Personally I used CodeCademy to learn the basics and rely on W3Schools to get a reminder of the correct syntax to use when I am not sure. Do not hesitate to Google what you are trying to achieve if you suspect that something already exists to do so in Python's built-in methods. I cannot speak for everyone there but this is what worked best for me so maybe it will suit your learning style as well !

Was this part not clear enough from description? Another user also asked about it.

/* faction == myfaction || */

EDIT:

I did not implement the first condition here. Let me adapt the ref sol.

An additional +1 is awarded if any of the following conditions are met:

• The agreeing knight shares the same faction as you.
• The agreeing knight has both the same rank and suit as your card.

didn't know you could increment the key by one, nice solution.

I'll say, but keep in mind that this approach is more concise and efficient than your method, especially for larger numbers.

cheater
you play with aim here

i just ran this code in chat gpt and it call it old style python =) sounds funny

"""is a list comprehension that formats each line from the lines list into a numbered string using the old-style Python string formatting (% operator). Let’s break it down step by step.© """ chatGPT

Wow... that's insane

Okay, thanks so much!

Okay, so first, for each vehicle type, compute the inverse weight as 1 / points. Normalize these weights to convert them into probabilities summing to 100%. This is done by dividing each weight by the total sum of all weights. Ensure the probabilities sum exactly to 100% by adjusting the last probability. For each vehicle type, compute the absolute difference between John's probability and the city's frequency and then sum all these differences. Subtract the total difference from 100% to calculate the match percentage: Match Percentage = 100 - Σ|P(Jhon) - P(City)|. If the match percentage is negative, set it to 0.

Here is an example: Points: [1, 3, 5, 6] City Frequencies: [58.82%, 19.61%, 11.76%, 9.81%] | Weight=[1, 1/3 ,1/5 ,1/6] | Sum of weights = 1.7 | Probabilities= [1/1.7, 0.33/1.7, 0.2/1.7, (1/6)/1.7]*100= | Probabilities = [58.82, 19.61, 11.76, 9.81] | In this case the sum is already 100%, so no adjustment is needed. | Differences= |58.82 - 58.82| + |19.61 - 19.61| + |11.76 - 11.76| + |9.81 - 9.81| = 0 | Match Percentage = 100 - 0 = 100.00%

No no, my goal is not to annoy users ;)

LOL. I was actually wondering if the precision issue was part of the challenge.

I should have lowered the precision a bit sooner, might have saved you all this mess xD

Random tests are vulnerable to input modification

Can I have some worked examples of the match percentage calculation? What formula do you use to derive the percentage?

The description is fine but the specifics are a mystery to me.

The "match percentage" in this context measures how closely John’s point-based probability distribution aligns with the actual frequency distribution of vehicle types in the city. The purpose is to assess how well John's point system predicts the city's actual vehicle distribution. A high match percentage means the point system effectively captures real-world probabilities, while a low percentage suggests it deviates significantly, potentially making the game less representative of actual trends.