Merge pull request #1832 from nouiz/fix_tests

setup.py

@@ -74,11 +74,11 @@ VERSION             = '%d.%d.%d%s' % (MAJOR, MINOR, MICRO, SUFFIX)
 
 def find_packages(where='.', exclude=()):
     out = []
-    stack=[(convert_path(where), '')]
+    stack = [(convert_path(where), '')]
     while stack:
         where, prefix = stack.pop(0)
         for name in os.listdir(where):
-            fn = os.path.join(where,name)
+            fn = os.path.join(where, name)
             if ('.' not in name and os.path.isdir(fn) and
                 os.path.isfile(os.path.join(fn, '__init__.py'))
             ):
@@ -146,6 +146,7 @@ def write_text(filename, text):
         a.close()
 """)
 
+
 def write_version_py(filename=os.path.join('theano', 'generated_version.py')):
     cnt = """
 # THIS FILE IS GENERATED FROM THEANO SETUP.PY
@@ -175,6 +176,7 @@ if not release:
                   'isrelease': str(ISRELEASED)}
     write_text(filename, text)
 
+
 def do_setup():
     write_version_py()
     setup(name=NAME,
@@ -199,7 +201,7 @@ def do_setup():
               'theano', 'math', 'numerical', 'symbolic', 'blas',
               'numpy', 'gpu', 'autodiff', 'differentiation'
           ]),
-          cmdclass = {'build_py': build_py,
+          cmdclass={'build_py': build_py,
                     'build_scripts': build_scripts}
     )
 if __name__ == "__main__":

theano/sandbox/test_rng_mrg.py

@@ -166,7 +166,8 @@ def test_consistency_cpu_parallel():
         rstate = theano.shared(rstate)
 
         new_rstate, sample = rng_mrg.mrg_uniform.new(rstate, ndim=None,
-                dtype=config.floatX, size=(n_substreams,))
+                                                     dtype=config.floatX,
+                                                     size=(n_substreams,))
         # Not really necessary, just mimicking
         # rng_mrg.MRG_RandomStreams' behavior
         sample.rstate = rstate
@@ -219,7 +220,8 @@ def test_consistency_GPU_serial():
             rstate = float32_shared_constructor(tmp_float_buf)
 
             new_rstate, sample = rng_mrg.GPU_mrg_uniform.new(rstate, ndim=None,
-                    dtype='float32', size=(1,))
+                                                             dtype='float32',
+                                                             size=(1,))
             rstate.default_update = new_rstate
 
             # Not really necessary, just mimicking
@@ -278,7 +280,8 @@ def test_consistency_GPU_parallel():
         rstate = float32_shared_constructor(tmp_float_buf)
 
         new_rstate, sample = rng_mrg.GPU_mrg_uniform.new(rstate, ndim=None,
-                dtype='float32', size=(n_substreams,))
+                                                         dtype='float32',
+                                                         size=(n_substreams,))
         rstate.default_update = new_rstate
 
         # Not really necessary, just mimicking
@@ -381,7 +384,8 @@ def test_consistency_GPUA_parallel():
         rstate = gpuarray_shared_constructor(rstate)
 
         new_rstate, sample = rng_mrg.GPUA_mrg_uniform.new(rstate, ndim=None,
-                dtype='float32', size=(n_substreams,))
+                                                          dtype='float32',
+                                                          size=(n_substreams,))
         rstate.default_update = new_rstate
 
         # Not really necessary, just mimicking
@@ -887,12 +891,14 @@ def test_multMatVect():
     g0 = rng_mrg.DotModulo()(A1, s1, m1, A2, s2, m2)
     f0 = theano.function([A1, s1, m1, A2, s2, m2], g0)
 
-    A1 = numpy.random.randint(0, numpy.iinfo(numpy.int32).max, (3, 3)).astype('int64')
-    s1 = numpy.random.randint(0, numpy.iinfo(numpy.int32).max, 3).astype('int32')
-    m1 = numpy.asarray(numpy.random.randint(numpy.iinfo(numpy.int32).max), dtype="int32")
-    A2 = numpy.random.randint(0, numpy.iinfo(numpy.int32).max, (3, 3)).astype('int64')
-    s2 = numpy.random.randint(0, numpy.iinfo(numpy.int32).max, 3).astype('int32')
-    m2 = numpy.asarray(numpy.random.randint(numpy.iinfo(numpy.int32).max), dtype="int32")
+    i32max = numpy.iinfo(numpy.int32).max
+
+    A1 = numpy.random.randint(0, i32max, (3, 3)).astype('int64')
+    s1 = numpy.random.randint(0, i32max, 3).astype('int32')
+    m1 = numpy.asarray(numpy.random.randint(i32max), dtype="int32")
+    A2 = numpy.random.randint(0, i32max, (3, 3)).astype('int64')
+    s2 = numpy.random.randint(0, i32max, 3).astype('int32')
+    m2 = numpy.asarray(numpy.random.randint(i32max), dtype="int32")
 
     f0.input_storage[0].storage[0] = A1
     f0.input_storage[1].storage[0] = s1
@@ -903,7 +909,8 @@ def test_multMatVect():
 
     r_a1 = rng_mrg.matVecModM(A1, s1, m1)
     r_a2 = rng_mrg.matVecModM(A2, s2, m2)
-    r_b = f0.fn()[0]
+    f0.fn()
+    r_b = f0.output_storage[0].value
 
     assert numpy.allclose(r_a1, r_b[:3])
     assert numpy.allclose(r_a2, r_b[3:])

theano/tensor/basic.py

@@ -2028,8 +2028,9 @@ def chi2sf(x, k):
     """chi squared survival function"""
 
 
-
-@_scal_elemwise_with_nfunc('real', 1, -1)
+#numpy.real(float32) return a view on the inputs.
+#@_scal_elemwise_with_nfunc('real', 1, -1)
+@_scal_elemwise
 def real(z):
     """Return real component of complex-valued tensor `z`"""
 _tensor_py_operators.real = property(real)

theano/tensor/tests/test_complex.py

@@ -5,30 +5,31 @@ from theano.tests import unittest_tools as utt
 
 from numpy.testing import dec
 
+
 class TestRealImag(unittest.TestCase):
 
     def test0(self):
-        x= zvector()
+        x = zvector()
         rng = numpy.random.RandomState(23)
-        xval = numpy.asarray(list(numpy.complex(rng.randn(), rng.randn()) for i in xrange(10)))
-        assert numpy.all( xval.real == theano.function([x], real(x))(xval))
-        assert numpy.all( xval.imag == theano.function([x], imag(x))(xval))
+        xval = numpy.asarray(list(numpy.complex(rng.randn(), rng.randn())
+                                  for i in xrange(10)))
+        assert numpy.all(xval.real == theano.function([x], real(x))(xval))
+        assert numpy.all(xval.imag == theano.function([x], imag(x))(xval))
 
     def test_on_real_input(self):
-        x= dvector()
+        x = dvector()
         rng = numpy.random.RandomState(23)
         xval = rng.randn(10)
-        numpy.all( 0 == theano.function([x], imag(x))(xval))
-        numpy.all( xval == theano.function([x], real(x))(xval))
-
+        numpy.all(0 == theano.function([x], imag(x))(xval))
+        numpy.all(xval == theano.function([x], real(x))(xval))
 
-        x= imatrix()
-        xval = numpy.asarray(rng.randn(3,3)*100, dtype='int32')
-        numpy.all( 0 == theano.function([x], imag(x))(xval))
-        numpy.all( xval == theano.function([x], real(x))(xval))
+        x = imatrix()
+        xval = numpy.asarray(rng.randn(3, 3) * 100, dtype='int32')
+        numpy.all(0 == theano.function([x], imag(x))(xval))
+        numpy.all(xval == theano.function([x], real(x))(xval))
 
     def test_cast(self):
-        x= zvector()
+        x = zvector()
         self.assertRaises(TypeError, cast, x, 'int32')
 
     def test_complex(self):
@@ -36,27 +37,27 @@ class TestRealImag(unittest.TestCase):
         m = fmatrix()
         c = complex(m[0], m[1])
         assert c.type == cvector
-        r,i = [real(c), imag(c)]
+        r, i = [real(c), imag(c)]
         assert r.type == fvector
         assert i.type == fvector
-        f = theano.function([m], [r,i] )
+        f = theano.function([m], [r, i])
 
-        mval = numpy.asarray(rng.randn(2,5), dtype='float32')
+        mval = numpy.asarray(rng.randn(2, 5), dtype='float32')
         rval, ival = f(mval)
-        assert numpy.all(rval == mval[0]), (rval,mval[0])
+        assert numpy.all(rval == mval[0]), (rval, mval[0])
         assert numpy.all(ival == mval[1]), (ival, mval[1])
 
-    @dec.knownfailureif(True,"Complex grads not enabled, see #178")
+    @dec.knownfailureif(True, "Complex grads not enabled, see #178")
     def test_complex_grads(self):
         def f(m):
             c = complex(m[0], m[1])
             return .5 * real(c) + .9 * imag(c)
 
         rng = numpy.random.RandomState(9333)
-        mval = numpy.asarray(rng.randn(2,5))
+        mval = numpy.asarray(rng.randn(2, 5))
         utt.verify_grad(f, [mval])
 
-    @dec.knownfailureif(True,"Complex grads not enabled, see #178")
+    @dec.knownfailureif(True, "Complex grads not enabled, see #178")
     def test_mul_mixed0(self):
 
         def f(a):
@@ -64,7 +65,7 @@ class TestRealImag(unittest.TestCase):
             return abs((ac)**2).sum()
 
         rng = numpy.random.RandomState(9333)
-        aval = numpy.asarray(rng.randn(2,5))
+        aval = numpy.asarray(rng.randn(2, 5))
         try:
             utt.verify_grad(f, [aval])
         except utt.verify_grad.E_grad, e:
@@ -72,7 +73,7 @@ class TestRealImag(unittest.TestCase):
             print e.analytic_grad
             raise
 
-    @dec.knownfailureif(True,"Complex grads not enabled, see #178")
+    @dec.knownfailureif(True, "Complex grads not enabled, see #178")
     def test_mul_mixed1(self):
 
         def f(a):
@@ -80,22 +81,23 @@ class TestRealImag(unittest.TestCase):
             return abs(ac).sum()
 
         rng = numpy.random.RandomState(9333)
-        aval = numpy.asarray(rng.randn(2,5))
+        aval = numpy.asarray(rng.randn(2, 5))
         try:
             utt.verify_grad(f, [aval])
         except utt.verify_grad.E_grad, e:
             print e.num_grad.gf
             print e.analytic_grad
             raise
-    @dec.knownfailureif(True,"Complex grads not enabled, see #178")
+
+    @dec.knownfailureif(True, "Complex grads not enabled, see #178")
     def test_mul_mixed(self):
 
-        def f(a,b):
+        def f(a, b):
             ac = complex(a[0], a[1])
             return abs((ac*b)**2).sum()
 
         rng = numpy.random.RandomState(9333)
-        aval = numpy.asarray(rng.randn(2,5))
+        aval = numpy.asarray(rng.randn(2, 5))
         bval = rng.randn(5)
         try:
             utt.verify_grad(f, [aval, bval])
@@ -104,22 +106,22 @@ class TestRealImag(unittest.TestCase):
             print e.analytic_grad
             raise
 
-    @dec.knownfailureif(True,"Complex grads not enabled, see #178")
+    @dec.knownfailureif(True, "Complex grads not enabled, see #178")
     def test_polar_grads(self):
         def f(m):
             c = complex_from_polar(abs(m[0]), m[1])
             return .5 * real(c) + .9 * imag(c)
 
         rng = numpy.random.RandomState(9333)
-        mval = numpy.asarray(rng.randn(2,5))
+        mval = numpy.asarray(rng.randn(2, 5))
         utt.verify_grad(f, [mval])
 
-    @dec.knownfailureif(True,"Complex grads not enabled, see #178")
+    @dec.knownfailureif(True, "Complex grads not enabled, see #178")
     def test_abs_grad(self):
         def f(m):
             c = complex(m[0], m[1])
             return .5 * abs(c)
 
         rng = numpy.random.RandomState(9333)
-        mval = numpy.asarray(rng.randn(2,5))
+        mval = numpy.asarray(rng.randn(2, 5))
         utt.verify_grad(f, [mval])