Remove the "VERBOSE" option, since you never want to change it.

[dunshire.git] / dunshire / games.py

diff --git a/dunshire/games.py b/dunshire/games.py
index 130176b63bf9a276541609ad70b25f2c0b7a7d79..51c97add2314a9de54f8bb7d76f56ac13d6a1dd9 100644 (file)
--- a/dunshire/games.py
+++ b/dunshire/games.py
@@ -887,49 +887,46 @@ class SymmetricLinearGame:
             >>> SLG.solution().game_value() < -ABS_TOL
             True
 
-        Tests
-        -----
-
         The following two games are problematic numerically, but we
         should be able to solve them::
 
-        >>> from dunshire import *
-        >>> L = [[-0.95237953890954685221, 1.83474556206462535712],
-        ...      [ 1.30481749924621448500, 1.65278664543326403447]]
-        >>> K = NonnegativeOrthant(2)
-        >>> e1 = [0.95477167524644313001, 0.63270781756540095397]
-        >>> e2 = [0.39633793037154141370, 0.10239281495640320530]
-        >>> SLG = SymmetricLinearGame(L, K, e1, e2)
-        >>> print(SLG.solution())
-        Game value: 18.767...
-        Player 1 optimal:
-          [-0.000...]
-          [ 9.766...]
-        Player 2 optimal:
-          [1.047...]
-          [0.000...]
+            >>> from dunshire import *
+            >>> L = [[-0.95237953890954685221, 1.83474556206462535712],
+            ...      [ 1.30481749924621448500, 1.65278664543326403447]]
+            >>> K = NonnegativeOrthant(2)
+            >>> e1 = [0.95477167524644313001, 0.63270781756540095397]
+            >>> e2 = [0.39633793037154141370, 0.10239281495640320530]
+            >>> SLG = SymmetricLinearGame(L, K, e1, e2)
+            >>> print(SLG.solution())
+            Game value: 18.767...
+            Player 1 optimal:
+              [-0.000...]
+              [ 9.766...]
+            Player 2 optimal:
+              [1.047...]
+              [0.000...]
 
         ::
 
-        >>> from dunshire import *
-        >>> L = [[1.54159395026049472754, 2.21344728574316684799],
-        ...      [1.33147433507846657541, 1.17913616272988108769]]
-        >>> K = NonnegativeOrthant(2)
-        >>> e1 = [0.39903040089404784307, 0.12377403622479113410]
-        >>> e2 = [0.15695181142215544612, 0.85527381344651265405]
-        >>> SLG = SymmetricLinearGame(L, K, e1, e2)
-        >>> print(SLG.solution())
-        Game value: 24.614...
-        Player 1 optimal:
-          [ 6.371...]
-          [-0.000...]
-        Player 2 optimal:
-          [2.506...]
-          [0.000...]
+            >>> from dunshire import *
+            >>> L = [[1.54159395026049472754, 2.21344728574316684799],
+            ...      [1.33147433507846657541, 1.17913616272988108769]]
+            >>> K = NonnegativeOrthant(2)
+            >>> e1 = [0.39903040089404784307, 0.12377403622479113410]
+            >>> e2 = [0.15695181142215544612, 0.85527381344651265405]
+            >>> SLG = SymmetricLinearGame(L, K, e1, e2)
+            >>> print(SLG.solution())
+            Game value: 24.614...
+            Player 1 optimal:
+              [ 6.371...]
+              [-0.000...]
+            Player 2 optimal:
+              [2.506...]
+              [0.000...]
 
         """
         try:
-            opts = {'show_progress': options.VERBOSE}
+            opts = {'show_progress': False}
             soln_dict = solvers.conelp(self._c(),
                                        self._G(),
                                        self._h(),
@@ -942,6 +939,7 @@ class SymmetricLinearGame:
                 # Oops, CVXOPT tried to take the square root of a
                 # negative number. Report some details about the game
                 # rather than just the underlying CVXOPT crash.
+                printing.options['dformat'] = options.DEBUG_FLOAT_FORMAT
                 raise PoorScalingException(self)
             else:
                 raise error
@@ -966,6 +964,7 @@ class SymmetricLinearGame:
         # that CVXOPT is convinced the problem is infeasible (and that
         # cannot happen).
         if soln_dict['status'] in ['primal infeasible', 'dual infeasible']:
+            printing.options['dformat'] = options.DEBUG_FLOAT_FORMAT
             raise GameUnsolvableException(self, soln_dict)
 
         # The "optimal" and "unknown" results, we actually treat the
@@ -979,11 +978,13 @@ class SymmetricLinearGame:
         # it) because otherwise CVXOPT might return "unknown" and give
         # us two points in the cone that are nowhere near optimal.
         if abs(p1_value - p2_value) > 2*options.ABS_TOL:
+            printing.options['dformat'] = options.DEBUG_FLOAT_FORMAT
             raise GameUnsolvableException(self, soln_dict)
 
         # And we also check that the points it gave us belong to the
         # cone, just in case...
         if (p1_optimal not in self._K) or (p2_optimal not in self._K):
+            printing.options['dformat'] = options.DEBUG_FLOAT_FORMAT
             raise GameUnsolvableException(self, soln_dict)
 
         # For the game value, we could use any of:
@@ -997,7 +998,7 @@ class SymmetricLinearGame:
         # makes the most sense to just use that, even if it means we
         # can't test the fact that p1_value/p2_value are close to the
         # payoff.
-        payoff = self.payoff(p1_optimal,p2_optimal)
+        payoff = self.payoff(p1_optimal, p2_optimal)
         return Solution(payoff, p1_optimal, p2_optimal)