various changes

setup.py

@@ -161,7 +161,6 @@ def do_setup():
           license=LICENSE,
           platforms=PLATFORMS,
           packages=find_packages(),
-          # 1.7.0 give too much warning related to numpy.diagonal.
           install_requires=['numpy>=1.9.1', 'scipy>=0.14', 'six>=1.9.0'],
           # pygments is a dependency for Sphinx code highlight
           extras_require={

theano/tensor/nlinalg.py

@@ -557,14 +557,10 @@ def qr(a, mode="reduced"):
         'raw'
           returns h, tau with dimensions (N, M), (K,)
 
-        The options 'reduced', 'complete', and 'raw' are new in numpy
-        1.8, see the notes for more information. The default is
-        'reduced' and to maintain backward compatibility with earlier
-        versions of numpy it can be
-        omitted. Note that array h returned in 'raw' mode is
+        Note that array h returned in 'raw' mode is
         transposed for calling Fortran.
 
-        Default mode is 'reduced' which is also default for numpy 1.8
+        Default mode is 'reduced'
 
     Returns
     -------

theano/tensor/tests/test_basic.py

@@ -6797,33 +6797,6 @@ class T_long_tensor(unittest.TestCase):
             assert numpy.all(matrix_ct.value == val)
 
     def test_too_big(self):
-        val = L(2 ** 63)
-        # NumPy 1.7 this will raise an exception
-        # NumPy 1.7.1 this will work
-        try:
-            cst = constant(val)
-            assert cst.value == val
-            assert cst.dtype == "uint64"
-        except OverflowError:
-            pass
-
-        try:
-            cst = constant([val, val])
-            assert cst.value[0] == val
-            assert cst.value[1] == val
-            assert cst.value.size == 2
-            assert cst.dtype == "uint64"
-        except TypeError:
-            pass
-        try:
-            cst = constant([[val, val]])
-            assert cst.value[0, 0] == val
-            assert cst.value[0, 1] == val
-            assert cst.value.size == 2
-            assert cst.dtype == "uint64"
-        except TypeError:
-            pass
-
         val = L(2 ** 64)
         # This fail for all NumPy version.
         self.assertRaises(Exception, constant, val)

theano/tensor/tests/test_opt.py

@@ -3003,9 +3003,6 @@ class Test_alloc_zero(unittest.TestCase):
                           for n in f.maker.fgraph.toposort()])
 
     def test_advancedincsubtensor_allocs0(self):
-        if tensor.inplace_increment is None:
-            raise SkipTest('NumPy version >= 1.8 not available')
-
         x = tensor.matrix()
         y = tensor.matrix()
         y0 = tensor.zeros_like(y)
@@ -3015,9 +3012,6 @@ class Test_alloc_zero(unittest.TestCase):
                           for n in f.maker.fgraph.toposort()])
 
     def test_advancedincsubtensor_allocs0t(self):
-        if tensor.inplace_increment is None:
-            raise SkipTest('NumPy version >= 1.8 not available')
-
         x = tensor.matrix()
         y = tensor.matrix()
         y0 = tensor.zeros_like(y)
@@ -3027,9 +3021,6 @@ class Test_alloc_zero(unittest.TestCase):
                           for n in f.maker.fgraph.toposort()])
 
     def test_advancedincsubtensor_allocs1(self):
-        if tensor.inplace_increment is None:
-            raise SkipTest('NumPy version >= 1.8 not available')
-
         x = tensor.matrix()
         y0 = tensor.constant(numpy.asarray(numpy.zeros_like((2, 2)),
                                            dtype=config.floatX))
@@ -3856,9 +3847,6 @@ class Test_local_useless_inc_subtensor_alloc(unittest.TestCase):
         self.mode = compile.mode.get_mode(mode)
 
     def test_advanced_inc_subtensor(self):
-        if tensor.inplace_increment is None:
-            raise SkipTest('NumPy version >= 1.8 not available')
-
         x = tensor.vector('x')
         y = tensor.scalar('y')
         i = tensor.matrix('i', dtype='int64')
@@ -3889,9 +3877,6 @@ class Test_local_useless_inc_subtensor_alloc(unittest.TestCase):
         self.assertTrue(check_stack_trace(f2, ops_to_check=tensor.AdvancedIncSubtensor))
 
     def test_advanced_inc_subtensor1(self):
-        if tensor.inplace_increment is None:
-            raise SkipTest('NumPy version >= 1.8 not available')
-
         x = tensor.vector('x')
         y = tensor.scalar('y')
         i = tensor.vector('i', dtype='int64')

theano/tensor/utils.py

@@ -22,9 +22,7 @@ def hash_from_ndarray(data):
     # python hash are not strong, so I always use md5 in order not to have a
     # too long hash, I call it again on the concatenation of all parts.
     if not data.flags["C_CONTIGUOUS"]:
-        # Version 1.7.1 and previous of NumPy allowed calling
-        # hash_from_code on an F-contiguous array, but more recent
-        # versions need a C-contiguous one.
+        # hash_from_code needs a C-contiguous array.
         data = numpy.ascontiguousarray(data)
     return hash_from_code(hash_from_code(data) +
                           hash_from_code(str(data.shape)) +

theano/tensor/var.py

@@ -88,16 +88,7 @@ class _tensor_py_operators(object):
             return True
         else:
             raise TypeError(
-                "Variables do not support boolean operations. This "
-                "can happen if you do a logical operation (<, <=, >, <=, "
-                "==, !=) between a numpy.ndarray and a Theano tensor"
-                "variable. Due to NumPy implementation before NumPy 1.8, "
-                "we cannot make the Python syntax work when the ndarray "
-                "is on the left, and this results in this error. To work "
-                "around that, either call "
-                "theano.tensor.{lt,le,eq,ne,gt,ge}(ndarray, tensor), or "
-                "use the Python syntax with the Theano tensor on the "
-                "left. Or update to NumPy 1.8 or above."
+                "Variables do not support boolean operations."
             )
 
     # BITWISE