Added block matrix dot product

GPy/util/block_matrices.py

@@ -35,6 +35,37 @@ def unblock(B):
         count_i += i
     return A
 
+def block_dot(A, B):
+    """
+    Element wise dot product on block matricies
+
+    +------+------+   +------+------+    +-------+-------+
+    |      |      |   |      |      |    |A11.B11|B12.B12|
+    | A11  | A12  |   | B11  | B12  |    |       |       |
+    +------+------+ o +------+------| =  +-------+-------+
+    |      |      |   |      |      |    |A21.B21|A22.B22|
+    | A21  | A22  |   | B21  | B22  |    |       |       |
+    +-------------+   +------+------+    +-------+-------+
+
+    ..Note
+        If either (A or B) of the diagonal matrices are stored as vectors then a more
+        efficient dot product using numpy broadcasting will be used, i.e. A11*B11
+    """
+    #Must have same number of blocks and be a block matrix
+    assert A.dtype is np.dtype('object'), "Must be a block matrix"
+    assert B.dtype is np.dtype('object'), "Must be a block matrix"
+    Ashape = A.shape
+    Bshape = B.shape
+    assert Ashape == Bshape
+    def f(A,B):
+        if Ashape[0] == Ashape[1] or Bshape[0] == Bshape[1]:
+            #FIXME: Careful if one is transpose of other, would make a matrix
+            return A*B
+        else:
+            return np.dot(A,B)
+    dot = np.vectorize(f, otypes = [np.object])
+    return dot(A,B)
+
 
 if __name__=='__main__':
     A = np.zeros((5,5))
@@ -43,6 +74,3 @@ if __name__=='__main__':
     print B
 
     assert np.all(unblock(B) == A)
-
-    import ipdb; ipdb.set_trace()  # XXX BREAKPOINT
-