file theano/gpuarray/tests/test_subtensor.py

theano/gpuarray/tests/test_subtensor.py

 from __future__ import absolute_import, print_function, division
-import numpy
+import numpy as np
 import unittest
 
 import theano
@@ -47,8 +47,8 @@ def test_advinc_subtensor1():
     # Test the second case in the opt local_gpu_advanced_incsubtensor1
     for shp in [(3, 3), (3, 3, 3)]:
         shared = gpuarray_shared_constructor
-        xval = numpy.arange(numpy.prod(shp), dtype='float32').reshape(shp) + 1
-        yval = numpy.empty((2,) + shp[1:], dtype='float32')
+        xval = np.arange(np.prod(shp), dtype='float32').reshape(shp) + 1
+        yval = np.empty((2,) + shp[1:], dtype='float32')
         yval[:] = 10
         x = shared(xval, name='x')
         y = tensor.tensor(dtype='float32',
@@ -61,7 +61,7 @@ def test_advinc_subtensor1():
         rval = f(yval)
         rep = xval.copy()
         rep[[0, 2]] += yval
-        assert numpy.allclose(rval, rep)
+        assert np.allclose(rval, rep)
 
 
 def test_advinc_subtensor1_dtype():
@@ -69,8 +69,8 @@ def test_advinc_subtensor1_dtype():
     shp = (3, 4)
     for dtype1, dtype2 in [('float32', 'int8'), ('float32', 'float64')]:
         shared = gpuarray_shared_constructor
-        xval = numpy.arange(numpy.prod(shp), dtype=dtype1).reshape(shp) + 1
-        yval = numpy.empty((2,) + shp[1:], dtype=dtype2)
+        xval = np.arange(np.prod(shp), dtype=dtype1).reshape(shp) + 1
+        yval = np.empty((2,) + shp[1:], dtype=dtype2)
         yval[:] = 10
         x = shared(xval, name='x')
         y = tensor.tensor(dtype=yval.dtype,
@@ -83,7 +83,7 @@ def test_advinc_subtensor1_dtype():
         rval = f(yval)
         rep = xval.copy()
         rep[[0, 2]] += yval
-        assert numpy.allclose(rval, rep)
+        assert np.allclose(rval, rep)
 
 
 def test_advinc_subtensor1_vector_scalar():
@@ -91,8 +91,8 @@ def test_advinc_subtensor1_vector_scalar():
     shp = (3,)
     for dtype1, dtype2 in [('float32', 'int8'), ('float32', 'float64')]:
         shared = gpuarray_shared_constructor
-        xval = numpy.arange(numpy.prod(shp), dtype=dtype1).reshape(shp) + 1
-        yval = numpy.asarray(10, dtype=dtype2)
+        xval = np.arange(np.prod(shp), dtype=dtype1).reshape(shp) + 1
+        yval = np.asarray(10, dtype=dtype2)
         x = shared(xval, name='x')
         y = tensor.tensor(dtype=yval.dtype,
                           broadcastable=(False,) * len(yval.shape),
@@ -104,14 +104,14 @@ def test_advinc_subtensor1_vector_scalar():
         rval = f(yval)
         rep = xval.copy()
         rep[[0, 2]] += yval
-        assert numpy.allclose(rval, rep)
+        assert np.allclose(rval, rep)
 
 
 def test_incsub_f16():
     shp = (3, 3)
     shared = gpuarray_shared_constructor
-    xval = numpy.arange(numpy.prod(shp), dtype='float16').reshape(shp) + 1
-    yval = numpy.empty((2,) + shp[1:], dtype='float16')
+    xval = np.arange(np.prod(shp), dtype='float16').reshape(shp) + 1
+    yval = np.empty((2,) + shp[1:], dtype='float16')
     yval[:] = 2
     x = shared(xval, name='x')
     y = tensor.tensor(dtype='float16',
@@ -124,7 +124,7 @@ def test_incsub_f16():
     rval = f(yval)
     rep = xval.copy()
     rep[[0, 2]] += yval
-    assert numpy.allclose(rval, rep)
+    assert np.allclose(rval, rep)
 
     expr = tensor.inc_subtensor(x[1:], y)
     f = theano.function([y], expr, mode=mode_with_gpu)
@@ -133,7 +133,7 @@ def test_incsub_f16():
     rval = f(yval)
     rep = xval.copy()
     rep[1:] += yval
-    assert numpy.allclose(rval, rep)
+    assert np.allclose(rval, rep)
 
 
 class G_advancedsubtensor(test_subtensor.TestAdvancedSubtensor):
@@ -159,7 +159,7 @@ def test_adv_subtensor():
     # Test the advancedsubtensor on gpu.
     shp = (2, 3, 4)
     shared = gpuarray_shared_constructor
-    xval = numpy.arange(numpy.prod(shp), dtype=theano.config.floatX).reshape(shp)
+    xval = np.arange(np.prod(shp), dtype=theano.config.floatX).reshape(shp)
     idx1, idx2 = tensor.ivectors('idx1', 'idx2')
     idxs = [idx1, None, slice(0, 2, 1), idx2, None]
     x = shared(xval, name='x')
@@ -171,27 +171,27 @@ def test_adv_subtensor():
     idx2_val = [0, 1]
     rval = f(idx1_val, idx2_val)
     rep = xval[idx1_val, None, slice(0, 2, 1), idx2_val, None]
-    assert numpy.allclose(rval, rep)
+    assert np.allclose(rval, rep)
 
 
 class test_gpudiagonal(unittest.TestCase):
     def test_matrix(self):
         x = tensor.matrix()
-        np_x = numpy.arange(77).reshape(7, 11).astype(theano.config.floatX)
+        np_x = np.arange(77).reshape(7, 11).astype(theano.config.floatX)
         fn = theano.function([x], GpuDiagonal()(x), mode=mode_with_gpu)
-        assert numpy.allclose(fn(np_x), np_x.diagonal())
+        assert np.allclose(fn(np_x), np_x.diagonal())
         fn = theano.function([x], GpuDiagonal(2)(x), mode=mode_with_gpu)
-        assert numpy.allclose(fn(np_x), np_x.diagonal(2))
+        assert np.allclose(fn(np_x), np_x.diagonal(2))
         fn = theano.function([x], GpuDiagonal(-3)(x), mode=mode_with_gpu)
-        assert numpy.allclose(fn(np_x), np_x.diagonal(-3))
+        assert np.allclose(fn(np_x), np_x.diagonal(-3))
 
     def test_tensor(self):
         x = tensor.tensor4()
-        np_x = numpy.arange(30107).reshape(7, 11, 17, 23).astype(theano.config.floatX)
+        np_x = np.arange(30107).reshape(7, 11, 17, 23).astype(theano.config.floatX)
         for offset, axis1, axis2 in [
                 (1, 0, 1), (-1, 0, 1), (0, 1, 0), (-2, 1, 0),
                 (-3, 1, 0), (-2, 2, 0), (3, 3, 0), (-1, 3, 2),
                 (2, 2, 3), (-1, 2, 1), (1, 3, 1), (-1, 1, 3)]:
-            assert numpy.allclose(
+            assert np.allclose(
                 GpuDiagonal(offset, axis1, axis2)(x).eval({x: np_x}),
                 np_x.diagonal(offset, axis1, axis2))