made the test compatible with the new way to allow easier debuging.

theano/tensor/tests/test_raw_random.py

@@ -9,59 +9,59 @@ from theano import tensor
 
 from theano import compile, gof
 
-def test_state_propagation():
-    x = tensor.vector()
-    rk = RandomKit('rk', 1000)
-    f = compile.function([x, (rk, [gof.Container(r = gof.generic, storage = [123], name='bla')])], rk.binomial(tensor.shape(x)), mode='FAST_COMPILE')
-    f['rk'] = 9873456
+class T_test_module(unittest.TestCase):
+    def test_state_propagation(self):
+        x = tensor.vector()
+        rk = RandomKit('rk', 1000)
+        f = compile.function([x, (rk, [gof.Container(r = gof.generic, storage = [123], name='bla')])], rk.binomial(tensor.shape(x)), mode='FAST_COMPILE')
+        f['rk'] = 9873456
     
-    rvals = [f([1,2,3,4,6, 7, 8]) for i in xrange(5)]
-    print rvals
-    for i in xrange(5-1):
-        for j in xrange(i+1, 5):
-            assert not N.all(rvals[i] == rvals[j])
+        rvals = [f([1,2,3,4,6, 7, 8]) for i in xrange(5)]
+        print rvals
+        for i in xrange(5-1):
+            for j in xrange(i+1, 5):
+                assert not N.all(rvals[i] == rvals[j])
 
-def test_B():
-    """Test that random numbers change from call to call!
-    
-    Also, make sure that the seeding strategy doesn't change without failing a test.
-
-    Random numbers can't be too random or experiments aren't repeatable.  Email theano-dev
-    before updating the `rvals` in this test.
-    """
-    class B(RModule):
-        def __init__(self):
-            super(B, self).__init__(self)
-
-            self.x = compile.Member(tensor.dvector())
-            self.r = self.random.uniform(tensor.shape(self.x))
-
-            self.f = compile.Method([self.x], self.r)
-    class E(RModule):
-        def __init__(self):
-            super(E, self).__init__(self)
-            self.b = B()
-            self.f = compile.Method([self.b.x], self.b.r)
+    def test_B(self):
+        """Test that random numbers change from call to call!
+        
+        Also, make sure that the seeding strategy doesn't change without failing a test.
+        
+        Random numbers can't be too random or experiments aren't repeatable.  Email theano-dev
+        before updating the `rvals` in this test.
+        """
+        class B(RModule):
+            def __init__(self):
+                super(B, self).__init__(self)
+                
+                self.x = compile.Member(tensor.dvector())
+                self.r = self.random.uniform(tensor.shape(self.x))
+                
+                self.f = compile.Method([self.x], self.r)
+        class E(RModule):
+            def __init__(self):
+                super(E, self).__init__(self)
+                self.b = B()
+                self.f = compile.Method([self.b.x], self.b.r)
 
-    b = E()
-    m = b.make(mode='FAST_COMPILE')
-
-    m.seed(1000)
+        b = E()
+        m = b.make(mode='FAST_COMPILE')
+        
+        m.seed(1000)
     #print m.f(N.ones(5))
     #print m.f(N.ones(5))
     #print m.f(N.ones(5))
-    rvals = ["0.74802375876 0.872308123517 0.294830748897 0.803123780003 0.6321109955",
-        "0.00168744844365 0.278638315678 0.725436793755 0.7788480779 0.629885140994",
-        "0.545561221664 0.0992011009108 0.847112593242 0.188015424144 0.158046201298",
-        "0.054382248842 0.563459168529 0.192757276954 0.360455221883 0.174805216702",
-        "0.961942907777 0.49657319422 0.0316111492826 0.0915054717012 0.195877184515"]
-
-    for i in xrange(5):
-        s = " ".join([str(n) for n in m.f(N.ones(5))])
-        print s
-        assert s == rvals[i]
+        rvals = ["0.74802375876 0.872308123517 0.294830748897 0.803123780003 0.6321109955",
+                 "0.00168744844365 0.278638315678 0.725436793755 0.7788480779 0.629885140994",
+                 "0.545561221664 0.0992011009108 0.847112593242 0.188015424144 0.158046201298",
+                 "0.054382248842 0.563459168529 0.192757276954 0.360455221883 0.174805216702",
+                 "0.961942907777 0.49657319422 0.0316111492826 0.0915054717012 0.195877184515"]
 
+        for i in xrange(5):
+            s = " ".join([str(n) for n in m.f(N.ones(5))])
+            print s
+            assert s == rvals[i]
 
 if __name__ == '__main__':
-    unittest.main()
-
+    from theano.tests import main
+    main("test_raw_random")