semvec/pypi-packages huggingface dataset:
MonteCarloPy 0.1
PyMonteCarlo is a module that has helper function for monte carlo simulations

# 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