jidt/course/Module03-MutualInformation/ScissorsPaperRockAnalysis-Python/ScissorsPaperRockAnalysis_Solution.ipynb

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    "# Scissors Paper Rock data analysis (modules 3 and 4)\n",
    "\n",
    "_Author_: Julio Correa, Joseph Lizier, 2020-; based on the original Matlab tutorials.\n",
    "\n",
    "We will analyse the uncertainties and information contents of some sample Scissors-Paper-Rock gameplay.\n",
    "\n",
    "## Initial Questions:\n",
    "\n",
    "* Why are we interested in using measures of information theory to analyse this data set?\n",
    "* What in particular might we wish to measure?\n",
    "* _Information theory is all about questions and answers_. What questions might we ask of the data? What hypotheses might we have about the answers?\n",
    "\n",
    "# Stage 1 - Familiarisation\n",
    "\n",
    "We've done a lot of the data plumbing for you, so that we can concentrate on computing the information-theoretic quantities. Data plumbing is an important part of any analysis though, so do take a look in more detail at how the code was set up at some point.\n",
    "\n",
    "Note that these utilies require the additional libraries: `pandas` and `re`"
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    "# Import the Scissors-Paper-Rock utilies\n",
    "import sprutils\n",
    "# Other libraries we require here:\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt"
   ]
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    "For now though, let's get things working and start to explore the data set.\n",
    "\n",
    "1. Please download a copy of the *data set* (following instructions on canvas). Unzip them to any convenient location on your computer. You can also download the solution code, though I trust you not to go straight to it until you've had an attempt at the task first!\n",
    "2. Open the folder where the gameplay data set is stored. Open any file in a text editor, which includes the data for a game between two named players. The file contains each iteration of the game on one line, with $\\{0,1,2\\}$ encoding the player's selections amongst _{scissors,paper,rock}_.\n",
    "3. Set the appropriate paths in the code below for the following:<br/>\n",
    "    For the `simpleinfotheory` scripts, make sure you haved gathered the new functions you wrote into your `simpleinfotheory.py` script, and make sure it is referencable from here (you may need to change the folder referenced below) before you run the import line in the next cell:"
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    "# Set path for where your Scissors-Paper-Rock data files are stored:\n",
    "sprutils.setDataPath(\"../../../Data/ScissorsPaperRock-AllYears/CSYS5030-ScissorsPaperRockData-Sample\")\n",
    "\n",
    "# Set the path for where your Matlab entropy scripts are from the previous modules\n",
    "# (if you are confident that they are working, or else the completed code solutions).\n",
    "import sys\n",
    "sys.path.append('../../Module1-Entropy/PythonCode/completed/')\n",
    "import simpleinfotheory"
   ]
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    "4. Run `sprutils.listPlayers(True)` below to print and also return a list of which player names you can analyse."
   ]
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     "text": [
      "Player names:\n",
      "Player1\n",
      "Player2\n",
      "Player3\n",
      "Player4\n",
      "Player5\n",
      "Player6\n",
      "Player7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Player1', 'Player2', 'Player3', 'Player4', 'Player5', 'Player6', 'Player7']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "sprutils.listPlayers(True)"
   ]
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    "&nbsp;&nbsp;&nbsp;&nbsp; You can run `players = sprutils.listPlayers()` and then access each player name after that function call via `players[0]`, `players[1]`  etc up to `players[len(players)-1]`."
   ]
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      "text/plain": [
       "'Player7'"
      ]
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   "source": [
    "players = sprutils.listPlayers()\n",
    "players[len(players)-1]"
   ]
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    "5. Run `sprutils.loadGamesForPlayer(name, True)`, where `name` is the name string for any player (e.g. `'Joe'`), to display the games (including moves and results) for that player. Note: You can call `sprutils.loadGamesForPlayer('*', True)`, i.e. with name `'*'`, to get the data for all players.<br/>\n",
    "    The function can be called as `games = sprutils.loadGamesForPlayer(name)` to return a list of the data for each game for that player, which will be used in our information-theoretic analysis later. Each item in the list, e.g. `games[i]`, is a 2D numpy array for the given game index, where:\n",
    "    * the first column `games[i][:,0]` is the moves of the named player ({0,1,2} for {scissors,paper,rock}),\n",
    "    * the second column `games[i][:,1]` is the moves of their opponent, and\n",
    "    * the third column `games[i][:,2]` is the result for this player ({1,0,-1} for {win,tie,loss})."
   ]
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     "text": [
      "Game 0 for * (20 iterations):\n",
      "Player1:\tscis,\tPlayer2:\trock,\tresult: los\n",
      "Player1:\tscis,\tPlayer2:\tpapr,\tresult: win\n",
      "Player1:\tpapr,\tPlayer2:\trock,\tresult: win\n",
      "Player1:\tscis,\tPlayer2:\tpapr,\tresult: win\n",
      "Player1:\trock,\tPlayer2:\tpapr,\tresult: los\n",
      "Player1:\tscis,\tPlayer2:\trock,\tresult: los\n",
      "Player1:\tpapr,\tPlayer2:\tscis,\tresult: los\n",
      "Player1:\tpapr,\tPlayer2:\tpapr,\tresult: tie\n",
      "Player1:\tscis,\tPlayer2:\trock,\tresult: los\n",
      "Player1:\tscis,\tPlayer2:\tpapr,\tresult: win\n",
      "Player1:\tscis,\tPlayer2:\tpapr,\tresult: win\n",
      "Player1:\tpapr,\tPlayer2:\tpapr,\tresult: tie\n",
      "Player1:\trock,\tPlayer2:\trock,\tresult: tie\n",
      "Player1:\trock,\tPlayer2:\trock,\tresult: tie\n",
      "Player1:\tscis,\tPlayer2:\trock,\tresult: los\n",
      "Player1:\tscis,\tPlayer2:\trock,\tresult: los\n",
      "Player1:\trock,\tPlayer2:\tscis,\tresult: win\n",
      "Player1:\trock,\tPlayer2:\tpapr,\tresult: los\n",
      "Player1:\tscis,\tPlayer2:\tpapr,\tresult: win\n",
      "Player1:\tpapr,\tPlayer2:\trock,\tresult: win\n",
      "\n",
      "Game 1 for * (20 iterations):\n",
      "Player2:\trock,\tPlayer1:\tscis,\tresult: win\n",
      "Player2:\tpapr,\tPlayer1:\tscis,\tresult: los\n",
      "Player2:\trock,\tPlayer1:\tpapr,\tresult: los\n",
      "Player2:\tpapr,\tPlayer1:\tscis,\tresult: los\n",
      "Player2:\tpapr,\tPlayer1:\trock,\tresult: win\n",
      "Player2:\trock,\tPlayer1:\tscis,\tresult: win\n",
      "Player2:\tscis,\tPlayer1:\tpapr,\tresult: win\n",
      "Player2:\tpapr,\tPlayer1:\tpapr,\tresult: tie\n",
      "Player2:\trock,\tPlayer1:\tscis,\tresult: win\n",
      "Player2:\tpapr,\tPlayer1:\tscis,\tresult: los\n",
      "Player2:\tpapr,\tPlayer1:\tscis,\tresult: los\n",
      "Player2:\tpapr,\tPlayer1:\tpapr,\tresult: tie\n",
      "Player2:\trock,\tPlayer1:\trock,\tresult: tie\n",
      "Player2:\trock,\tPlayer1:\trock,\tresult: tie\n",
      "Player2:\trock,\tPlayer1:\tscis,\tresult: win\n",
      "Player2:\trock,\tPlayer1:\tscis,\tresult: win\n",
      "Player2:\tscis,\tPlayer1:\trock,\tresult: los\n",
      "Player2:\tpapr,\tPlayer1:\trock,\tresult: win\n",
      "Player2:\tpapr,\tPlayer1:\tscis,\tresult: los\n",
      "Player2:\trock,\tPlayer1:\tpapr,\tresult: los\n",
      "Game 2 for * (20 iterations):\n",
      "Player3:\tscis,\tPlayer2:\tscis,\tresult: tie\n",
      "Player3:\tscis,\tPlayer2:\trock,\tresult: los\n",
      "Player3:\tpapr,\tPlayer2:\tpapr,\tresult: tie\n",
      "Player3:\tscis,\tPlayer2:\trock,\tresult: los\n",
      "Player3:\tpapr,\tPlayer2:\trock,\tresult: win\n",
      "Player3:\tscis,\tPlayer2:\tpapr,\tresult: win\n",
      "Player3:\tpapr,\tPlayer2:\tpapr,\tresult: tie\n",
      "Player3:\trock,\tPlayer2:\tpapr,\tresult: los\n",
      "Player3:\tpapr,\tPlayer2:\tpapr,\tresult: tie\n",
      "Player3:\tscis,\tPlayer2:\tpapr,\tresult: win\n",
      "Player3:\tscis,\tPlayer2:\tpapr,\tresult: win\n",
      "Player3:\tscis,\tPlayer2:\trock,\tresult: los\n",
      "Player3:\tscis,\tPlayer2:\tpapr,\tresult: win\n",
      "Player3:\tscis,\tPlayer2:\tpapr,\tresult: win\n",
      "Player3:\tpapr,\tPlayer2:\trock,\tresult: win\n",
      "Player3:\tscis,\tPlayer2:\trock,\tresult: los\n",
      "Player3:\tscis,\tPlayer2:\tpapr,\tresult: win\n",
      "Player3:\tscis,\tPlayer2:\trock,\tresult: los\n",
      "Player3:\tscis,\tPlayer2:\tpapr,\tresult: win\n",
      "Player3:\tpapr,\tPlayer2:\tpapr,\tresult: tie\n",
      "\n",
      "Game 3 for * (20 iterations):\n",
      "Player2:\tscis,\tPlayer3:\tscis,\tresult: tie\n",
      "Player2:\trock,\tPlayer3:\tscis,\tresult: win\n",
      "Player2:\tpapr,\tPlayer3:\tpapr,\tresult: tie\n",
      "Player2:\trock,\tPlayer3:\tscis,\tresult: win\n",
      "Player2:\trock,\tPlayer3:\tpapr,\tresult: los\n",
      "Player2:\tpapr,\tPlayer3:\tscis,\tresult: los\n",
      "Player2:\tpapr,\tPlayer3:\tpapr,\tresult: tie\n",
      "Player2:\tpapr,\tPlayer3:\trock,\tresult: win\n",
      "Player2:\tpapr,\tPlayer3:\tpapr,\tresult: tie\n",
      "Player2:\tpapr,\tPlayer3:\tscis,\tresult: los\n",
      "Player2:\tpapr,\tPlayer3:\tscis,\tresult: los\n",
      "Player2:\trock,\tPlayer3:\tscis,\tresult: win\n",
      "Player2:\tpapr,\tPlayer3:\tscis,\tresult: los\n",
      "Player2:\tpapr,\tPlayer3:\tscis,\tresult: los\n",
      "Player2:\trock,\tPlayer3:\tpapr,\tresult: los\n",
      "Player2:\trock,\tPlayer3:\tscis,\tresult: win\n",
      "Player2:\tpapr,\tPlayer3:\tscis,\tresult: los\n",
      "Player2:\trock,\tPlayer3:\tscis,\tresult: win\n",
      "Player2:\tpapr,\tPlayer3:\tscis,\tresult: los\n",
      "Player2:\tpapr,\tPlayer3:\tpapr,\tresult: tie\n",
      "Game 4 for * (40 iterations):\n",
      "Player4:\trock,\tPlayer5:\tscis,\tresult: win\n",
      "Player4:\tscis,\tPlayer5:\tscis,\tresult: tie\n",
      "Player4:\trock,\tPlayer5:\tscis,\tresult: win\n",
      "Player4:\tpapr,\tPlayer5:\tscis,\tresult: los\n",
      "Player4:\trock,\tPlayer5:\tscis,\tresult: win\n",
      "Player4:\trock,\tPlayer5:\tscis,\tresult: win\n",
      "Player4:\tpapr,\tPlayer5:\tpapr,\tresult: tie\n",
      "Player4:\tscis,\tPlayer5:\tpapr,\tresult: win\n",
      "Player4:\trock,\tPlayer5:\tscis,\tresult: win\n",
      "Player4:\trock,\tPlayer5:\tscis,\tresult: win\n",
      "Player4:\tpapr,\tPlayer5:\tscis,\tresult: los\n",
      "Player4:\trock,\tPlayer5:\trock,\tresult: tie\n",
      "Player4:\tpapr,\tPlayer5:\trock,\tresult: win\n",
      "Player4:\trock,\tPlayer5:\tscis,\tresult: win\n",
      "Player4:\tpapr,\tPlayer5:\tpapr,\tresult: tie\n",
      "Player4:\tpapr,\tPlayer5:\tscis,\tresult: los\n",
      "Player4:\tpapr,\tPlayer5:\tscis,\tresult: los\n",
      "Player4:\trock,\tPlayer5:\tscis,\tresult: win\n",
      "Player4:\tpapr,\tPlayer5:\tscis,\tresult: los\n",
      "Player4:\tscis,\tPlayer5:\tpapr,\tresult: win\n",
      "Player4:\tscis,\tPlayer5:\trock,\tresult: los\n",
      "Player4:\trock,\tPlayer5:\trock,\tresult: tie\n",
      "Player4:\tscis,\tPlayer5:\tpapr,\tresult: win\n",
      "Player4:\tpapr,\tPlayer5:\trock,\tresult: win\n",
      "Player4:\tscis,\tPlayer5:\tscis,\tresult: tie\n",
      "Player4:\trock,\tPlayer5:\tscis,\tresult: win\n",
      "Player4:\tpapr,\tPlayer5:\trock,\tresult: win\n",
      "Player4:\trock,\tPlayer5:\trock,\tresult: tie\n",
      "Player4:\tscis,\tPlayer5:\tscis,\tresult: tie\n",
      "Player4:\tscis,\tPlayer5:\tscis,\tresult: tie\n",
      "Player4:\trock,\tPlayer5:\trock,\tresult: tie\n",
      "Player4:\trock,\tPlayer5:\tpapr,\tresult: los\n",
      "Player4:\tpapr,\tPlayer5:\tscis,\tresult: los\n",
      "Player4:\trock,\tPlayer5:\tscis,\tresult: win\n",
      "Player4:\trock,\tPlayer5:\trock,\tresult: tie\n",
      "Player4:\trock,\tPlayer5:\tpapr,\tresult: los\n",
      "Player4:\tscis,\tPlayer5:\tpapr,\tresult: win\n",
      "Player4:\tpapr,\tPlayer5:\tscis,\tresult: los\n",
      "Player4:\trock,\tPlayer5:\tpapr,\tresult: los\n",
      "Player4:\trock,\tPlayer5:\trock,\tresult: tie\n",
      "\n",
      "Game 5 for * (40 iterations):\n",
      "Player5:\tscis,\tPlayer4:\trock,\tresult: los\n",
      "Player5:\tscis,\tPlayer4:\tscis,\tresult: tie\n",
      "Player5:\tscis,\tPlayer4:\trock,\tresult: los\n",
      "Player5:\tscis,\tPlayer4:\tpapr,\tresult: win\n",
      "Player5:\tscis,\tPlayer4:\trock,\tresult: los\n",
      "Player5:\tscis,\tPlayer4:\trock,\tresult: los\n",
      "Player5:\tpapr,\tPlayer4:\tpapr,\tresult: tie\n",
      "Player5:\tpapr,\tPlayer4:\tscis,\tresult: los\n",
      "Player5:\tscis,\tPlayer4:\trock,\tresult: los\n",
      "Player5:\tscis,\tPlayer4:\trock,\tresult: los\n",
      "Player5:\tscis,\tPlayer4:\tpapr,\tresult: win\n",
      "Player5:\trock,\tPlayer4:\trock,\tresult: tie\n",
      "Player5:\trock,\tPlayer4:\tpapr,\tresult: los\n",
      "Player5:\tscis,\tPlayer4:\trock,\tresult: los\n",
      "Player5:\tpapr,\tPlayer4:\tpapr,\tresult: tie\n",
      "Player5:\tscis,\tPlayer4:\tpapr,\tresult: win\n",
      "Player5:\tscis,\tPlayer4:\tpapr,\tresult: win\n",
      "Player5:\tscis,\tPlayer4:\trock,\tresult: los\n",
      "Player5:\tscis,\tPlayer4:\tpapr,\tresult: win\n",
      "Player5:\tpapr,\tPlayer4:\tscis,\tresult: los\n",
      "Player5:\trock,\tPlayer4:\tscis,\tresult: win\n",
      "Player5:\trock,\tPlayer4:\trock,\tresult: tie\n",
      "Player5:\tpapr,\tPlayer4:\tscis,\tresult: los\n",
      "Player5:\trock,\tPlayer4:\tpapr,\tresult: los\n",
      "Player5:\tscis,\tPlayer4:\tscis,\tresult: tie\n",
      "Player5:\tscis,\tPlayer4:\trock,\tresult: los\n",
      "Player5:\trock,\tPlayer4:\tpapr,\tresult: los\n",
      "Player5:\trock,\tPlayer4:\trock,\tresult: tie\n",
      "Player5:\tscis,\tPlayer4:\tscis,\tresult: tie\n",
      "Player5:\tscis,\tPlayer4:\tscis,\tresult: tie\n",
      "Player5:\trock,\tPlayer4:\trock,\tresult: tie\n",
      "Player5:\tpapr,\tPlayer4:\trock,\tresult: win\n",
      "Player5:\tscis,\tPlayer4:\tpapr,\tresult: win\n",
      "Player5:\tscis,\tPlayer4:\trock,\tresult: los\n",
      "Player5:\trock,\tPlayer4:\trock,\tresult: tie\n",
      "Player5:\tpapr,\tPlayer4:\trock,\tresult: win\n",
      "Player5:\tpapr,\tPlayer4:\tscis,\tresult: los\n",
      "Player5:\tscis,\tPlayer4:\tpapr,\tresult: win\n",
      "Player5:\tpapr,\tPlayer4:\trock,\tresult: win\n",
      "Player5:\trock,\tPlayer4:\trock,\tresult: tie\n",
      "Game 6 for * (50 iterations):\n",
      "Player6:\tscis,\tPlayer7:\trock,\tresult: los\n",
      "Player6:\trock,\tPlayer7:\trock,\tresult: tie\n",
      "Player6:\trock,\tPlayer7:\trock,\tresult: tie\n",
      "Player6:\tscis,\tPlayer7:\tscis,\tresult: tie\n",
      "Player6:\tscis,\tPlayer7:\tpapr,\tresult: win\n",
      "Player6:\trock,\tPlayer7:\tscis,\tresult: win\n",
      "Player6:\tscis,\tPlayer7:\tpapr,\tresult: win\n",
      "Player6:\trock,\tPlayer7:\trock,\tresult: tie\n",
      "Player6:\tscis,\tPlayer7:\trock,\tresult: los\n",
      "Player6:\trock,\tPlayer7:\tscis,\tresult: win\n",
      "Player6:\tscis,\tPlayer7:\tscis,\tresult: tie\n",
      "Player6:\trock,\tPlayer7:\trock,\tresult: tie\n",
      "Player6:\tscis,\tPlayer7:\tpapr,\tresult: win\n",
      "Player6:\trock,\tPlayer7:\tpapr,\tresult: los\n",
      "Player6:\tscis,\tPlayer7:\tpapr,\tresult: win\n",
      "Player6:\tscis,\tPlayer7:\tscis,\tresult: tie\n",
      "Player6:\trock,\tPlayer7:\tpapr,\tresult: los\n",
      "Player6:\tscis,\tPlayer7:\tpapr,\tresult: win\n",
      "Player6:\trock,\tPlayer7:\trock,\tresult: tie\n",
      "Player6:\tscis,\tPlayer7:\trock,\tresult: los\n",
      "Player6:\trock,\tPlayer7:\trock,\tresult: tie\n",
      "Player6:\tscis,\tPlayer7:\tscis,\tresult: tie\n",
      "Player6:\trock,\tPlayer7:\tpapr,\tresult: los\n",
      "Player6:\trock,\tPlayer7:\tscis,\tresult: win\n",
      "Player6:\trock,\tPlayer7:\tscis,\tresult: win\n",
      "Player6:\tscis,\tPlayer7:\tpapr,\tresult: win\n",
      "Player6:\trock,\tPlayer7:\tscis,\tresult: win\n",
      "Player6:\tscis,\tPlayer7:\tpapr,\tresult: win\n",
      "Player6:\trock,\tPlayer7:\tscis,\tresult: win\n",
      "Player6:\tscis,\tPlayer7:\tpapr,\tresult: win\n",
      "Player6:\trock,\tPlayer7:\trock,\tresult: tie\n",
      "Player6:\tscis,\tPlayer7:\tscis,\tresult: tie\n",
      "Player6:\trock,\tPlayer7:\tpapr,\tresult: los\n",
      "Player6:\trock,\tPlayer7:\tpapr,\tresult: los\n",
      "Player6:\tscis,\tPlayer7:\trock,\tresult: los\n",
      "Player6:\tscis,\tPlayer7:\trock,\tresult: los\n",
      "Player6:\trock,\tPlayer7:\tscis,\tresult: win\n",
      "Player6:\trock,\tPlayer7:\tpapr,\tresult: los\n",
      "Player6:\tscis,\tPlayer7:\tpapr,\tresult: win\n",
      "Player6:\trock,\tPlayer7:\tscis,\tresult: win\n",
      "Player6:\tscis,\tPlayer7:\tscis,\tresult: tie\n",
      "Player6:\trock,\tPlayer7:\tpapr,\tresult: los\n",
      "Player6:\tscis,\tPlayer7:\tpapr,\tresult: win\n",
      "Player6:\tscis,\tPlayer7:\tscis,\tresult: tie\n",
      "Player6:\trock,\tPlayer7:\trock,\tresult: tie\n",
      "Player6:\tscis,\tPlayer7:\tscis,\tresult: tie\n",
      "Player6:\tscis,\tPlayer7:\trock,\tresult: los\n",
      "Player6:\trock,\tPlayer7:\tscis,\tresult: win\n",
      "Player6:\tscis,\tPlayer7:\trock,\tresult: los\n",
      "Player6:\trock,\tPlayer7:\tscis,\tresult: win\n",
      "\n",
      "Game 7 for * (50 iterations):\n",
      "Player7:\trock,\tPlayer6:\tscis,\tresult: win\n",
      "Player7:\trock,\tPlayer6:\trock,\tresult: tie\n",
      "Player7:\trock,\tPlayer6:\trock,\tresult: tie\n",
      "Player7:\tscis,\tPlayer6:\tscis,\tresult: tie\n",
      "Player7:\tpapr,\tPlayer6:\tscis,\tresult: los\n",
      "Player7:\tscis,\tPlayer6:\trock,\tresult: los\n",
      "Player7:\tpapr,\tPlayer6:\tscis,\tresult: los\n",
      "Player7:\trock,\tPlayer6:\trock,\tresult: tie\n",
      "Player7:\trock,\tPlayer6:\tscis,\tresult: win\n",
      "Player7:\tscis,\tPlayer6:\trock,\tresult: los\n",
      "Player7:\tscis,\tPlayer6:\tscis,\tresult: tie\n",
      "Player7:\trock,\tPlayer6:\trock,\tresult: tie\n",
      "Player7:\tpapr,\tPlayer6:\tscis,\tresult: los\n",
      "Player7:\tpapr,\tPlayer6:\trock,\tresult: win\n",
      "Player7:\tpapr,\tPlayer6:\tscis,\tresult: los\n",
      "Player7:\tscis,\tPlayer6:\tscis,\tresult: tie\n",
      "Player7:\tpapr,\tPlayer6:\trock,\tresult: win\n",
      "Player7:\tpapr,\tPlayer6:\tscis,\tresult: los\n",
      "Player7:\trock,\tPlayer6:\trock,\tresult: tie\n",
      "Player7:\trock,\tPlayer6:\tscis,\tresult: win\n",
      "Player7:\trock,\tPlayer6:\trock,\tresult: tie\n",
      "Player7:\tscis,\tPlayer6:\tscis,\tresult: tie\n",
      "Player7:\tpapr,\tPlayer6:\trock,\tresult: win\n",
      "Player7:\tscis,\tPlayer6:\trock,\tresult: los\n",
      "Player7:\tscis,\tPlayer6:\trock,\tresult: los\n",
      "Player7:\tpapr,\tPlayer6:\tscis,\tresult: los\n",
      "Player7:\tscis,\tPlayer6:\trock,\tresult: los\n",
      "Player7:\tpapr,\tPlayer6:\tscis,\tresult: los\n",
      "Player7:\tscis,\tPlayer6:\trock,\tresult: los\n",
      "Player7:\tpapr,\tPlayer6:\tscis,\tresult: los\n",
      "Player7:\trock,\tPlayer6:\trock,\tresult: tie\n",
      "Player7:\tscis,\tPlayer6:\tscis,\tresult: tie\n",
      "Player7:\tpapr,\tPlayer6:\trock,\tresult: win\n",
      "Player7:\tpapr,\tPlayer6:\trock,\tresult: win\n",
      "Player7:\trock,\tPlayer6:\tscis,\tresult: win\n",
      "Player7:\trock,\tPlayer6:\tscis,\tresult: win\n",
      "Player7:\tscis,\tPlayer6:\trock,\tresult: los\n",
      "Player7:\tpapr,\tPlayer6:\trock,\tresult: win\n",
      "Player7:\tpapr,\tPlayer6:\tscis,\tresult: los\n",
      "Player7:\tscis,\tPlayer6:\trock,\tresult: los\n",
      "Player7:\tscis,\tPlayer6:\tscis,\tresult: tie\n",
      "Player7:\tpapr,\tPlayer6:\trock,\tresult: win\n",
      "Player7:\tpapr,\tPlayer6:\tscis,\tresult: los\n",
      "Player7:\tscis,\tPlayer6:\tscis,\tresult: tie\n",
      "Player7:\trock,\tPlayer6:\trock,\tresult: tie\n",
      "Player7:\tscis,\tPlayer6:\tscis,\tresult: tie\n",
      "Player7:\trock,\tPlayer6:\tscis,\tresult: win\n",
      "Player7:\tscis,\tPlayer6:\trock,\tresult: los\n",
      "Player7:\trock,\tPlayer6:\tscis,\tresult: win\n",
      "Player7:\tscis,\tPlayer6:\trock,\tresult: los\n",
      "Game 8 for * (20 iterations):\n",
      "Player1:\tpapr,\tPlayer3:\tscis,\tresult: los\n",
      "Player1:\tscis,\tPlayer3:\tscis,\tresult: tie\n",
      "Player1:\trock,\tPlayer3:\tscis,\tresult: win\n",
      "Player1:\tscis,\tPlayer3:\tscis,\tresult: tie\n",
      "Player1:\tpapr,\tPlayer3:\tscis,\tresult: los\n",
      "Player1:\tpapr,\tPlayer3:\tscis,\tresult: los\n",
      "Player1:\trock,\tPlayer3:\tpapr,\tresult: los\n",
      "Player1:\tscis,\tPlayer3:\tpapr,\tresult: win\n",
      "Player1:\tscis,\tPlayer3:\tpapr,\tresult: win\n",
      "Player1:\tpapr,\tPlayer3:\trock,\tresult: win\n",
      "Player1:\tpapr,\tPlayer3:\tscis,\tresult: los\n",
      "Player1:\tpapr,\tPlayer3:\trock,\tresult: win\n",
      "Player1:\tpapr,\tPlayer3:\tscis,\tresult: los\n",
      "Player1:\trock,\tPlayer3:\tscis,\tresult: win\n",
      "Player1:\trock,\tPlayer3:\tscis,\tresult: win\n",
      "Player1:\trock,\tPlayer3:\tscis,\tresult: win\n",
      "Player1:\tpapr,\tPlayer3:\tscis,\tresult: los\n",
      "Player1:\trock,\tPlayer3:\tpapr,\tresult: los\n",
      "Player1:\tpapr,\tPlayer3:\tscis,\tresult: los\n",
      "Player1:\tpapr,\tPlayer3:\tpapr,\tresult: tie\n",
      "\n",
      "Game 9 for * (20 iterations):\n",
      "Player3:\tscis,\tPlayer1:\tpapr,\tresult: win\n",
      "Player3:\tscis,\tPlayer1:\tscis,\tresult: tie\n",
      "Player3:\tscis,\tPlayer1:\trock,\tresult: los\n",
      "Player3:\tscis,\tPlayer1:\tscis,\tresult: tie\n",
      "Player3:\tscis,\tPlayer1:\tpapr,\tresult: win\n",
      "Player3:\tscis,\tPlayer1:\tpapr,\tresult: win\n",
      "Player3:\tpapr,\tPlayer1:\trock,\tresult: win\n",
      "Player3:\tpapr,\tPlayer1:\tscis,\tresult: los\n",
      "Player3:\tpapr,\tPlayer1:\tscis,\tresult: los\n",
      "Player3:\trock,\tPlayer1:\tpapr,\tresult: los\n",
      "Player3:\tscis,\tPlayer1:\tpapr,\tresult: win\n",
      "Player3:\trock,\tPlayer1:\tpapr,\tresult: los\n",
      "Player3:\tscis,\tPlayer1:\tpapr,\tresult: win\n",
      "Player3:\tscis,\tPlayer1:\trock,\tresult: los\n",
      "Player3:\tscis,\tPlayer1:\trock,\tresult: los\n",
      "Player3:\tscis,\tPlayer1:\trock,\tresult: los\n",
      "Player3:\tscis,\tPlayer1:\tpapr,\tresult: win\n",
      "Player3:\tpapr,\tPlayer1:\trock,\tresult: win\n",
      "Player3:\tscis,\tPlayer1:\tpapr,\tresult: win\n",
      "Player3:\tpapr,\tPlayer1:\tpapr,\tresult: tie\n"
     ]
    }
   ],
   "source": [
    "agData = sprutils.loadGamesForPlayer('*', True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d82034c-3b86-4d54-835b-63d20bfb0f76",
   "metadata": {},
   "source": [
    "# Stage 2 - Entropy calculations\n",
    "\n",
    "We will analyse the uncertainty in various player's moves using Shannon entropy, and consider whether this relates to their performance in the game. (Do you have a hypothesis on this?)\n",
    "\n",
    "1. See the function `computeEntropyForPlayer(name)` below. This aims to compute the entropy of moves for a given named player, over all the iterations in all of their games. The code retrieves the data for each game of this player using `loadGamesForPlayer(name)`, then loops over each game. Fill out the missing parts of code:\n",
    "    * In the loop, pull out the moves for that player (and their results), and append them into the arrays used to store these values over all iterations. A helpful hint is that if you had a 2D matrix data, and you wanted to pull out the first column of its contents, you would do this as: `data[:,0]` (but an extra reshape is need to keep it as a column).\n",
    "    * Compute the entropy over the players' moves, using our `simpleinfotheory.entropyempirical()` function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ce7fd29-0a29-4e85-8598-c8b5d6ddec5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"function computeEntropyForPlayer()\n",
    "\n",
    "Compute the entropy of moves for a given player, across all games/iterations\n",
    "\n",
    "Inputs:\n",
    "- name - name of the player\n",
    "- verbose - whether to print entropy out\n",
    "\n",
    "Outputs:\n",
    "- calculatedEntropy\n",
    "- winRate\n",
    "- lossRate\n",
    "- numGames\n",
    "\n",
    "Copyright (C) 2020-, Julio Correa, Joseph T. Lizier\n",
    "Distributed under GNU General Public License v3\n",
    "\"\"\"\n",
    "def computeEntropyForPlayer(name: str, verbose: bool=False):\n",
    "    \n",
    "    # Step 1: load all of the player's games' data:\n",
    "    games = sprutils.loadGamesForPlayer(name)\n",
    "    \n",
    "    # Step 2: the player's moves are in the first column, pull these from\n",
    "    #  each game into an array of samples that we can compute entropy on:\n",
    "    moves = np.empty((0,1)) # empty column\n",
    "    results = np.empty((0,1)) # empty column\n",
    "    for gm in games:\n",
    "        # First column of numpy array gm is the player's move, second is opponent's\n",
    "        #  and third is the result.\n",
    "        # Pull out the player's moves in this game (first column of gm):\n",
    "        #  (reshape is required to keep it as a column rather than row vector)\n",
    "        movesInThisGame = gm[:,0].reshape(gm.shape[0],1)\n",
    "        # Pull out the results in this game (third column of gm) as a column:\n",
    "        resultsInThisGame = gm[:,2].reshape(gm.shape[0],1)\n",
    "        # Append this player's moves to the array we're storing over all iterations:\n",
    "        moves = np.row_stack((moves, movesInThisGame)) if moves.size else movesInThisGame\n",
    "        # Append this player's results to the array over all iterations:\n",
    "        results = np.row_stack((results, resultsInThisGame)) if results.size else resultsInThisGame\n",
    "    \n",
    "    # Step 3: compute the entropy for this player's moves using our existing scripts:\n",
    "    # (Don't forget that if your entropy script is returning a tuple, you just want the [0] entry)\n",
    "    calculatedEntropy = simpleinfotheory.entropyempirical(moves)[0] # [0] takes the value only\n",
    "\n",
    "    # Step 4: compute the win and loss rates:\n",
    "    winRate = np.sum(results == 1)/len(results)\n",
    "    lossRate = np.sum(results == -1)/len(results)\n",
    "    numGames = len(results)\n",
    "\n",
    "    if (verbose):\n",
    "        print('Entropy for %s over %d iterations: %.04f' % (name, numGames, calculatedEntropy))\n",
    "\n",
    "    return calculatedEntropy, winRate, lossRate, numGames"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "327ad1b5-d51c-4373-a775-967ac36cde17",
   "metadata": {},
   "source": [
    "2. Call the script for a few different players, e.g. `computeEntropyForPlayer('Joe')`, and compare.\n",
    "3. Now call it using all players' data at once, in a single calculation: `computeEntropyForPlayer('*')`. What implicit assumption(s) are we making when we analyse the data in this way? What question is it asking of the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "67d3d38c-cd53-490c-a7df-7c138de04a1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.5733842558479327, 0.36666666666666664, 0.36666666666666664, 300)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "computeEntropyForPlayer(players[0])\n",
    "computeEntropyForPlayer('*')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ad5c13-ba52-4fe9-83ff-1eded1c2f081",
   "metadata": {},
   "source": [
    "4. See the function `computeEntropyForAllPlayers()` below. This aims to compute entropy of moves for each player in turn (considering each player separately), then plots these, and looks for relationships between entropy and win/loss rates. Fill out the missing parts of code:\n",
    "    * In the loop over player names, use our previous function `computeEntropyForPlayer` to compute the entropy for that player.\n",
    "    * Once we have the entropy for each player and their win / loss ratios, compute the correlation between entropy and win ratio, and entropy and loss ratio. _HINT_: Use the `stats.pearson()` or `stats.spearmanr()` correlation functions from the `scipy.stats` library -- make sure that you check what is returned from this function call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e84ac281-ec3a-4de7-8f40-8151e65d35d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"function computeEntropyForAllPlayers()\n",
    "\n",
    "Compute the entropy of moves for each player, across all games/iterations\n",
    "\n",
    "Outputs:\n",
    "- names\n",
    "- entropies\n",
    "- winRates\n",
    "- lossRates\n",
    "\n",
    "Copyright (C) 2020-, Julio Correa, Joseph T. Lizier\n",
    "Distributed under GNU General Public License v3\n",
    "\"\"\"\n",
    "def computeEntropyForAllPlayers():\n",
    "    \n",
    "    # Step 1: load all of the player's names:\n",
    "    names = sprutils.listPlayers()\n",
    "    # Step 2: compute entropy for each player\n",
    "    index = 0\n",
    "    entropies = np.zeros(len(names))\n",
    "    winRates = np.zeros(len(names))\n",
    "    lossRates = np.zeros(len(names))\n",
    "    \n",
    "    for name in names:\n",
    "        # Compute the entropy for the moves of this player.\n",
    "        # HINT: use the script that you just completed passing in name\n",
    "        calculatedEntropy, winRate, lossRate, numGames = computeEntropyForPlayer(name)\n",
    "        print('{} = {:.03f} bits,\\twin rate = {:.03f},\\tloss rate = {:.03f}, num games = {}'.\\\n",
    "              format(name, calculatedEntropy, winRate, lossRate, numGames))\n",
    "        \n",
    "        entropies[index] = calculatedEntropy\n",
    "        winRates[index] = winRate\n",
    "        lossRates[index] = lossRate\n",
    "\n",
    "        index += 1\n",
    "    \n",
    "    # Plot the winRates and lossRates versus entropies:\n",
    "    plt.figure();\n",
    "    plt.scatter(entropies, winRates, c='red', marker='x');\n",
    "    plt.title('Win rates versus entropies of single players')\n",
    "    plt.xlabel('Entropy of moves (bits)')\n",
    "    plt.ylabel('Win rate')\n",
    "    \n",
    "    plt.figure();\n",
    "    plt.scatter(entropies, lossRates, c='red', marker='x');\n",
    "    plt.title('Loss rates versus entropies of single players')\n",
    "    plt.xlabel('Entropy of moves (bits)')\n",
    "    plt.ylabel('Loss rate')\n",
    "\n",
    "    # Compute correlations and check if these are statistically significant:\n",
    "    # Are these statistically significant?\n",
    "    winToEntropyCorr, winToEntropyCorrPValue = stats.pearsonr(winRates,entropies)\n",
    "    lossToEntropyCorr, lossToEntropyCorrPValue = stats.pearsonr(lossRates,entropies)\n",
    "\n",
    "    print('Correlation of win  rate to entropy is: {:.04f} (pValue {:.04f})'.\\\n",
    "        format(winToEntropyCorr, winToEntropyCorrPValue))\n",
    "    print('Correlation of loss rate to entropy is: {:.04f} (pValue: {:.04f})'.\\\n",
    "        format(lossToEntropyCorr, lossToEntropyCorrPValue))\n",
    "\n",
    "    return names, entropies, winRates, lossRates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5ba6f7-48aa-4479-91f5-71c9d75402ce",
   "metadata": {},
   "source": [
    "5. Call the script to see the entropies of each player, the plots and correlation analyses on how this related to performance. Whose moves was there most uncertainty about? Did this correlate to wins? What about losses? Does this match your hypothesis?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8387951-f475-48ca-ba13-d873bc009368",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Player1 = 1.573 bits,\twin rate = 0.400,\tloss rate = 0.425, num games = 40\n",
      "Player2 = 1.297 bits,\twin rate = 0.350,\tloss rate = 0.425, num games = 40\n",
      "Player3 = 1.196 bits,\twin rate = 0.450,\tloss rate = 0.350, num games = 40\n",
      "Player4 = 1.515 bits,\twin rate = 0.425,\tloss rate = 0.275, num games = 40\n",
      "Player5 = 1.472 bits,\twin rate = 0.275,\tloss rate = 0.425, num games = 40\n",
      "Player6 = 1.000 bits,\twin rate = 0.400,\tloss rate = 0.280, num games = 50\n",
      "Player7 = 1.581 bits,\twin rate = 0.280,\tloss rate = 0.400, num games = 50\n",
      "Correlation of win  rate to entropy is: -0.4175 (pValue 0.3513)\n",
      "Correlation of loss rate to entropy is: 0.4723 (pValue: 0.2845)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(['Player1', 'Player2', 'Player3', 'Player4', 'Player5', 'Player6', 'Player7'],\n",
       " array([1.5729262 , 1.29708926, 1.1964264 , 1.51544065, 1.4722463 ,\n",
       "        1.        , 1.58087864]),\n",
       " array([0.4  , 0.35 , 0.45 , 0.425, 0.275, 0.4  , 0.28 ]),\n",
       " array([0.425, 0.425, 0.35 , 0.275, 0.425, 0.28 , 0.4  ]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fysrulp+fjzt37uCzzz7TOaYDBgwAoHv+VKbivausrLpjNmrUKHz++ecYP348du7ciV9//RW//fYbmjZtqnM+vvbaa/jll19w+vRp3L59GytWrMDw4cMl/dR0Xyo7Xz/99FO8/fbb2Lp1K3r06AFra2sMGTIE6enpsseBqsZH6emB6NGjBxYsWIDExEQkJiZq/+cGoA1He/fu1T4Mfr9fX9bH3Q9pV9i6dSuuX7+OzZs3w9XVVVuuz0PJFSoeNN6xY0el6xs3bqz9c7du3dCtWzeUlpbi0KFD+OyzzzB16lTY29tj5MiRVfbRtm1bdOnSBatWrcLEiROxatUqNG/eHCEhIZJxKBQK7Nu3T/sBdLd7yyo7HvqMsSK03vuwfWUfQq6urli5ciUA4MyZM9i4cSOioqJQUlKCZcuWVdq/ra0tACAyMhLPPvtspXVat25dafn9uPd4VHyo3/3lhQp//fWXdpwV7ty5g/z8fEkYyMnJkbRVGVtbW5iZmeGrr76qcn2FwYMHY/DgwSguLsbBgwcRHR2NUaNGwc3NDf7+/pVub2Njgzt37uDvv/+WhCYhBHJycrRXrmrK29sbGzZsgBACR48exerVqzFnzhyYmZlhxowZtWrz7jED/4bnu+Xk5FR7lcnKygrGxsYIDw/H5MmTK63j7u4uO4aK966ysqrez4KCAmzbtg2zZs2SHIPi4mJcuXJFp/6oUaPw9ttvY8mSJejatStycnIkY67NvlT2/7Varcbs2bMxe/ZsXL58WXu1adCgQfjjjz8qbZfkMTDRA9G9e3cYGxvju+++w4kTJzBv3jztOo1Gg44dO+Lrr7/GxYsXtbctDKHiL5e7w4QQAitWrNCpq1KpKr1i8fTTT2PDhg0oLS2Fn5+fXv0aGxvDz88Pnp6eWLduHX7//fdqAxMAvPDCC3jllVeQnJyMH3/8EdOmTYOxsbFkHB999BEuXbqkcyujNqoaY8WH1dGjRyXzaf3www/VtteqVSvMnDkTmzZtwu+//15lvdatW6Nly5Y4cuQIPvzww/vejwpVvX9V8ff3h5mZGb755hs899xz2vKsrCzs2bMHw4cP19lm3bp1eO2117Sv169fDwDVfqvr6aefxocffggbGxu9PsiB8n0JCgpCkyZNsHPnTqSmplYZmHr16oV58+bhm2++wRtvvKEt37RpE65fv45evXrp1WdVFAoFOnTogIULF2L16tXVvrf68vPzg0qlQlxcnCQ0Hzx4EH/++We1gcnc3Bw9evRAamoq2rdvDxMTk1qN4ZdffsHly5e1V7RKS0sRFxcHDw8PODk5VbqNQqGAEELnHydffvklSktLdeqbmppiwoQJ+Pzzz5GSkoKOHTtqH114kPtyN3t7e4wbNw5HjhzBokWLOJ3IfWBgogfC0tISnTt3xtatW2FkZCT5SwAovy1XMWu2Ps8v1ZU+ffrAxMQEYWFheOutt3Dr1i0sXboU//zzj05db29vbN68GUuXLoWPjw+MjIzg6+uLkSNHYt26dRgwYABef/11dOnSBY0aNUJWVhYSEhIwePBgDB06FMuWLcOePXswcOBAuLi44NatW9qrCr1795Yda1hYGKZNm4awsDAUFxfrPJMSGBiICRMm4IUXXsChQ4fQvXt3qNVqZGdnIzk5Gd7e3njllVeq7UOfMTZr1gy9e/dGdHQ0rKys4Orqil9++QWbN2+WtHX06FFERETgueeeQ8uWLWFiYoI9e/bg6NGjslcgvvjiC/Tv3x99+/bFuHHj4OjoiCtXruDUqVP4/fffJV+51lfFFZG4uDi0aNECpqam8Pb2rrJ+kyZN8N577+Gdd97BmDFjEBYWhvz8fMyePRumpqaYNWuWpL6JiQn++9//4tq1a3jyySeRkpKCuXPnon///tXecp46dSo2bdqE7t2744033kD79u1RVlaGjIwM7Nq1C2+++Sb8/Pzw/vvvIysrC7169YKTkxOuXr2KxYsXo1GjRggKCqqy/T59+qBv3754++23UVhYiMDAQBw9ehSzZs1Cp06dEB4eXuNjuW3bNsTExGDIkCFo0aIFhBDYvHkzrl69ij59+tS4vXtZW1tj2rRp2nNs6NChyMrKwuzZs+Hg4AAjo+qfHlm8eDGeeuopdOvWDa+88grc3NxQVFSEs2fP4scff9Q+11UdW1tb9OzZE++99x7UajViYmLwxx9/VDu1gKWlJbp3745PPvkEtra2cHNzQ1JSElauXIkmTZpUus2rr76KefPm4fDhw/jyyy/rZF/8/Pzw9NNPo3379rCyssKpU6ewdu1a+Pv7MyzdDwM+cE6PmLfeeksAEL6+vjrrtm7dKgAIExMTcf36dcm6qr4lV9m3d2bNmiX0OW1dXV3FwIEDK133448/ig4dOghTU1Ph6Ogo/u///k/8/PPPOmO4cuWKGD58uGjSpIlQKBSSfm/fvi3mz5+vbcfCwkJ4enqKiRMnivT0dCFE+Td8hg4dKlxdXYVKpRI2NjYiKChI/PDDD7LjrzBq1CgBQAQGBlZZ56uvvhJ+fn5CrVYLMzMz4eHhIcaMGSMOHTqkrRMUFCTatWuns62+Y8zOzhbDhw8X1tbWQqPRiNGjR2u/OVXxbaXLly+LcePGCU9PT6FWq4WFhYVo3769WLhwoc5XqStz5MgRMWLECGFnZycaNWokmjVrJnr27CmWLVumrVPxLbl7v2FW2Tl08eJFERISIho3biwAaL/xWFH37q+L3+3LL78U7du3FyYmJkKj0YjBgweLEydOSOpUnJ9Hjx4VwcHBwszMTFhbW4tXXnlFXLt2TVL33m+OCVH+Nf2ZM2eK1q1ba/vx9vYWb7zxhvabWtu2bRP9+/cXjo6OwsTERNjZ2YkBAwaIffv2yR7Lmzdvirffflu4urqKRo0aCQcHB/HKK6/oTGeh77fk/vjjDxEWFiY8PDyEmZmZ0Gg0okuXLmL16tXV7mtVx7qyb7qVlZWJuXPnCicnJ2FiYiLat28vtm3bJjp06CCGDh1a7bYV5S+++KJwdHQUjRo1Ek2bNhUBAQFi7ty5svsHQEyePFnExMQIDw8P0ahRI+Hp6SnWrVsnqVfZeZaVlSWGDRsmrKysROPGjUW/fv3E8ePHK33fKwQHBwtra2vtFCT30mdfqjuPZ8yYIXx9fYWVlZVQqVSiRYsW4o033tB+w5ZqRyGEEA87pBERNWTjxo3Dd999h2vXrhl6KI+0CxcuwNPTE7NmzcI777xTZ/0oFApMnjwZn3/+eZ31USE3Nxeurq6YMmWK5NEFqv94S46IiAzuyJEjiI2NRUBAACwtLXH69GnMmzcPlpaWeOmllww9vPtW8Ruan3zyCYyMjPD6668bekhUQwxMRERkcGq1GocOHcLKlStx9epVaDQaBAcH4z//+Y/s1AINwZdffok5c+bAzc0N69atg6Ojo6GHRDXEW3JEREREMjhxJREREZEMBiYiIiIiGQxMRERERDL40HctlZWV4a+//kLjxo2r/MkJIiIiql+EECgqKkLz5s1lJ0W9GwNTLf31119wdnY29DCIiIioFjIzM6v82ZvKMDDVUsUPrGZmZsLS0tLAoyEiIiJ9FBYWwtnZWfJD6fpgYKqlittwlpaWDExEREQNTE0fp+FD30REREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIOBiYiIiEgGAxMRERGRDAYmIiIiIhkMTEREREQyGJiIHgcFBUBWVuXrsrLK1xMRUZUMHphiYmLg7u4OU1NT+Pj4YN++fXptt3//fiiVSnTs2FFSvnr1aigUCp3l1q1bD6RfoganoADo1w8ICgIyM6XrMjPLy/v1Y2giIqqGQQNTXFwcpk6dinfffRepqano1q0b+vfvj4yMjGq3KygowJgxY9CrV69K11taWiI7O1uymJqa3ne/RA1SURGQmwucPw8EB/8bmjIzy1+fP1++vqjIkKMkIqrXFEIIYajO/fz80LlzZyxdulRb1qZNGwwZMgTR0dFVbjdy5Ei0bNkSxsbG2Lp1K9LS0rTrVq9ejalTp+Lq1asPvN+7FRYWQqPRoKCggD++S/Xf3eGoRQtg7VogPPzf14mJgLOzoUdJRFTnavv5bbArTCUlJTh8+DBCQkIk5SEhIUhJSalyu1WrVuHcuXOYNWtWlXWuXbsGV1dXODk54emnn0Zqaup991tcXIzCwkLJQtRgODuXh6IWLcpDUmAgwxIRUQ0YLDDl5eWhtLQU9vb2knJ7e3vk5ORUuk16ejpmzJiBdevWQalUVlrH09MTq1evxg8//IDY2FiYmpoiMDAQ6enpte4XAKKjo6HRaLSLMz9gqKFxdi6/snS3tWsZloiI9GDwh74VCoXktRBCpwwASktLMWrUKMyePRutWrWqsr2uXbti9OjR6NChA7p164aNGzeiVatW+Oyzz2rVb4XIyEgUFBRol8x7H54lqu8yM8tvw90tPFz3QXAiItJR+WWah8DW1hbGxsY6V3Vyc3N1rv4AQFFREQ4dOoTU1FREREQAAMrKyiCEgFKpxK5du9CzZ0+d7YyMjPDkk09qrzDVtN8KKpUKKpWqxvtJVC9U9wxTcDBvyxERyTDYFSYTExP4+PggPj5eUh4fH4+AgACd+paWljh27BjS0tK0y6RJk9C6dWukpaXBz8+v0n6EEEhLS4ODg0Ot+iVq8LKypGEpMREICJA+0xQcXPU8TUREZLgrTAAwbdo0hIeHw9fXF/7+/li+fDkyMjIwadIkAOW3wS5duoQ1a9bAyMgIXl5eku3t7OxgamoqKZ89eza6du2Kli1borCwEJ9++inS0tKwZMkSvfsleqQ0bgzY2ZX/+e4rSRUPggcHl69v3NhAAyQiqv8MGphCQ0ORn5+POXPmIDs7G15eXti+fTtcXV0BANnZ2TWeG+nq1auYMGECcnJyoNFo0KlTJ+zduxddunTRu1+iR4pGA+zYUT7PkpOTdJ2zM5CUVB6WNBrDjI+IqAEw6DxMDRnnYSIiImp4Gtw8TEREREQNBQMTERERkQwGJiIiIiIZDExEREREMhiYiIiIiGQwMBERERHJYGAiIiIiksHARERERCSDgYmIiIhIBgMTERERkQwGJiIiIiIZDExEREREMhiYiIiIiGQwMBERERHJYGAiIiIiksHARERERCSDgYmIiIhIBgMTERERkQwGJiIiIiIZDExEREREMhiYiIiIiGQwMBERERHJYGAiIiIiksHARERERCSDgYmIiIhIBgMTERERkQwGJiIiIiIZDExEREREMhiYiIiIiGQwMBERERHJYGAiIiIiksHARERERCSDgYmIiIhIBgMTERERkQwGJiIiIiIZDExEREREMhiYiIiIGrKCAiArq/J1WVnl6+m+GTwwxcTEwN3dHaampvDx8cG+ffv02m7//v1QKpXo2LGjpHzFihXo1q0brKysYGVlhd69e+PXX3+V1ImKioJCoZAszZo1e1C7RERE9HAUFAD9+gFBQUBmpnRdZmZ5eb9+DE0PgEEDU1xcHKZOnYp3330Xqamp6NatG/r374+MjIxqtysoKMCYMWPQq1cvnXWJiYkICwtDQkICDhw4ABcXF4SEhODSpUuSeu3atUN2drZ2OXbs2APdNyIiojpXVATk5gLnzwPBwf+GpszM8tfnz5evLyoy5CgfCQohhDBU535+fujcuTOWLl2qLWvTpg2GDBmC6OjoKrcbOXIkWrZsCWNjY2zduhVpaWlV1i0tLYWVlRU+//xzjBkzBkD5FSa57eQUFhZCo9GgoKAAlpaWtW6HiIjovtwdjlq0ANauBcLD/32dmAg4Oxt6lPVGbT+/DXaFqaSkBIcPH0ZISIikPCQkBCkpKVVut2rVKpw7dw6zZs3Sq58bN27g9u3bsLa2lpSnp6ejefPmcHd3x8iRI3H+/Plq2ykuLkZhYaFkISIiMjhn5/JQ1KJFeUgKDGRYqgMGC0x5eXkoLS2Fvb29pNze3h45OTmVbpOeno4ZM2Zg3bp1UCqVevUzY8YMODo6onfv3toyPz8/rFmzBjt37sSKFSuQk5ODgIAA5OfnV9lOdHQ0NBqNdnHmCUhERPWFs3P5laW7rV3LsPQAGfyhb4VCIXkthNApA8pvrY0aNQqzZ89Gq1at9Gp73rx5iI2NxebNm2Fqaqot79+/P4YNGwZvb2/07t0bP/30EwDg66+/rrKtyMhIFBQUaJfMex+uIyIiMpTMzPLbcHcLD9d9EJxqTb/LNHXA1tYWxsbGOleTcnNzda46AUBRUREOHTqE1NRUREREAADKysoghIBSqcSuXbvQs2dPbf358+fjww8/xO7du9G+fftqx6JWq+Ht7Y309PQq66hUKqhUqprsIhERUd2r7hmm4GDelntADHaFycTEBD4+PoiPj5eUx8fHIyAgQKe+paUljh07hrS0NO0yadIktG7dGmlpafDz89PW/eSTT/DBBx9gx44d8PX1lR1LcXExTp06BQcHh/vfMSIiooclK0salhITgYAA6TNNwcFVz9NEejPYFSYAmDZtGsLDw+Hr6wt/f38sX74cGRkZmDRpEoDy22CXLl3CmjVrYGRkBC8vL8n2dnZ2MDU1lZTPmzcP7733HtavXw83NzftFSwLCwtYWFgAAKZPn45BgwbBxcUFubm5mDt3LgoLCzF27NiHtOdEREQPQOPGgJ1d+Z/vvpJU8SB4cHD5+saNDTTAR4dBA1NoaCjy8/MxZ84cZGdnw8vLC9u3b4erqysAIDs7W3ZOpnvFxMSgpKQEw4cPl5TPmjULUVFRAICsrCyEhYUhLy8PTZs2RdeuXXHw4EFtv0RERA2CRgPs2FE+z5KTk3SdszOQlFQeljQaw4zvEWLQeZgaMs7DRERE1PA0uHmYiIiIiBoKBiYiIiIiGQxMRERERDIYmIiIiIhkMDARERERyWBgIiIiIpLBwEREREQkg4GJiIiISAYDExEREZEMBiYiIiIiGQxMRERERDIYmIiIiIhkMDARERERyWBgIiIiIpLBwEREREQkg4GJiIiISAYDExEREZEMBiYiIiIiGQxMRERERDIYmIiIiIhkMDARERERyWBgIiIiIpLBwEREREQkg4GJiIiISAYDExEREZEMBiYiIiIiGQxMRERERDIYmIiIiIhkMDARERERyWBgIiIiIpLBwEREREQkg4GJiIiISAYDExEREZEMBiYiIiIiGQxMRERERDIYmIiIiIhkMDDVFwUFQFZW5euyssrXExERNXQN9PPO4IEpJiYG7u7uMDU1hY+PD/bt26fXdvv374dSqUTHjh111m3atAlt27aFSqVC27ZtsWXLlgfWb50oKAD69QOCgoDMTOm6zMzy8n796u1JREREpJcG/Hln0MAUFxeHqVOn4t1330Vqaiq6deuG/v37IyMjo9rtCgoKMGbMGPTq1Utn3YEDBxAaGorw8HAcOXIE4eHhGDFiBP73v//dd791pqgIyM0Fzp8HgoP/PYkyM8tfnz9fvr6oyDDjIyIiehAa8OedQgghDNW5n58fOnfujKVLl2rL2rRpgyFDhiA6OrrK7UaOHImWLVvC2NgYW7duRVpamnZdaGgoCgsL8fPPP2vL+vXrBysrK8TGxt5Xv3crLCyERqNBQUEBLC0t9d3lqt19srRoAaxdC4SH//s6MRFwdr7/foiIiAzJwJ93tf38NtgVppKSEhw+fBghISGS8pCQEKSkpFS53apVq3Du3DnMmjWr0vUHDhzQabNv377aNmvbb3FxMQoLCyXLA+XsXH6StGhRftIEBjIsERHRo6eBft4ZLDDl5eWhtLQU9vb2knJ7e3vk5ORUuk16ejpmzJiBdevWQalUVlonJyen2jZr0y8AREdHQ6PRaBfnunhDnZ3Lk/bd1q6ttycPERFRrTTAzzuDP/StUCgkr4UQOmUAUFpailGjRmH27Nlo1arVfbepb78VIiMjUVBQoF0y731Y7UHIzCy/LHm38HDdB+OIiIgasgb4eWewwGRrawtjY2Odqzq5ubk6V38AoKioCIcOHUJERASUSiWUSiXmzJmDI0eOQKlUYs+ePQCAZs2aVdtmTfutoFKpYGlpKVkeqHvv6e7f/+/lyrsfjCMiImrIGujnncECk4mJCXx8fBAfHy8pj4+PR0BAgE59S0tLHDt2DGlpadpl0qRJaN26NdLS0uDn5wcA8Pf312lz165d2jZr2u9DkZUlPXkSE4GAAOk93uDgquetICIiagga8Odd5Q8CPSTTpk1DeHg4fH194e/vj+XLlyMjIwOTJk0CUH4b7NKlS1izZg2MjIzg5eUl2d7Ozg6mpqaS8tdffx3du3fHxx9/jMGDB+P777/H7t27kZycrHe/D13jxoCdXfmf737greLBuODg8vWNGxtmfERERA9CA/68M2hgCg0NRX5+PubMmYPs7Gx4eXlh+/btcHV1BQBkZ2fXeG6kgIAAbNiwATNnzsR7770HDw8PxMXFaa9A6dPvQ6fRADt2lM874eQkXefsDCQllZ88Go1hxkdERPQgNODPO4POw9SQPfB5mIiIiKjONbh5mIiIiIgaCgYmIiIiIhkMTEREREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIOBiYiIiEgGAxMRERGRDAYmIiIiIhkMTEREREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIOBiYiIiEgGAxMRERGRDAYmIiIiIhkMTEREREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIOBiYiIiEgGAxMRERGRDAYmIiIiIhkMTEREREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIOBiYiIiEgGAxMRERGRDAYmIiIiIhkMTEREREQyGJiIiIiIZDAwEREREcmodWAqKSnB6dOncefOnfsaQExMDNzd3WFqagofHx/s27evyrrJyckIDAyEjY0NzMzM4OnpiYULF0rqBAcHQ6FQ6CwDBw7U1omKitJZ36xZs/vaDyIiInp0KWu6wY0bNzBlyhR8/fXXAIAzZ86gRYsWeO2119C8eXPMmDFD77bi4uIwdepUxMTEIDAwEF988QX69++PkydPwsXFRae+Wq1GREQE2rdvD7VajeTkZEycOBFqtRoTJkwAAGzevBklJSXabfLz89GhQwc899xzkrbatWuH3bt3a18bGxvX6DgQERHR46PGV5giIyNx5MgRJCYmwtTUVFveu3dvxMXF1aitBQsW4KWXXsL48ePRpk0bLFq0CM7Ozli6dGml9Tt16oSwsDC0a9cObm5uGD16NPr27Su5KmVtbY1mzZppl/j4eJibm+sEJqVSKanXtGnTGo2diIiIHh81Dkxbt27F559/jqeeegoKhUJb3rZtW5w7d07vdkpKSnD48GGEhIRIykNCQpCSkqJXG6mpqUhJSUFQUFCVdVauXImRI0dCrVZLytPT09G8eXO4u7tj5MiROH/+fLV9FRcXo7CwULIQERHR46HGgenvv/+GnZ2dTvn169clAUpOXl4eSktLYW9vLym3t7dHTk5Otds6OTlBpVLB19cXkydPxvjx4yut9+uvv+L48eM66/38/LBmzRrs3LkTK1asQE5ODgICApCfn19ln9HR0dBoNNrF2dlZzz0lIiKihq7GgenJJ5/ETz/9pH1dEZJWrFgBf3//Gg/g3pAlhJANXvv27cOhQ4ewbNkyLFq0CLGxsZXWW7lyJby8vNClSxdJef/+/TFs2DB4e3ujd+/e2v2peC6rMpGRkSgoKNAumZmZ+uweERERPQJq/NB3dHQ0+vXrh5MnT+LOnTtYvHgxTpw4gQMHDiApKUnvdmxtbWFsbKxzNSk3N1fnqtO93N3dAQDe3t64fPkyoqKiEBYWJqlz48YNbNiwAXPmzJEdi1qthre3N9LT06uso1KpoFKpZNsiIiKiR0+NrzAFBARg//79uHHjBjw8PLBr1y7Y29vjwIED8PHx0bsdExMT+Pj4ID4+XlIeHx+PgIAAvdsRQqC4uFinfOPGjSguLsbo0aNl2yguLsapU6fg4OCgd79ERET0+KjxFSag/MpOdbev9DVt2jSEh4fD19cX/v7+WL58OTIyMjBp0iQA5bfBLl26hDVr1gAAlixZAhcXF3h6egIon5dp/vz5mDJlik7bK1euxJAhQ2BjY6Ozbvr06Rg0aBBcXFyQm5uLuXPnorCwEGPHjr3vfSIiIqJHT40Dk7GxMbKzs3Ue/M7Pz4ednR1KS0v1bis0NBT5+fmYM2cOsrOz4eXlhe3bt8PV1RUAkJ2djYyMDG39srIyREZG4sKFC1AqlfDw8MBHH32EiRMnSto9c+YMkpOTsWvXrkr7zcrKQlhYGPLy8tC0aVN07doVBw8e1PZLREREdDeFEELUZAMjIyPk5OToBKa//voLHh4euHnz5gMdYH1VWFgIjUaDgoICWFpaGno4REREpIfafn7rfYXp008/BVD+rbYvv/wSFhYW2nWlpaXYu3ev9lYZERER0aNE78BU8ZttQggsW7ZM8lMiJiYmcHNzw7Jlyx78CImIiIgMTO/AdOHCBQBAjx49sHnzZlhZWdXZoIiIiIjqkxo/9J2QkFAX4yAiIiKqt2o1rUBWVhZ++OEHZGRkoKSkRLJuwYIFD2RgRERERPVFjQPTL7/8gmeeeQbu7u44ffo0vLy8cPHiRQgh0Llz57oYIxEREZFB1Xim78jISLz55ps4fvw4TE1NsWnTJmRmZiIoKAjPPfdcXYyRiIiIyKBqHJhOnTqlnRFbqVTi5s2bsLCwwJw5c/Dxxx8/8AESERERGVqNA5Nardb+dlvz5s1x7tw57bq8vLwHNzIiIiKieqLGzzB17doV+/fvR9u2bTFw4EC8+eabOHbsGDZv3oyuXbvWxRiJiIiIDKrGgWnBggW4du0aACAqKgrXrl1DXFwcnnjiCe3klkRERESPkhoFptLSUmRmZqJ9+/YAAHNzc8TExNTJwIiIiIjqixo9w2RsbIy+ffvi6tWrdTQcIiIiovqnxg99e3t74/z583UxFiIiIqJ6qcaB6T//+Q+mT5+Obdu2ITs7G4WFhZKFiIiI6FGjEEKImmxgZPRvxlIoFNo/CyGgUChQWlr64EZXjxUWFkKj0aCgoACWlpaGHg4RERHpobaf3/zxXSIiIiIZNQ5MQUFBdTEOIiIionqrxs8wERERET1uGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJKPGgeny5csIDw9H8+bNoVQqYWxsLFmIiIiIHjU1nlZg3LhxyMjIwHvvvQcHBwfJ5JVEREREj6IaB6bk5GTs27cPHTt2rIPhEBEREdU/Nb4l5+zsjBr+mgoRERFRg1bjwLRo0SLMmDEDFy9erIPhEBEREdU/Nb4lFxoaihs3bsDDwwPm5uZo1KiRZP2VK1ce2OCIiIiI6oMaB6ZFixbVwTCIiIiI6q8aB6axY8fWxTiIiIiI6i29AlNhYSEsLS21f65ORT0iIiKiR4VegcnKygrZ2dmws7NDkyZNKp17SQgBhUKB0tLSBz5IIiIiIkPSKzDt2bMH1tbW2j9zskoiIiJ6nCiEnpMqnT17Fk888URdj6fBKCwshEajQUFBAW9DEhERNRC1/fzWex6mVq1awdnZGWPGjMGqVas4DxMRERE9NvT+llxSUhKSkpKQmJiIiIgI3Lp1Cy4uLujZsyd69OiBHj16wNHRsS7HSkT0cBUUAEVFgJOT7rqsLKBxY0CjefjjIqKHTu8rTN26dcPMmTOxe/duXL16FQkJCXjhhRdw4cIFTJgwAS4uLmjdunWNBxATEwN3d3eYmprCx8cH+/btq7JucnIyAgMDYWNjAzMzM3h6emLhwoWSOqtXr4ZCodBZbt26Vet+iegxVFAA9OsHBAUBmZnSdZmZ5eX9+pXXI6JHXo3nYQKARo0aoXv37njyySfh7++PnTt3YsWKFTh79myN2omLi8PUqVMRExODwMBAfPHFF+jfvz9OnjwJFxcXnfpqtRoRERFo37491Go1kpOTMXHiRKjVakyYMEFbz9LSEqdPn5Zsa2pqWut+iegxVFQE5OYC588DwcFAYiLg7FweloKDy8sr6vEqE9EjT++HvgHg1q1bSElJQUJCAhITE/Hbb7/B3d0dQUFB6N69O4KCgmp0W87Pzw+dO3fG0qVLtWVt2rTBkCFDEB0drVcbzz77LNRqNdauXQug/ArT1KlTcfXq1Trtlw99Ez0G7g5HLVoAa9cC4eH/vq4IUUTUYNT281vvK0xBQUH47bff4OHhge7du2PKlCkICgqCvb19rQZcUlKCw4cPY8aMGZLykJAQpKSk6NVGamoqUlJSMHfuXEn5tWvX4OrqitLSUnTs2BEffPABOnXqdF/9FhcXo7i4WPtabgJPInoEODuXh6KK0BQYWF7OsET02NH7GaaUlBTY2tqiR48e6NWrF3r27FnrsAQAeXl5KC0t1WnD3t4eOTk51W7r5OQElUoFX19fTJ48GePHj9eu8/T0xOrVq/HDDz8gNjYWpqamCAwMRHp6+n31Gx0dDY1Go12c+Rcl0ePB2bn8ytLd1q5lWCJ6zOgdmK5evYrly5fD3NwcH3/8MRwdHeHt7Y2IiAh89913+Pvvv2s1gHsnwayYMbw6+/btw6FDh7Bs2TIsWrQIsbGx2nVdu3bF6NGj0aFDB3Tr1g0bN25Eq1at8Nlnn91Xv5GRkSgoKNAumfc+BEpEj6bMzPLbcHcLD9d9EJyIHml635JTq9Xo168f+vXrBwAoKipCcnIyEhISMG/ePDz//PNo2bIljh8/rld7tra2MDY21rmqk5ubK3vlyt3dHQDg7e2Ny5cvIyoqCmFhYZXWNTIywpNPPqm9wlTbflUqFVQqlex+EdEjpLpnmO5+EJyIHnl6X2G6l1qthrW1NaytrWFlZQWlUolTp07pvb2JiQl8fHwQHx8vKY+Pj0dAQIDe7QghJM8WVbY+LS0NDg4OD7RfInrEZWVJw1JiIhAQUP7fFi3+DU1ZWYYdJxE9FHpfYSorK8OhQ4eQmJiIhIQE7N+/H9evX4ejoyN69OiBJUuWoEePHjXqfNq0aQgPD4evry/8/f2xfPlyZGRkYNKkSQDKb4NdunQJa9asAQAsWbIELi4u8PT0BFA+L9P8+fMxZcoUbZuzZ89G165d0bJlSxQWFuLTTz9FWloalixZone/RERo3Biwsyv/891Xku5+ENzOrrweET3y9A5MTZo0wfXr1+Hg4IDg4GAsWLAAPXr0gIeHR607Dw0NRX5+PubMmYPs7Gx4eXlh+/btcHV1BQBkZ2cjIyNDW7+srAyRkZG4cOEClEolPDw88NFHH2HixInaOlevXsWECROQk5MDjUaDTp06Ye/evejSpYve/RIRQaMBduyofKZvZ2cgKYkzfRM9RvSeh+mLL75Ajx490KpVq7oeU4PAeZiIiIganjqfh+nuqzhEREREj5NaP/RNRERE9LhgYCIiIiKSwcBEREREJIOBiYiIiEgGAxMRERGRDAYmIiIiIhkMTEREREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIOBiYiIiEgGAxMRERGRDAYmIiIiIhkMTEREREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIOBiYiIiEgGAxMRERGRDAYmIiIiIhkMTEREREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIOBiYiIiEgGAxMRERGRDAYmIiIiIhkMTEREREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIOBiYiIiEiGwQNTTEwM3N3dYWpqCh8fH+zbt6/KusnJyQgMDISNjQ3MzMzg6emJhQsXSuqsWLEC3bp1g5WVFaysrNC7d2/8+uuvkjpRUVFQKBSSpVmzZnWyf0RERNTwKQ3ZeVxcHKZOnYqYmBgEBgbiiy++QP/+/XHy5Em4uLjo1Fer1YiIiED79u2hVquRnJyMiRMnQq1WY8KECQCAxMREhIWFISAgAKamppg3bx5CQkJw4sQJODo6attq164ddu/erX1tbGxc9ztMREREDZJCCCEM1bmfnx86d+6MpUuXasvatGmDIUOGIDo6Wq82nn32WajVaqxdu7bS9aWlpbCyssLnn3+OMWPGACi/wrR161akpaXVeuyFhYXQaDQoKCiApaVlrdshIiKih6e2n98GuyVXUlKCw4cPIyQkRFIeEhKClJQUvdpITU1FSkoKgoKCqqxz48YN3L59G9bW1pLy9PR0NG/eHO7u7hg5ciTOnz9fbV/FxcUoLCyULERERPR4MFhgysvLQ2lpKezt7SXl9vb2yMnJqXZbJycnqFQq+Pr6YvLkyRg/fnyVdWfMmAFHR0f07t1bW+bn54c1a9Zg586dWLFiBXJychAQEID8/Pwq24mOjoZGo9Euzs7Oeu4pERERNXQGf+hboVBIXgshdMrutW/fPhw6dAjLli3DokWLEBsbW2m9efPmITY2Fps3b4apqam2vH///hg2bBi8vb3Ru3dv/PTTTwCAr7/+uso+IyMjUVBQoF0yMzP13UUiIiJq4Az20LetrS2MjY11ribl5ubqXHW6l7u7OwDA29sbly9fRlRUFMLCwiR15s+fjw8//BC7d+9G+/btq21PrVbD29sb6enpVdZRqVRQqVTVtkNERESPJoNdYTIxMYGPjw/i4+Ml5fHx8QgICNC7HSEEiouLJWWffPIJPvjgA+zYsQO+vr6ybRQXF+PUqVNwcHDQu18iIiJ6fBh0WoFp06YhPDwcvr6+8Pf3x/Lly5GRkYFJkyYBKL8NdunSJaxZswYAsGTJEri4uMDT0xNA+bxM8+fPx5QpU7Rtzps3D++99x7Wr18PNzc37RUsCwsLWFhYAACmT5+OQYMGwcXFBbm5uZg7dy4KCwsxduzYh7n7RERE1EAYNDCFhoYiPz8fc+bMQXZ2Nry8vLB9+3a4uroCALKzs5GRkaGtX1ZWhsjISFy4cAFKpRIeHh746KOPMHHiRG2dmJgYlJSUYPjw4ZK+Zs2ahaioKABAVlYWwsLCkJeXh6ZNm6Jr1644ePCgtl8iIiKiuxl0HqaGjPMwERERNTwNbh4mIiIiooaCgYmIiIhIBgMTERERkQwGJiIiIiIZDExEREREMhiYiIiIiGQwMBERERHJYGAiIiIiksHARERERCSDgYmIiIhIBgMTERERkQwGJiIiIiIZDExEREREMhiYiIiIiGQwMBERERHJYGAiIiIiksHARERERCSDgYmIiIhIBgMTERERkQwGJiIiIiIZDExEREREMhiYiIiIiGQwMBERERHJYGAiIiIiksHARERERCSDgYmIiIhIBgMTERERkQwGJiIiIiIZDExEREREMhiYiIiIiGQwMBERERHJYGAiIiIiksHARERERCSDgYmIiIhIBgMTERERkQwGJiIiIiIZDExEREREMgwemGJiYuDu7g5TU1P4+Phg3759VdZNTk5GYGAgbGxsYGZmBk9PTyxcuFCn3qZNm9C2bVuoVCq0bdsWW7Zsua9+iYiI6PFm0MAUFxeHqVOn4t1330Vqaiq6deuG/v37IyMjo9L6arUaERER2Lt3L06dOoWZM2di5syZWL58ubbOgQMHEBoaivDwcBw5cgTh4eEYMWIE/ve//9W6XyIiInq8KYQQwlCd+/n5oXPnzli6dKm2rE2bNhgyZAiio6P1auPZZ5+FWq3G2rVrAQChoaEoLCzEzz//rK3Tr18/WFlZITY29oH1W1hYCI1Gg4KCAlhaWuq1DRERERlWbT+/DXaFqaSkBIcPH0ZISIikPCQkBCkpKXq1kZqaipSUFAQFBWnLDhw4oNNm3759tW3Wtt/i4mIUFhZKFiIiIno8GCww5eXlobS0FPb29pJye3t75OTkVLutk5MTVCoVfH19MXnyZIwfP167Licnp9o2a9tvdHQ0NBqNdnF2dtZrP4mIiKjhM/hD3wqFQvJaCKFTdq99+/bh0KFDWLZsGRYtWqS91VaTNmvab2RkJAoKCrRLZmZmtWMkIiKiR4fSUB3b2trC2NhY56pObm6uztWfe7m7uwMAvL29cfnyZURFRSEsLAwA0KxZs2rbrG2/KpUKKpVKv50jIiKiR4rBrjCZmJjAx8cH8fHxkvL4+HgEBATo3Y4QAsXFxdrX/v7+Om3u2rVL2+aD6peIiIgeHwa7wgQA06ZNQ3h4OHx9feHv74/ly5cjIyMDkyZNAlB+G+zSpUtYs2YNAGDJkiVwcXGBp6cngPJ5mebPn48pU6Zo23z99dfRvXt3fPzxxxg8eDC+//577N69G8nJyXr3S0RERHQ3gwam0NBQ5OfnY86cOcjOzoaXlxe2b98OV1dXAEB2drZkbqSysjJERkbiwoULUCqV8PDwwEcffYSJEydq6wQEBGDDhg2YOXMm3nvvPXh4eCAuLg5+fn5690tERER0N4POw9SQcR4mIiKihqfBzcNERERE1FAwMBERERHJYGAiIiIiksHARERERCSDgYmIiIhIBgMTERERkQwGJiIiIiIZDExEREREMhiYiIiIiGQwMBERERHJYGAiIiIiksHARERERCSDgYmIiIhIBgMTERERkQwGJiIiIiIZDExEREREMhiYiIiIiGQwMBERERHJYGAiIiIiksHARERERCSDgYmIiIhIBgMTERERkQwGJiIiIiIZDExEREREMhiYiIiIiGQwMBERERHJYGAiIiKi+qGgAMjKqnxdVlb5egNhYCIiIiLDKygA+vUDgoKAzEzpuszM8vJ+/QwWmhiYiIiIyPCKioDcXOD8eSA4+N/QlJlZ/vr8+fL1RUUGGR4DExERERmekxOQmAi0aPFvaEpJ+TcstWhRvt7JySDDUxqkVyIiIqJ7OTuXh6KKkBQYWF5eEZacnQ02NF5hIiIiovrD2RlYu1ZatnatQcMSwMBERERE9UlmJhAeLi0LD9d9EPwhY2AiIiKi+uHuB7xbtAD275c+02TA0MTARERERIaXlaX7gHdAgO6D4FXN01TH+NA3ERERGV7jxoCdXfmf737A++4Hwe3syusZAAMTERFRbRQUlM8JVNnX3LOyyj/YNZqHP66GSqMBduyo/Jg6OwNJSQY9pga/JRcTEwN3d3eYmprCx8cH+/btq7Lu5s2b0adPHzRt2hSWlpbw9/fHzp07JXWCg4OhUCh0loEDB2rrREVF6axv1qxZne0jERE9Yur5rNQNlkZT9TxLTk4GDaAGDUxxcXGYOnUq3n33XaSmpqJbt27o378/MjIyKq2/d+9e9OnTB9u3b8fhw4fRo0cPDBo0CKmpqdo6mzdvRnZ2tnY5fvw4jI2N8dxzz0naateunaTesWPH6nRfiYjoEVLPZ6WmB08hhBCG6tzPzw+dO3fG0qVLtWVt2rTBkCFDEB0drVcb7dq1Q2hoKN5///1K1y9atAjvv/8+srOzoVarAZRfYdq6dSvS0tJqPfbCwkJoNBoUFBTA0tKy1u0QEVEDde83utauLf/6+90PLRt47iDSVdvPb4NdYSopKcHhw4cREhIiKQ8JCUFKSopebZSVlaGoqAjW1tZV1lm5ciVGjhypDUsV0tPT0bx5c7i7u2PkyJE4f/58tX0VFxejsLBQshAR0WOs4mHkim9wBQYyLD3CDBaY8vLyUFpaCnt7e0m5vb09cnJy9Grjv//9L65fv44RI0ZUuv7XX3/F8ePHMX78eEm5n58f1qxZg507d2LFihXIyclBQEAA8vPzq+wrOjoaGo1GuzjzfwQiIqqns1LTg2fwh74VCoXktRBCp6wysbGxiIqKQlxcHOwqvoZ4j5UrV8LLywtdunSRlPfv3x/Dhg2Dt7c3evfujZ9++gkA8PXXX1fZX2RkJAoKCrRLpoFnHCUionqgns5KTQ+ewQKTra0tjI2Nda4m5ebm6lx1uldcXBxeeuklbNy4Eb179660zo0bN7Bhwwadq0uVUavV8Pb2Rnp6epV1VCoVLC0tJQsRET3G6vGs1PTgGSwwmZiYwMfHB/Hx8ZLy+Ph4BAQEVLldbGwsxo0bh/Xr10umCrjXxo0bUVxcjNGjR8uOpbi4GKdOnYKDg4P+O0BERI+vej4rNT14Bp24ctq0aQgPD4evry/8/f2xfPlyZGRkYNKkSQDKb4NdunQJa9asAVAelsaMGYPFixeja9eu2qtTZmZm0NwzN8PKlSsxZMgQ2NjY6PQ7ffp0DBo0CC4uLsjNzcXcuXNRWFiIsWPH1vEeExHRI6Gez0pND55BA1NoaCjy8/MxZ84cZGdnw8vLC9u3b4erqysAIDs7WzIn0xdffIE7d+5g8uTJmDx5srZ87NixWL16tfb1mTNnkJycjF27dlXab1ZWFsLCwpCXl4emTZuia9euOHjwoLZfIiKiatXzWanpwTPoPEwNGedhIiIianga3DxMRERERA0FAxMRERGRDAYmIiIiIhkMTEREREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIOBiYiIiEiGQX8apSGrmCC9sLDQwCMhIiIifVV8btf0h04YmGqpqKgIAOBc8YOLRERE1GAUFRVBU4Pf+uNvydVSWVkZ/vrrLzRu3BgKheKBtl1YWAhnZ2dkZmbyd+r0xGNWOzxutcPjVnM8ZrXD41ZzcsdMCIGioiI0b94cRkb6P5nEK0y1ZGRkBKd7f6H6AbO0tOT/IDXEY1Y7PG61w+NWczxmtcPjVnPVHbOaXFmqwIe+iYiIiGQwMBERERHJYGCqh1QqFWbNmgWVSmXooTQYPGa1w+NWOzxuNcdjVjs8bjVXV8eMD30TERERyeAVJiIiIiIZDExEREREMhiYiIiIiGQwMBERERHJYGB6yPbu3YtBgwahefPmUCgU2Lp1q+w2SUlJ8PHxgampKVq0aIFly5bV/UDrmZoet+zsbIwaNQqtW7eGkZERpk6d+lDGWZ/U9Jht3rwZffr0QdOmTWFpaQl/f3/s3Lnz4Qy2HqnpcUtOTkZgYCBsbGxgZmYGT09PLFy48OEMth6pzd9tFfbv3w+lUomOHTvW2fjqo5oes8TERCgUCp3ljz/+eDgDridqc64VFxfj3XffhaurK1QqFTw8PPDVV1/VqF8Gpofs+vXr6NChAz7//HO96l+4cAEDBgxAt27dkJqainfeeQevvfYaNm3aVMcjrV9qetyKi4vRtGlTvPvuu+jQoUMdj65+qukx27t3L/r06YPt27fj8OHD6NGjBwYNGoTU1NQ6Hmn9UtPjplarERERgb179+LUqVOYOXMmZs6cieXLl9fxSOuXmh63CgUFBRgzZgx69epVRyOrv2p7zE6fPo3s7Gzt0rJlyzoaYf1Um+M2YsQI/PLLL1i5ciVOnz6N2NhYeHp61qxjQQYDQGzZsqXaOm+99Zbw9PSUlE2cOFF07dq1DkdWv+lz3O4WFBQkXn/99TobT0NQ02NWoW3btmL27NkPfkANRG2P29ChQ8Xo0aMf/IAaiJoct9DQUDFz5kwxa9Ys0aFDhzodV32mzzFLSEgQAMQ///zzUMbUEOhz3H7++Weh0WhEfn7+ffXFK0z13IEDBxASEiIp69u3Lw4dOoTbt28baFT0OCgrK0NRURGsra0NPZQGJTU1FSkpKQgKCjL0UOq9VatW4dy5c5g1a5ahh9KgdOrUCQ4ODujVqxcSEhIMPZx674cffoCvry/mzZsHR0dHtGrVCtOnT8fNmzdr1A5/fLeey8nJgb29vaTM3t4ed+7cQV5eHhwcHAw0MnrU/fe//8X169cxYsQIQw+lQXBycsLff/+NO3fuICoqCuPHjzf0kOq19PR0zJgxA/v27YNSyY8ifTg4OGD58uXw8fFBcXEx1q5di169eiExMRHdu3c39PDqrfPnzyM5ORmmpqbYsmUL8vLy8Oqrr+LKlSs1eo6JZ2kDoFAoJK/F/5+c/d5yogclNjYWUVFR+P7772FnZ2fo4TQI+/btw7Vr13Dw4EHMmDEDTzzxBMLCwgw9rHqptLQUo0aNwuzZs9GqVStDD6fBaN26NVq3bq197e/vj8zMTMyfP5+BqRplZWVQKBRYt24dNBoNAGDBggUYPnw4lixZAjMzM73aYWCq55o1a4acnBxJWW5uLpRKJWxsbAw0KnqUxcXF4aWXXsK3336L3r17G3o4DYa7uzsAwNvbG5cvX0ZUVBQDUxWKiopw6NAhpKamIiIiAkD5h5oQAkqlErt27ULPnj0NPMqGoWvXrvjmm28MPYx6zcHBAY6OjtqwBABt2rSBEAJZWVl6PzTPwFTP+fv748cff5SU7dq1C76+vmjUqJGBRkWPqtjYWLz44ouIjY3FwIEDDT2cBksIgeLiYkMPo96ytLTEsWPHJGUxMTHYs2cPvvvuO234JHmpqal8NENGYGAgvv32W1y7dg0WFhYAgDNnzsDIyAhOTk56t8PA9JBdu3YNZ8+e1b6+cOEC0tLSYG1tDRcXF0RGRuLSpUtYs2YNAGDSpEn4/PPPMW3aNLz88ss4cOAAVq5cidjYWEPtgkHU9LgBQFpamnbbv//+G2lpaTAxMUHbtm0f9vANoqbHLDY2FmPGjMHixYvRtWtX7ZVNMzMzyb/MHnU1PW5LliyBi4uL9ivKycnJmD9/PqZMmWKQ8RtKTY6bkZERvLy8JNvb2dnB1NRUp/xRVtNzbdGiRXBzc0O7du1QUlKCb775Bps2bXrsppmp6XEbNWoUPvjgA7zwwguYPXs28vLy8H//93948cUX9b4dB4DTCjxsFV8LvXcZO3asEEKIsWPHiqCgIMk2iYmJolOnTsLExES4ubmJpUuXPvyBG1htjltl9V1dXR/62A2lpscsKCio2vqPi5oet08//VS0a9dOmJubC0tLS9GpUycRExMjSktLDbMDBlKb/0fv9jhOK1DTY/bxxx8LDw8PYWpqKqysrMRTTz0lfvrpJ8MM3oBqc66dOnVK9O7dW5iZmQknJycxbdo0cePGjRr1qxDi/z9BTERERESV4jxMRERERDIYmIiIiIhkMDARERERyWBgIiIiIpLBwEREREQkg4GJiIiISAYDExEREZEMBiYieuQJITBhwgRYW1tDoVBoZ4F/lK1cuRIhISHa1+PGjcOQIUOq3SY4OBhTp06tdZ/Hjh2Dk5MTrl+/Xus2iOorBiaiR9S4ceOgUCh0ln79+undRmJiIhQKBa5evVp3A30IduzYgdWrV2Pbtm3Izs5+5H9+o7i4GO+//z7ee++9Gm23efNmfPDBB9rXbm5uWLRokd7be3t7o0uXLli4cGGN+iVqCBiYiB5h/fr1Q3Z2tmSpi98hLCkpeeBtPkjnzp2Dg4MDAgIC0KxZMyiVj/bPaG7atAkWFhbo1q1bjbaztrZG48aN76vvF154AUuXLkVpael9tUNU3zAwET3CVCoVmjVrJlmsrKy06xUKBb788ksMHToU5ubmaNmyJX744QcAwMWLF9GjRw8AgJWVFRQKBcaNGweg/NZNREQEpk2bBltbW/Tp0wcAkJSUhC5dukClUsHBwQEzZszAnTt3tP1VbBcREYEmTZrAxsYGM2fORMUvNM2ZMwfe3t46++Hj44P333+/yv2srt9x48ZhypQpyMjIgEKhgJubW6VtrF69Gk2aNMG2bdvQunVrmJubY/jw4bh+/Tq+/vpruLm5wcrKClOmTJGEgX/++QdjxoyBlZUVzM3N0b9/f6SnpwMACgoKYGZmhh07dkj62rx5M9RqNa5duwYAuHTpEkJDQ2FlZQUbGxsMHjwYFy9e1NZPTExEly5doFar0aRJEwQGBuLPP/+s8nhs2LABzzzzTKXrZs+eDTs7O1haWmLixImSsHv3Lbng4GD8+eefeOONN7RXJwHgzz//xKBBg2BlZQW1Wo127dph+/bt2jb69u2L/Px8JCUlVTk+ogbpAfwOHhHVQ2PHjhWDBw+utg4A4eTkJNavXy/S09PFa6+9JiwsLER+fr64c+eO2LRpkwAgTp8+LbKzs8XVq1eFEOU/1GthYSH+7//+T/zxxx/i1KlTIisrS5ibm4tXX31VnDp1SmzZskXY2tqKWbNmafur2O71118Xf/zxh/jmm2+Eubm5WL58uRBCiMzMTGFkZCR+/fVX7TZHjhwRCoVCnDt3rtJ9kOv36tWrYs6cOcLJyUlkZ2eL3NzcSttZtWqVaNSokejTp4/4/fffRVJSkrCxsREhISFixIgR4sSJE+LHH38UJiYmYsOGDdrtnnnmGdGmTRuxd+9ekZaWJvr27SueeOIJUVJSIoQQYtiwYWL06NGSvoYNGybCwsKEEEJcv35dtGzZUrz44ovi6NGj4uTJk2LUqFGidevWori4WNy+fVtoNBoxffp0cfbsWXHy5EmxevVq8eeff1b5vjZp0kQyRiHKzwcLCwsRGhoqjh8/LrZt2yaaNm0q3nnnHcn78/rrrwshhMjPzxdOTk5izpw5Ijs7W2RnZwshhBg4cKDo06ePOHr0qDh37pz48ccfRVJSkqSvLl26iKioqCrHR9QQMTARPaLGjh0rjI2NhVqtlixz5szR1gEgZs6cqX197do1oVAoxM8//yyE+PdXwf/55x9J20FBQaJjx46SsnfeeUe0bt1alJWVacuWLFkiLCwsRGlpqXa7Nm3aSOq8/fbbok2bNtrX/fv3F6+88or29dSpU0VwcHCV+6lPvwsXLhSurq5VtiFEeWACIM6ePastmzhxojA3NxdFRUXasr59+4qJEycKIYQ4c+aMACD279+vXZ+XlyfMzMzExo0bhRBCbN68WVhYWIjr168LIYQoKCgQpqam2l+ZX7lypc74i4uLhZmZmdi5c6fIz88XAERiYmK146/wzz//CABi7969kvKxY8cKa2tr7TiEEGLp0qU6709FYBJCCFdXV7Fw4UJJO97e3rJhaOjQoWLcuHF6jZeooeAtOaJHWI8ePZCWliZZJk+eLKnTvn177Z/VajUaN26M3Nxc2bZ9fX0lr0+dOgV/f3/trRsACAwMxLVr15CVlaUt69q1q6SOv78/0tPTtbe5Xn75ZcTGxuLWrVu4ffs21q1bhxdffLHKcejbrz7Mzc3h4eGhfW1vbw83NzdYWFhIyiqOz6lTp6BUKuHn56ddb2Njg9atW+PUqVMAgIEDB0KpVGpvdW7atAmNGzfWfoPt8OHDOHv2LBo3bgwLCwtYWFjA2toat27dwrlz52BtbY1x48ahb9++GDRoEBYvXozs7Owq9+HmzZsAAFNTU511HTp0gLm5ufa1v78/rl27hszMTL2P0WuvvYa5c+ciMDAQs2bNwtGjR3XqmJmZ4caNG3q3SdQQMDARPcLUajWeeOIJyWJtbS2p06hRI8lrhUKBsrIyvdq+mxBCEloqyira1NegQYOgUqmwZcsW/PjjjyguLsawYcOqrP+g+gUqPxbVHZ+Kfqobk4mJCYYPH47169cDANavX4/Q0FDtg+dlZWXw8fHRCbZnzpzBqFGjAACrVq3CgQMHEBAQgLi4OLRq1QoHDx6stG8bGxsoFAr8888/eu93TY7T+PHjcf78eYSHh+PYsWPw9fXFZ599Jqlz5coVNG3aVO82iRoCBiYiqpKJiQkA6PWNp7Zt2yIlJUUSIlJSUtC4cWM4Ojpqy+79oD948CBatmwJY2NjAIBSqcTYsWOxatUqrFq1CiNHjpRcFaltv3Whbdu2uHPnDv73v/9py/Lz83HmzBm0adNGW/b8889jx44dOHHiBBISEvD8889r13Xu3Bnp6emws7PTCbcajUZbr1OnToiMjERKSgq8vLy0AexeJiYmaNu2LU6ePKmz7siRI9orUED5sbewsICTk1OVbVX23js7O2PSpEnYvHkz3nzzTaxYsUKy/vjx4+jUqVOlbRI1VAxMRI+w4uJi5OTkSJa8vDy9t3d1dYVCocC2bdvw999/a7/VVZlXX30VmZmZmDJlCv744w98//33mDVrFqZNmwYjo3//qsnMzMS0adNw+vRpxMbG4rPPPsPrr78uaWv8+PHYs2cPfv7552pvx9Wk37rQsmVLDB48GC+//DKSk5Nx5MgRjB49Go6Ojhg8eLC2XlBQEOzt7fH888/Dzc0NXbt21a57/vnnYWtri8GDB2Pfvn24cOECkpKS8PrrryMrKwsXLlxAZGQkDhw4gD///BO7du3SCWT36tu3L5KTk3XKS0pK8NJLL+HkyZP4+eefMWvWLERERFR5nNzc3LB3715cunRJe95MnToVO3fuxIULF/D7779jz549krFcvHgRly5dQu/evWt8PInqMwYmokfYjh074ODgIFmeeuopvbd3dHTE7NmzMWPGDNjb2yMiIqLautu3b8evv/6KDh06YNKkSXjppZcwc+ZMSb0xY8bg5s2b6NKlCyZPnowpU6ZgwoQJkjotW7ZEQEAAWrduLXk+6H76rSurVq2Cj48Pnn76afj7+0MIge3bt0tu5SkUCoSFheHIkSOSq0tA+XNTe/fuhYuLC5599lm0adMGL774Im7evAlLS0uYm5vjjz/+wLBhw9CqVStMmDABERERmDhxYpVjevnll7F9+3YUFBRIynv16oWWLVuie/fuGDFiBAYNGoSoqKgq25kzZw4uXrwIDw8P7S220tJSTJ48GW3atEG/fv3QunVrxMTEaLeJjY1FSEgIXF1da3IYieo9hajqJjwR0QMWHByMjh07ys4eLYSAp6cnJk6ciGnTpj2cwT1iRowYob2N97AUFxejZcuWiI2NRWBg4EPrl+hh4BUmIqpXcnNzsWDBAly6dAkvvPCCoYfTYH3yySeSb/c9DH/++SfeffddhiV6JD3avw9ARA2Ovb09bG1tsXz5csms5FQzrq6umDJlykPts1WrVmjVqtVD7ZPoYeEtOSIiIiIZvCVHREREJIOBiYiIiEgGAxMRERGRDAYmIiIiIhkMTEREREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJOP/ATidY2mHWCGgAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "computeEntropyForAllPlayers()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14fc5575-32be-494c-88a3-2ea2a241fdf8",
   "metadata": {},
   "source": [
    "7. _Challenge_: are these correlation values statistically significant? Look up theory on how to compute whether a correlation value is statistically significant. To add this to the code above, you can check out the other return values from the `stats.pearson()` or `stats.spearmanr()` correlation functions.\n",
    "\n",
    "We will continue to investigate relationships between variables in this data set once we have learned about the mutual information."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f847f202-c739-4437-bb24-033ff58d1e32",
   "metadata": {},
   "source": [
    "# Stage 3 - Conditional entropy calculations\n",
    "\n",
    "Take a moment to reflect on our initial questions, right up the top of this notebook.\n",
    "\n",
    "We will now analyse the conditional uncertainty in the player's moves, given their previous move, and consider whether this relates to their performance in the game. (Do you have a hypothesis on this?).\n",
    "_The coding is very similar to what you already did in stage 2 previously._\n",
    "\n",
    "1. See the function `computeConditionalEntropyForPlayer(name)` below.\n",
    "This aims to compute the entropy of moves for a given named player, conditioned on their previous move, over all the iterations in all of their games. The code retrieves the data for each game of this player using `loadGamesForPlayer(name)`, then loops over each game. Fill out the missing parts of code:\n",
    "    * In the loop, pull out the moves for that player, their previous moves (and the results on the current, not previous, move), and append them into the arrays used to store these values over all iterations. Take care:\n",
    "        * You can only pull out moves which have a paired sample of a previous move in the given game. This means the moves from the 2nd iteration onwards. A helpful hint is that if you had a 2D matrix data, and you wanted to pull out the first column of its contents, but only from the 2nd row onwards, you could first pull out the first column as `myColumn = data[:,0]` and then pull the 2nd row onwards as `myColumn[1:]`. (You could do this in one go as: `data[1:,0]`).\n",
    "        * Similarly, you can only pull out previous moves which have a paired sample of a next move in the given game. This means the moves up to the 2nd last iteration. A helpful hint there is that if you had a 2D matrix data, and you wanted to pull out the first column 1 of its contents, but only up to the 2nd last row, you would first pull out the first column as `myColumn = data[:,0]` and then pull out all rows but the last as `myColumn[:-1]`. (You could do this in one go as: `data[:-1,0]`).\n",
    "        * You should also only pull out samples of results that relate to the current (but not previous) moves.\n",
    "    * Compute the conditional entropy over the players' moves given their previous moves, using your (or my) `simpleinfotheory.conditionalentropyempirical()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "12ecef23-76f6-4416-9dfa-9674e45ec5b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"function computeConditionalEntropyForPlayer()\n",
    "\n",
    "Compute the conditional entropy of moves for a given player, conditioned on\n",
    " their previous move across all games/iterations\n",
    "\n",
    "Inputs:\n",
    "- name - name of the player\n",
    "- verbose - whether to print entropy out\n",
    "\n",
    "Outputs:\n",
    "- calculatedEntropy\n",
    "- winRate\n",
    "- lossRate\n",
    "- numGames\n",
    "\n",
    "Copyright (C) 2020-, Julio Correa, Joseph T. Lizier\n",
    "Distributed under GNU General Public License v3\n",
    "\"\"\"\n",
    "def computeConditionalEntropyForPlayer(name: str, verbose: bool=False):\n",
    "\n",
    "    # Step 1: load all of the player's games' data:\n",
    "    games = sprutils.loadGamesForPlayer(name)\n",
    "    \n",
    "    # Step 2: the player's moves are in the first column, pull these from\n",
    "    #  each game into arrays of samples that we can compute conditional entropy on:\n",
    "    nextMoves = np.empty((0,1)) # empty column\n",
    "    previousMoves = np.empty((0,1)) # empty column\n",
    "    results = np.empty((0,1)) # empty column\n",
    "    \n",
    "    for gm in games:\n",
    "        # First column of numpy array gm is the player's move, second is opponent's\n",
    "        #  and third is the result.\n",
    "        # Pull out the player's moves in this game (first column of gm):\n",
    "        #  (reshape is required to keep it as a column rather than row vector)\n",
    "        movesInThisGame = gm[:,0].reshape(gm.shape[0],1)\n",
    "        # Pull out the results in this game (third column of gm) as a column:\n",
    "        resultsInThisGame = gm[:,2].reshape(gm.shape[0],1)\n",
    "        \n",
    "        # Append this player's moves to the array we're storing over all iterations.\n",
    "        #  TAKE CARE: Can we take all samples here, or only a limited number that\n",
    "        #  we're able to match up properly to compute conditional entropy?\n",
    "        nextMoves = np.row_stack((nextMoves, movesInThisGame[1:])) if nextMoves.size else movesInThisGame[1:]\n",
    "        previousMoves = np.row_stack((previousMoves, movesInThisGame[:-1])) if previousMoves.size else movesInThisGame[:-1]\n",
    "        # Append this player's results to the array over all iterations:\n",
    "        #  Which results do we want here -- those of the previous iteration or this one?\n",
    "        results = np.row_stack((results, resultsInThisGame[1:])) if results.size else resultsInThisGame[1:]\n",
    "    \n",
    "    # Step 3: compute the condtional entropy for this player's moves using our existing scripts:\n",
    "    calculatedEntropy = simpleinfotheory.conditionalentropyempirical(nextMoves, previousMoves)\n",
    "    \n",
    "    # Step 4: compute the win and loss rates:\n",
    "    winRate = np.sum(results == 1)/len(results)\n",
    "    lossRate = np.sum(results == -1)/len(results)\n",
    "    numGames = len(results)\n",
    "\n",
    "    if verbose:\n",
    "        print('Conditional entropy for {} over {} iterations: {:.04f}'.\\\n",
    "                format(name, numGames, calculatedEntropy))\n",
    "                          \n",
    "    return calculatedEntropy, winRate, lossRate, numGames"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebd57e5f-11f8-47e7-97df-b8b698570f36",
   "metadata": {},
   "source": [
    "2. Call the script for a few different players, e.g. `computeConditionalEntropyForPlayer('Joe')`, and compare.\n",
    "3. Now call it to compute the conditional entropy using samples for all players' data in the one calculation: `computeConditionalEntropyForPlayer('*')`. What implicit assumption are we making when we analyse the data in this way?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0175f515-e9e4-4a23-9720-819164641a91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional entropy for Player1 over 38 iterations: 1.5188\n",
      "Conditional entropy for * over 290 iterations: 1.5602\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.5601520681645509, 0.36551724137931035, 0.36551724137931035, 290)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "computeConditionalEntropyForPlayer(players[0], True)\n",
    "computeConditionalEntropyForPlayer('*', True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d1a270a-1258-498b-ab13-e93e8ecba317",
   "metadata": {},
   "source": [
    "4. See the function `computeConditionalEntropyForAllPlayers()` below.\n",
    "This aims to compute conditional entropy of moves for each player in turn (considering each player separately), then plots these, and looks for relationships between conditional entropy and win/loss rates. Fill out the missing parts of code:\n",
    "    * In the loop over player names, use our previous function `computeConditionalEntropyForPlayer()` to compute the conditional entropy for that player.\n",
    "    * Once we have the conditional entropy for each player and their win / loss ratios, compute the correlation between conditional entropy and win ratio, and entropy and loss ratio. _HINT_: Use the `stats.pearson()` or `stats.spearmanr()` correlation functions from the `scipy.stats` library -- make sure that you check what is returned from this function call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30c27c2a-a8b2-4808-ae9e-42a2c6e5a621",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"function computeConditionalEntropyForAllPlayers()\n",
    "\n",
    "Compute the conditional entropy of moves for each player, conditioned on their previous move,\n",
    " across all games/iterations\n",
    "\n",
    "Outputs:\n",
    "- names\n",
    "- entropies\n",
    "- winRates\n",
    "- lossRates\n",
    "\n",
    "Copyright (C) 2020-, Julio Correa, Joseph T. Lizier\n",
    "Distributed under GNU General Public License v3\n",
    "\"\"\"\n",
    "def computeConditionalEntropyForAllPlayers():\n",
    "\n",
    "    # Step 1: load all of the player's names:\n",
    "    names = sprutils.listPlayers()\n",
    "    # Step 2: compute entropy for each player\n",
    "    index = 0\n",
    "    entropies = np.zeros(len(names))\n",
    "    winRates = np.zeros(len(names))\n",
    "    lossRates = np.zeros(len(names))\n",
    "    \n",
    "    for name in names:\n",
    "        # Compute the entropy for the moves of this player.\n",
    "        # HINT: use the script that you just completed passing in name\n",
    "        calculatedEntropy, winRate, lossRate, numGames = computeConditionalEntropyForPlayer(name)\n",
    "        print('{} = {:.03f} bits,\\twin rate = {:.03f},\\tloss rate = {:.03f}, num games = {}'.\\\n",
    "              format(name, calculatedEntropy, winRate, lossRate, numGames))\n",
    "\n",
    "        entropies[index] = calculatedEntropy\n",
    "        winRates[index] = winRate\n",
    "        lossRates[index] = lossRate\n",
    "\n",
    "        index += 1\n",
    "   \n",
    "    # Plot the winRates and lossRates versus entropies:\n",
    "    plt.figure();\n",
    "    plt.scatter(entropies, winRates, c='red', marker='x');\n",
    "    plt.title('Win rates versus cond entropies of single players')\n",
    "    plt.xlabel('Entropy of moves (bits)')\n",
    "    plt.ylabel('Win rate')\n",
    "    \n",
    "    plt.figure();\n",
    "    plt.scatter(entropies, lossRates, c='red', marker='x');\n",
    "    plt.title('Loss rates versus cond entropies of single players')\n",
    "    plt.xlabel('Entropy of moves (bits)')\n",
    "    plt.ylabel('Loss rate')\n",
    "\n",
    "    # Compute correlations and check if these are statistically significant:\n",
    "    # Are these statistically significant?\n",
    "    winToEntropyCorr, winToEntropyCorrPValue = stats.pearsonr(winRates,entropies)\n",
    "    lossToEntropyCorr, lossToEntropyCorrPValue = stats.pearsonr(lossRates,entropies)\n",
    "\n",
    "    print('Correlation of win  rate to entropy is: {:.04f} (pValue {:.04f})'.\\\n",
    "        format(winToEntropyCorr, winToEntropyCorrPValue))\n",
    "    print('Correlation of loss rate to entropy is: {:.04f} (pValue: {:.04f})'.\\\n",
    "        format(lossToEntropyCorr, lossToEntropyCorrPValue))\n",
    "\n",
    "    return names, entropies, winRates, lossRates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db7d11fd-a292-4b38-8509-d3b274f0b52e",
   "metadata": {},
   "source": [
    "5. Call the script to see the conditional entropies of each player, the plots and correlation analyses on how this related to performance. Whose was most (conditionally) uncertainty? Did this correlate to wins? What about losses? Does this match your hypothesis?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2ee18289-7e48-4a4d-a1b1-d80838b7d0a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Player1 = 1.519 bits,\twin rate = 0.421,\tloss rate = 0.395, num games = 38\n",
      "Player2 = 1.147 bits,\twin rate = 0.342,\tloss rate = 0.447, num games = 38\n",
      "Player3 = 1.168 bits,\twin rate = 0.447,\tloss rate = 0.368, num games = 38\n",
      "Player4 = 1.464 bits,\twin rate = 0.410,\tloss rate = 0.282, num games = 39\n",
      "Player5 = 1.440 bits,\twin rate = 0.282,\tloss rate = 0.410, num games = 39\n",
      "Player6 = 0.730 bits,\twin rate = 0.408,\tloss rate = 0.265, num games = 49\n",
      "Player7 = 1.407 bits,\twin rate = 0.265,\tloss rate = 0.408, num games = 49\n",
      "Correlation of win  rate to entropy is: -0.2820 (pValue 0.5401)\n",
      "Correlation of loss rate to entropy is: 0.4219 (pValue: 0.3457)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(['Player1', 'Player2', 'Player3', 'Player4', 'Player5', 'Player6', 'Player7'],\n",
       " array([1.51878492, 1.14747619, 1.16839833, 1.4636343 , 1.44030896,\n",
       "        0.72993957, 1.40677332]),\n",
       " array([0.42105263, 0.34210526, 0.44736842, 0.41025641, 0.28205128,\n",
       "        0.40816327, 0.26530612]),\n",
       " array([0.39473684, 0.44736842, 0.36842105, 0.28205128, 0.41025641,\n",
       "        0.26530612, 0.40816327]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "computeConditionalEntropyForAllPlayers()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed568f2c-fcaa-40c7-946d-03e392da9786",
   "metadata": {},
   "source": [
    "6. _Challenge_: are these correlation values statistically significant? As per stage 2, look up theory on how to compute whether a correlation value is statistically significant. To add this to the code above, you can check out the other return values from the `stats.pearson()` or `stats.spearmanr()` correlation functions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e908780-5e9a-42ea-b5ad-f37e4effe453",
   "metadata": {},
   "source": [
    "# Stage 4 - Mutual information calculations\n",
    "\n",
    "We will now analyse the mutual information in the player's previous moves to their next move, and consider whether this relates to their performance in the game. Different to the above, here we're going to look at relationships not only to the player's own previous move but also to their opponent's previous move. (Do you have a hypothesis on this?).\n",
    "_The coding is very similar to what you already did in stage 3 above._\n",
    "\n",
    "1. See the function `computeConditionalEntropyForPlayer(name)` below.\n",
    "This aims to compute the mutual information of moves for a given named player to their previous move (or those of their opponent), over all the iterations in all of their games. The code retrieves the data for each game of this player using `loadGamesForPlayer(name)`, then loops over each game. Fill out the missing parts of code:\n",
    "    * In the loop, pull out the moves for that player, their previous moves or that of their opponent (and the results on the current, not previous, move), and append them into the arrays used to store these values over all iterations. Take note of how you performed the similar operations for the conditional entropy.\n",
    "    * Compute the mutual information between the moves and previous moves, using our `simpleinfotheory.mutualinformationempirical()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "32b3f184-a71e-42ea-b248-c13102c553a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"function computeMutualInformationForPlayer()\n",
    "\n",
    "Compute the mutual information of moves for a given player with their own\n",
    " previous move, or the previous move of their opponent\n",
    "\n",
    "Inputs:\n",
    "- name - name of the player\n",
    "- fromSelf (boolean, default True) - if true, take MI from the player's own previous move; if false\n",
    "    take MI from opponent's previous move.\n",
    "- verbose - whether to print entropy out\n",
    "\n",
    "Outputs:\n",
    "- calculatedMI\n",
    "- winRate\n",
    "- lossRate\n",
    "- numGames\n",
    "\n",
    "Copyright (C) 2020-, Julio Correa, Joseph T. Lizier\n",
    "Distributed under GNU General Public License v3\n",
    "\"\"\"\n",
    "def computeMutualInformationForPlayer(name: str, fromSelf: bool=True, verbose: bool=False):\n",
    "\n",
    "    # Step 1: load all of the player's games' data:\n",
    "    games = sprutils.loadGamesForPlayer(name)\n",
    "    \n",
    "    # Step 2: the player's moves are in the first column, pull these from\n",
    "    #  each game into arrays of samples that we can compute mutual info on:\n",
    "    nextMoves = np.empty((0,1)) # empty column\n",
    "    previousMoves = np.empty((0,1)) # empty column\n",
    "    results = np.empty((0,1)) # empty column\n",
    "    \n",
    "    for gm in games:\n",
    "        # First column of numpy array gm is the player's move, second is opponent's\n",
    "        #  and third is the result.\n",
    "        # Pull out the player's moves in this game (first column of gm):\n",
    "        #  (reshape is required to keep it as a column rather than row vector)\n",
    "        movesInThisGame = gm[:,0].reshape(gm.shape[0],1)\n",
    "        # Pull out the opponent's moves in this game (second column of gm):\n",
    "        opponentsMovesInThisGame = gm[:,1].reshape(gm.shape[0],1)\n",
    "        # Pull out the results in this game (third column of gm) as a column:\n",
    "        resultsInThisGame = gm[:,2].reshape(gm.shape[0],1)\n",
    "        \n",
    "        # Append this player's moves to the array we're storing over all iterations.\n",
    "        #  TAKE CARE: Can we take all samples here, or only a limited number that\n",
    "        #  we're able to match up properly to compute mutual information?\n",
    "        nextMoves = np.row_stack((nextMoves, movesInThisGame[1:])) if nextMoves.size else movesInThisGame[1:]\n",
    "        if fromSelf:\n",
    "            # Grab the previous moves from this player:\n",
    "\t\t\t# HINT: This will be the same thing you did in computeConditionalEntropyForPlayer:\n",
    "            previousMoves = np.row_stack((previousMoves, movesInThisGame[:-1])) if previousMoves.size else movesInThisGame[:-1]\n",
    "        else:\n",
    "            # Grab the previous moves from their opponent:\n",
    "            previousMoves = np.row_stack((previousMoves, opponentsMovesInThisGame[:-1])) if previousMoves.size else opponentsMovesInThisGame[:-1]\n",
    "        # Append this player's results to the array over all iterations:\n",
    "        #  Which results do we want here -- those of the previous iteration or this one?\n",
    "        results = np.row_stack((results, resultsInThisGame[1:])) if results.size else resultsInThisGame[1:]\n",
    "    \n",
    "    # Step 3: compute the mutual information for this player's moves using our existing scripts:\n",
    "    calculatedMI = simpleinfotheory.mutualinformationempirical(nextMoves, previousMoves)[0] # Just pulling the first element of the tuple\n",
    "    \n",
    "    # Step 4: compute the win and loss rates:\n",
    "    winRate = np.sum(results == 1)/len(results)\n",
    "    lossRate = np.sum(results == -1)/len(results)\n",
    "    numGames = len(results)\n",
    "\n",
    "    if verbose:\n",
    "        print('MI for {} over {} iterations: {:.04f} bits'.\\\n",
    "                format(name, numGames, calculatedMI))\n",
    "                          \n",
    "    return calculatedMI, winRate, lossRate, numGames"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46adc1ee-3d73-4622-b131-4b547b918892",
   "metadata": {},
   "source": [
    "2. Call the script for a few different players, e.g. `computeMutualInformationForPlayer('Joe', True)`, and compare.\n",
    "3. Now call it to compute the mutual information using samples for all players' data in the one calculation: `computeMutualInformationForPlayer('*', True)`. What implicit assumption are we making when we analyse the data in this way?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da0e86fa-73b0-41d1-b4d1-eda9bb3019de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MI for Player1 over 38 iterations: 0.0591 bits\n",
      "MI for Player1 over 38 iterations: 0.1230 bits\n",
      "MI for * over 290 iterations: 0.0161 bits\n",
      "MI for * over 290 iterations: 0.0197 bits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.01965213650181097, 0.36551724137931035, 0.36551724137931035, 290)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "computeMutualInformationForPlayer(players[0], True, True)\n",
    "computeMutualInformationForPlayer(players[0], False, True)\n",
    "computeMutualInformationForPlayer('*', True, True)\n",
    "computeMutualInformationForPlayer('*', False, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f9a5afc-5da3-4cad-9449-934cac0e6792",
   "metadata": {},
   "source": [
    "4. See the function `computeMutualInformationForAllPlayers()` below.\n",
    "This aims to compute the mutual information of moves to previous moves for each player in turn (considering each player separately), then plots these, and looks for relationships between the mutual information and win/loss rates. Fill out the missing parts of code:\n",
    "    * In the loop over player names, use our previous function `computeMutualInformationForPlayer()` to compute the mutual information for that player. Take care: are we computing MI from our own previous moves or that of our opponent?\n",
    "    * Once we have the mutual information for each player and their win / loss ratios, compute the correlation between mutual information and win ratio, and entropy and loss ratio. _HINT_: Use the `stats.pearson()` or `stats.spearmanr()` correlation functions from the `scipy.stats` library -- make sure that you check what is returned from this function call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cc376fbd-6c8e-431e-ba33-a7cb03003072",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"function computeMutualInformationForAllPlayers()\n",
    "\n",
    "Compute the mutual information of moves for each player with their own\n",
    " previous move, or the previous move of their opponent, across all games/iterations.\n",
    "\n",
    "Inputs:\n",
    "- fromSelf (boolean, default True) - if true, take MI from the player's own previous move; if false\n",
    "    take MI from opponent's previous move.\n",
    "\n",
    "Outputs:\n",
    "- names\n",
    "- mutualInfos\n",
    "- winRates\n",
    "- lossRates\n",
    "\n",
    "Copyright (C) 2020-, Julio Correa, Joseph T. Lizier\n",
    "Distributed under GNU General Public License v3\n",
    "\"\"\"\n",
    "def computeMutualInformationForAllPlayers(fromSelf: bool = True):\n",
    "\n",
    "    # Step 1: load all of the player's names:\n",
    "    names = sprutils.listPlayers()\n",
    "    # Step 2: compute mutual info for each player\n",
    "    index = 0\n",
    "    mutualInfos = np.zeros(len(names))\n",
    "    winRates = np.zeros(len(names))\n",
    "    lossRates = np.zeros(len(names))\n",
    "    \n",
    "    for name in names:\n",
    "        # Compute the mutual info for the moves of this player.\n",
    "        # HINT: use the script that you just completed passing in name and fromSelf\n",
    "        calculatedMI, winRate, lossRate, numGames = computeMutualInformationForPlayer(name, fromSelf)\n",
    "        print('{} = {:.03f} bits,\\twin rate = {:.03f},\\tloss rate = {:.03f}, num games = {}'.\\\n",
    "              format(name, calculatedMI, winRate, lossRate, numGames))\n",
    "\n",
    "        mutualInfos[index] = calculatedMI\n",
    "        winRates[index] = winRate\n",
    "        lossRates[index] = lossRate\n",
    "\n",
    "        index += 1\n",
    "   \n",
    "    # Plot the winRates and lossRates versus mutualInfos:\n",
    "    plt.figure();\n",
    "    plt.scatter(mutualInfos, winRates, c='red', marker='x');\n",
    "    plt.title('Win rates versus mutual information for single players')\n",
    "    plt.xlabel('Mutual information of moves (bits)')\n",
    "    plt.ylabel('Win rate')\n",
    "    \n",
    "    plt.figure();\n",
    "    plt.scatter(mutualInfos, lossRates, c='red', marker='x');\n",
    "    plt.title('Loss rates versus mutual information of single players')\n",
    "    plt.xlabel('Mutual information of moves (bits)')\n",
    "    plt.ylabel('Loss rate')\n",
    "\n",
    "    # Compute correlations and check if these are statistically significant:\n",
    "    # Are these statistically significant?\n",
    "    winToMICorr, winToMICorrPValue = stats.pearsonr(winRates,mutualInfos)\n",
    "    lossToMICorr, lossToMICorrPValue = stats.pearsonr(lossRates,mutualInfos)\n",
    "\n",
    "    print('Correlation of win  rate to MI is: {:.04f} (pValue {:.04f})'.\\\n",
    "        format(winToMICorr, winToMICorrPValue))\n",
    "    print('Correlation of loss rate to MI is: {:.04f} (pValue: {:.04f})'.\\\n",
    "        format(lossToMICorr, lossToMICorrPValue))\n",
    "\n",
    "    return names, mutualInfos, winRates, lossRates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ca15ace-a27e-4c09-b0f7-6467659cb9bd",
   "metadata": {},
   "source": [
    "5. Call the script to see the mutual information of each player, the plots and correlation analyses on how this related to performance. Do this for MI from the players' own previous move (pass argument `fromSelf=True`) and from their opponent (pass argument `fromSelf=False`). Who reflected the most information in their moves? Did this correlate to wins? What about losses? Does this match your hypothesis?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7a2b2b7f-4b2c-4ca0-8c47-9466e3456bc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Player1 = 0.059 bits,\twin rate = 0.421,\tloss rate = 0.395, num games = 38\n",
      "Player2 = 0.078 bits,\twin rate = 0.342,\tloss rate = 0.447, num games = 38\n",
      "Player3 = 0.057 bits,\twin rate = 0.447,\tloss rate = 0.368, num games = 38\n",
      "Player4 = 0.063 bits,\twin rate = 0.410,\tloss rate = 0.282, num games = 39\n",
      "Player5 = 0.045 bits,\twin rate = 0.282,\tloss rate = 0.410, num games = 39\n",
      "Player6 = 0.270 bits,\twin rate = 0.408,\tloss rate = 0.265, num games = 49\n",
      "Player7 = 0.170 bits,\twin rate = 0.265,\tloss rate = 0.408, num games = 49\n",
      "Correlation of win  rate to MI is: -0.0634 (pValue 0.8926)\n",
      "Correlation of loss rate to MI is: -0.4803 (pValue: 0.2753)\n",
      "Player1 = 0.123 bits,\twin rate = 0.421,\tloss rate = 0.395, num games = 38\n",
      "Player2 = 0.045 bits,\twin rate = 0.342,\tloss rate = 0.447, num games = 38\n",
      "Player3 = 0.075 bits,\twin rate = 0.447,\tloss rate = 0.368, num games = 38\n",
      "Player4 = 0.098 bits,\twin rate = 0.410,\tloss rate = 0.282, num games = 39\n",
      "Player5 = 0.125 bits,\twin rate = 0.282,\tloss rate = 0.410, num games = 39\n",
      "Player6 = 0.009 bits,\twin rate = 0.408,\tloss rate = 0.265, num games = 49\n",
      "Player7 = 0.042 bits,\twin rate = 0.265,\tloss rate = 0.408, num games = 49\n",
      "Correlation of win  rate to MI is: 0.0402 (pValue 0.9317)\n",
      "Correlation of loss rate to MI is: 0.2489 (pValue: 0.5904)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(['Player1', 'Player2', 'Player3', 'Player4', 'Player5', 'Player6', 'Player7'],\n",
       " array([0.12304366, 0.04535072, 0.0751021 , 0.09757387, 0.12466105,\n",
       "        0.00938279, 0.04201142]),\n",
       " array([0.42105263, 0.34210526, 0.44736842, 0.41025641, 0.28205128,\n",
       "        0.40816327, 0.26530612]),\n",
       " array([0.39473684, 0.44736842, 0.36842105, 0.28205128, 0.41025641,\n",
       "        0.26530612, 0.40816327]))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "computeMutualInformationForAllPlayers(True)\n",
    "computeMutualInformationForAllPlayers(False)"
   ]
  },
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    "6. _Challenge_: are these correlation values statistically significant? As above, look up theory on how to compute whether a correlation value is statistically significant. To add this to the code above, you can check out the other return values from the `stats.pearson()` or `stats.spearmanr()` correlation functions."
   ]
  },
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   "source": [
    "# Stage 5 - Further analysis\n",
    "\n",
    "Are there additional analyses that you would like to perform here?\n",
    "\n",
    "E.g. measuring mutual information from (jointly) the previous move of the player and their opponent, to the player's next move. What would you hypothesise about that? Or, is there any mutual information between concurrent moves? What would that mean?"
   ]
  }
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