Unit 5 Session 2 (Click for link to problem statements)
TIP102 Unit 5 Session 2 Standard (Click for link to problem statements)
Understand what the interviewer is asking for by using test cases and questions about the problem.
- Established a set (2-3) of test cases to verify their own solution later.
- Established a set (1-2) of edge cases to verify their solution handles complexities.
- Have fully understood the problem and have no clarifying questions.
- Have you verified any Time/Space Constraints for this problem?
HAPPY CASE
Input: player1 = Player("Mario", "Standard", [1, 2, 1, 1, 3]), opponents = [Player("Luigi", "Standard", [2, 1, 3, 2, 2]), Player("Peach", "Standard", [3, 3, 2, 3, 1])]
Output: 1
Explanation: Mario's average race outcome is 1.6, which is lower than Luigi's 2.0 and Peach's 2.4, so Mario gets 1st place.
EDGE CASE
Input: player1 = Player("Mario", "Standard", [2, 2, 2, 2, 2]), opponents = [Player("Luigi", "Standard", [2, 2, 2, 2, 2])]
Output: 1
Explanation: Both players have the same average race outcome, so Mario gets 1st place because the function counts the player itself as 1st.
EDGE CASE
Input: player1 = Player("Mario", "Standard", [1, 1, 1, 1, 1]), opponents = []
Output: 1
Explanation: Without any opponents, Mario automatically gets 1st place.
Match what this problem looks like to known categories of problems, e.g. Linked List or Dynamic Programming, and strategies or patterns in those categories.
For Object-Oriented Programming problems, we want to consider the following approaches:
Plan the solution with appropriate visualizations and pseudocode.
General Idea: Calculate the average race outcome for the player and each opponent. Count how many opponents have a better (lower) average race outcome than the player to determine their rank.
1) Calculate the average race outcome for the player.
2) Initialize the rank to 1.
3) Iterate over each opponent:
a) Calculate the average race outcome for the opponent.
b) If the opponent's average is better (lower), increase the player's rank.
4) Return the player's rank.
⚠️ Common Mistakes
Implement the code to solve the algorithm.
class Player:
def __init__(self, character, kart, outcomes):
self.character = character
self.kart = kart
self.items = []
self.race_outcomes = outcomes
def get_tournament_place(self, opponents):
# Calculate own average race outcome
own_average = sum(self.race_outcomes) / len(self.race_outcomes) if self.race_outcomes else float('inf')
# Start counting at 1 for the player themselves
rank = 1
# Calculate and compare with each opponent's average race outcome
for opponent in opponents:
opponent_average = sum(opponent.race_outcomes) / len(opponent.race_outcomes)
if opponent_average < own_average:
rank += 1 # The opponent did better (lower) than we did!
return rank
Review the code by running specific example(s) and recording values (watchlist) of your code's variables along the way.
Example:
player1 = Player("Mario", "Standard", [1, 2, 1, 1, 3])
player2 = Player("Luigi", "Standard", [2, 1, 3, 2, 2])
player3 = Player("Peach", "Standard", [3, 3, 2, 3, 1])
opponents = [player2, player3]
print(f"{player1.character} was number {player1.get_tournament_place(opponents)}")
# Expected Output: "Mario was number 1"
Evaluate the performance of your algorithm and state any strong/weak or future potential work.
- Time Complexity: O(N * M) where N is the number of opponents and M is the number of races, as we need to calculate the average race outcome for each player.
- Space Complexity: O(1) because we are only using a constant amount of extra space to store the rank and averages.