printing.options['dformat'] = '%.7f'
solvers.options['show_progress'] = False
+
class Solution:
"""
A representation of the solution of a linear game. It should contain
the value of the game, and both players' strategies.
"""
- def __init__(self, p1_value, p2_value, p1_optimal, p2_optimal):
- self._player1_value = p1_value
- self._player2_value = p2_value
+ def __init__(self, game_value, p1_optimal, p2_optimal):
+ self._game_value = game_value
self._player1_optimal = p1_optimal
self._player2_optimal = p2_optimal
* The optimal strategy of player two.
"""
- # The string representations of the player strategy matrices
- # already contain trailing newlines.
+
tpl = 'Game value: {:.7f}\n' \
- 'Player 1 optimal: {!s}' \
- 'Player 2 optimal: {!s}'
- return tpl.format(self.game_value(),
- self.player1_optimal().trans(),
- self.player2_optimal().trans())
+ 'Player 1 optimal:{:s}\n' \
+ 'Player 2 optimal:{:s}\n'
+
+ p1 = '\n{!s}'.format(self.player1_optimal())
+ p1 = '\n '.join(p1.splitlines())
+ p2 = '\n{!s}'.format(self.player2_optimal())
+ p2 = '\n '.join(p2.splitlines())
+
+ return tpl.format(self.game_value(), p1, p2)
- def game_value(self):
- return ((self.player1_value() + self.player2_value()) / 2.0)
- def player1_value(self):
- return self._player1_value
+ def game_value(self):
+ return self._game_value
- def player2_value(self):
- return self._player2_value
def player1_optimal(self):
return self._player1_optimal
+
def player2_optimal(self):
return self._player2_optimal
A = matrix([0, self._e1], (1, K.dimension() + 1), 'd')
soln_dict = solvers.conelp(c, G, h, C.cvxopt_dims(), A, b)
+
+ if soln_dict['status'] != 'optimal':
+ raise GameUnsolvableException(soln_dict)
+
p1_value = soln_dict['x'][0]
- p2_value = soln_dict['y'][0]
p1_optimal = soln_dict['x'][1:]
p2_optimal = soln_dict['z'][self._K.dimension():]
- soln = Solution(p1_value, p2_value, p1_optimal, p2_optimal)
-
- #if soln_dict['status'] != 'optimal':
- raise GameUnsolvableException(soln_dict['status'], soln, soln_dict)
- return soln
+ return Solution(p1_value, p1_optimal, p2_optimal)