WHOT AI

Custom Built

• Decision Tree Algorithm
• Random Forest Alogirthm
• Word2Vec/Tokenizers
• AI Model Simulation System

Introduction

WHOT is a Nigerian Originated card game, somewhat similar to UNO, where player(s) have to match the card placed with same shapes or numbers. Official Game Doc https://en.wikipedia.org/wiki/Whot!

Data Generation.

Generated the dataset to train the model by creating a program using the constraints and logic of the game.

Base Data Generation Code is located in ./data/data.py

Parallelized Data Generated Code is located in ./data/data-pool.py

7 Features, 5 Input Features (Card 1,Card 2,Card 3,Card 4,Played), 1 Target Feature (Action)

id,Card 1,Card 2,Card 3,Card 4,Played,Action
0,circle 1,circle 2,circle 3,circle 4,circle 1,circle 1
1,circle 1,circle 2,circle 3,circle 4,circle 2,circle 1
2,circle 1,circle 2,circle 3,circle 4,circle 3,circle 1
...

Time Complexity Analysis (Before Parallelization)

The time complexity of this program can be broken down into multiple parts:

• Initializing the store and deck, which is O(1) since it has a fixed size.
• The stacks function has a time complexity of O(nk), where n is the number of players and k is the number of cards per player. This is because for each player, the function iterates through k cards to remove them from the deck.
• The combinations function has a time complexity of O(C(n, k)), where n is the number of elements in the deck and k is the size of the combination. In this case, it is O(C(54, 4)).
• The nested loop that generates the 'box' list has a time complexity of O(C(n, k) * n), where n is the number of elements in the deck and k is the size of the combination. In this case, it is O(C(54, 4) * 54).
• The loop that generates the 'data' list has a time complexity of O(C(n, k) * n), which is the same as the loop generating the 'box' list.
• The most time-consuming part of the program is the nested loop that generates the 'box' list and the loop that generates the 'data' list.
• The overall time complexity of the program is: O(C(n, k) * n)
• In this specific case, the time complexity is: O(C(54, 4) * 54)

Time Complexity Analysis (After Parallelization)

• The time complexity of the nested loop generating the 'box' list is O(C(54, 4) * 54). After parallelizing this loop, the time complexity becomes O(C(54, 4) * 54 / P). The time complexity is reduced by a factor of the number of cores (P) available on the system.
• The time complexity of the process_data function is O(C(n, k) * n), which is the same as the loop generating the 'box' list. When parallelized using P cores, the time complexity becomes O(C(54, 4) * 54 / P).
• The rest of the program has the same time complexity as before, which doesn't change significantly when parallelized.

Serialized Objects

I have already trained the model, it is saved in the ./objects/whotmodel alongside the Word2Vec Tokens using in training, saved in ./objects/whottokens