cut old commented test from test_opt

theano/tensor/tests/test_opt.py

@@ -78,55 +78,8 @@ def test_add_canonizer_problem0():
     r = segment_labels * 5
     f = function([label], r)
 
-
-
-
-# class _test_inplace_opt(unittest.TestCase):
-
-#     def test_straightforward(self):
-#         x, y, z = inputs()
-#         e = x + y + z
-#         g = Env([x, y], [e])
-#         self.failUnless(str(g) == "[Broadcast{Add}(Broadcast{Add}(x, y), z)]")
-#         inplace_optimizer.optimize(g)
-#         self.failUnless(str(g) == "[Broadcast{Add}{0: 0}(Broadcast{Add}{0: 0}(x, y), z)]")
-
-#     def test_multiple_uses(self):
-#         x, y, z = inputs()
-#         e0 = x + y
-#         e1 = x * y
-#         g = Env([x, y], [e0, e1])
-#         self.failUnless(str(g) == "[Broadcast{Add}(x, y), Broadcast{Mul}(x, y)]")
-#         inplace_optimizer.optimize(g)
-#         self.failUnless(str(g) == "[Broadcast{Add}{0: 0}(x, y), Broadcast{Mul}(x, y)]" \
-#             or str(g) == "[Broadcast{Add}(x, y), Broadcast{Mul}{0: 0}(x, y)]")
-
-#     def test_user_inplace(self):
-#         x, y, z = inputs()
-#         e0 = x + y
-#         e1 = tensor._mul_inplace(x, y)
-#         g = Env([x, y], [e0, e1])
-#         self.failUnless(str(g) == "[Broadcast{Add}(x, y), Broadcast{Mul}{0: 0}(x, y)]")
-#         inplace_optimizer.optimize(g)
-#         self.failUnless(str(g) == "[Broadcast{Add}(x, y), Broadcast{Mul}{0: 0}(x, y)]")
-
-#     def test_inplace_on_second_argument(self):
-#         x, y, z = inputs()
-#         e0 = x + y
-#         e1 = tensor._mul_inplace(x, z)
-#         g = Env([x, y], [e0, e1])
-#         self.failUnless(str(g) == "[Broadcast{Add}(x, y), Broadcast{Mul}{0: 0}(x, z)]")
-#         inplace_optimizer.optimize(g)
-#         self.failUnless(str(g) == "[Broadcast{Add}{0: 1}(x, y), Broadcast{Mul}{0: 0}(x, z)]")
-
-
-
-
-
 from theano.tensor import *
 
-#from sandbox import pprint
-
 class test_greedy_distribute(unittest.TestCase):
     def test_main(self):
         a, b, c, d, x, y, z = matrices('abcdxyz')
@@ -597,191 +550,6 @@ def test_local_shape_lift_dot():
                 print pprint(g.outputs[0]), args_to_result[(x,y)]
                 assert pprint(g.outputs[0]) == args_to_result[(x,y)]
         
-#     def test_plusmin(self):
-#         x, y, z = inputs()
-#         a, b, c, d = more_inputs()
-# #        e = x - x
-# #        e = (2.0 + x) - (2.0 + y)
-# #        e = (2.0 + x) - (4.0 + y)
-# #        e = x - (y - z)
-# #        e = (x + y) - x
-# #        e = (x - y) + (y - z) + (z - x)
-# #        e = (a - b) + (b - c) + (c - d)
-# #        e = x + -y
-# #        e = a - b - b + a + b + c + b - c
-# #        e = x + log(y) - x + y
-#         e = 2.0 + x + 4.0
-#         g = Env([x, y, z, a, b, c, d], [e])
-#         print g
-#         gof.ConstantFinder().optimize(g)
-#         addfn = lambda *inputs: sum(inputs)
-#         subfn = lambda x, y: x - y
-#         negfn = lambda x: -x
-#         Canonizer(Add, Sub, Neg, addfn, subfn, negfn).optimize(g)
-#         print g
-
-#     def test_both(self):
-#         x, y, z = inputs()
-#         a, b, c, d = more_inputs()
-#         e0 = (x * y / x)
-#         e = e0 + e0 - e0
-#         g = Env([x, y, z, a, b, c, d], [e])
-#         print g
-#         gof.ConstantFinder().optimize(g)
-#         mulfn = lambda *inputs: reduce(lambda x, y: x * y, (1,) + inputs)
-#         divfn = lambda x, y: x / y
-#         invfn = lambda x: 1 / x
-#         Canonizer(Mul, Div, Inv, mulfn, divfn, invfn).optimize(g)
-#         addfn = lambda *inputs: reduce(lambda x, y: x + y, (0,) + inputs)
-#         subfn = lambda x, y: x - y
-#         negfn = lambda x: -x
-#         Canonizer(Add, Sub, Neg, addfn, subfn, negfn).optimize(g)
-#         print g
-
-#     def test_group_powers(self):
-#         x, y, z, a, b, c, d = floats('xyzabcd')
-
-###################
-#         c1, c2 = constant(1.), constant(2.)
-#         #e = pow(x, c1) * pow(x, y) / pow(x, 7.0) # <-- fucked
-#         #f = -- moving from div(mul.out, pow.out) to pow(x, sub.out)
-#         e = div(mul(pow(x, 2.0), pow(x, y)), pow(x, 7.0))
-
-#         g = Env([x, y, z, a, b, c, d], [e])
-#         print g
-#         print g.inputs, g.outputs, g.orphans
-#         f = sub(add(2.0, y), add(7.0))
-#         g.replace(e, pow(x, f))
-#         print g
-#         print g.inputs, g.outputs, g.orphans
-#         g.replace(f, sub(add(2.0, y), add(7.0))) # -- moving from sub(add.out, add.out) to sub(add.out, add.out)
-#         print g
-#         print g.inputs, g.outputs, g.orphans
-###################
-
-# #        e = x * exp(y) * exp(z)
-# #        e = x * pow(x, y) * pow(x, z)
-# #        e = pow(x, y) / pow(x, z)
-#         e = pow(x, 2.0) * pow(x, y) / pow(x, 7.0) # <-- fucked
-# #        e = pow(x - x, y)
-# #        e = pow(x, 2.0 + y - 7.0)
-# #        e = pow(x, 2.0) * pow(x, y) / pow(x, 7.0) / pow(x, z)
-# #        e = pow(x, 2.0 + y - 7.0 - z)
-# #        e = x ** y / x ** y
-# #        e = x ** y / x ** (y - 1.0)
-# #        e = exp(x) * a * exp(y) / exp(z)
-#         g = Env([x, y, z, a, b, c, d], [e])
-#         g.extend(gof.PrintListener(g))
-#         print g, g.orphans
-#         mulfn = lambda *inputs: reduce(lambda x, y: x * y, (1,) + inputs)
-#         divfn = lambda x, y: x / y
-#         invfn = lambda x: 1 / x
-#         Canonizer(mul, div, inv, mulfn, divfn, invfn, group_powers).optimize(g)
-#         print g, g.orphans
-#         addfn = lambda *inputs: reduce(lambda x, y: x + y, (0,) + inputs)
-#         subfn = lambda x, y: x - y
-#         negfn = lambda x: -x
-#         Canonizer(add, sub, neg, addfn, subfn, negfn).optimize(g)
-#         print g, g.orphans
-#         pow2one_float.optimize(g)
-#         pow2x_float.optimize(g)
-#         print g, g.orphans
-        
-
-
-
-
-
-
-
-# class _test_cliques(unittest.TestCase):
-
-#     def test_straightforward(self):
-#         x, y, z = inputs()
-#         m = y * z
-#         d = tensor.dot(x, m)
-#         d.name = 'd'
-#         e = x + y + d
-#         g = Env([x, y, z], [e])
-#         cliques = find_cliques(g)
-#         self.failUnless(len(cliques) == 2)
-#         (i1, o1), (i2, o2) = cliques
-#         self.failUnless(str(Env(i1, o1)) == "[Broadcast{Add}(Broadcast{Add}(x, y), d)]")
-#         self.failUnless(str(Env(i2, o2)) == "[Broadcast{Mul}(y, z)]")
-# #         print g
-# #         for i, o in find_cliques(g):
-# #             print "-->", Env(i, [o])
-
-#     def test_broadcasting(self):
-#         x, y, z = inputs([0]*1, [0]*2, [0]*3)
-#         e = x + y + z
-#         g = Env([x, y, z], [e])
-#         lift_dimshuffle.optimize(g)
-#         self.failUnless(len(find_cliques(g, through_broadcast = True)) == 1)
-#         self.failUnless(len(find_cliques(g, through_broadcast = False)) == 2)
-# #         print g
-# #         for i, o in find_cliques(g, True):
-# #             print "-->", Env(i, [o])
-
-
-# # class _test_clique_opt(unittest.TestCase):
-
-# #     def test_straightforward(self):
-# #         x, y, z = inputs()
-# #         e = x ** 2.0 #x * x
-# #         g = Env([x], [e])
-# #         gof.ConstantFinder().optimize(g)
-# #         opt = CliqueOptimizer(through_broadcast = False,
-# #                               scalar_optimizer = scalar_opt.opt2,
-# #                               make_composite = False)
-# #         print g
-# #         opt.optimize(g)
-# #         print g
-
-# #     def test_inplace(self):
-# #         x, y, z = inputs()
-# #         #e = tensor._add_inplace(x, y + z)
-# #         e = x + tensor._add_inplace(y, z)
-# #         g = Env([x, y, z], [e])
-# #         opt = CliqueOptimizer(through_broadcast = False,
-# #                               scalar_optimizer = None,
-# #                               make_composite = True)
-# #         print g
-# #         opt.optimize(g)
-# #         print g
-# # #        print g.outputs[0].owner.c_code(['x', 'y', 'z'], ['e'], dict(fail = "FAIL;", id = 0))
-# #         print gof.OpWiseCLinker(g).make_function()(numpy.ones((5, 5)), numpy.ones((5, 5)), numpy.ones((5, 5)))
-
-# #     def test_straightforward(self):
-# #         x, y, z = inputs()
-# #         e = x + y + z
-# #         g = Env([x, y, z], [e])
-# #         opt = CliqueOptimizer(through_broadcast = False,
-# #                               scalar_optimizer = None,
-# #                               make_composite = True)
-# #         print g
-# #         opt.optimize(g)
-# #         print g
-# # #        print g.outputs[0].owner.c_code(['x', 'y', 'z'], ['e'], dict(fail = "FAIL;", id = 0))
-# #         print gof.OpWiseCLinker(g).make_function()(numpy.ones((5, 5)), numpy.ones((5, 5)), numpy.ones((5, 5)))
-
-# #     def test_straightforward2(self):
-# #         x, y, z = inputs()
-# #         m = y * z
-# #         d = tensor.dot(x, m)
-# #         d.name = 'd'
-# #         e = x + y + d
-# #         g = Env([x, y, z], [e])
-# #         opt = CliqueOptimizer(through_broadcast = False,
-# #                               scalar_optimizer = None,
-# #                               make_composite = True)
-# #         print g
-# #         opt.optimize(g)
-# #         print g
-# # #        print g.outputs[0].owner.c_code(['x', 'y', 'z'], ['e'], dict(fail = "FAIL;", id = 0))
-# #         print gof.OpWiseCLinker(g).make_function()(numpy.ones((5, 5)), numpy.ones((5, 5)), numpy.ones((5, 5)))
-
-
 def test_const_type_in_mul_canonizer():
     input = dmatrix()
     w = dmatrix()