Add the dtype param to tensor.{zeros,ones}_like as numpy. Test them.

theano/tensor/basic.py

@@ -2338,17 +2338,19 @@ pprint.assign(fill, printing.FunctionPrinter('fill'))
 
 
 @constructor
-def ones_like(model):
-    """WRITEME"""
-    #return Ones(model.type.ndim)(shape(model))
-    ret= fill(model, constant(1.0, dtype=model.type.dtype))
+def ones_like(model, dtype=None):
+    """equivalent of numpy.ones_like"""
+    if dtype is None:
+        dtype = model.type.dtype
+    ret= fill(model, constant(1.0, dtype=dtype))
     return ret
 
 @constructor
-def zeros_like(model):
-    """WRITEME"""
-    #return Zeros(model.type.ndim)(shape(model))
-    return fill(model, constant(0.0, dtype=model.type.dtype))
+def zeros_like(model, dtype=None):
+    """equivalent of numpy.zeros_like"""
+    if dtype is None:
+        dtype = model.type.dtype
+    return fill(model, constant(0.0, dtype=dtype))
 
 
 def zeros(shape, dtype=config.floatX):

theano/tensor/tests/test_basic.py

@@ -915,6 +915,15 @@ ErfcInplaceTester = makeBroadcastTester(op = inplace.erfc_inplace,
                                         inplace = True,
                                         skip = skip_scipy)
 
+ZerosLikeTester =  makeBroadcastTester(op = zeros_like,
+                                        expected = numpy.zeros_like,
+                                        good = _good_broadcast_unary_normal,
+                                        grad = _grad_broadcast_unary_normal)
+
+OnesLikeTester =  makeBroadcastTester(op = ones_like,
+                                        expected = numpy.ones_like,
+                                        good = _good_broadcast_unary_normal,
+                                        grad = _grad_broadcast_unary_normal)
 
 DotTester = makeTester(name = 'DotTester',
                         op = dot,