Changeset 997

Timestamp:

10/03/11 17:06:43 (8 months ago)

Author:

apdavison

Message:

AbstractRNG is now a new-style class (ticket:194)

Location:

trunk

Files:

• src/common.py (modified) (1 diff)
• src/random.py (modified) (1 diff)
• test/unittests/test_random.py (modified) (2 diffs)

trunk/src/common.py

@@ -328 +328 @@
         Connect a source of spikes to a synaptic target.
 
-        source and target can both be individual cells or lists of cells, in
-        which case all possible connections are made with probability p, using
-        either the random number generator supplied, or the default rng
-        otherwise. Weights should be in nA or µS.
+        source and target can both be individual cells or populations/assemblies
+        of cells, in which case all possible connections are made with
+        probability p, using either the random number generator supplied, or the
+        default rng otherwise. Weights should be in nA or µS.
         """
         if isinstance(source, IDMixin):

trunk/src/random.py

@@ -42 +42 @@
 logger = logging.getLogger("PyNN")
  
-class AbstractRNG:
+class AbstractRNG(object):
     """Abstract class for wrapping random number generators. The idea is to be
     able to use either simulator-native rngs, which may be more efficient, or a

trunk/test/unittests/test_random.py

@@ -168 +168 @@
                                        rng=self.rnglist[0], boundaries=[0.0, 1.0],
                                        constrain="clip")
-        vals = rd.next(100)
+        vals = rd.next(1000)
         assert vals.min() == 0
         assert vals.max() < 1.0
@@ -175 +175 @@
                                        rng=self.rnglist[0], boundaries=[0.0, 1.0],
                                        constrain="redraw")
-        vals = rd.next(100)
+        vals = rd.next(1000)
         assert vals.min() >= 0
         assert vals.max() < 1.0
-        assert abs(vals.mean() - 0.5) < 0.05
+        assert abs(vals.mean() - 0.5) < 0.05, vals.mean()
         val = rd.next()
         rd = random.RandomDistribution(distribution='uniform', parameters=[-1.0, 1.0],