knows how to solve a linear game.
"""
-# These few are used only for tests.
-from math import sqrt
-from random import randint, uniform
-from unittest import TestCase
-
-# These are mostly actually needed.
from cvxopt import matrix, printing, solvers
-from cones import CartesianProduct, IceCream, NonnegativeOrthant
-from errors import GameUnsolvableException
-from matrices import append_col, append_row, identity, inner_product
-import options
+from .cones import CartesianProduct
+from .errors import GameUnsolvableException
+from .matrices import append_col, append_row, identity
+from . import options
printing.options['dformat'] = options.FLOAT_FORMAT
solvers.options['show_progress'] = options.VERBOSE
Examples
--------
- >>> from cones import NonnegativeOrthant
+ >>> from dunshire import *
>>> K = NonnegativeOrthant(3)
>>> L = [[1,-5,-15],[-1,2,-3],[-12,-15,1]]
>>> e1 = [1,1,1]
Lists can (and probably should) be used for every argument::
- >>> from cones import NonnegativeOrthant
+ >>> from dunshire import *
>>> K = NonnegativeOrthant(2)
>>> L = [[1,0],[0,1]]
>>> e1 = [1,1]
>>> import cvxopt
>>> import numpy
- >>> from cones import NonnegativeOrthant
+ >>> from dunshire import *
>>> K = NonnegativeOrthant(2)
>>> L = [[1,0],[0,1]]
>>> e1 = cvxopt.matrix([1,1])
otherwise indexed by columns::
>>> import cvxopt
- >>> from cones import NonnegativeOrthant
+ >>> from dunshire import *
>>> K = NonnegativeOrthant(2)
>>> L = [[1,2],[3,4]]
>>> e1 = [1,1]
This example is computed in Gowda and Ravindran in the section
"The value of a Z-transformation"::
- >>> from cones import NonnegativeOrthant
+ >>> from dunshire import *
>>> K = NonnegativeOrthant(3)
>>> L = [[1,-5,-15],[-1,2,-3],[-12,-15,1]]
>>> e1 = [1,1,1]
The value of the following game can be computed using the fact
that the identity is invertible::
- >>> from cones import NonnegativeOrthant
+ >>> from dunshire import *
>>> K = NonnegativeOrthant(3)
>>> L = [[1,0,0],[0,1,0],[0,0,1]]
>>> e1 = [1,2,3]
# what happened.
soln_dict = solvers.conelp(c, G, h, C.cvxopt_dims(), A, b)
+ # The optimal strategies are named ``p`` and ``q`` in the
+ # background documentation, and we need to extract them from
+ # the CVXOPT ``x`` and ``z`` variables. The objective values
+ # :math:`nu` and :math:`omega` can also be found in the CVXOPT
+ # ``x`` and ``y`` variables; however, they're stored
+ # conveniently as separate entries in the solution dictionary.
p1_value = -soln_dict['primal objective']
p2_value = -soln_dict['dual objective']
p1_optimal = soln_dict['x'][1:]
# worst, since they indicate that CVXOPT is convinced the
# problem is infeasible (and that cannot happen).
if soln_dict['status'] in ['primal infeasible', 'dual infeasible']:
- raise GameUnsolvableException(soln_dict)
+ raise GameUnsolvableException(self, soln_dict)
elif soln_dict['status'] == 'unknown':
# When we get a status of "unknown", we may still be able
# to salvage a solution out of the returned
# objectives match (within a tolerance) and that the
# primal/dual optimal solutions are within the cone (to a
# tolerance as well).
- if (abs(p1_value - p2_value) > options.ABS_TOL):
- raise GameUnsolvableException(soln_dict)
+ if abs(p1_value - p2_value) > options.ABS_TOL:
+ raise GameUnsolvableException(self, soln_dict)
if (p1_optimal not in self._K) or (p2_optimal not in self._K):
- raise GameUnsolvableException(soln_dict)
+ raise GameUnsolvableException(self, soln_dict)
return Solution(p1_value, p1_optimal, p2_optimal)
Examples
--------
- >>> from cones import NonnegativeOrthant
+ >>> from dunshire import *
>>> K = NonnegativeOrthant(3)
>>> L = [[1,-5,-15],[-1,2,-3],[-12,-15,1]]
>>> e1 = [1,1,1]
self._K,
self._e2,
self._e1)
-
-
-class SymmetricLinearGameTest(TestCase):
- """
- Tests for the SymmetricLinearGame and Solution classes.
- """
-
- def random_orthant_params(self):
- """
- Generate the ``L``, ``K``, ``e1``, and ``e2`` parameters for a
- random game over the nonnegative orthant.
- """
- ambient_dim = randint(1, 10)
- K = NonnegativeOrthant(ambient_dim)
- e1 = [uniform(0.1, 10) for idx in range(K.dimension())]
- e2 = [uniform(0.1, 10) for idx in range(K.dimension())]
- L = [[uniform(-10, 10) for i in range(K.dimension())]
- for j in range(K.dimension())]
- return (L, K, e1, e2)
-
-
- def random_icecream_params(self):
- """
- Generate the ``L``, ``K``, ``e1``, and ``e2`` parameters for a
- random game over the ice cream cone.
- """
- # Use a minimum dimension of two to avoid divide-by-zero in
- # the fudge factor we make up later.
- ambient_dim = randint(2, 10)
- K = IceCream(ambient_dim)
- e1 = [1] # Set the "height" of e1 to one
- e2 = [1] # And the same for e2
-
- # If we choose the rest of the components of e1,e2 randomly
- # between 0 and 1, then the largest the squared norm of the
- # non-height part of e1,e2 could be is the 1*(dim(K) - 1). We
- # need to make it less than one (the height of the cone) so
- # that the whole thing is in the cone. The norm of the
- # non-height part is sqrt(dim(K) - 1), and we can divide by
- # twice that.
- fudge_factor = 1.0 / (2.0*sqrt(K.dimension() - 1.0))
- e1 += [fudge_factor*uniform(0, 1) for idx in range(K.dimension() - 1)]
- e2 += [fudge_factor*uniform(0, 1) for idx in range(K.dimension() - 1)]
- L = [[uniform(-10, 10) for i in range(K.dimension())]
- for j in range(K.dimension())]
-
- return (L, K, e1, e2)
-
-
- def assert_within_tol(self, first, second):
- """
- Test that ``first`` and ``second`` are equal within our default
- tolerance.
- """
- self.assertTrue(abs(first - second) < options.ABS_TOL)
-
-
- def assert_solution_exists(self, L, K, e1, e2):
- """
- Given the parameters needed to construct a SymmetricLinearGame,
- ensure that that game has a solution.
- """
- G = SymmetricLinearGame(L, K, e1, e2)
- soln = G.solution()
- L_matrix = matrix(L).trans()
- expected = inner_product(L_matrix*soln.player1_optimal(),
- soln.player2_optimal())
- self.assert_within_tol(soln.game_value(), expected)
-
- def test_solution_exists_nonnegative_orthant(self):
- """
- Every linear game has a solution, so we should be able to solve
- every symmetric linear game over the NonnegativeOrthant. Pick
- some parameters randomly and give it a shot. The resulting
- optimal solutions should give us the optimal game value when we
- apply the payoff operator to them.
- """
- (L, K, e1, e2) = self.random_orthant_params()
- self.assert_solution_exists(L, K, e1, e2)
-
- def test_solution_exists_ice_cream(self):
- """
- Like :meth:`test_solution_exists_nonnegative_orthant`, except
- over the ice cream cone.
- """
- (L, K, e1, e2) = self.random_icecream_params()
- self.assert_solution_exists(L, K, e1, e2)
-
-
- def test_negative_value_Z_operator(self):
- """
- Test the example given in Gowda/Ravindran of a Z-matrix with
- negative game value on the nonnegative orthant.
- """
- K = NonnegativeOrthant(2)
- e1 = [1,1]
- e2 = e1
- L = [[1,-2],[-2,1]]
- G = SymmetricLinearGame(L, K, e1, e2)
- self.assertTrue(G.solution().game_value() < -options.ABS_TOL)
-
-
- def test_nonnegative_scaling_orthant(self):
- """
- Test that scaling ``L`` by a nonnegative number scales the value
- of the game by the same number. Use the nonnegative orthant as
- our cone.
- """
- (L, K, e1, e2) = self.random_orthant_params()
- L = matrix(L) # So that we can scale it by alpha below.
- G1 = SymmetricLinearGame(L, K, e1, e2)
- value1 = G1.solution().game_value()
- alpha = uniform(0.1, 10)
-
- G2 = SymmetricLinearGame(alpha*L, K, e1, e2)
- value2 = G2.solution().game_value()
- self.assert_within_tol(alpha*value1, value2)
-
-
- def test_nonnegative_scaling_icecream(self):
- """
- The same test as :meth:`test_nonnegative_scaling_orthant`,
- except over the ice cream cone.
- """
- (L, K, e1, e2) = self.random_icecream_params()
- L = matrix(L) # So that we can scale it by alpha below.
-
- G1 = SymmetricLinearGame(L, K, e1, e2)
- value1 = G1.solution().game_value()
- alpha = uniform(0.1, 10)
-
- G2 = SymmetricLinearGame(alpha*L, K, e1, e2)
- value2 = G2.solution().game_value()
- self.assert_within_tol(alpha*value1, value2)
-
projects / dunshire.git / blobdiff