fixed sandbox/multinomial tests to not require a perform method

theano/sandbox/test_multinomial.py

 
 import numpy
+import theano
 from theano import tensor, shared, function
 import multinomial
+from theano.compile.mode import get_default_mode, predefined_linkers
+
+def run_with_c(f):
+    mode = get_default_mode()
+    linker_orig = mode.linker
+    if linker_orig == predefined_linkers['py']:
+        mode.linker = predefined_linkers['c|py']
+    try:
+        f(mode)
+    finally:
+        mode.linker = linker_orig
+
 
 def test_multimomial_0():
     # This tests the multinomial Op directly, not going through the
@@ -12,8 +25,9 @@ def test_multimomial_0():
 
     m = multinomial.Multinomial('auto')(p,u)
 
+    def body(mode):
         #the m*2 allows the multinomial to reuse output
-    f = function([p,u], m*2, allow_input_downcast=True)
+        f = function([p,u], m*2, allow_input_downcast=True, mode=mode)
 
 
         # test that both first and second samples can be drawn
@@ -35,14 +49,17 @@ def test_multimomial_0():
         r = f([[.2,.8] ], [.25])
         assert numpy.allclose(r, [[0,2]]), r
 
+    run_with_c(body)
+
 
 #TODO: check a bigger example (make sure blocking on GPU is handled correctly)
 def test_multinomial_large():
     # DEBUG_MODE will test this on GPU
+    def body(mode):
         p = tensor.fmatrix()
         u = tensor.fvector()
         m = multinomial.Multinomial('auto')(p,u)
-    f = function([p,u], m*2, allow_input_downcast=True)
+        f = function([p,u], m*2, allow_input_downcast=True, mode=mode)
 
         pval = numpy.arange(10000 * 4, dtype='float32').reshape((10000, 4))+0.1
         pval = pval / pval.sum(axis=1)[:,None]
@@ -54,6 +71,7 @@ def test_multinomial_large():
         assert numpy.allclose(mval.sum(axis=1), 2)
         asdf = numpy.asarray([0, 0, 2, 0])+0*pval
         assert numpy.allclose(mval, asdf) #broadcast over all rows
+    run_with_c(body)
 
 
 def test_multinomial_dtypes():