Clean up a few pylint warnings and make "doc" a phony target.

[dunshire.git] / test / symmetric_linear_game_test.py

diff --git a/test/symmetric_linear_game_test.py b/test/symmetric_linear_game_test.py
index f61356d7ece50cf6df2807181587920175123264..69c352ace384e0fda2f60023d6577bc37baaf315 100644 (file)
--- a/test/symmetric_linear_game_test.py
+++ b/test/symmetric_linear_game_test.py
@@ -39,8 +39,8 @@ def random_matrix(dims):
         (3, 3)
 
     """
-    return matrix([[uniform(-10, 10) for i in range(dims)]
-                   for j in range(dims)])
+    return matrix([[uniform(-10, 10) for _ in range(dims)]
+                   for _ in range(dims)])
 
 
 def random_nonnegative_matrix(dims):
@@ -194,7 +194,7 @@ def random_lyapunov_like_icecream(dims):
 
     """
     a = matrix([uniform(-10, 10)], (1, 1))
-    b = matrix([uniform(-10, 10) for idx in range(dims-1)], (dims-1, 1))
+    b = matrix([uniform(-10, 10) for _ in range(dims-1)], (dims-1, 1))
     D = random_skew_symmetric_matrix(dims-1) + a*identity(dims-1)
     row1 = append_col(a, b.trans())
     row2 = append_col(b, D)
@@ -208,8 +208,8 @@ def random_orthant_params():
     """
     ambient_dim = randint(1, 10)
     K = NonnegativeOrthant(ambient_dim)
-    e1 = [uniform(0.5, 10) for idx in range(K.dimension())]
-    e2 = [uniform(0.5, 10) for idx in range(K.dimension())]
+    e1 = [uniform(0.5, 10) for _ in range(K.dimension())]
+    e2 = [uniform(0.5, 10) for _ in range(K.dimension())]
     L = random_matrix(K.dimension())
     return (L, K, matrix(e1), matrix(e2))
 
@@ -234,8 +234,8 @@ def random_icecream_params():
     # 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)]
+    e1 += [fudge_factor*uniform(0, 1) for _ in range(K.dimension() - 1)]
+    e2 += [fudge_factor*uniform(0, 1) for _ in range(K.dimension() - 1)]
     L = random_matrix(K.dimension())
 
     return (L, K, matrix(e1), matrix(e2))