X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=test%2Frandomgen.py;h=9fee2f749cbcf8037f2c64d41fe42e17731381a4;hb=10dfe7d7edb69701dd8f4f955f3f325706d77e47;hp=395408c6626ec4ee48dcb95e3b2e684daea88f4a;hpb=17055c563e53d29a6e72195b335e0c00f846cf78;p=dunshire.git

diff --git a/test/randomgen.py b/test/randomgen.py
index 395408c..9fee2f7 100644
--- a/test/randomgen.py
+++ b/test/randomgen.py
@@ -9,7 +9,7 @@ from dunshire.cones import NonnegativeOrthant, IceCream
 from dunshire.games import SymmetricLinearGame
 from dunshire.matrices import (append_col, append_row, identity)
 
-MAX_COND = 250
+MAX_COND = 100
 """
 The maximum condition number of a randomly-generated game.
 """
@@ -314,11 +314,17 @@ def random_orthant_game():
     SymmetricLinearGame
         A random game over some nonnegative orthant.
 
+    Examples
+    --------
+
+        >>> random_orthant_game()
+        <dunshire.games.SymmetricLinearGame object at 0x...>
+
     """
     ambient_dim = random_natural() + 1
     K = NonnegativeOrthant(ambient_dim)
-    e1 = [random_nn_scalar() for _ in range(K.dimension())]
-    e2 = [random_nn_scalar() for _ in range(K.dimension())]
+    e1 = [0.1 + random_nn_scalar() for _ in range(K.dimension())]
+    e2 = [0.1 + random_nn_scalar() for _ in range(K.dimension())]
     L = random_matrix(K.dimension())
     G = SymmetricLinearGame(L, K, e1, e2)
 
@@ -343,6 +349,12 @@ def random_icecream_game():
     SymmetricLinearGame
         A random game over some ice-cream cone.
 
+    Examples
+    --------
+
+        >>> random_icecream_game()
+        <dunshire.games.SymmetricLinearGame object at 0x...>
+
     """
     # Use a minimum dimension of two to avoid divide-by-zero in
     # the fudge factor we make up later.
@@ -387,6 +399,12 @@ def random_ll_orthant_game():
         A random game over some nonnegative orthant whose ``payoff`` method
         is based on a Lyapunov-like ``L`` operator.
 
+    Examples
+    --------
+
+        >>> random_ll_orthant_game()
+        <dunshire.games.SymmetricLinearGame object at 0x...>
+
     """
     G = random_orthant_game()
     L = random_diagonal_matrix(G._K.dimension())
@@ -419,6 +437,12 @@ def random_ll_icecream_game():
         A random game over some ice-cream cone whose ``payoff`` method
         is based on a Lyapunov-like ``L`` operator.
 
+    Examples
+    --------
+
+        >>> random_ll_icecream_game()
+        <dunshire.games.SymmetricLinearGame object at 0x...>
+
     """
     G = random_icecream_game()
     L = random_lyapunov_like_icecream(G._K.dimension())
@@ -452,6 +476,12 @@ def random_positive_orthant_game():
         A random game over some nonnegative orthant whose ``payoff`` method
         is based on a positive ``L`` operator.
 
+    Examples
+    --------
+
+        >>> random_positive_orthant_game()
+        <dunshire.games.SymmetricLinearGame object at 0x...>
+
     """
 
     G = random_orthant_game()
@@ -487,6 +517,22 @@ def random_nn_scaling(G):
     (float, SymmetricLinearGame)
         A pair containing the both the scaling factor and the new scaled game.
 
+    Examples
+    --------
+
+        >>> from dunshire.matrices import norm
+        >>> from dunshire.options import ABS_TOL
+        >>> G = random_orthant_game()
+        >>> (alpha, H) = random_nn_scaling(G)
+        >>> alpha >= 0
+        True
+        >>> G._K == H._K
+        True
+        >>> norm(G._e1 - H._e1) < ABS_TOL
+        True
+        >>> norm(G._e2 - H._e2) < ABS_TOL
+        True
+
     """
     alpha = random_nn_scalar()
     H = SymmetricLinearGame(alpha*G._L.trans(), G._K, G._e1, G._e2)
@@ -518,6 +564,20 @@ def random_translation(G):
         A pair containing the both the translation distance and the new
         scaled game.
 
+    Examples
+    --------
+
+        >>> from dunshire.matrices import norm
+        >>> from dunshire.options import ABS_TOL
+        >>> G = random_orthant_game()
+        >>> (alpha, H) = random_translation(G)
+        >>> G._K == H._K
+        True
+        >>> norm(G._e1 - H._e1) < ABS_TOL
+        True
+        >>> norm(G._e2 - H._e2) < ABS_TOL
+        True
+
     """
     alpha = random_scalar()
     tensor_prod = G._e1 * G._e2.trans()