from cvxopt import matrix, printing, solvers
from .cones import CartesianProduct
from .errors import GameUnsolvableException, PoorScalingException
-from .matrices import append_col, append_row, condition_number, identity
+from .matrices import (append_col, append_row, condition_number, identity,
+ inner_product)
from . import options
printing.options['dformat'] = options.FLOAT_FORMAT
self.condition())
+ def L(self):
+ """
+ Return the matrix ``L`` passed to the constructor.
+
+ Returns
+ -------
+
+ matrix
+ The matrix that defines this game's :meth:`payoff` operator.
+
+ Examples
+ --------
+
+ >>> from dunshire import *
+ >>> K = NonnegativeOrthant(3)
+ >>> L = [[1,-5,-15],[-1,2,-3],[-12,-15,1]]
+ >>> e1 = [1,1,1]
+ >>> e2 = [1,2,3]
+ >>> SLG = SymmetricLinearGame(L, K, e1, e2)
+ >>> print(SLG.L())
+ [ 1 -5 -15]
+ [ -1 2 -3]
+ [-12 -15 1]
+ <BLANKLINE>
+
+ """
+ return self._L
+
+
+ def K(self):
+ """
+ Return the cone over which this game is played.
+
+ Returns
+ -------
+
+ SymmetricCone
+ The :class:`SymmetricCone` over which this game is played.
+
+ Examples
+ --------
+
+ >>> from dunshire import *
+ >>> K = NonnegativeOrthant(3)
+ >>> L = [[1,-5,-15],[-1,2,-3],[-12,-15,1]]
+ >>> e1 = [1,1,1]
+ >>> e2 = [1,2,3]
+ >>> SLG = SymmetricLinearGame(L, K, e1, e2)
+ >>> print(SLG.K())
+ Nonnegative orthant in the real 3-space
+
+ """
+ return self._K
+
+
+ def e1(self):
+ """
+ Return player one's interior point.
+
+ Returns
+ -------
+
+ matrix
+ The point interior to :meth:`K` affiliated with player one.
+
+ Examples
+ --------
+
+ >>> from dunshire import *
+ >>> K = NonnegativeOrthant(3)
+ >>> L = [[1,-5,-15],[-1,2,-3],[-12,-15,1]]
+ >>> e1 = [1,1,1]
+ >>> e2 = [1,2,3]
+ >>> SLG = SymmetricLinearGame(L, K, e1, e2)
+ >>> print(SLG.e1())
+ [ 1]
+ [ 1]
+ [ 1]
+ <BLANKLINE>
+
+ """
+ return self._e1
+
+
+ def e2(self):
+ """
+ Return player two's interior point.
+
+ Returns
+ -------
+
+ matrix
+ The point interior to :meth:`K` affiliated with player one.
+
+ Examples
+ --------
+
+ >>> from dunshire import *
+ >>> K = NonnegativeOrthant(3)
+ >>> L = [[1,-5,-15],[-1,2,-3],[-12,-15,1]]
+ >>> e1 = [1,1,1]
+ >>> e2 = [1,2,3]
+ >>> SLG = SymmetricLinearGame(L, K, e1, e2)
+ >>> print(SLG.e2())
+ [ 1]
+ [ 2]
+ [ 3]
+ <BLANKLINE>
+
+ """
+ return self._e2
+
+
+ def payoff(self, strategy1, strategy2):
+ r"""
+ Return the payoff associated with ``strategy1`` and ``strategy2``.
+
+ The payoff operator takes pairs of strategies to a real
+ number. For example, if player one's strategy is :math:`x` and
+ player two's strategy is :math:`y`, then the associated payoff
+ is :math:`\left\langle L\left(x\right),y \right\rangle` \in
+ \mathbb{R}. Here, :math:`L` denotes the same linear operator as
+ :meth:`L`. This method computes the payoff given the two
+ players' strategies.
+
+ Parameters
+ ----------
+
+ strategy1 : matrix
+ Player one's strategy.
+
+ strategy2 : matrix
+ Player two's strategy.
+
+ Returns
+ -------
+
+ float
+ The payoff for the game when player one plays ``strategy1``
+ and player two plays ``strategy2``.
+
+ Examples
+ --------
+
+ The value of the game should be the payoff at the optimal
+ strategies::
+
+ >>> from dunshire import *
+ >>> from dunshire.options import ABS_TOL
+ >>> K = NonnegativeOrthant(3)
+ >>> L = [[1,-5,-15],[-1,2,-3],[-12,-15,1]]
+ >>> e1 = [1,1,1]
+ >>> e2 = [1,1,1]
+ >>> SLG = SymmetricLinearGame(L, K, e1, e2)
+ >>> soln = SLG.solution()
+ >>> x_bar = soln.player1_optimal()
+ >>> y_bar = soln.player2_optimal()
+ >>> abs(SLG.payoff(x_bar, y_bar) - soln.game_value()) < ABS_TOL
+ True
+
+ """
+ return inner_product(self.L()*strategy1, strategy2)
+
+
+ def dimension(self):
+ """
+ Return the dimension of this game.
+
+ The dimension of a game is not needed for the theory, but it is
+ useful for the implementation. We define the dimension of a game
+ to be the dimension of its underlying cone. Or what is the same,
+ the dimension of the space from which the strategies are chosen.
+
+ Returns
+ -------
+
+ int
+ The dimension of the cone :meth:`K`, or of the space where
+ this game is played.
+
+ Examples
+ --------
+
+ The dimension of a game over the nonnegative quadrant in the
+ plane should be two (the dimension of the plane)::
+
+ >>> from dunshire import *
+ >>> K = NonnegativeOrthant(2)
+ >>> L = [[1,-5],[-1,2]]
+ >>> e1 = [1,1]
+ >>> e2 = [1,4]
+ >>> SLG = SymmetricLinearGame(L, K, e1, e2)
+ >>> SLG.dimension()
+ 2
+
+ """
+ return self.K().dimension()
+
+
def _zero(self):
"""
Return a column of zeros that fits ``K``.
-------
matrix
- A ``K.dimension()``-by-``1`` column vector of zeros.
+ A ``self.dimension()``-by-``1`` column vector of zeros.
Examples
--------
<BLANKLINE>
"""
- return matrix(0, (self._K.dimension(), 1), tc='d')
+ return matrix(0, (self.dimension(), 1), tc='d')
def _A(self):
-------
matrix
- A ``1``-by-``(1 + K.dimension())`` row vector. Its first
+ A ``1``-by-``(1 + self.dimension())`` row vector. Its first
entry is zero, and the rest are the entries of ``e2``.
Examples
<BLANKLINE>
"""
- return matrix([0, self._e2], (1, self._K.dimension() + 1), 'd')
+ return matrix([0, self._e2], (1, self.dimension() + 1), 'd')
-------
matrix
- A ``2*K.dimension()``-by-``1 + K.dimension()`` matrix.
+ A ``2*self.dimension()``-by-``(1 + self.dimension())`` matrix.
Examples
--------
<BLANKLINE>
"""
- I = identity(self._K.dimension())
- return append_row(append_col(self._zero(), -I),
+ identity_matrix = identity(self.dimension())
+ return append_row(append_col(self._zero(), -identity_matrix),
append_col(self._e1, -self._L))
-------
matrix
- A ``K.dimension()``-by-``1`` column vector.
+ A ``self.dimension()``-by-``1`` column vector.
Examples
--------
-------
matrix
- A ``2*K.dimension()``-by-``1`` column vector of zeros.
+ A ``2*self.dimension()``-by-``1`` column vector of zeros.
Examples
--------
return matrix([self._zero(), self._zero()])
- def _b(self):
+
+ @staticmethod
+ def _b():
"""
Return the ``b`` vector used in our CVXOPT construction.
The vector ``b`` appears on the right-hand side of :math:`Ax =
b` in the statement of the CVXOPT conelp program.
+ This method is static because the dimensions and entries of
+ ``b`` are known beforehand, and don't depend on any other
+ properties of the game.
+
.. warning::
It is not safe to cache any of the matrices passed to
p1_value = -soln_dict['primal objective']
p2_value = -soln_dict['dual objective']
p1_optimal = soln_dict['x'][1:]
- p2_optimal = soln_dict['z'][self._K.dimension():]
+ p2_optimal = soln_dict['z'][self.dimension():]
# The "status" field contains "optimal" if everything went
# according to plan. Other possible values are "primal
projects / dunshire.git / blobdiff