You have collected reviews from four movie reviewers where the reviewers are numbered 0-3. Each reviewer has rated six movies where the movies are numbered 100- 105. The ratings range from 1 (terrible) to 5 (excellent). The reviews are shown in the following table: 100 101 102 103 104 105 3 1 1 1 4 2 1 4 2 4 3 1 4 4 1 5 4 4 2 Write a class that stores this data using a 2D array. Based on this information your class' member predictRating should allow the user to enter ratings for any three movies. The program should then find the reviewer whose ratings most closely match the ratings input by the user. It should then predict the user's interest in the other movies by outputting the ratings by the reviewer for the movies that were not rated by the user. Use the Cartesian distance as the metric to determine how close the reviewer's movie ratings are to the ratings input by the user. This technique is a simple version of the nearest neighbor classification algorithm. For example, if the user inputs a rating of 5 for movie 102, 2 for movie 104, and 5 for movie 105, then the closest match is reviewer 0 with a distance of sqrt ((5-5)^2 + (2- 1) ^2 + (5-5)^2) = 1. The program would then predict a rating of 3 for movie 100, a rating of 1 for movie 101, and a rating of 2 for movie 103. Note: User the STL Array/vector to create a 2D array/vector. To store the data in a 2D array the movie ID’s must be mapped to 0-5. Store -1 to represent unrated movies.
You have collected reviews from four movie reviewers where the reviewers are numbered 0-3. Each reviewer has rated six movies where the movies are numbered 100- 105. The ratings range from 1 (terrible) to 5 (excellent). The reviews are shown in the following table: 100 101 102 103 104 105 3 1 1 1 4 2 1 4 2 4 3 1 4 4 1 5 4 4 2 Write a class that stores this data using a 2D array. Based on this information your class' member predictRating should allow the user to enter ratings for any three movies. The program should then find the reviewer whose ratings most closely match the ratings input by the user. It should then predict the user's interest in the other movies by outputting the ratings by the reviewer for the movies that were not rated by the user. Use the Cartesian distance as the metric to determine how close the reviewer's movie ratings are to the ratings input by the user. This technique is a simple version of the nearest neighbor classification algorithm. For example, if the user inputs a rating of 5 for movie 102, 2 for movie 104, and 5 for movie 105, then the closest match is reviewer 0 with a distance of sqrt ((5-5)^2 + (2- 1) ^2 + (5-5)^2) = 1. The program would then predict a rating of 3 for movie 100, a rating of 1 for movie 101, and a rating of 2 for movie 103. Note: User the STL Array/vector to create a 2D array/vector. To store the data in a 2D array the movie ID’s must be mapped to 0-5. Store -1 to represent unrated movies.
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Transcribed Image Text:You have collected reviews from four movie reviewers where the reviewers are
numbered 0-3. Each reviewer has rated six movies where the movies are numbered 100-
105. The ratings range from 1 (terrible) to 5 (excellent).
The reviews are shown in the following table:
100
101
102
103
104
105
3
1
5
2
1
5
4
2
1
4
4
2
3
1
2
4
4
1
3
5
1
4
2
4
2
Write a class that stores this data using a 2D array. Based on this information your
class' member predictRating should allow the user to enter ratings for any three movies. The program should
then find the reviewer whose ratings most closely match the ratings input by the user. It
should then predict the user's interest in the other movies by outputting the ratings by the
reviewer for the movies that were not rated by the user. Use the Cartesian distance as the
metric to determine how close the reviewer's movie ratings are to the ratings input by the
user. This technique is a simple version of the nearest neighbor classification algorithm.
For example, if the user inputs a rating of 5 for movie 102, 2 for movie 104, and 5 for
movie 105, then the closest match is reviewer 0 with a distance of sqrt((5-5)^2 +
1. The program would then predict a rating of 3 for movie 100, a
(2-
1)^2 + (5-5)^2)
rating of 1 for movie 101, and a rating of 2 for movie 103.
Note: User the STL Array/vector to create a 2D array/vector. To store the data in a 2D array the movie ID's must be mapped to 0-5. Store -1 to
represent unrated movies.
For the toolbar, press ALT+F10 (PC) or ALT+FN+F10 (Mac).
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