disabling RModule tests

eab932a1 · james@X40 · d7aebfad · eab932a1 · eab932a1 · eab932a1
--- a/theano/__init__.py
+++ b/theano/__init__.py
@@ -43,7 +43,7 @@ from compile import \
    Mode, \
    predefined_modes, predefined_linkers, predefined_optimizers, \
    FunctionMaker, function, OpFromGraph, \
-    Component, External, Member, KitComponent, Method, \
+    Component, External, Member, Method, \
    Composite, ComponentList, ComponentDict, Module, \
    ProfileMode

--- a/theano/tensor/deprecated/test_rmodule.py
+++ b/theano/tensor/deprecated/test_rmodule.py
@@ -4,183 +4,70 @@ import sys
 import unittest
 import numpy as N
-from theano.tensor.rmodule import *
+from theano.tensor.deprecated.rmodule import *
 from theano import tensor
 from theano import compile, gof
-class T_RandomStreams(unittest.TestCase):
+if 0:
-    def test_basics(self):
+    class T_test_module(unittest.TestCase):
-        m = Module()
+        def test_state_propagation(self):
-        m.random = RandomStreams(234)
+            if 1:
-        m.fn = Method([], m.random.uniform((2,2)))
+                print >> sys.stderr, "RModule deprecated"
-        m.gn = Method([], m.random.normal((2,2)))
+            else:
-        made = m.make()
+                x = tensor.vector()
-        made.random.initialize()
+                rk = RandomKit('rk', 1000)
+                f = compile.function([x, (rk, [gof.Container(r = gof.generic, storage = [123], name='bla')])], rk.binomial(tensor.shape(x)))
-        fn_val0 = made.fn()
+                print "RK", rk.value
-        fn_val1 = made.fn()
+                f['rk'] = 9873456
+                print "RK", rk.value
-        gn_val0 = made.gn()
+                rvals = [f([1,2,3,4,6, 7, 8]) for i in xrange(5)]
-        rng_seed = numpy.random.RandomState(234).randint(2**30)
+                print rvals
-        rng = numpy.random.RandomState(int(rng_seed)) #int() is for 32bit
+                for i in xrange(5-1):
+                    for j in xrange(i+1, 5):
-        #print fn_val0
+                        assert not N.all(rvals[i] == rvals[j])
-        numpy_val0 = rng.uniform(size=(2,2))
-        numpy_val1 = rng.uniform(size=(2,2))
+        def test_B(self):
-        #print numpy_val0
+            """Test that random numbers change from call to call!
-        assert numpy.all(fn_val0 == numpy_val0)
+            Also, make sure that the seeding strategy doesn't change without failing a test.
-        assert numpy.all(fn_val1 == numpy_val1)
+            Random numbers can't be too random or experiments aren't repeatable.  Email theano-dev
-    def test_seed_in_initialize(self):
+            before updating the `rvals` in this test.
-        m = Module()
+            """
-        m.random = RandomStreams(234)
+            class B(RModule):
-        m.fn = Method([], m.random.uniform((2,2)))
+                def __init__(self):
-        made = m.make()
+                    super(B, self).__init__()
-        made.random.initialize(seed=888)
+                    self.x = compile.Member(tensor.dvector())
-        fn_val0 = made.fn()
+                    self.r = self.random.uniform(tensor.shape(self.x))
-        fn_val1 = made.fn()
+                    self.f = compile.Method([self.x], self.r)
-        rng_seed = numpy.random.RandomState(888).randint(2**30)
+            class E(RModule):
-        rng = numpy.random.RandomState(int(rng_seed))  #int() is for 32bit
+                def __init__(self):
+                    super(E, self).__init__()
-        #print fn_val0
+                    self.b = B()
-        numpy_val0 = rng.uniform(size=(2,2))
+                    self.f = compile.Method([self.b.x], self.b.r)
-        numpy_val1 = rng.uniform(size=(2,2))
-        #print numpy_val0
+            b = E()
+            m = b.make()
-        assert numpy.all(fn_val0 == numpy_val0)
-        assert numpy.all(fn_val1 == numpy_val1)
+            m.seed(1000)
+        #print m.f(N.ones(5))
-    def test_seed_fn(self):
+        #print m.f(N.ones(5))
-        m = Module()
+        #print m.f(N.ones(5))
-        m.random = RandomStreams(234)
+            rvals = ["0.74802375876 0.872308123517 0.294830748897 0.803123780003 0.6321109955",
-        m.fn = Method([], m.random.uniform((2,2)))
+                     "0.00168744844365 0.278638315678 0.725436793755 0.7788480779 0.629885140994",
-        made = m.make()
+                     "0.545561221664 0.0992011009108 0.847112593242 0.188015424144 0.158046201298",
-        made.random.initialize(seed=789)
+                     "0.054382248842 0.563459168529 0.192757276954 0.360455221883 0.174805216702",
+                     "0.961942907777 0.49657319422 0.0316111492826 0.0915054717012 0.195877184515"]
-        made.random.seed(888)
+            for i in xrange(5):
-        fn_val0 = made.fn()
+                s = " ".join([str(n) for n in m.f(N.ones(5))])
-        fn_val1 = made.fn()
+                print s
+                assert s == rvals[i]
-        rng_seed = numpy.random.RandomState(888).randint(2**30)
-        rng = numpy.random.RandomState(int(rng_seed))  #int() is for 32bit
-        #print fn_val0
-        numpy_val0 = rng.uniform(size=(2,2))
-        numpy_val1 = rng.uniform(size=(2,2))
-        #print numpy_val0
-        assert numpy.all(fn_val0 == numpy_val0)
-        assert numpy.all(fn_val1 == numpy_val1)
-    def test_getitem(self):
-        m = Module()
-        m.random = RandomStreams(234)
-        out = m.random.uniform((2,2))
-        m.fn = Method([], out)
-        made = m.make()
-        made.random.initialize(seed=789)
-        made.random.seed(888)
-        rng = numpy.random.RandomState()
-        rng.set_state(made.random[out.rng].get_state())
-        fn_val0 = made.fn()
-        fn_val1 = made.fn()
-        numpy_val0 = rng.uniform(size=(2,2))
-        numpy_val1 = rng.uniform(size=(2,2))
-        assert numpy.all(fn_val0 == numpy_val0)
-        assert numpy.all(fn_val1 == numpy_val1)
-    def test_setitem(self):
-        m = Module()
-        m.random = RandomStreams(234)
-        out = m.random.uniform((2,2))
-        m.fn = Method([], out)
-        made = m.make()
-        made.random.initialize(seed=789)
-        made.random.seed(888)
-        rng = numpy.random.RandomState(823874)
-        made.random[out.rng] = numpy.random.RandomState(823874)
-        fn_val0 = made.fn()
-        fn_val1 = made.fn()
-        numpy_val0 = rng.uniform(size=(2,2))
-        numpy_val1 = rng.uniform(size=(2,2))
-        assert numpy.all(fn_val0 == numpy_val0)
-        assert numpy.all(fn_val1 == numpy_val1)
-class T_test_module(unittest.TestCase):
-    def test_state_propagation(self):
-        if 1:
-            print >> sys.stderr, "RModule deprecated"
-        else:
-            x = tensor.vector()
-            rk = RandomKit('rk', 1000)
-            f = compile.function([x, (rk, [gof.Container(r = gof.generic, storage = [123], name='bla')])], rk.binomial(tensor.shape(x)))
-            print "RK", rk.value
-            f['rk'] = 9873456
-            print "RK", rk.value
-            rvals = [f([1,2,3,4,6, 7, 8]) for i in xrange(5)]
-            print rvals
-            for i in xrange(5-1):
-                for j in xrange(i+1, 5):
-                    assert not N.all(rvals[i] == rvals[j])
-    def test_B(self):
-        """Test that random numbers change from call to call!
-        Also, make sure that the seeding strategy doesn't change without failing a test.
-        Random numbers can't be too random or experiments aren't repeatable.  Email theano-dev
-        before updating the `rvals` in this test.
-        """
-        class B(RModule):
-            def __init__(self):
-                super(B, self).__init__()
-                self.x = compile.Member(tensor.dvector())
-                self.r = self.random.uniform(tensor.shape(self.x))
-                self.f = compile.Method([self.x], self.r)
-        class E(RModule):
-            def __init__(self):
-                super(E, self).__init__()
-                self.b = B()
-                self.f = compile.Method([self.b.x], self.b.r)
-        b = E()
-        m = b.make()
-        m.seed(1000)
-    #print m.f(N.ones(5))
-    #print m.f(N.ones(5))
-    #print m.f(N.ones(5))
-        rvals = ["0.74802375876 0.872308123517 0.294830748897 0.803123780003 0.6321109955",
-                 "0.00168744844365 0.278638315678 0.725436793755 0.7788480779 0.629885140994",
-                 "0.545561221664 0.0992011009108 0.847112593242 0.188015424144 0.158046201298",
-                 "0.054382248842 0.563459168529 0.192757276954 0.360455221883 0.174805216702",
-                 "0.961942907777 0.49657319422 0.0316111492826 0.0915054717012 0.195877184515"]
-        for i in xrange(5):
-            s = " ".join([str(n) for n in m.f(N.ones(5))])
-            print s
-            assert s == rvals[i]
 if __name__ == '__main__':
    from theano.tests import main

--- a/theano/tensor/deprecated/test_rmodule.pyc
+++ b/theano/tensor/deprecated/test_rmodule.pyc