite>

sqlite> select * from keyword limit 10;

movie_id keyword

---------- ------------------------------

407 dog

407 parole

407 parole officer

407 prison

407 puppy

3699 alien

3699 bunker

3699 car crash

3699 kidnapping

3699 minimal cast

sqlite>

You may also explore the schema and any table information using the sqlite system catalog table called sqlite_master, or the sqlite PRAGMA functions (please see

the sqlite documentation for details), as shown in the examples below:

cs3311@wagner:~/sqlite/a2$ sqlite3 a2.db

SQLite version 3.27.2 2019-02-25 16:06:06

Enter ".help" for Usage hints.

sqlite> .tables

acting actor director genre keyword movie rating

sqlite> pragma table_info(movie);

0|id|integer|1||1

1|title|character varying(256)|1||0

2|year|integer|0||0

3|content_rating|character varying(9)|0||0

4|duration|integer|0||0

5|lang|character varying(10)|0||0

6|country|character varying(20)|0||0

7|gross|bigint|0||0

8|budget|bigint|0||0

9|director_id|integer|0||0

sqlite>

sqlite> select * from sqlite_master where name="movie";

table|movie|movie|6|CREATE TABLE movie (

id integer NOT NULL,

title character varying(256) NOT NULL,

year integer,

content_rating character varying(9),

duration integer,

lang character varying(10),

country character varying(20),

gross bigint,

budget bigint,

director_id integer,

PRIMARY KEY (id),

FOREIGN KEY (director_id) REFERENCES director(id)

)

sqlite>

The Sample Python program

By following the steps above, you can experience the provided sample program and study its code as follows:

cs3311@wagner:~/sqlite/a2$ ./sample

Usage: ./sample YEAR

cs3311@wagner:~/sqlite/a2$ ./sample 1989

A Nightmare on Elm Street 5: The Dream Child (1989)

Back to the Future Part II (1989)

Batman (1989)

Bill & Ted's Excellent Adventure (1989)

Black Rain (1989)

Born on the Fourth of July (1989)

Dead Poets Society (1989)

Do the Right Thing (1989)

Driving Miss Daisy (1989)

Friday the 13th Part VIII: Jason Takes Manhattan (1989)

Glory (1989)

Halloween 5 (1989)

Henry V (1989)

Indiana Jones and the Last Crusade (1989)

Licence to Kill (1989)

Major League (1989)

New York Stories (1989)

Pet Sematary (1989)

2021/4/9 COMP3311 21T1 - Assignment 2

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Road House (1989)

Roger & Me (1989)

Sea of Love (1989)

Sex, Lies, and Videotape (1989)

Star Trek V: The Final Frontier (1989)

Tango & Cash (1989)

The Abyss (1989)

The Blood of Heroes (1989)

The Toxic Avenger Part II (1989)

Troop Beverly Hills (1989)

UHF (1989)

Warlock (1989)

We're No Angels (1989)

When Harry Met Sally... (1989)

cs3311@wagner:~/sqlite/a2$ ls

a2.db sample

cs3311@wagner:~/sqlite/a2$

cs3311@wagner:~/sqlite/a2$ cat sample

#!/usr/bin/python3

"""

This is a sample minimal program for COMP3311 21T1 Assignment 2 to illustrate how to write an

executable python code, take in a command line argument, and connect to a sqlite3 db.

For simplicity (easier to read and follow), it does not include error checking,

exception Handling and comments.

"""

import sqlite3,sys

if len(sys.argv) != 2 :

print("Usage:",sys.argv[0],"YEAR")

sys.exit(1)

year = sys.argv[1]

con = sqlite3.connect('a2.db')

cur = con.cursor()

cur.execute('SELECT title, year FROM movie WHERE year ={} ORDER BY title'.format(year))

while True:

t = cur.fetchone()

if t == None:

break

x,y = t

print('{} ({})'.format(x,y))

con.close()

cs3311@wagner:~/sqlite/a2$

Submission and Testing

We will auto-mark your submission on wagner as follows:

create a testing subdirectory

place a modified a2.db file (with the same schema but probably slightly modified data) into that directory

place your submitted files in that directory and make them executable

run a series of tests using your submitted msearch, toprank and shortest scripts

manually inspect your submitted Python code (if time allows)

Time Requirement: in order to avoid indefinitely running scripts that block the marking process, any scripts that run for more than 2 minutes for a particular task on

wagner will be terminated and considered failed for that test.

Your submitted code must be complete so that when we do the above, your Python will work just as it did in your assignment directory and with a database with the

identical Schema (with either the same or slightly modified dataset) to yours (a2.db). For submission simplicity, please only submit the 3 required Python files. In

other words, do NOT break a Python program into multiple Python files (so each submitted file should be a self-contained Python program). If your code does not

work when installed for testing on wagner, as described above (for example, it works on your home machine perfectly, but we need to edit one line in order to have

it running properly on a CSE machine; OR it does not pass the auto-marking tests due to extra debugging messages, etc), you will be penalised by an additional 30%

administrative penalty. A testing script for formatting sanity check (based on the examples below) will be available in Week 8.

Before you submit, you should test out whether the files you submit will work by following a similar sequence of steps to those noted above on wagner

(wagner.cse.unsw.edu.au).

Tasks

For each task, you are required to implement an executable Python program command that takes in commandline arguments and displays the result in a specified

format (which we will use for auto-marking). We assume the sample output format for each task below to test your programs. If any field of your output is empty, you

should hide that field and its related formatting text (for example, in ./msearch for "star war"below, a comma and a space will not be printed for year if it is

empty). Of course, you are free to print debugging information temporarily, but please remember to eventually disable or remove them when your solution is

2021/4/9 COMP3311 21T1 - Assignment 2

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submitted for marking. For all the tasks below, unless it is specified explicitly, we assume 'movies' will include any titles stored in the movie table (i.e., including movies and TV shows etc).

For each task below, output nothing if there is no result returned from the database. If the task does not specify particular output order, your program can output in

any row order. If multiple columns are involved in your output, your output should follow the same column order as the sample output presented in each task below.

Task A: List movie information by its title, actor or director substrings (6 marks)

The msearch (movie search) script lists the movie title, year, content rating, IMDB score and genres of those movies with the title, actor, or director matching the

given substring (case insensitive), one per line. It accepts any number of given substrings (but at least one). If more than one substring are provided, they should be

considered as a conjunctive selection query (i.e., via AND). For example, consider

msearch SUBSTRING-1 SUBSTRING-2 SUBSTRING-3 ...

A movie will be in its output if each of SUBSTRING-1, SUBSTRING-2, SUBSTRING-3 ... appears in its title, actor or director.

The output of Multiple genres of a movie should be concatenated in one line delimited by a comma and sorted alphabetically in ascending order, as shown by an

example below. The rows are ordered by year (in descending order, rows with empty year at the end), then by IMDB rating (descending, rows with empty rating at the

end if any) and finally by title (ascending). The required output format is elaborated further by the examples below:

wagner$ ./msearch "james fRAnco"

1. Sausage Party (2016, R, 7.5) [Adventure,Animation,Comedy,Fantasy]

2. The Little Prince (2015, PG, 7.8) [Adventure,Animation,Drama,Family,Fantasy]

3. The Interview (2014, R, 6.6) [Comedy]

4. This Is the End (2013, R, 6.7) [Comedy,Fantasy]

5. Homefront (2013, R, 6.5) [Action,Crime,Drama,Thriller]

6. Oz the Great and Powerful (2013, PG, 6.4) [Adventure,Family,Fantasy]

7. Palo Alto (2013, R, 6.3) [Drama]

8. The Iceman (2012, R, 6.9) [Biography,Crime,Drama]

9. Spring Breakers (2012, R, 5.3) [Crime,Drama]

10. Rise of the Planet of the Apes (2011, PG-13, 7.6) [Action,Drama,Sci-Fi,Thriller]

11. Your Highness (2011, R, 5.6) [Adventure,Comedy,Fantasy]

12. 127 Hours (2010, R, 7.6) [Adventure,Biography,Drama,Thriller]

13. Date Night (2010, PG-13, 6.3) [Comedy,Crime,Romance,Thriller]

14. Eat Pray Love (2010, PG-13, 5.7) [Drama,Romance]

15. Milk (2008, R, 7.6) [Biography,Drama,History]

16. Pineapple Express (2008, R, 7.0) [Action,Comedy,Crime]

17. In the Valley of Elah (2007, R, 7.2) [Crime,Drama,Mystery,Thriller]

18. Spider-Man 3 (2007, PG-13, 6.2) [Action,Adventure,Romance]

19. The Dead Girl (2006, R, 6.7) [Crime,Drama,Mystery]

20. Flyboys (2006, PG-13, 6.6) [Action,Adventure,Drama,History,Romance,War]

21. The Great Raid (2005, R, 6.7) [Action,Drama,War]

22. Spider-Man 2 (2004, PG-13, 7.3) [Action,Adventure,Fantasy,Romance]

23. Spider-Man (2002, PG-13, 7.3) [Action,Adventure,Fantasy,Romance]

24. City by the Sea (2002, R, 6.2) [Crime,Drama,Mystery,Thriller]

25. Deuces Wild (2002, R, 5.6) [Action,Crime,Drama]

26. Whatever It Takes (2000, PG-13, 5.5) [Comedy,Drama,Romance]

wagner$ ./msearch "JOHN smith"

wagner$ ./msearch "star WAR"

1. Star Wars: Episode III - Revenge of the Sith (2005, PG-13, 7.6) [Action,Adventure,Fantasy,Sci-Fi]

2. Star Wars: Episode II - Attack of the Clones (2002, PG, 6.7) [Action,Adventure,Fantasy,Sci-Fi]

3. Star Wars: Episode I - The Phantom Menace (1999, PG, 6.5) [Action,Adventure,Fantasy,Sci-Fi]

4. Star Wars: Episode VI - Return of the Jedi (1983, PG, 8.4) [Action,Adventure,Fantasy,Sci-Fi]

5. Star Wars: Episode V - The Empire Strikes Back (1980, PG, 8.8) [Action,Adventure,Fantasy,Sci-Fi]

6. Star Wars: Episode IV - A New Hope (1977, PG, 8.7) [Action,Adventure,Fantasy,Sci-Fi]

7. Star Wars: The Clone Wars (TV-PG, 7.9) [Action,Adventure,Animation,Drama,Fantasy,Sci-Fi]

wagner$

Please note the output formatting of the last movie above (No. 7), i.e., it does not have year. For all the tasks in this assignment, as mentioned previously, your

program shall output nothing if there are no results. If there are N results, it shall output exactly N lines of output without any extra lines or messages. Therefore, we may also use the line count to measure the size of an output to determine its preliminary correctness. For example:

wagner$ ./msearch "james franco"|wc -l

26

wagner$ ./msearch "john smith" |wc -l

0

wagner$

More examples:

wagner$ ./msearch "happy"

1. Happy Christmas (2014, R, 5.6) [Comedy,Drama]

2. Another Happy Day (2011, R, 6.0) [Comedy,Drama]

3. Happy Feet 2 (2011, PG, 5.9) [Animation,Comedy,Family,Musical]

4. The Pursuit of Happyness (2006, PG-13, 8.0) [Biography,Drama]

5. Happy Feet (2006, PG, 6.5) [Animation,Comedy,Family,Music,Romance]

6. Happy, Texas (1999, PG-13, 6.3) [Comedy,Crime,Romance]

7. Happy Gilmore (1996, PG-13, 7.0) [Comedy,Sport]

8. Happy Valley (TV-MA, 8.5) [Crime,Drama]

wagner$ ./msearch "ALBert"

1. The Secret Life of Pets (2016, PG, 6.8) [Animation,Comedy,Family]

2. The Big Short (2015, R, 7.8) [Biography,Comedy,Drama,History]

3. Concussion (2015, PG-13, 7.1) [Biography,Drama,Sport]

4. A Most Violent Year (2014, R, 7.0) [Action,Crime,Drama,Thriller]

5. Baggage Claim (2013, PG-13, 5.0) [Comedy]

6. Skyfall (2012, PG-13, 7.8) [Action,Adventure,Thriller]

7. Drive (2011, R, 7.8) [Crime,Drama]

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8. Albert Nobbs (2011, R, 6.7) [Drama]

9. The Book of Eli (2010, R, 6.9) [Action,Adventure,Drama,Thriller]

10. Shine a Light (2008, PG-13, 7.2) [Biography,Documentary,Music]

11. The Bourne Ultimatum (2007, PG-13, 8.1) [Action,Mystery,Thriller]

12. The Simpsons Movie (2007, PG-13, 7.4) [Adventure,Animation,Comedy]

13. Once in a Lifetime: The Extraordinary Story of the New York Cosmos (2006, PG-13, 7.3) [Documentary,Sport]

14. A Good Year (2006, PG-13, 6.9) [Comedy,Drama,Romance]

15. Before Sunset (2004, R, 8.0) [Drama,Romance]

16. A Very Long Engagement (2004, R, 7.7) [Drama,Mystery,Romance,War]

17. Fat Albert (2004, PG, 4.3) [Comedy,Family,Fantasy,Romance]

18. Big Fish (2003, PG-13, 8.0) [Adventure,Drama,Fantasy]

19. Irreversible (2002, Not Rated, 7.4) [Crime,Drama,Mystery,Thriller]

20. From Hell (2001, R, 6.8) [Horror,Mystery,Thriller]

21. Erin Brockovich (2000, R, 7.3) [Biography,Drama]

22. Ready to Rumble (2000, PG-13, 5.3) [Comedy,Sport]

23. The Muse (1999, PG-13, 5.6) [Comedy]

24. Breakfast of Champions (1999, R, 4.6) [Comedy]

25. Out of Sight (1998, R, 7.0) [Crime,Drama,Romance,Thriller]

26. 54 (1998, R, 5.8) [Drama,Music]

27. Critical Care (1997, R, 6.0) [Comedy,Drama]

28. The Brothers McMullen (1995, R, 6.6) [Comedy,Drama,Romance]

29. Menace II Society (1993, R, 7.5) [Crime,Drama,Thriller]

30. Twilight Zone: The Movie (1983, PG, 6.5) [Fantasy,Horror,Sci-Fi]

31. Dragonslayer (1981, PG, 6.7) [Action,Adventure,Fantasy]

32. Private Benjamin (1980, R, 6.1) [Comedy,War]

33. Taxi Driver (1976, R, 8.3) [Crime,Drama]

34. Willy Wonka & the Chocolate Factory (1971, G, 7.8) [Family,Fantasy,Musical]

35. The Party's Over (1965, 7.3) [Drama]

36. Tom Jones (1963, Unrated, 6.8) [Adventure,Comedy,History]

37. The Longest Day (1962, G, 7.8) [Action,Drama,History,War]

wagner$ ./msearch "albert" "david"

1. A Most Violent Year (2014, R, 7.0) [Action,Crime,Drama,Thriller]

2. Baggage Claim (2013, PG-13, 5.0) [Comedy]

3. The Simpsons Movie (2007, PG-13, 7.4) [Adventure,Animation,Comedy]

wagner$ ./msearch "albert" "david" "simpson"

1. The Simpsons Movie (2007, PG-13, 7.4) [Adventure,Animation,Comedy]

wagner$ ./msearch "tom hanks" "spielberg" "Ryan"

1. Bridge of Spies (2015, PG-13, 7.6) [Drama,History,Thriller]

2. Saving Private Ryan (1998, R, 8.6) [Action,Drama,War]

wagner$ ./msearch "star war" "Natalie PORTMAN" "Lucas"

1. Star Wars: Episode III - Revenge of the Sith (2005, PG-13, 7.6) [Action,Adventure,Fantasy,Sci-Fi]

2. Star Wars: Episode II - Attack of the Clones (2002, PG, 6.7) [Action,Adventure,Fantasy,Sci-Fi]

3. Star Wars: Episode I - The Phantom Menace (1999, PG, 6.5) [Action,Adventure,Fantasy,Sci-Fi]

Wagner$ ./msearch "Star war" "Natalie PORTMAN" "Lucas" "revenge"

1. Star Wars: Episode III - Revenge of the Sith (2005, PG-13, 7.6) [Action,Adventure,Fantasy,Sci-Fi]

wagner$

Task B: Top ranked movies (6 marks)

The toprank script ranks movies based on their IMDB rating. It considers movies with given genres and the minimum acceptable IMDB score to be listed in the

output. It takes in 2 commandline arguments:

./toprank Genres MinRating

where Genres is a list of genres (based on case insensitive matching) separated by '&', MinRating is the minimum acceptable IMDB score. The output is ranked by

IMDB score and then by the number of votes (both in descending order). We interpret '&' as conjunction, i.e., the selected movies shall contain all the specified

genres. When Genres is an empty string, perform the same ranking but on movies with any genres. The required output format is elaborated further by the examples

below:

wagner$ ./toprank "Action&Sci-Fi&Adventure"

Usage: ./toprank Genres MinRating

wagner$ ./toprank "Action&Sci-Fi&Adventure" 8.5

1. Inception (2010, PG-13, English) [8.8, 1468200]

2. Star Wars: Episode V - The Empire Strikes Back (1980, PG, English) [8.8, 837759]

3. Daredevil (TV-MA, English) [8.8, 213483]

4. Star Wars: Episode IV - A New Hope (1977, PG, English) [8.7, 911097]

wagner$ ./toprank "Sci-Fi&Adventure&Action" 8

1. Inception (2010, PG-13, English) [8.8, 1468200]

2. Star Wars: Episode V - The Empire Strikes Back (1980, PG, English) [8.8, 837759]

3. Daredevil (TV-MA, English) [8.8, 213483]

4. Star Wars: Episode IV - A New Hope (1977, PG, English) [8.7, 911097]

5. Star Wars: Episode VI - Return of the Jedi (1983, PG, English) [8.4, 681857]

6. Aliens (1986, R, English) [8.4, 488537]

7. Stargate SG-1 (TV-14, English) [8.4, 63982]

8. Captain America: Civil War (2016, PG-13, English) [8.2, 272670]

9. Godzilla Resurgence (2016, Japanese) [8.2, 374]

10. The Avengers (2012, PG-13, English) [8.1, 995415]

11. Guardians of the Galaxy (2014, PG-13, English) [8.1, 682155]

12. Mad Max: Fury Road (2015, R, English) [8.1, 552503]

13. Deadpool (2016, R, English) [8.1, 479047]

14. Destiny (2014, English) [8.1, 3089]

15. X-Men: Days of Future Past (2014, PG-13, English) [8.0, 514125]

16. Star Trek (2009, PG-13, English) [8.0, 504419]

17. Serenity (2005, PG-13, English) [8.0, 242599]

18. The Iron Giant (1999, PG, English) [8.0, 128455]

2021/4/9 COMP3311 21T1 - Assignment 2

https://www.cse.unsw.edu.au/~cs3311/21T1/assignments/a2/index.html 7/7

wagner$ ./toprank "Sci-Fi&Adventure&Action" 8.175

1. Inception (2010, PG-13, English) [8.8, 1468200]

2. Star Wars: Episode V - The Empire Strikes Back (1980, PG, English) [8.8, 837759]

3. Daredevil (TV-MA, English) [8.8, 213483]

4. Star Wars: Episode IV - A New Hope (1977, PG, English) [8.7, 911097]

5. Star Wars: Episode VI - Return of the Jedi (1983, PG, English) [8.4, 681857]

6. Aliens (1986, R, English) [8.4, 488537]

7. Stargate SG-1 (TV-14, English) [8.4, 63982]

8. Captain America: Civil War (2016, PG-13, English) [8.2, 272670]

9. Godzilla Resurgence (2016, Japanese) [8.2, 374]

wagner$ ./toprank "" 8.8

1. Towering Inferno (English) [9.5, 10]

2. The Shawshank Redemption (1994, R, English) [9.3, 1689764]

3. The Godfather (1972, R, English) [9.2, 1155770]

4. Dekalog (TV-MA, Polish) [9.1, 12590]

5. The Dark Knight (2008, PG-13, English) [9.0, 1676169]

6. The Godfather: Part II (1974, R, English) [9.0, 790926]

7. Fargo (TV-MA, English) [9.0, 170055]

8. Pulp Fiction (1994, R, English) [8.9, 1324680]

9. The Lord of the Rings: The Return of the King (2003, PG-13, English) [8.9, 1215718]

10. Schindler's List (1993, R, English) [8.9, 865020]

11. The Good, the Bad and the Ugly (1966, Approved, Italian) [8.9, 503509]

12. 12 Angry Men (1957, Not Rated, English) [8.9, 447785]

13. Inception (2010, PG-13, English) [8.8, 1468200]

14. Fight Club (1999, R, English) [8.8, 1347461]

15. Forrest Gump (1994, PG-13, English) [8.8, 1251222]

16. The Lord of the Rings: The Fellowship of the Ring (2001, PG-13, English) [8.8, 1238746]

17. Star Wars: Episode V - The Empire Strikes Back (1980, PG, English) [8.8, 837759]

18. Daredevil (TV-MA, English) [8.8, 213483]

19. It's Always Sunny in Philadelphia (TV-MA, English) [8.8, 133415]

wagner$

Task C: Six degrees of Kevin Bacon (8 marks)

This task is inspired by the Six degrees of Kevin Bacon. The shortest script takes in two actor names (with case insensitive matching), and lists the shortest path

(up to Six Degrees of Separation) between two given actors. In other words, if two actors are not connected within six degrees, you can assume that they are not

connected at all (i.e. output nothing). The output will be a list of actors, the movies and the years, as illustrated in the examples below. If there are more than one

'shortest paths', output all of them (all output lines are sorted ASCII-betically in ascending order). Reference (Wikipedia): Six Degrees of Kevin Bacon

wagner$ ./shortest "tom cruise" "Jeremy Renner"

1. Tom Cruise was in Mission: Impossible - Ghost Protocol (2011) with Jeremy Renner

2. Tom Cruise was in Mission: Impossible - Rogue Nation (2015) with Jeremy Renner

wagner$ ./shortest "chris evans" "Scarlett Johansson"

1. Chris Evans was in Captain America: Civil War (2016) with Scarlett Johansson

2. Chris Evans was in Captain America: The Winter Soldier (2014) with Scarlett Johansson

wagner$ ./shortest "tom cruise" "Robert Downey Jr."

1. Tom Cruise was in Days of Thunder (1990) with Robert Duvall; Robert Duvall was in Lucky You (2007) with Robert Downey

2. Tom Cruise was in Days of Thunder (1990) with Robert Duvall; Robert Duvall was in The Judge (2014) with Robert Downey

3. Tom Cruise was in Jack Reacher (2012) with Robert Duvall; Robert Duvall was in Lucky You (2007) with Robert Downey Jr.

4. Tom Cruise was in Jack Reacher (2012) with Robert Duvall; Robert Duvall was in The Judge (2014) with Robert Downey Jr.

5. Tom Cruise was in Mission: Impossible (1996) with Kristin Scott Thomas; Kristin Scott Thomas was in Richard III (1995)

6. Tom Cruise was in Mission: Impossible III (2006) with Eddie Marsan; Eddie Marsan was in Sherlock Holmes (2009) with Ro

7. Tom Cruise was in Mission: Impossible III (2006) with Eddie Marsan; Eddie Marsan was in Sherlock Holmes: A Game of Sha

8. Tom Cruise was in The Firm (1993) with Holly Hunter; Holly Hunter was in Home for the Holidays (1995) with Robert Down

wagner$ ./shortest "brad pitt" "will smith"

1. Brad Pitt was in Burn After Reading (2008) with Kevin Sussman; Kevin Sussman was in Hitch (2005) with Will Smith

2. Brad Pitt was in Ocean's Thirteen (2007) with Matt Damon; Matt Damon was in The Legend of Bagger Vance (2000) with Wil

3. Brad Pitt was in True Romance (1993) with Michael Rapaport; Michael Rapaport was in Hitch (2005) with Will Smith

wagner$ ./shortest "chris evans" "bill clinton"

1. Chris Evans was in Captain America: Civil War (2016) with Robert Downey Jr.; Robert Downey Jr. was in Kiss Kiss Bang B

2. Chris Evans was in Captain America: Civil War (2016) with Robert Downey Jr.; Robert Downey Jr. was in Kiss Kiss Bang B

3. Chris Evans was in Captain America: Civil War (2016) with Robert Downey Jr.; Robert Downey Jr. was in Lucky You (2007)

4. Chris Evans was in Captain America: Civil War (2016) with Robert Downey Jr.; Robert Downey Jr. was in Lucky You (2007)

5. Chris Evans was in Captain America: Civil War (2016) with Robert Downey Jr.; Robert Downey Jr. was in The Judge (2014)

6. Chris Evans was in Captain America: Civil War (2016) with Robert Downey Jr.; Robert Downey Jr. was in The Judge (2014)

7. Chris Evans was in Captain America: Civil War (2016) with Robert Downey Jr.; Robert Downey Jr. was in The Soloist (200

8. Chris Evans was in Captain America: Civil War (2016) with Robert Downey Jr.; Robert Downey Jr. was in The Soloist (200

...

45. Chris Evans was in The Iceman (2012) with James Franco; James Franco was in City by the Sea (2002) with Robert De Nir

46. Chris Evans was in The Iceman (2012) with James Franco; James Franco was in City by the Sea (2002) with Robert De Nir

47. Chris Evans was in The Iceman (2012) with James Franco; James Franco was in In the Valley of Elah (2007) with Charliz

48. Chris Evans was in The Iceman (2012) with James Franco; James Franco was in In the Valley of Elah (2007) with Charliz

49. Chris Evans was in The Losers (2010) with Jason Patric; Jason Patric was in The Lost Boys (1987) with Dianne Wiest; D

50. Chris Evans was in The Losers (2010) with Jason Patric; Jason Patric was in The Lost Boys (1987) with Dianne Wiest; D

 

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