merge

theano/tensor/elemwise.py

@@ -67,6 +67,9 @@ class DimShuffle(Op):
       DimShuffle((False, False, False), [2, 0, 1]) -> AxBxC to CxAxB
       DimShuffle((False, False), [0, 'x', 1]) -> AxB to Ax1xB
       DimShuffle((False, False), [1, 'x', 0]) -> AxB to Bx1xA
+
+    The reordering of the dimensions can be done in numpy with the transpose function.
+    Adding, subtracting dimensions can be done with reshape.
     """
 
     def __init__(self, input_broadcastable, new_order, inplace = False):

theano/tensor/tests/test_basic.py

@@ -1944,6 +1944,20 @@ def test_convert_to_complex():
             b = value(numpy.ones(3, dtype='complex128'))
             f = function([a],basic.convert_to_complex128(a))
             assert a.type.values_eq_approx(b.data, f(a.data))
+        for t in ['int8','int16','int32','int64','float32']:
+            a = value(numpy.ones(3, dtype=t))
+            b = value(numpy.ones(3, dtype='complex64'))
+            f = function([a],basic.convert_to_complex64(a))
+            assert a.type.values_eq_approx(b.data, f(a.data))
+
+        #this work, but should we allow it? How well it is implemented?
+        for t in ['float64']:
+            a = value(numpy.ones(3, dtype=t))
+            b = value(numpy.ones(3, dtype='complex64'))
+            f = function([a],basic.convert_to_complex64(a))
+            assert a.type.values_eq_approx(b.data, f(a.data))
+      
+
     
 def test_bug_complext_10_august_09():
     v0 = dmatrix()