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        "author": "Roshan J Mehta",
        "author_email": "sonicroshan122@gmail.com",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "# PyMonteCarlo\n\nPyMonteCarlo is a module that has helper function for monte carlo simulations\n\n## Getting Started\n\n### Installing PyMonteCarlo\n\n```\npip install (coming soon)\n```\n\n### Basics\n\n```python\nfrom PyMonteCarlo.mcs import MonteCarloSimulaterController as mcs\n\n#Flip A Coin. Output between 0 - 1\nmcs.flip_a_coin()\n#Roll Dice. Output between 1 - 6\nmcs.roll_a_dice()\n```\n\n\n## QuickStart Guide\nWe Will Create A Monte Carlo Simulator On A Rock, Papper, Scissor Game.\nYou Can Find This Game In Examples Folder In PyMonteCarlo Folder\n\n### Defining\n\n```python\nfrom PyMonteCarlo import MonteCarloSimulaterController as mcs\n\n\ncontroller = mcs.MonteCarloSimulaterController(actions = [\"ROCK\", \"PAPER\", \"SCISSOR\"], #All The Actions\n                                           results = [\"PLAYER_1_WON\", \"PLAYER_2_WON\", \"TIE\"]) #All The Results\n```\n\n### Create Game Login\n```python\ndef play(player1_move, player2_move):\n    \"\"\"Takes Two Player Input And Decide The Winner\"\"\"\n    players = [player1_move, player2_move]\n\n    if player1_move == player2_move:\n        #They Both Tied\n        return \"TIE\"\n\n    moves = {\"ROCK\" : \"SCISSOR\", #Rock beats scissor\n              \"SCISSOR\" : \"PAPER\",\n              \"PAPER\" : \"ROCK\"}\n\n    for player_index in range(len(players)):\n        player_id = \"PLAYER_1_WON\" if player_index == 0 else \"PLAYER_2_WON\"\n        for move in moves:\n            if move == players[player_index] and moves[move] == players[1 if player_index == 0 else 0]:\n                return player_id\n ```\n\n\n### Creating Simulation\n\n```python\n#The Main Simulations\nfor _ in range(1000):\n    player1_action = controller.take_action() #Randomly takes action between rock, paper, scissor\n    player2_action = controller.take_action()\n\n    \"\"\"Also You Can Do This\n    player2_action = controller.take_action(available_actions=[\"ROCK\",\"PAPER\"])\n    If You Want To Change The Available Outputs\n    \"\"\"\n\n\n    winner = play(player1_action, player2_action)\n\n    controller.add_result(winner) #Adds The Result To The Controller\n```\n\n\n### Viewing The Results\n\n```python\nprint(controller.results_count()) #Returns How Many Times Each Result Occurs\nprint(controller.max_result(strength=True)) #Returns The Maximum Times Occuring Result With Its Strenght Between 0 - 1. 0 means bad and 1 means amazing.\nprint(controller.avg_result(strength=True)) #Returns Average Result And Its Strength\nprint(controller.median_result(strength=True)) #Returns Median Result With Its Strength\n\n\"\"\"Output\n{'PLAYER_1_WON': 348, 'PLAYER_2_WON': 316, 'TIE': 336}\n('PLAYER_1_WON', 0.348)\n('TIE', 0.336)\n('TIE', 0.336)\n\"\"\"\n```\n\n# Contributing\nIf you have any suggestion either contact sonicroshan122@gmail or send a pull request\n\n\n## Authors\n\nRoshan Jignesh Mehta - sonicroshan122@gmail\n\n\n## Future\n\nThis Features Will Be Added In The Future\n\n* Monte Carlo Tree Search\n* Ploting The Monte Carlo Simulation Results And Action\n\n",
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