fixed tests

__init__.py

@@ -9,7 +9,7 @@ from gof import \
      Type, Generic, generic, \
      object2, utils
 
-from compile import function, eval_outputs, fast_compute, OpFromGraph
+from compile import FunctionMaker, function, OpFromGraph #, eval_outputs, fast_compute
 
 import tensor
 import tensor_random

gof/graph.py

@@ -358,8 +358,6 @@ def clone_get_equiv(i, o, copy_inputs_and_orphans = True):
         else:
             d[input] = input
 
-
-
     for apply in io_toposort(i, o):
         for input in apply.inputs:
             if input not in d:
@@ -374,6 +372,10 @@ def clone_get_equiv(i, o, copy_inputs_and_orphans = True):
         for output, new_output in zip(apply.outputs, new_apply.outputs):
             d[output] = new_output
 
+    for output in o:
+        if output not in d:
+            d[output] = output.clone()
+
     return d
 
 def general_toposort(r_out, deps, debug_print = False):

tensor_random.py

@@ -113,11 +113,16 @@ from collections import deque
 
 class RandomKit(SymbolicInputKit):
 
+    def __init__(self, name, value = None):
+        super(RandomKit, self).__init__(name)
+        self.value = value
+
     def gen(self, op, *args, **kwargs):
         r = gof.generic()
         new_r, out = op(r, *args, **kwargs)
         self.add_input(SymbolicInput(r, update = new_r))
         out.rng = r
+        out.auto = self
         return out
 
     def distribute(self, value, indices, containers):
@@ -135,7 +140,18 @@ class RandomKit(SymbolicInputKit):
     def binomial(self, *args, **kwargs):
         return self.gen(binomial, *args, **kwargs)
 
-rk = RandomKit('rk')
+    def uniform(self, *args, **kwargs):
+        return self.gen(uniform, *args, **kwargs)
+
+    def normal(self, *args, **kwargs):
+        return self.gen(normal, *args, **kwargs)
+
+    def random_integers(self, *args, **kwargs):
+        return self.gen(random_integers, *args, **kwargs)
+
+
+
+rk = RandomKit('rk', 0xBAD5EED)