mirror of https://github.com/jlizier/jidt
46 lines
1.6 KiB
Matlab
Executable File
46 lines
1.6 KiB
Matlab
Executable File
% function [calculatedEntropy, winRate, lossRate] = computeEntropyForPlayer(name)
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%
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% Compute the entropy of moves for a given player, across all games/iterations
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%
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% Input:
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% - name of the player
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function [calculatedEntropy, winRate, lossRate, numGames] = computeEntropyForPlayer(name)
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% Step 1: load all of the player's games' data:
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games = loadGamesForPlayer(name);
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% Step 2: the player's moves are in the first column, pull these from
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% each game into an array of samples that we can compute entropy on:
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moves = [];
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results = [];
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for gameIndex = 1:length(games)
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% Load data from game gameIndex into the variable game
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game = games{gameIndex};
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% First column of game is the player's move, second is opponent's
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% and third is the result.
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% Pull out the player's moves in this game (first column of game):
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movesInThisGame = ????;
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% Pull out the results in this game (third column of game):
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resultsInThisGame = ????;
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% Append this player's moves to the array we're storing over all iterations:
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moves = [moves; movesInThisGame];
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% Append this player's results to the array over all iterations:
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results = [results; resultsInThisGame];
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end
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% Step 3: compute the entropy for this player's moves using our existing scripts:
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calculatedEntropy = ????;
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% Step 4: compute the win and loss rates:
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winRate = sum(results == 1)./length(results);
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lossRate = sum(results == -1)./length(results);
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numGames = length(results);
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if (nargout == 0)
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fprintf('Entropy for %s over %d iterations: %.4f bits\n', ...
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name, length(moves), calculatedEntropy);
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end
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end
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