from dunshire.cones import NonnegativeOrthant
from dunshire.games import SymmetricLinearGame
-from dunshire.matrices import eigenvalues_re, inner_product
+from dunshire.matrices import eigenvalues_re, inner_product, norm
from dunshire import options
from .randomgen import (RANDOM_MAX, random_icecream_game,
random_ll_icecream_game, random_ll_orthant_game,
random_nn_scaling, random_orthant_game,
random_positive_orthant_game, random_translation)
-EPSILON = (1 + RANDOM_MAX)*options.ABS_TOL
-"""
-This is the tolerance constant including fudge factors that we use to
-determine whether or not two numbers are equal in tests.
-
-Often we will want to compare two solutions, say for games that are
-equivalent. If the first game value is low by ``ABS_TOL`` and the second
-is high by ``ABS_TOL``, then the total could be off by ``2*ABS_TOL``. We
-also subject solutions to translations and scalings, which adds to or
-scales their error. If the first game is low by ``ABS_TOL`` and the
-second is high by ``ABS_TOL`` before scaling, then after scaling, the
-second could be high by ``RANDOM_MAX*ABS_TOL``. That is the rationale
-for the factor of ``1 + RANDOM_MAX`` in ``EPSILON``. Since ``1 +
-RANDOM_MAX`` is greater than ``2*ABS_TOL``, we don't need to handle the
-first issue mentioned (both solutions off by the same amount in opposite
-directions).
-"""
# Tell pylint to shut up about the large number of methods.
class SymmetricLinearGameTest(TestCase): # pylint: disable=R0904
modifier : float
A scaling factor (default: 1) applied to the default
- ``EPSILON`` for this comparison. If you have a poorly-
+ tolerance for this comparison. If you have a poorly-
conditioned matrix, for example, you may want to set this
greater than one.
"""
- self.assertTrue(abs(first - second) < EPSILON*modifier)
-
-
- def assert_solution_exists(self, G):
- """
- Given a SymmetricLinearGame, ensure that it has a solution.
- """
- soln = G.solution()
-
- expected = G.payoff(soln.player1_optimal(), soln.player2_optimal())
- self.assert_within_tol(soln.game_value(), expected, G.condition())
+ self.assertTrue(abs(first - second) < options.ABS_TOL*modifier)
self.assertTrue(G.condition() >= 1.0)
- def test_solution_exists_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.
- """
- G = random_orthant_game()
- self.assert_solution_exists(G)
-
-
- def test_solution_exists_icecream(self):
- """
- Like :meth:`test_solution_exists_nonnegative_orthant`, except
- over the ice cream cone.
- """
- G = random_icecream_game()
- self.assert_solution_exists(G)
-
-
- 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 assert_scaling_works(self, G):
"""
Test that scaling ``L`` by a nonnegative number scales the value
(alpha, H) = random_nn_scaling(G)
value1 = G.solution().game_value()
value2 = H.solution().game_value()
- self.assert_within_tol(alpha*value1, value2, H.condition())
+ modifier = 4*max(abs(alpha), 1)
+ self.assert_within_tol(alpha*value1, value2, modifier)
def test_scaling_orthant(self):
(alpha, H) = random_translation(G)
value2 = H.solution().game_value()
- self.assert_within_tol(value1 + alpha, value2, H.condition())
+ modifier = 4*max(abs(alpha), 1)
+ self.assert_within_tol(value1 + alpha, value2, modifier)
# Make sure the same optimal pair works.
- self.assert_within_tol(value2,
- H.payoff(x_bar, y_bar),
- H.condition())
+ self.assert_within_tol(value2, H.payoff(x_bar, y_bar), modifier)
def test_translation_orthant(self):
y_bar = soln1.player2_optimal()
soln2 = H.solution()
- self.assert_within_tol(-soln1.game_value(),
- soln2.game_value(),
- H.condition())
+ # The modifier of 4 is because each could be off by 2*ABS_TOL,
+ # which is how far apart the primal/dual objectives have been
+ # observed being.
+ self.assert_within_tol(-soln1.game_value(), soln2.game_value(), 4)
+
+ # Make sure the switched optimal pair works. Since x_bar and
+ # y_bar come from G, we use the same modifier.
+ self.assert_within_tol(soln2.game_value(), H.payoff(y_bar, x_bar), 4)
- # Make sure the switched optimal pair works.
- self.assert_within_tol(soln2.game_value(),
- H.payoff(y_bar, x_bar),
- H.condition())
def test_opposite_game_orthant(self):
value = soln.game_value()
ip1 = inner_product(y_bar, G.L()*x_bar - value*G.e1())
- self.assert_within_tol(ip1, 0, G.condition())
+ self.assert_within_tol(ip1, 0)
ip2 = inner_product(value*G.e2() - G.L().trans()*y_bar, x_bar)
- self.assert_within_tol(ip2, 0, G.condition())
+ self.assert_within_tol(ip2, 0)
def test_orthogonality_orthant(self):
# fudge factors.
eigs = eigenvalues_re(G.L())
- if soln.game_value() > EPSILON:
+ if soln.game_value() > options.ABS_TOL:
# L should be positive stable
positive_stable = all([eig > -options.ABS_TOL for eig in eigs])
self.assertTrue(positive_stable)
- elif soln.game_value() < -EPSILON:
+ elif soln.game_value() < -options.ABS_TOL:
# L should be negative stable
negative_stable = all([eig < options.ABS_TOL for eig in eigs])
self.assertTrue(negative_stable)
# The dual game's value should always equal the primal's.
+ # The modifier of 4 is because even though the games are dual,
+ # CVXOPT doesn't know that, and each could be off by 2*ABS_TOL.
dualsoln = G.dual().solution()
- self.assert_within_tol(dualsoln.game_value(),
- soln.game_value(),
- G.condition())
+ self.assert_within_tol(dualsoln.game_value(), soln.game_value(), 4)
def test_lyapunov_orthant(self):
projects / dunshire.git / blobdiff