Added uniform and binomial op-creating functions with examples in test.

_test_tensor_random.py

@@ -80,6 +80,22 @@ class T_Random(unittest.TestCase):
         self.failUnless(d0.owner.op == d1.owner.op)
         self.failUnless(hash(d0.owner.op) == hash(d1.owner.op))
 
+    def test6(self):
+
+        x = tensor.vector()
+        u = uniform(9999,x,0.,10.)
+        fu = compile.function([x],[u])
+        res1 = fu(numpy.zeros((3)))
+        res2 = fu(numpy.zeros((3)))
+        self.failUnless(str(res1[0]).startswith('8.23389'))
+        self.failUnless(str(res2[0]).startswith('5.45926'))
+
+        b = binomial(121212,x,1,0.8)
+        fb = compile.function([x],[b])
+        res1 = fb(numpy.zeros((10)))
+        res2 = fb(numpy.zeros((10)))
+        self.failUnless(list(res1) == [1,0,1,1,1,1,1,1,1,1])
+        self.failUnless(list(res2) == [1,1,0,1,1,1,0,0,1,1])
 
 if __name__ == '__main__':
     unittest.main()

tensor_random.py

@@ -120,4 +120,26 @@ class NumpyGenerator(gof.op.Op):
                     (shape, self.ndim) )
         output_storage[0][0] = self.fn(rng, size=shape)
 
+def uniform(seed, template, low=0.,high=1.):
+    """
+    Return a multivariate uniform(low,high)
+    random variable in a tensor of the same shape as template
+    (template can either be a tensor or a shape tuple). Each element of the
+    resulting tensor is sampled independently. low and high can
+    be scalars or have the same shape as the template (or broadcastable
+    to it).
+    """
+    return RandomState(seed).gen_like(('uniform',{'low':low,'high':high}),template)
+
+def binomial(seed, template, n=1, p=0.5):
+    """
+    Return a multivariate binomial(n,p) random variable in a tensor of the same shape as template
+    (template can either be a tensor or a shape tuple). Each element of the
+    resulting tensor is sampled independently. low and high can
+    be scalars or have the same shape as the template (or broadcastable
+    to it).
+    """
+    return RandomState(seed).gen_like(('binomial',{'n':n,'p':p}),template)
+
+