soln2 = H.solution()
value1 = soln1.game_value()
value2 = soln2.game_value()
- modifier1 = G.epsilon_scale(soln1)
- modifier2 = H.epsilon_scale(soln2)
+ modifier1 = G.tolerance_scale(soln1)
+ modifier2 = H.tolerance_scale(soln2)
modifier = max(modifier1, modifier2)
self.assert_within_tol(alpha*value1, value2, modifier)
(alpha, H) = random_translation(G)
value2 = H.solution().game_value()
- modifier = G.epsilon_scale(soln1)
+ modifier = G.tolerance_scale(soln1)
self.assert_within_tol(value1 + alpha, value2, modifier)
# Make sure the same optimal pair works.
y_bar = soln1.player2_optimal()
soln2 = H.solution()
- mod = G.epsilon_scale(soln1)
+ mod = G.tolerance_scale(soln1)
self.assert_within_tol(-soln1.game_value(), soln2.game_value(), mod)
# Make sure the switched optimal pair works. Since x_bar and
ip1 = inner_product(y_bar, G.L()*x_bar - value*G.e1())
ip2 = inner_product(value*G.e2() - G.L().trans()*y_bar, x_bar)
- modifier = G.epsilon_scale(soln)
+ modifier = G.tolerance_scale(soln)
self.assert_within_tol(ip1, 0, modifier)
self.assert_within_tol(ip2, 0, modifier)
self.assertTrue(negative_stable)
dualsoln = G.dual().solution()
- mod = G.epsilon_scale(soln)
+ mod = G.tolerance_scale(soln)
self.assert_within_tol(dualsoln.game_value(), soln.game_value(), mod)