Use utt.fetch_seed() instead of a fixed seed in test_sharedrandomstreams

theano/tensor/tests/test_shared_randomstreams.py

@@ -2,7 +2,7 @@ __docformat__ = "restructuredtext en"
 
 import sys
 import unittest
-import numpy 
+import numpy
 
 from theano.tensor import raw_random
 from theano.tensor.shared_randomstreams import RandomStreams
@@ -11,7 +11,7 @@ from theano import function
 from theano import tensor
 from theano import compile, gof
 
-from theano.tests import unittest_tools
+from theano.tests import unittest_tools as utt
 
 class T_SharedRandomStreams(unittest.TestCase):
 
@@ -30,7 +30,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         assert isinstance(rv_u.rng.value, numpy.random.RandomState)
 
     def test_basics(self):
-        random = RandomStreams(234)
+        random = RandomStreams(utt.fetch_seed())
         fn = function([], random.uniform((2,2)), updates=random.updates())
         gn = function([], random.normal((2,2)), updates=random.updates())
 
@@ -39,7 +39,7 @@ class T_SharedRandomStreams(unittest.TestCase):
 
         gn_val0 = gn()
 
-        rng_seed = numpy.random.RandomState(234).randint(2**30)
+        rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
         rng = numpy.random.RandomState(int(rng_seed)) #int() is for 32bit
 
         #print fn_val0
@@ -58,12 +58,12 @@ class T_SharedRandomStreams(unittest.TestCase):
         random = RandomStreams(234)
         fn = function([], random.uniform((2,2)), updates=random.updates())
 
-        random.seed(888)
+        random.seed(utt.fetch_seed())
 
         fn_val0 = fn()
         fn_val1 = fn()
 
-        rng_seed = numpy.random.RandomState(888).randint(2**30)
+        rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
         rng = numpy.random.RandomState(int(rng_seed))  #int() is for 32bit
 
         #print fn_val0
@@ -80,7 +80,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         out = random.uniform((2,2))
         fn = function([], out, updates=random.updates())
 
-        random.seed(888)
+        random.seed(utt.fetch_seed())
 
         rng = numpy.random.RandomState()
         rng.set_state(random[out.rng].get_state()) #tests getitem
@@ -100,8 +100,8 @@ class T_SharedRandomStreams(unittest.TestCase):
 
         random.seed(888)
 
-        rng = numpy.random.RandomState(823874)
-        random[out.rng] = numpy.random.RandomState(823874)
+        rng = numpy.random.RandomState(utt.fetch_seed())
+        random[out.rng] = numpy.random.RandomState(utt.fetch_seed())
 
         fn_val0 = fn()
         fn_val1 = fn()
@@ -111,15 +111,15 @@ class T_SharedRandomStreams(unittest.TestCase):
         assert numpy.all(fn_val1 == numpy_val1)
 
     def test_permutation(self):
-        """Test that RandomStreams.uniform generates the same results as numpy"""
+        """Test that RandomStreams.permutation generates the same results as numpy"""
         # Check over two calls to see if the random state is correctly updated.
-        random = RandomStreams(234)
+        random = RandomStreams(utt.fetch_seed())
         fn = function([], random.permutation((20,), 10), updates=random.updates())
 
         fn_val0 = fn()
         fn_val1 = fn()
 
-        rng_seed = numpy.random.RandomState(234).randint(2**30)
+        rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
         rng = numpy.random.RandomState(int(rng_seed)) #int() is for 32bit
 
         # rng.permutation outputs one vector at a time, so we iterate.
@@ -132,13 +132,13 @@ class T_SharedRandomStreams(unittest.TestCase):
     def test_multinomial(self):
         """Test that RandomStreams.multinomial generates the same results as numpy"""
         # Check over two calls to see if the random state is correctly updated.
-        random = RandomStreams(234)
+        random = RandomStreams(utt.fetch_seed())
         fn = function([], random.multinomial((4,4), 1, [0.1]*10), updates=random.updates())
 
         fn_val0 = fn()
         fn_val1 = fn()
 
-        rng_seed = numpy.random.RandomState(234).randint(2**30)
+        rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
         rng = numpy.random.RandomState(int(rng_seed)) #int() is for 32bit
         numpy_val0 = rng.multinomial(1, [0.1]*10, size=(4,4))
         numpy_val1 = rng.multinomial(1, [0.1]*10, size=(4,4))
@@ -153,11 +153,12 @@ class T_SharedRandomStreams(unittest.TestCase):
         # On matrices, for each row, the elements of that row should be shuffled.
         # Note that this differs from numpy.random.shuffle, where all the elements
         # of the matrix are shuffled.
-        random = RandomStreams(234)
+        random = RandomStreams(utt.fetch_seed())
         m_input = tensor.dmatrix()
         f = function([m_input], random.shuffle_row_elements(m_input), updates=random.updates())
 
-        val_rng = numpy.random.RandomState(unittest_tools.fetch_seed())
+        # Generate the elements to be shuffled
+        val_rng = numpy.random.RandomState(utt.fetch_seed()+42)
         in_mval = val_rng.uniform(-2, 2, size=(20,5))
         fn_mval0 = f(in_mval)
         fn_mval1 = f(in_mval)
@@ -168,7 +169,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         assert not numpy.all(in_mval == fn_mval1)
         assert not numpy.all(fn_mval0 == fn_mval1)
 
-        rng_seed = numpy.random.RandomState(234).randint(2**30)
+        rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
         rng = numpy.random.RandomState(int(rng_seed))
         numpy_mval0 = in_mval.copy()
         numpy_mval1 = in_mval.copy()
@@ -182,7 +183,7 @@ class T_SharedRandomStreams(unittest.TestCase):
 
         # On vectors, the behaviour is the same as numpy.random.shuffle,
         # except that it does not work in place, but returns a shuffled vector.
-        random1 = RandomStreams(234)
+        random1 = RandomStreams(utt.fetch_seed())
         v_input = tensor.dvector()
         f1 = function([v_input], random1.shuffle_row_elements(v_input))
 
@@ -203,7 +204,7 @@ class T_SharedRandomStreams(unittest.TestCase):
 
     def test_default_updates(self):
         # Basic case: default_updates
-        random_a = RandomStreams(234)
+        random_a = RandomStreams(utt.fetch_seed())
         out_a = random_a.uniform((2,2))
         fn_a = function([], out_a)
         fn_a_val0 = fn_a()
@@ -214,7 +215,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         assert numpy.all(abs(nearly_zeros()) < 1e-5)
 
         # Explicit updates #1
-        random_b = RandomStreams(234)
+        random_b = RandomStreams(utt.fetch_seed())
         out_b = random_b.uniform((2,2))
         fn_b = function([], out_b, updates=random_b.updates())
         fn_b_val0 = fn_b()
@@ -223,7 +224,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         assert numpy.all(fn_b_val1 == fn_a_val1)
 
         # Explicit updates #2
-        random_c = RandomStreams(234)
+        random_c = RandomStreams(utt.fetch_seed())
         out_c = random_c.uniform((2,2))
         fn_c = function([], out_c, updates=[out_c.update])
         fn_c_val0 = fn_c()
@@ -232,7 +233,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         assert numpy.all(fn_c_val1 == fn_a_val1)
 
         # No updates at all
-        random_d = RandomStreams(234)
+        random_d = RandomStreams(utt.fetch_seed())
         out_d = random_d.uniform((2,2))
         fn_d = function([], out_d, no_default_updates=True)
         fn_d_val0 = fn_d()
@@ -241,7 +242,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         assert numpy.all(fn_d_val1 == fn_d_val0)
 
         # No updates for out
-        random_e = RandomStreams(234)
+        random_e = RandomStreams(utt.fetch_seed())
         out_e = random_e.uniform((2,2))
         fn_e = function([], out_e, no_default_updates=[out_e.rng])
         fn_e_val0 = fn_e()