pep8

theano/sandbox/multinomial.py

@@ -172,7 +172,6 @@ class MultinomialFromUniform(Op):
 
         nb_multi = pvals.shape[0]
         nb_outcomes = pvals.shape[1]
-
         # For each multinomial, loop over each possible outcome
         for c in range(n_samples):
             for n in range(nb_multi):

theano/sandbox/tests/test_rng_mrg.py

@@ -15,13 +15,12 @@ from theano import tensor, config
 from theano.sandbox import rng_mrg
 from theano.sandbox.rng_mrg import MRG_RandomStreams
 from theano.sandbox.cuda import cuda_available
+from theano.tests import unittest_tools as utt
+from theano.tests.unittest_tools import attr
 
 if cuda_available:
     from theano.sandbox.cuda import float32_shared_constructor
 
-from theano.tests import unittest_tools as utt
-from theano.tests.unittest_tools import attr
-
 # TODO: test gpu
 # Done in test_consistency_GPU_{serial,parallel}
 
@@ -474,8 +473,8 @@ def basictest(f, steps, sample_size, prefix="", allow_01=False, inputs=None,
         else:
             alpha = 1.0 / (1 + i)
             mean = alpha * ival + (1 - alpha) * mean
-            avg_var = (alpha * numpy.mean((ival - target_avg) ** 2)
-                       + (1 - alpha) * avg_var)
+            avg_var = (alpha * numpy.mean((ival - target_avg) ** 2) +
+                       (1 - alpha) * avg_var)
             min_ = min(min_, ival.min())
             max_ = max(max_, ival.max())
         if not allow_01:
@@ -487,7 +486,8 @@ def basictest(f, steps, sample_size, prefix="", allow_01=False, inputs=None,
         # print prefix, 'mean diff with mean', diff
         assert numpy.all(diff < mean_rtol * (1 + abs(target_avg))), (
             'bad mean? %s %s' % (mean, target_avg))
-    else:  # if target_avg is a scalar, then we can do the mean of
+    else:
+        # if target_avg is a scalar, then we can do the mean of
         # `mean` to get something more precise
         mean = numpy.mean(mean)
         # print prefix, 'mean', mean
@@ -507,11 +507,11 @@ def basictest(f, steps, sample_size, prefix="", allow_01=False, inputs=None,
 
 
 def test_uniform():
-# TODO: test param low, high
-# TODO: test size=None
-# TODO: test ndim!=size.ndim
-# TODO: test bad seed
-# TODO: test size=Var, with shape that change from call to call
+    # TODO: test param low, high
+    # TODO: test size=None
+    # TODO: test ndim!=size.ndim
+    # TODO: test bad seed
+    # TODO: test size=Var, with shape that change from call to call
     if (mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
             mode == 'Mode' and config.linker in ['py']):
         sample_size = (10, 100)
@@ -531,7 +531,7 @@ def test_uniform():
             ((), (), [], []),
             ]:
 
-        #### TEST CPU IMPLEMENTATION ####
+        # TEST CPU IMPLEMENTATION
         # The python and C implementation are tested with DebugMode
         # print ''
         # print 'ON CPU with size=(%s):' % str(size)
@@ -598,13 +598,13 @@ def test_uniform():
 
 @attr('slow')
 def test_binomial():
-# TODO: test size=None, ndim=X
-# TODO: test size=X, ndim!=X.ndim
-# TODO: test random seed in legal value(!=0 and other)
-# TODO: test sample_size not a multiple of guessed #streams
-# TODO: test size=Var, with shape that change from call to call
-# we test size in a tuple of int and a tensor.shape.
-# we test the param p with int.
+    # TODO: test size=None, ndim=X
+    # TODO: test size=X, ndim!=X.ndim
+    # TODO: test random seed in legal value(!=0 and other)
+    # TODO: test sample_size not a multiple of guessed #streams
+    # TODO: test size=Var, with shape that change from call to call
+    # we test size in a tuple of int and a tensor.shape.
+    # we test the param p with int.
 
     if (mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
             mode == 'Mode' and config.linker in ['py']):
@@ -617,7 +617,6 @@ def test_binomial():
         rtol = 0.01
 
     x = tensor.matrix()
-    v = tensor.vector()
     for mean in [0.1, 0.5]:
         for size, const_size, var_input, input in [
                 (sample_size, sample_size, [], []),
@@ -821,7 +820,8 @@ def test_multinomial():
     f = theano.function([], m, mode=mode_)
     # theano.printing.debugprint(f)
     out = f()
-    basic_multinomialtest(f, steps, sample_size, pvals, n_samples=1, prefix='mrg ')
+    basic_multinomialtest(f, steps, sample_size, pvals, n_samples=1,
+                          prefix='mrg ')
 
     sys.stdout.flush()
 
@@ -842,7 +842,8 @@ def test_multinomial():
         # theano.printing.debugprint(f)
         gpu_out = f()
         sys.stdout.flush()
-        basic_multinomialtest(f, steps, sample_size, pvals, n_samples=1, prefix='gpu mrg ')
+        basic_multinomialtest(f, steps, sample_size, pvals, n_samples=1,
+                              prefix='gpu mrg ')
         numpy.testing.assert_array_almost_equal(out, gpu_out, decimal=6)
 
 
@@ -863,15 +864,18 @@ def test_multinomial_n_samples():
     R = MRG_RandomStreams(234, use_cuda=False)
 
     for n_samples, steps in zip([5, 10, 100, 1000], [20, 10, 1, 1]):
-        m = R.multinomial(pvals=pvals, n=n_samples, dtype=config.floatX, nstreams=30 * 256)
+        m = R.multinomial(pvals=pvals, n=n_samples,
+                          dtype=config.floatX, nstreams=30 * 256)
         f = theano.function([], m, mode=mode_)
-        basic_multinomialtest(f, steps, sample_size, pvals, n_samples, prefix='mrg ')
+        basic_multinomialtest(f, steps, sample_size, pvals,
+                              n_samples, prefix='mrg ')
         sys.stdout.flush()
 
         if mode != 'FAST_COMPILE' and cuda_available:
             R = MRG_RandomStreams(234, use_cuda=True)
             pvals = numpy.asarray(pvals, dtype='float32')
-            n = R.multinomial(pvals=pvals, n=n_samples, dtype='float32', nstreams=30 * 256)
+            n = R.multinomial(pvals=pvals, n=n_samples,
+                              dtype='float32', nstreams=30 * 256)
             assert n.dtype == 'float32'
             f = theano.function(
                 [],
@@ -879,7 +883,8 @@ def test_multinomial_n_samples():
                 mode=mode_.including('gpu'))
 
             sys.stdout.flush()
-            basic_multinomialtest(f, steps, sample_size, pvals, n_samples, prefix='gpu mrg ')
+            basic_multinomialtest(f, steps, sample_size, pvals,
+                                  n_samples, prefix='gpu mrg ')
 
 
 class T_MRG(unittest.TestCase):
@@ -1039,7 +1044,6 @@ def test_seed_fn():
 
 if __name__ == "__main__":
     rng = MRG_RandomStreams(numpy.random.randint(2147462579))
-    import time
     print(theano.__file__)
     pvals = theano.tensor.fmatrix()
     for i in range(10):

theano/tests/test_flake8.py

@@ -102,7 +102,6 @@ whitelist_flake8 = [
     "sandbox/debug.py",
     "sandbox/tests/test_theano_object.py",
     "sandbox/tests/test_scan.py",
-    "sandbox/tests/test_rng_mrg.py",
     "sandbox/tests/test_neighbourhoods.py",
     "sandbox/tests/__init__.py",
     "sandbox/cuda/var.py",