# PyMonteCarlo
PyMonteCarlo is a module that has helper function for monte carlo simulations
## Getting Started
### Installing PyMonteCarlo
```
pip install (coming soon)
```
### Basics
```python
from PyMonteCarlo.mcs import MonteCarloSimulaterController as mcs
#Flip A Coin. Output between 0 - 1
mcs.flip_a_coin()
#Roll Dice. Output between 1 - 6
mcs.roll_a_dice()
```
## QuickStart Guide
We Will Create A Monte Carlo Simulator On A Rock, Papper, Scissor Game.
You Can Find This Game In Examples Folder In PyMonteCarlo Folder
### Defining
```python
from PyMonteCarlo import MonteCarloSimulaterController as mcs
controller = mcs.MonteCarloSimulaterController(actions = ["ROCK", "PAPER", "SCISSOR"], #All The Actions
results = ["PLAYER_1_WON", "PLAYER_2_WON", "TIE"]) #All The Results
```
### Create Game Login
```python
def play(player1_move, player2_move):
"""Takes Two Player Input And Decide The Winner"""
players = [player1_move, player2_move]
if player1_move == player2_move:
#They Both Tied
return "TIE"
moves = {"ROCK" : "SCISSOR", #Rock beats scissor
"SCISSOR" : "PAPER",
"PAPER" : "ROCK"}
for player_index in range(len(players)):
player_id = "PLAYER_1_WON" if player_index == 0 else "PLAYER_2_WON"
for move in moves:
if move == players[player_index] and moves[move] == players[1 if player_index == 0 else 0]:
return player_id
```
### Creating Simulation
```python
#The Main Simulations
for _ in range(1000):
player1_action = controller.take_action() #Randomly takes action between rock, paper, scissor
player2_action = controller.take_action()
"""Also You Can Do This
player2_action = controller.take_action(available_actions=["ROCK","PAPER"])
If You Want To Change The Available Outputs
"""
winner = play(player1_action, player2_action)
controller.add_result(winner) #Adds The Result To The Controller
```
### Viewing The Results
```python
print(controller.results_count()) #Returns How Many Times Each Result Occurs
print(controller.max_result(strength=True)) #Returns The Maximum Times Occuring Result With Its Strenght Between 0 - 1. 0 means bad and 1 means amazing.
print(controller.avg_result(strength=True)) #Returns Average Result And Its Strength
print(controller.median_result(strength=True)) #Returns Median Result With Its Strength
"""Output
{'PLAYER_1_WON': 348, 'PLAYER_2_WON': 316, 'TIE': 336}
('PLAYER_1_WON', 0.348)
('TIE', 0.336)
('TIE', 0.336)
"""
```
# Contributing
If you have any suggestion either contact sonicroshan122@gmail or send a pull request
## Authors
Roshan Jignesh Mehta - sonicroshan122@gmail
## Future
This Features Will Be Added In The Future
* Monte Carlo Tree Search
* Ploting The Monte Carlo Simulation Results And Action
