Merge pull request #5853 from Amrithasuresh/master

theano/__init__.py

@@ -134,7 +134,7 @@ if (config.device.startswith('cuda') or
     import theano.gpuarray
 
 # Use config.numpy to call numpy.seterr
-import numpy
+import numpy as np
 
 if config.numpy.seterr_all == 'None':
     _all = None
@@ -156,7 +156,7 @@ if config.numpy.seterr_invalid == 'None':
     _invalid = None
 else:
     _invalid = config.numpy.seterr_invalid
-numpy.seterr(
+np.seterr(
     all=_all,
     divide=_divide,
     over=_over,

theano/configdefaults.py

@@ -3,7 +3,7 @@ import errno
 import os
 import sys
 import logging
-import numpy
+import numpy as np
 import platform
 import textwrap
 import re
@@ -1052,7 +1052,7 @@ AddConfigVar('profiling.ignore_first_call',
 AddConfigVar('optdb.position_cutoff',
              'Where to stop eariler during optimization. It represent the'
              ' position of the optimizer where to stop.',
-             FloatParam(numpy.inf),
+             FloatParam(np.inf),
              in_c_key=False)
 
 AddConfigVar('optdb.max_use_ratio',
@@ -1106,11 +1106,11 @@ def default_blas_ldflags():
     global numpy
     warn_record = []
     try:
-        if (hasattr(numpy.distutils, '__config__') and
-                numpy.distutils.__config__):
+        if (hasattr(np.distutils, '__config__') and
+                np.distutils.__config__):
             # If the old private interface is available use it as it
             # don't print information to the user.
-            blas_info = numpy.distutils.__config__.blas_opt_info
+            blas_info = np.distutils.__config__.blas_opt_info
         else:
             # We do this import only here, as in some setup, if we
             # just import theano and exit, with the import at global
@@ -1494,7 +1494,7 @@ compiledir_format_dict = {
     "python_bitwidth": local_bitwidth(),
     "python_int_bitwidth": python_int_bitwidth(),
     "theano_version": theano.__version__,
-    "numpy_version": numpy.__version__,
+    "numpy_version": np.__version__,
     "gxx_version": gcc_version_str.replace(" ", "_"),
     "hostname": socket.gethostname()}
 

theano/gof/tests/test_params_type.py

 from __future__ import absolute_import, print_function, division
 import theano
-import numpy
+import numpy as np
 from unittest import TestCase
 from theano.gof import Op, COp, Apply
 from theano import Generic
@@ -121,21 +121,21 @@ class TestParamsType(TestCase):
                          npy_scalar=TensorType('float64', tuple()))
         wp2 = ParamsType(a=Generic(), array=TensorType('int64', (False,)), floatting=Scalar('float64'),
                          npy_scalar=TensorType('float64', tuple()))
-        w1 = Params(wp1, a=1, array=numpy.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
-        w2 = Params(wp2, a=1, array=numpy.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
+        w1 = Params(wp1, a=1, array=np.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=np.asarray(12))
+        w2 = Params(wp2, a=1, array=np.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=np.asarray(12))
         assert w1 == w2
         assert not (w1 != w2)
         assert hash(w1) == hash(w2)
         # Changing attributes names only (a -> other_name).
         wp2_other = ParamsType(other_name=Generic(), array=TensorType('int64', (False,)), floatting=Scalar('float64'),
                                npy_scalar=TensorType('float64', tuple()))
-        w2 = Params(wp2_other, other_name=1, array=numpy.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
+        w2 = Params(wp2_other, other_name=1, array=np.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=np.asarray(12))
         assert w1 != w2
         # Changing attributes values only (now a=2).
-        w2 = Params(wp2, a=2, array=numpy.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
+        w2 = Params(wp2, a=2, array=np.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=np.asarray(12))
         assert w1 != w2
         # Changing NumPy array values (5 -> -5).
-        w2 = Params(wp2, a=1, array=numpy.asarray([1, 2, 4, -5, 7]), floatting=-4.5, npy_scalar=numpy.asarray(12))
+        w2 = Params(wp2, a=1, array=np.asarray([1, 2, 4, -5, 7]), floatting=-4.5, npy_scalar=np.asarray(12))
         assert w1 != w2
 
     def test_hash_and_eq_params_type(self):
@@ -168,7 +168,7 @@ class TestParamsType(TestCase):
     def test_params_type_filtering(self):
         shape_tensor5 = (1, 2, 2, 3, 2)
         size_tensor5 = shape_tensor5[0] * shape_tensor5[1] * shape_tensor5[2] * shape_tensor5[3] * shape_tensor5[4]
-        random_tensor = numpy.random.normal(size=size_tensor5).reshape(shape_tensor5)
+        random_tensor = np.random.normal(size=size_tensor5).reshape(shape_tensor5)
 
         w = ParamsType(a1=TensorType('int32', (False, False)),
                        a2=TensorType('float64', (False, False, False, False, False)),
@@ -176,7 +176,7 @@ class TestParamsType(TestCase):
 
         # With a value that does not match the params type.
         o = Params(w,
-                   a1=numpy.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int64'),
+                   a1=np.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int64'),
                    a2=random_tensor.astype('float32'),
                    a3=2000)
         # should fail (o.a1 is not int32, o.a2 is not float64)
@@ -188,7 +188,7 @@ class TestParamsType(TestCase):
 
         # With a value that matches the params type.
         o1 = Params(w,
-                    a1=numpy.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int32'),
+                    a1=np.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int32'),
                     a2=random_tensor.astype('float64'),
                     a3=2000)
         # All should pass.
@@ -198,7 +198,7 @@ class TestParamsType(TestCase):
 
         # Check values_eq and values_eq_approx.
         o2 = Params(w,
-                    a1=numpy.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int32'),
+                    a1=np.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int32'),
                     a2=random_tensor.astype('float64'),
                     a3=2000)
         assert w.values_eq(o1, o2)
@@ -208,7 +208,7 @@ class TestParamsType(TestCase):
         # NB: I don't know exactly which kind of differences is rejected by values_eq but accepted by values_eq_approx.
         # So, I just play a little with float values.
         o3 = Params(w,
-                    a1=numpy.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int32'),
+                    a1=np.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int32'),
                     a2=(random_tensor.astype('float32') * 10 / 2.2 * 2.19999999999 / 10).astype('float64'),
                     a3=2000.0 - 0.00000000000000001)
         assert w.values_eq_approx(o1, o3)
@@ -250,7 +250,7 @@ class TestParamsType(TestCase):
         f1 = theano.function([x], y1)
         f2 = theano.function([x], y2)
         shape = (100, 100)
-        vx = numpy.random.normal(size=shape[0] * shape[1]).astype('float64').reshape(*shape)
+        vx = np.random.normal(size=shape[0] * shape[1]).astype('float64').reshape(*shape)
         vy1 = f1(vx)
         vy2 = f2(vx)
         ref = a * (vx**2) + b * vx + c

theano/gpuarray/rng_mrg.py

@@ -7,7 +7,7 @@ http://www.iro.umontreal.ca/~simardr/ssj/indexe.html
 """
 from __future__ import absolute_import, print_function, division
 
-import numpy
+import numpy as np
 
 from theano import Apply, tensor
 from theano.gof import local_optimizer
@@ -170,7 +170,7 @@ class GPUA_mrg_uniform(GpuKernelBase, mrg_uniform_base):
         o_rstate, o_sample = out
         inplace = int(self.inplace)
         ndim = self.output_type.ndim
-        o_type_num = numpy.asarray(0, dtype=self.output_type.dtype).dtype.num
+        o_type_num = np.asarray(0, dtype=self.output_type.dtype).dtype.num
         fail = sub['fail']
         ctx = sub['params']
         kname = self.gpu_kernels(node, nodename)[0].objvar

theano/scalar/basic_scipy.py

@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
 # Definitions of theano.scalar ops that have their python implementation taken
 # from SciPy. As SciPy is not always available, we treat them separately.
 
-import numpy
+import numpy as np
 
 import theano
 from theano.gradient import grad_not_implemented
@@ -43,8 +43,8 @@ class Erf(UnaryScalarOp):
             else:
                 return [x.zeros_like()]
 
-        cst = numpy.asarray(2. / numpy.sqrt(numpy.pi),
-                            dtype=upcast(x.type.dtype, gz.type.dtype))
+        cst = np.asarray(2. / np.sqrt(np.pi),
+                         dtype=upcast(x.type.dtype, gz.type.dtype))
         return gz * cst * exp(-x * x),
 
     def c_code(self, node, name, inp, out, sub):
@@ -74,8 +74,8 @@ class Erfc(UnaryScalarOp):
             else:
                 return [x.zeros_like()]
 
-        cst = numpy.asarray(2. / numpy.sqrt(numpy.pi),
-                            dtype=upcast(x.type.dtype, gz.type.dtype))
+        cst = np.asarray(2. / np.sqrt(np.pi),
+                         dtype=upcast(x.type.dtype, gz.type.dtype))
         return - gz * cst * exp(-x * x),
 
     def c_code(self, node, name, inp, out, sub):
@@ -120,8 +120,8 @@ class Erfcx(UnaryScalarOp):
             else:
                 return [x.zeros_like()]
 
-        cst = numpy.asarray(2. / numpy.sqrt(numpy.pi),
-                            dtype=upcast(x.type.dtype, gz.type.dtype))
+        cst = np.asarray(2. / np.sqrt(np.pi),
+                         dtype=upcast(x.type.dtype, gz.type.dtype))
         return gz * (-cst + (2. * x) * erfcx(x)),
 
 erfcx = Erfcx(upgrade_to_float_no_complex, name='erfcx')
@@ -155,8 +155,8 @@ class Erfinv(UnaryScalarOp):
             else:
                 return [x.zeros_like()]
 
-        cst = numpy.asarray(numpy.sqrt(numpy.pi) / 2.,
-                            dtype=upcast(x.type.dtype, gz.type.dtype))
+        cst = np.asarray(np.sqrt(np.pi) / 2.,
+                         dtype=upcast(x.type.dtype, gz.type.dtype))
         return gz * cst * exp(erfinv(x) ** 2),
 
     # TODO: erfinv() is not provided by the C standard library
@@ -188,8 +188,8 @@ class Erfcinv(UnaryScalarOp):
             else:
                 return [x.zeros_like()]
 
-        cst = numpy.asarray(numpy.sqrt(numpy.pi) / 2.,
-                            dtype=upcast(x.type.dtype, gz.type.dtype))
+        cst = np.asarray(np.sqrt(np.pi) / 2.,
+                         dtype=upcast(x.type.dtype, gz.type.dtype))
         return - gz * cst * exp(erfcinv(x) ** 2),
 
     # TODO: erfcinv() is not provided by the C standard library

theano/scalar/sharedvar.py

@@ -16,7 +16,7 @@ way (as scan does) to create a shared variable of this kind.
 
 """
 from __future__ import absolute_import, print_function, division
-import numpy
+import numpy as np
 from six import integer_types
 
 from theano.compile import SharedVariable
@@ -48,15 +48,15 @@ def shared(value, name=None, strict=False, allow_downcast=None):
     We implement this using 0-d tensors for now.
 
     """
-    if not isinstance(value, (numpy.number, float, integer_types, complex)):
+    if not isinstance(value, (np.number, float, integer_types, complex)):
         raise TypeError()
     try:
         dtype = value.dtype
     except AttributeError:
-        dtype = numpy.asarray(value).dtype
+        dtype = np.asarray(value).dtype
 
     dtype = str(dtype)
-    value = getattr(numpy, dtype)(value)
+    value = getattr(np, dtype)(value)
     scalar_type = Scalar(dtype=dtype)
     rval = ScalarSharedVariable(
         type=scalar_type,