Answer:
an electronic device for storing and processing data, typically in binary form, according to instructions given to it in a variable program
Explanation:
Answer:
a divice to play games on, learn on, and other helpful things
Explanation:
Ex: If the input is:
Enter the number of integers in your list: 5
Enter the 5 integers:
50
60
140
200
75
Enter the threshold value: 100
the output is:
The integers that are less than or equal to 100 are:
50
60
75
The 5 indicates that there are five integers in the list, namely 50, 60, 140, 200, and 75. The 100 indicates that the program should output all integers less than or equal to 100, so the program outputs 50, 60, and 75. Such functionality is common on sites like Amazon, where a user can filter results. Your code must define and call the following two functions: def get_user_values() def output_ints_less_than_or_equal_to_threshold(user_values, upper_threshold) Utilizing functions will help to make your main very clean and intuitive.
Answer:
def output_ints_less_than_or_equal_to_threshold(user_values, upper_threshold):
for value in user_values:
if value < upper_threshold:
print(value)
def get_user_values():
n = int(input())
lst = []
for i in range(n):
lst.append(int(input()))
return lst
if __name__ == '__main__':
userValues = get_user_values()
upperThreshold = int(input())
output_ints_less_than_or_equal_to_threshold(userValues, upperThreshold)
Explanation:
Answer:
Greedy is an algorithmic paradigm that builds up a solution piece by piece, always choosing the next piece that offers the most obvious and immediate benefit. Greedy algorithms are used for optimization problems. An optimization problem can be solved using Greedy if the problem has the following property: At every step, we can make a choice that looks best at the moment, and we get the optimal solution of the complete problem.
If a Greedy Algorithm can solve a problem, then it generally becomes the best method to solve that problem as the Greedy algorithms are in general more efficient than other techniques like Dynamic Programming. But Greedy algorithms cannot always be applied. For example, the Fractional Knapsack problem (See this) can be solved using Greedy, but 0-1 Knapsack cannot be solved using Greedy.
The following are some standard algorithms that are Greedy algorithms.
1) Kruskal’s Minimum Spanning Tree (MST): In Kruskal’s algorithm, we create an MST by picking edges one by one. The Greedy Choice is to pick the smallest weight edge that doesn’t cause a cycle in the MST constructed so far.
2) Prim’s Minimum Spanning Tree: In Prim’s algorithm also, we create an MST by picking edges one by one. We maintain two sets: a set of the vertices already included in MST and the set of the vertices not yet included. The Greedy Choice is to pick the smallest weight edge that connects the two sets.
3) Dijkstra’s Shortest Path: Dijkstra’s algorithm is very similar to Prim’s algorithm. The shortest-path tree is built up, edge by edge. We maintain two sets: a set of the vertices already included in the tree and the set of the vertices not yet included. The Greedy Choice is to pick the edge that connects the two sets and is on the smallest weight path from source to the set that contains not yet included vertices.
4) Huffman Coding: Huffman Coding is a loss-less compression technique. It assigns variable-length bit codes to different characters. The Greedy Choice is to assign the least bit length code to the most frequent character. The greedy algorithms are sometimes also used to get an approximation for Hard optimization problems. For example, the Traveling Salesman Problem is an NP-Hard problem. A Greedy choice for this problem is to pick the nearest unvisited city from the current city at every step. These solutions don’t always produce the best optimal solution but can be used to get an approximately optimal solution.
Answer:
See explaination
Explanation:
The program code
import java.util.ArrayList;
public class Main
{
public static void main(String[] args)
{
ArrayList<AirlineTicket> tickets = new ArrayList<AirlineTicket>();
//This creates a randomized list of passengers
addPassengers(tickets);
for(AirlineTicket elem: tickets)
{
System.out.println(elem);
}
//This creates a TicketOrganizer object
TicketOrganizer ticketOrganizer = new TicketOrganizer(tickets);
//These are the methods of the ticketOrganizer in action
System.out.println("\nPassengers Ordered by Boarding Group:");
ticketOrganizer.printPassengersByBoardingGroup();
System.out.println("\nPassengers in line who can board together:");
ticketOrganizer.canBoardTogether();
}
//Do not touch this method! It is adding random passengers to the AirlineTicket array
public static void addPassengers(ArrayList<AirlineTicket> tickets)
{
String[] seats = {"A","B","C","D","E","F"};
for(int index = 0; index< 15; index++)
{
int random = (int)(Math.random() * 5);
AirlineTicket ticket = new AirlineTicket("Passenger " + (index+1), seats[random], ((int)(Math.random()*5)+1), ((int)(Math.random()*8)+1));
tickets.add(ticket);
}
}
}
class TicketOrganizer
{
private ArrayList<AirlineTicket> tickets ;
//constructor with parameter as an arraylist of AirlineTicket
public TicketOrganizer(ArrayList<AirlineTicket> tickets)
{
this.tickets = tickets;
}
//methhods to return the arraylist of airlines
public ArrayList<AirlineTicket> getTickets()
{ //re-organize the ticket first
ArrayList<AirlineTicket> tickets_organized = new ArrayList<AirlineTicket>();
for(int i=1; i<=5; i++)
{
for(AirlineTicket ticket: tickets)
{
if(ticket.getBoardingGroup() == i)
{
tickets_organized.add(ticket);
}
}
}
return tickets_organized;
}
//print the tickets based on boardingGroup
public void printPassengersByBoardingGroup()
{
int count = 0;
for(int i=1; i<=5; i++)
{
System.out.println("Boarding Group " + i + ":");
for(AirlineTicket ticket : tickets)
{
if(ticket.getBoardingGroup() == i)
{
System.out.println("Passenger " + ticket.getName());
}
}
}
See attachment for sample output
B)Positive
Answer:
positive
Explanation:
it shows good customer service
Answer:
Yes
Explanation:
I will say Yes. There are very much limitless possible and future we can expect from social media and even more from social networking. Its capabilities from upcoming software in the future. Since the Internet is in its early days of growing and cloud computing being introduced there are way more possibilities in the future.
Social media can be duely applied by CIT (Computer Information Technologies) graduates. It can be used to improve them in their career and also will helps them connect professionally. Quora is one such platform which is not exactly like LinkedIn but pretty much serves the purpose.
Answer:
short_names = ["Gus", "Bob", "Ann"]
print(short_names[0])
print(short_names[1])
print(short_names[2])
Explanation:
There are some typos in your code. In addition to the missing part of the code, I corrected the typos.
First of all, initialize the list called short_names. The list starts with "[" and ends with "]". Between those, there are must be the names (Since each name is a string, they must be written between "" and there must be a semicolon between each name)
Then, you can print each name by writing the name of the list and the index of the names between brackets (Index implies the position of the element and it starts with 0)