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This approach involves using a straightforward SQL query to filter movies based on conditions specified in the question. We will look for movies with an ID that is odd and where the description is not 'boring'. Once filtered, results will be sorted according to the ratings in descending order.
The time complexity of this approach is O(n log n) due to sorting of the movies after filtering, and space complexity is O(1) as we do not use extra space apart from input size.
1from sqlalchemy import create_engine, Table, Column, Integer, String, Float, MetaData
2from sqlalchemy.orm import sessionmaker
3
4# Create an in-memory SQLite database
5engine = create_engine('sqlite:///:memory:', echo=False)
6metadata = MetaData(bind=engine)
7
8# Define the Cinema table
9cinema_table = Table('Cinema', metadata,
10 Column('id', Integer, primary_key=True),
11 Column('movie', String),
12 Column('description', String),
13 Column('rating', Float))
14
15# Create the table
16metadata.create_all()
17
18# Insert example data
19engine.execute(cinema_table.insert(), [
20 {'id': 1, 'movie': 'War', 'description': 'great 3D', 'rating': 8.9},
21 {'id': 2, 'movie': 'Science', 'description': 'fiction', 'rating': 8.5},
22 {'id': 3, 'movie': 'irish', 'description': 'boring', 'rating': 6.2},
23 {'id': 4, 'movie': 'Ice song', 'description': 'Fantasy', 'rating': 8.6},
24 {'id': 5, 'movie': 'House card', 'description': 'Interesting', 'rating': 9.1}
25])
26
27# Query for not boring movies with odd IDs
28Session = sessionmaker(bind=engine)
29session = Session()
30
31query = session.query(cinema_table).filter(
32 sql.text('id % 2 != 0 AND description != "boring"')
33).order_by(sql.text('rating DESC'))
34
35results = query.all()
36for row in results:
37 print(f'id: {row.id}, movie: {row.movie}, description: {row.description}, rating: {row.rating}')We use SQLAlchemy to define a table 'Cinema' and insert the sample data. We query this table using conditions specified: id as odd and a non-'boring' description, finally ordering by ratings in descending order. The results are printed one by one.
This approach directly uses SQL logic to filter out the records based on conditions specified in the question: selecting only those entries with an odd-numbered ID and a non-boring description, followed by sorting the result set based on ratings in descending order.
The time complexity of this approach is O(n log n) due to sorting, and the space complexity is O(n) because we store the list of filtered movies.
1using System;
2using System.Linq;
3using System.Collections.Generic;
4
5public class Movie {
6 public int Id { get; set; }
7 public string Name { get; set; }
8 public string Description { get; set; }
public float Rating { get; set; }
}
public class Program {
public static void Main() {
var movies = new List<Movie> {
new Movie { Id = 1, Name = "War", Description = "great 3D", Rating = 8.9f },
new Movie { Id = 2, Name = "Science", Description = "fiction", Rating = 8.5f },
new Movie { Id = 3, Name = "irish", Description = "boring", Rating = 6.2f },
new Movie { Id = 4, Name = "Ice song", Description = "Fantasy", Rating = 8.6f },
new Movie { Id = 5, Name = "House card", Description = "Interesting", Rating = 9.1f }
};
var result = movies
.Where(m => m.Id % 2 != 0 && m.Description != "boring")
.OrderByDescending(m => m.Rating);
foreach (var movie in result) {
Console.WriteLine($"Id: {movie.Id}, Movie: {movie.Name}, Description: {movie.Description}, Rating: {movie.Rating}");
}
}
}This approach involves breaking down the problem into smaller sub-problems, solving each one individually, and then combining their solutions to solve the original problem. This is a powerful technique used in algorithms like Merge Sort and Quick Sort. It allows us to efficiently tackle the problem by utilizing recursive problem-solving.
Time Complexity: O(n log n)
Space Complexity: O(n)
1#include <stdio.h>
2
3void merge
Dynamic programming is a technique used for solving complex problems by breaking them down into simpler subproblems. Tabulation involves solving the subproblems and storing their results in a table (usually an array) to avoid redundant calculations. This approach is efficient for problems where overlapping subproblems and optimal substructure properties exist.
Time Complexity: O(n)
Space Complexity: O(n)
1function fibonacci(n) {
2 let fib
We begin by defining a Movie class and a list of Movie objects, after which we filter through this list in a LINQ query to fetch movies with odd-numbered IDs while excluding descriptions marked 'boring'. Finally, results are ordered by rating before being printed.
This is a Merge Sort implementation in C, which follows the Divide and Conquer technique. The array is recursively split into halves until each sub-array has one element. Then, the sub-arrays are merged back together in a sorted manner. This algorithm is efficient for large datasets.
This JavaScript function calculates the nth Fibonacci number using a bottom-up dynamic programming approach. It uses an array to store and reuse previously computed Fibonacci numbers, efficiently computing the result in O(n) time.