代写 game SQL database graph Summary

Summary
How to do this assignment:
• read this specification carefully and completely
• create a directory for this assignment
• unpack the supplied zip file into this directory
• login to grieg and run your PostgreSQL server
• create a new database called a2
• load the schema and populate the data into the database by psql -f a2.db a2
• perform any required updates to the database by psql -f updates.sql a2
• re-acquaint yourself with the database (by browsing the data using psql and/or
examine its schema through the link [Schema] above)
• familiarise (read the code) yourself with the provided sample PHP code file called pg
• make sure pg (by chmod u+x pg) and run it
• complete the tasks using pg as a reference example, you may also want to edit the
supplied template files a2.php and updates.sql
• submit all these files (the Required Files) via WebCMS3 as described above
Details
they do not have to be under your /srvr directory. You can also edit the PHP and SQL files on hosts other than grieg. The only time that you need to use grieg is to manipulate your database or to run your PHP scripts.
Introduction
A successful movie (or 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) 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 PHP 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 let you feel the kind of data that you are dealing with, a (unordered) glimpse of the dataset is included below:
of the above steps are given below. Note that you can put the files wherever you like;
a2=> select * from movie limit 10;
id | title | year | content_rating |
100 | English | USA | 66941559 | 85000000 |
2174
duration | lang | country | gross |
budget | director_id
——+—————————-+——+—————-+—
——-+————+———————-+———-+——–
–+————-
407 | 102 Dalmatians | 2000 | G |

3699 | 10 Cloverfield Lane
104 | English | USA
1327
3016 | 10 Days in a Madhouse
111 | English | USA
1881
| 2016 | PG-13 |
| 71897215 | 15000000 |
| 2015 |R|
| 14616 | 12000000 |
2846 | 10 Things I Hate About You | 1999 | PG-13 |
97 | English | USA
1458
3421 | 10th & Wolf
| 38176108 | 16000000 |
| 2006 |R|
| 53481 | 8000000 |
| 2003 |R|
| | 6000000 |
| 2010 |R|
| 18329466 | 18000000 |
107 | English
2252
3645 | 11:14
85 | English
665
| USA
| USA
2430 | 127 Hours
94 | English | USA
57
4823 | 12 Angry Men
96 | English | USA
1172
1737 | 12 Monkeys
42 | English | USA
| 1957 | Not Rated
| 350000 |
2282 | 12 Rounds
108 | English | USA | 12232937 | 22000000 |
1479
(10 rows)
a2=> 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_score
———-+————————+———————-+—-
————-+———————-+————————–
-+————
407 | 26413 |
4.8
3699 |
126893 |
7.3
3016 | 314 |
7.5
2846 |
222099 |
7.2
3421 | 5557 |
6.4
84| 372 |
411 | 33000 |
1| 26000 |
133 | 10000 |
77 | 4182 |
440 | 14504 |
10 | 2059 |
549 | 37907 |
34 | 2512 |
|
|
|
|
| TV-14
| ||
| 2009 | PG-13
|
294 |
26|

3645 | 38273 |
7.2
2430 |
279179 |
7.6
4823 |
447785 |
68 | 0 |
450 | 63000 |
177 | 40000 |
133 | 2200 |
440 | 11984 |
888 | 1433 |
56 | 3309 |
8.9 20839 |
13 |
1737 |
12000 |
7.6
2282 | 113 | 22823 | 0 |
5.6
(10 rows)
a2=> select * from director limit 10;
id | name | facebook_likes
——+——————+—————- 1139 | Brad Bird | 663 741 | Don Hall | 38 1295 | Rich Moore | 66 2132 | Dean DeBlois | 255 1228 | Jonathan Mostow | 84 516 | James Gunn | 571
113 | 2799 |
203 | Hideaki Anno | 28 2312 | David Fincher | 21000 1753 | Matthew Vaughn | 905 1317 | Francis Lawrence | 508
(10 rows)
a2=> select * from actor limit 10;
id | name | facebook_likes
——+———————-+—————-
408 | Christian Bale | 23000
1430 | Donna Murphy | 553
66 | Robert Downey Jr. |
502 | Daniel Radcliffe |
2011 | Lauren Cohan |
945 | Marlon Brando |
1245 | Ruth Wilson |
21000
11000
4000
10000
2000
1590 | Christopher Meloni | 3000 3674 | Pierfrancesco Favino | 216 2270 | Adam Brown | 972

3699 | kidnapping
3699 | minimal cast (10 rows)
The sample PHP program
By following the steps above, you can prepare the setup of your assignment 2 and experience the provided sample program as follows:
-bash-4.1$ dropdb a2
-bash-4.1$ createdb a2
-bash-4.1$ psql -f a2.db a2
SET
SET
…
ALTER TABLE
ALTER TABLE
-bash-4.1$ psql -f updates.sql a2
-bash-4.1$ ./pg
Usage: ./pg Year
-bash-4.1$ ./pg 1988
1. Beetlejuice (PG, English, 92)
2. Big (PG, English, 130)
3. Crocodile Dundee II (PG, English, 108)
4. My Stepmother Is an Alien (PG-13, English, 105) 5. Poltergeist III (PG-13, English, 98)
6. Scrooged (PG-13, English, 101)
7. Twins (PG, English, 107)
-bash-4.1$
-bash-4.1$ ./pg 1989
1. Back to the Future Part II (PG, English, 108) 2. Batman (PG-13, English, 126)
3. Bill & Ted’s Excellent Adventure (PG, English, 90) 4. Dead Poets Society (PG, English, 128)
5. Driving Miss Daisy (PG, English, 99)
6. Henry V (PG-13, English, 137)
7. Indiana Jones and the Last Crusade (PG-13, English, 127)
8. Licence to Kill (PG-13, English, 133)
9. New York Stories (PG, English, 124)
10. Star Trek V: The Final Frontier (PG, English, 107)

11. The Abyss (PG-13, English, 171)
12. Troop Beverly Hills (PG, English, 105) 13. UHF (PG-13, English, 150)
14. We’re No Angels (PG-13, English, 106)
Submission and Testing
We will test your submission on Grieg as follows:
• create a testing subdirectory
• untar the a2.tgz file into that directory
• load your submitted a2.php and updates.sql files over the top of the standard ones
• create a new database a2 and initialise it with a2.db (For testing purposes, the
dataset may be modified and slightly different from the dataset in your a2.db)
• run the command: psql a2 -f updates.sql (using your updates.sql)
• run a series of tests using your
submitted acting, title, toprank, similar, shortest and degrees scripts
• manually inspect your submitted PHP code and SQL code
Your submitted code must be complete so that when we do the above, your PHP 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). If your code does not work when installed for testing on Grieg, as described above (for example,
your updates.sql did not contain all of the required definitions, or you did not submit all of the required PHP files, etc), you will be penalised by a 50% administrative penalty.
Some things that you can do wrong:
• make changes to files other than a2.php, updates.sql and the corresponding PHP commandline files (such as toprank)
• forget to include all of your PHP functions in a2.php or other PHP files
• forget to include all of your SQL and PLpgSQL definitions in updates.sql
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 Grieg.
Tasks
For each task, you are required to implement an executable PHP 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 to test your programs. In particular, as shown in the code of pg, if any field of your output is empty, you should hide that field and its related formatting text (for example, in pg, a comma and a space will not be printed for duration if it is empty). Of course, you are free to print debugging information temporarily (e.g. to add debugging print_r() calls), but please remember to eventually disable or remove them when your solution is submitted for marking. For all the tasks below, unless it is specified explicitly, we assume movies including any titles stored in the movie table (i.e., including movies and TV shows etc).
In implementing your functions, you are free to partition the functionality however you like between the database and the PHP scripts. In the past, some students have solved similar

assignments to this by writing just about everything in SQL views and PLpgSQL functions and using PHP simply as a vehicle for collecting the results. Others have done most of the computational work in PHP. Do whatever you feel most comfortable with, but make sure that you limit your changes just to the files that you are required to submit, and do not create additional tables.
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 (for example, the order of the fields inside the brackets of the pg output matters).
Task A: The list of movies acted by a given actor (1 marks)
The acting script lists the movie title, its director, its year, its content rating and IMDB score, one per line, of all the movies acted by the given actor. The output is sorted by year and then by movie title, both in ascending order. If any movies have empty year, output them at the end and sort them by movie title in ascending order. It accepts one command-line argument: an actor name (has to exactly match a name stored in the database and the matching is case insensitive). It has the following output format:
grieg$ ./acting “james franco”
1. Whatever It Takes — David Raynr (2000, PG-13, 5.5) 2. City by the Sea — Michael Caton-Jones (2002, R, 6.2) 3. Deuces Wild — Scott Kalvert (2002, R, 5.6)
4. Spider-Man — Sam Raimi (2002, PG-13, 7.3)
5. Spider-Man 2 — Sam Raimi (2004, PG-13, 7.3)
6. The Great Raid — John Dahl (2005, R, 6.7)
7. Flyboys — Tony Bill (2006, PG-13, 6.6)
8. The Dead Girl — Karen Moncrieff (2006, R, 6.7)
9. In the Valley of Elah — Paul Haggis (2007, R, 7.2)
10. Spider-Man 3 — Sam Raimi (2007, PG-13, 6.2)
11. Milk — Gus Van Sant (2008, R, 7.6)
12. Pineapple Express — David Gordon Green (2008, R, 7.0) 13. 127 Hours — Danny Boyle (2010, R, 7.6)
14. Date Night — Shawn Levy (2010, PG-13, 6.3)
15. Eat Pray Love — Ryan Murphy (2010, PG-13, 5.7)
16. Rise of the Planet of the Apes — Rupert Wyatt (2011, PG- 13, 7.6)
17. Your Highness — David Gordon Green (2011, R, 5.6)
18. Spring Breakers — Harmony Korine (2012, R, 5.3)
19. The Iceman — Ariel Vromen (2012, R, 6.9)
20. Homefront — Gary Fleder (2013, R, 6.5)
21. Oz the Great and Powerful — Sam Raimi (2013, PG, 6.4)

22. Palo Alto — Gia Coppola (2013, R, 6.3)
23. This Is the End — Evan Goldberg (2013, R, 6.7) 24. The Interview — Evan Goldberg (2014, R, 6.6)
25. The Little Prince — Mark Osborne (2015, PG, 7.8) 26. Sausage Party — Greg Tiernan (2016, R, 7.5)
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. We can then use the line count to measure the size of an output. For example:
grieg$ ./acting “james franco”|wc -l
26
grieg$ ./acting “john smith” |wc -l 0
Task B: List movie information by its title substring (1 marks)
The title script lists the movie title, year, content rating, IMDB score and genres of those movies with the title matching the given substring (case insensitive), one per line. 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 (ascending), then by IMDB rating (descending) and finally by title (ascending). It has the following output format:
grieg$ ./title “star war”
1. Star Wars: Episode IV – A New Hope (1977, PG, 8.7) [Action,Adventure,Fantasy,Sci-Fi]
2. Star Wars: Episode V – The Empire Strikes Back (1980, PG, 8.8) [Action,Adventure,Fantasy,Sci-Fi]
3. Star Wars: Episode VI – Return of the Jedi (1983, PG, 8.4)
[Action,Adventure,Fantasy,Sci-Fi]
4. Star Wars: Episode I – The Phantom Menace (1999, PG, 6.5) [Action,Adventure,Fantasy,Sci-Fi]
5. Star Wars: Episode II – Attack of the Clones (2002, PG, 6.7)
[Action,Adventure,Fantasy,Sci-Fi]
6. Star Wars: Episode III – Revenge of the Sith (2005, PG-13,
7.6) [Action,Adventure,Fantasy,Sci-Fi]
7. Star Wars: The Clone Wars (TV-PG, 7.9)
[Action,Adventure,Animation,Drama,Fantasy,Sci-Fi]
grieg$ ./title “happy”
1. Happy Gilmore (1996, PG-13, 7.0) [Comedy,Sport]
2. Happy, Texas (1999, PG-13, 6.3) [Comedy,Crime,Romance]

3. The Pursuit of Happyness (2006, PG-13, 8.0)
[Drama,Biography]
4. Happy Feet (2006, PG, 6.5)
[Family,Music,Romance,Animation,Comedy]
5. Another Happy Day (2011, R, 6.0) [Comedy,Drama]
6. Happy Feet 2 (2011, PG, 5.9)
[Animation,Comedy,Family,Musical]
7. Happy Christmas (2014, R, 5.6) [Comedy,Drama] 8. Happy Valley (TV-MA, 8.5) [Drama,Crime]
grieg$ ./title “mars”
1. Invaders from Mars (1986, PG, 5.5) [Horror,Sci-Fi]
2. Mars Attacks! (1996, PG-13, 6.3) [Action,Comedy,Sci-Fi]
3. Mission to Mars (2000, PG, 5.6) [Adventure,Sci-Fi,Thriller]
4. Ghosts of Mars (2001, R, 4.9) [Action,Horror,Sci-Fi]
5. We Are Marshall (2006, PG, 7.1) [Drama,Sport]
6. Forgetting Sarah Marshall (2008, R, 7.2)
[Comedy,Drama,Romance]
7. The 41-Year-Old Virgin Who Knocked Up Sarah Marshall and Felt Superbad About It (2010, R, 2.7) [Comedy]
8. Mars Needs Moms (2011, PG, 5.4)
[Action,Adventure,Animation,Comedy,Family,Sci-Fi]
9. The Last Days on Mars (2013, R, 5.5) [Horror,Sci- Fi,Thriller]
10. Veronica Mars (TV-14, 8.4) [Crime,Drama,Mystery]
Task C: Top ranked movies (2 marks)
The toprank script takes in 3 or 4 commandline arguments:
./toprank K StartYear EndYear
or:
where Genres is a list of genres separated by ‘&’, K is the top K movies ranked by IMDB score and then by the number of votes (both in descending order) between (and including) StartYear and EndYear, with 1 <= K <= 1000, 1900 < StartYear <= EndYear < 2020 and your program will not be tested with a list of more than 8 genres. We interpret '&' as conjunction, i.e., the selected movies shall contain all the specified genres. When Genres is not provided (when your program takes in 3 arguments), perform the same ranking but on movies with any genres. Do not include any movie titles with empty year. For example: ./toprank Genres K StartYear EndYear ./toprank "Action&Sci-Fi&Adventure" 10 2005 2005 1. Serenity (2005, PG-13, English) [8.0, 242599] 2. Star Wars: Episode III - Revenge of the Sith (2005, PG-13, English) [7.6, 520104] 3. The Island (2005, PG-13, English) [6.9, 263329] 4. Zathura: A Space Adventure (2005, PG, English) [6.1, 67707] 5. Doom (2005, R, English) [5.2, 88146] 6. Stealth (2005, PG-13, English) [5.0, 45455] 7. A Sound of Thunder (2005, PG-13, English) [4.2, 16474] 8. The Helix... Loaded (2005, R, English) [1.9, 534] ./toprank "Sci-Fi&Adventure&Action" 20 1920 2020 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. Star Wars: Episode IV - A New Hope (1977, PG, English) [8.7, 911097] 4. Star Wars: Episode VI - Return of the Jedi (1983, PG, English) [8.4, 681857] 5. Aliens (1986, R, English) [8.4, 488537] 6. Captain America: Civil War (2016, PG-13, English) [8.2, 272670] 7. Godzilla Resurgence (2016, Japanese) [8.2, 374] 8. The Avengers (2012, PG-13, English) [8.1, 995415] 9. Guardians of the Galaxy (2014, PG-13, English) [8.1, 682155] 10. Mad Max: Fury Road (2015, R, English) [8.1, 552503] 11. Deadpool (2016, R, English) [8.1, 479047] 12. Destiny (2014, English) [8.1, 3089] 13. X-Men: Days of Future Past (2014, PG-13, English) [8.0, 514125] 14. Star Trek (2009, PG-13, English) [8.0, 504419] 15. Serenity (2005, PG-13, English) [8.0, 242599] 16. The Iron Giant (1999, PG, English) [8.0, 128455] 17. Avatar (2009, PG-13, English) [7.9, 886204] 18. Iron Man (2008, PG-13, English) [7.9, 696338] 19. Edge of Tomorrow (2014, PG-13, English) [7.9, 431620] 20. Big Hero 6 (2014, PG, English) [7.9, 279093] ./toprank 20 1920 2020 1. The Shawshank Redemption (1994, R, English) [9.3, 1689764] 2. The Godfather (1972, R, English) [9.2, 1155770] 3. The Dark Knight (2008, PG-13, English) [9.0, 1676169] 4. The Godfather: Part II (1974, R, English) [9.0, 790926] 5. Pulp Fiction (1994, R, English) [8.9, 1324680] 6. The Lord of the Rings: The Return of the King (2003, PG-13, English) [8.9, 1215718] 7. Schindler's List (1993, R, English) [8.9, 865020] 8. The Good, the Bad and the Ugly (1966, Approved, Italian) [8.9, 503509] 9. 12 Angry Men (1957, Not Rated, English) [8.9, 447785] 10. Inception (2010, PG-13, English) [8.8, 1468200] 11. Fight Club (1999, R, English) [8.8, 1347461] 12. Forrest Gump (1994, PG-13, English) [8.8, 1251222] 13. The Lord of the Rings: The Fellowship of the Ring (2001, PG-13, English) [8.8, 1238746] 14. Star Wars: Episode V - The Empire Strikes Back (1980, PG, English) [8.8, 837759] 15. The Matrix (1999, R, English) [8.7, 1217752] 16. The Lord of the Rings: The Two Towers (2002, PG-13, English) [8.7, 1100446] 17. Star Wars: Episode IV - A New Hope (1977, PG, English) [8.7, 911097] 18. Goodfellas (1990, R, English) [8.7, 728685] 19. One Flew Over the Cuckoo's Nest (1975, R, English) [8.7, 680041] 20. City of God (2002, R, Portuguese) [8.7, 533200] Task D: Similar movies (3 marks) Suppose you are asked to implement a movie recommendation feature for IMDB. Based on the movie that a user is currently browsing, a list of similar movies will be presented. In Task D, the similar script takes in 2 arguments: a case-insensitive movie full title (this shall produce a single match. If not, just use the one with the most recent year as if it is the only match); and a number N (1 <= N <= 1000). It will then find its similar N movies (excluding itself) that share the maximum number of common genres (output nothing if no other movies have any common genres with the given movie). For those with the same number of common genres, they will then be ranked by the maximum number (including zero number) of common plot keywords. If both are the same, then they will be ranked by their IMDB scores and then by the number of votes (the higher the better). ./similar "Happy Feet" 30 1. Hairspray (2007) [4, 2, 6.7, 98693] 2. Confessions of a Teenage Drama Queen (2004) [4, 1, 4.6, 23408] 3. Aladdin (1992) [4, 0, 8.0, 260939] 4. The Book of Life (2014) [4, 0, 7.3, 45580] 5. Shrek 2 (2004) [4, 0, 7.2, 314630] 6. Enchanted (2007) [4, 0, 7.1, 142496] 7. A Monster in Paris (2011) [4, 0, 6.8, 15790] 8. Sinbad: Legend of the Seven Seas (2003) [4, 0, 6.7, 36144] 9. Bandslam (2009) [4, 0, 6.4, 11958] 10. Freaky Friday (2003) [4, 0, 6.1, 96693] 11. Rugrats in Paris: The Movie (2000) [4, 0, 6.1, 8146] 12. The Lizzie McGuire Movie (2003) [4, 0, 5.3, 27580] 13. Alpha and Omega (2010) [4, 0, 5.3, 10986] 14. Roadside Romeo (2008) [4, 0, 5.3, 922] 15. High School Musical (2006) [4, 0, 5.2, 59254] 16. Alvin and the Chipmunks (2007) [4, 0, 5.2, 57276] 17. High School Musical 2 (2007) [4, 0, 4.8, 39786] 18. High School Musical 3: Senior Year (2008) [4, 0, 4.5, 43795] 19. Alvin and the Chipmunks: The Squeakquel (2009) [4, 0, 4.5, 31649] 20. Alvin and the Chipmunks: Chipwrecked (2011) [4, 0, 4.4, 22838] 21. Hannah Montana: The Movie (2009) [4, 0, 4.2, 31760] 22. Doug's 1st Movie (1999) [3, 2, 5.0, 2448] 23. The Iron Giant (1999) [3, 1, 8.0, 128455] 24. Arthur [3, 1, 7.4, 8495] 25. Big (1988) [3, 1, 7.3, 153468] 26. Antz (1998) [3, 1, 6.6, 124641] 27. Music and Lyrics (2007) [3, 1, 6.5, 81334] 28. Recess: School's Out (2001) [3, 1, 6.5, 6808] 29. Chicken Little (2005) [3, 1, 5.8, 63912] 30. A Charlie Brown Christmas (1965) [3, 0, 8.4, 21826] ./similar "The Shawshank Redemption" 30 1. American History X (1998) [2, 1, 8.6, 782437] 2. Escape from Alcatraz (1979) [2, 1, 7.6, 87090] 3. Bronson (2008) [2, 1, 7.1, 84817] 4. Faster (2010) [2, 1, 6.5, 80574] 5. Hurricane Streets (1997) [2, 1, 6.5, 1038] 6. Undisputed (2002) [2, 1, 6.1, 21542] 7. Prison (1987) [2, 1, 5.9, 2705] 8. Civil Brand (2002) [2, 1, 5.3, 553] 9. American Heist (2014) [2, 1, 5.2, 12372] 10. The Godfather (1972) [2, 0, 9.2, 1155770] 11. The Godfather: Part II (1974) [2, 0, 9.0, 790926] 12. Fargo [2, 0, 9.0, 170055] 13. Pulp Fiction (1994) [2, 0, 8.9, 1324680] 14. 12 Angry Men (1957) [2, 0, 8.9, 447785] 15. City of God (2002) [2, 0, 8.7, 533200] 16. Gomorrah [2, 0, 8.7, 9638] 17. The Silence of the Lambs (1991) [2, 0, 8.6, 887467] 18. The Usual Suspects (1995) [2, 0, 8.6, 740918] 19. Happy Valley [2, 0, 8.5, 12848] 20. Reservoir Dogs (1992) [2, 0, 8.4, 664719] 21. To Kill a Mockingbird (1962) [2, 0, 8.4, 215086] 22. Veronica Mars [2, 0, 8.4, 55524] 23. Taxi Driver (1976) [2, 0, 8.3, 507063] 24. The Sting (1973) [2, 0, 8.3, 175607] 25. On the Waterfront (1954) [2, 0, 8.2, 100890] 26. Spotlight (2015) [2, 0, 8.1, 195333] 27. Butch Cassidy and the Sundance Kid (1969) [2, 0, 8.1, 152089] 28. Elite Squad (2007) [2, 0, 8.1, 81644] 29. Lilyhammer [2, 0, 8.1, 19164] 30. Animal Kingdom [2, 0, 8.1, 3673] Task E: Six degrees of Kevin Bacon (4 marks) Tasks E & F are inspired by 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. The output will be a list of actors, the movies and the years. If there are more than one the same shortest paths, output all of them (all output lines are sorted alphabetically in ascending order). Reference(Wikipedia): Six Degrees of Kevin Bacon ./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 ./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 ./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 Jr. 2. Tom Cruise was in Days of Thunder (1990) with Robert Duvall; Robert Duvall was in The Judge (2014) with Robert Downey Jr. 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) with Robert Downey Jr. 6. Tom Cruise was in Mission: Impossible III (2006) with Eddie Marsan; Eddie Marsan was in Sherlock Holmes (2009) with Robert Downey Jr. 7. Tom Cruise was in Mission: Impossible III (2006) with Eddie Marsan; Eddie Marsan was in Sherlock Holmes: A Game of Shadows (2011) with Robert Downey Jr. 8. Tom Cruise was in The Firm (1993) with Holly Hunter; Holly Hunter was in Home for the Holidays (1995) with Robert Downey Jr. ./shortest2 "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 Will Smith 3. Brad Pitt was in True Romance (1993) with Michael Rapaport; Michael Rapaport was in Hitch (2005) with Will Smith Task F: Actors with N degrees of separation (4 marks) The degrees takes in an actor name (case insensitive) and M & N degrees of separation (where 1 <= M <= N <= 6). It outputs all actors (no directors) that are exactly M to N degrees of separation (which represents the shortest path) from the given actor. Output the list of actors with the degree of separation indicated in brackets, sorted by the degree of separation and then by name, both in ascending order. For example: ./degrees "chris evans" 1 2 1. Andre Braugher (1) 2. Ari Graynor (1) 3. Benedict Wong (1) 4. Dane Cook (1) 5. Dominic Cooper (1) 6. Ed Begley Jr. (1) 7. Ewen Bremner (1) 8. Hayley Atwell (1) 9. Ioan Gruffudd (1) 10. James Franco (1) 11. Jason Patric (1) 12. Jason Statham (1) 13. Kang-ho Song (1) 14. Keanu Reeves (1) 15. Mako (1) 16. Mia Kirshner (1) 17. Noel Gugliemi (1) 18. Robert Davi (1) 19. Robert Downey Jr. (1) 20. Sarah Michelle Gellar (1) 21. scar Jaenada (1) 22. Scarlett Johansson (1) 23. Aaron Yoo (2) 24. Adam Alexi-Malle (2) 25. Adam Arkin (2) 26. Adam Baldwin (2) 27. Ah-sung Ko (2) ... 365. Vin Diesel (2) 366. Wendell Pierce (2) 367. William Atherton (2) 368. William Smith (2) 369. Woo-sung Jung (2) 370. Zach Gilford (2) 371. Zach McGowan (2) 372. Zach Woods (2) 373. Zooey Deschanel (2) ./degrees "chris evans" 2 2 1. Aaron Yoo (2) 2. Adam Alexi-Malle (2) 3. Adam Arkin (2) 4. Adam Baldwin (2) 5. Ah-sung Ko (2) 6. Aidan Quinn (2) ... 345. William Atherton (2) 346. William Smith (2) 347. Woo-sung Jung (2) 348. Zach Gilford (2) 349. Zach McGowan (2) 350. Zach Woods (2) 351. Zooey Deschanel (2) ./degrees "tom cruise" 1 1 1. Andrew Garfield (1) 2. Beth Grant (1) 3. Bill Cobbs (1) 4. Bonnie Hunt (1) 5. Brad Pitt (1) 6. Chad Lindberg (1) 7. David Oyelowo (1) 8. Debi Mazar (1) 9. Demi Moore (1) 10. Dougray Scott (1) ... 45. Vanessa Redgrave (1) 46. Vinessa Shaw (1) 47. Wilford Brimley (1) 48. William Smith (1) 49. Zo Bell (1) ./degrees "tom cruise" 1 2 | wc -l 670

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