Update new kron doc, and add a test with numpy.kron.

theano/sandbox/linalg/kron.py

@@ -4,7 +4,11 @@ from theano import tensor
 def kron(a, b):
     """ Kronecker product
 
-    Same as scipy.linalg.kron(a, b) or numpy.kron(a, b)
+    Same as scipy.linalg.kron(a, b).
+
+    :note: numpy.kron(a, b) != scipy.linalg.kron(a, b)!
+        They don't have the same shape and order when
+        a.ndim != b.ndim != 2.
 
     :param a: array_like
     :param b: array_like

theano/sandbox/linalg/tests/test_kron.py

@@ -14,9 +14,6 @@ try:
 except ImportError:
     imported_scipy = False
 
-if not imported_scipy:
-    raise SkipTest('Kron Op need the scipy package to be installed')
-
 
 class TestKron(utt.InferShapeTester):
 
@@ -27,6 +24,9 @@ class TestKron(utt.InferShapeTester):
         self.op = kron
 
     def test_perform(self):
+        if not imported_scipy:
+            raise SkipTest('kron tests need the scipy package to be installed')
+
         for shp0 in [(2,), (2, 3), (2, 3, 4), (2, 3, 4, 5)]:
             for shp1 in [(6,), (6, 7), (6, 7, 8), (6, 7, 8, 9)]:
                 if len(shp0) + len(shp1) == 2:
@@ -41,6 +41,21 @@ class TestKron(utt.InferShapeTester):
                 out = f(a, b)
                 assert numpy.allclose(out, scipy.linalg.kron(a, b))
 
+    def test_numpy_2d(self):
+        for shp0 in [(2, 3)]:
+            for shp1 in [(6, 7)]:
+                if len(shp0) + len(shp1) == 2:
+                    continue
+                x = tensor.tensor(dtype='floatX',
+                                  broadcastable=(False,) * len(shp0))
+                y = tensor.tensor(dtype='floatX',
+                                  broadcastable=(False,) * len(shp1))
+                f = function([x, y], kron(x, y))
+                a = numpy.asarray(self.rng.rand(*shp0)).astype(floatX)
+                b = self.rng.rand(*shp1).astype(floatX)
+                out = f(a, b)
+                assert numpy.allclose(out, numpy.kron(a, b))
+
 
 if __name__ == "__main__":
     t = TestKron('setUp')