Updated numpy as np

theano/tests/test_2nd_order_grads.py

@@ -6,7 +6,7 @@ from six.moves import xrange
 from theano.tests import unittest_tools as utt
 import theano
 from theano import tensor
-import numpy
+import numpy as np
 
 utt.seed_rng()
 
@@ -14,27 +14,27 @@ utt.seed_rng()
 def test001_jacobian_vector():
     x = tensor.vector()
     y = x * 2
-    rng = numpy.random.RandomState(seed=utt.fetch_seed())
+    rng = np.random.RandomState(seed=utt.fetch_seed())
 
     # test when the jacobian is called with a tensor as wrt
     Jx = tensor.jacobian(y, x)
     f = theano.function([x], Jx)
     vx = rng.uniform(size=(10,)).astype(theano.config.floatX)
-    assert numpy.allclose(f(vx), numpy.eye(10) * 2)
+    assert np.allclose(f(vx), np.eye(10) * 2)
 
     # test when the jacobian is called with a tuple as wrt
     Jx = tensor.jacobian(y, (x,))
     assert isinstance(Jx, tuple)
     f = theano.function([x], Jx[0])
     vx = rng.uniform(size=(10,)).astype(theano.config.floatX)
-    assert numpy.allclose(f(vx), numpy.eye(10) * 2)
+    assert np.allclose(f(vx), np.eye(10) * 2)
 
     # test when the jacobian is called with a list as wrt
     Jx = tensor.jacobian(y, [x])
     assert isinstance(Jx, list)
     f = theano.function([x], Jx[0])
     vx = rng.uniform(size=(10,)).astype(theano.config.floatX)
-    assert numpy.allclose(f(vx), numpy.eye(10) * 2)
+    assert np.allclose(f(vx), np.eye(10) * 2)
 
     # test when the jacobian is called with a list of two elements
     z = tensor.vector()
@@ -44,19 +44,19 @@ def test001_jacobian_vector():
     vx = rng.uniform(size=(10,)).astype(theano.config.floatX)
     vz = rng.uniform(size=(10,)).astype(theano.config.floatX)
     vJs = f(vx, vz)
-    evx = numpy.zeros((10, 10))
-    evz = numpy.zeros((10, 10))
-    numpy.fill_diagonal(evx, vx)
-    numpy.fill_diagonal(evz, vz)
-    assert numpy.allclose(vJs[0], evz)
-    assert numpy.allclose(vJs[1], evx)
+    evx = np.zeros((10, 10))
+    evz = np.zeros((10, 10))
+    np.fill_diagonal(evx, vx)
+    np.fill_diagonal(evz, vz)
+    assert np.allclose(vJs[0], evz)
+    assert np.allclose(vJs[1], evx)
 
 
 def test002_jacobian_matrix():
     x = tensor.matrix()
     y = 2 * x.sum(axis=0)
-    rng = numpy.random.RandomState(seed=utt.fetch_seed())
-    ev = numpy.zeros((10, 10, 10))
+    rng = np.random.RandomState(seed=utt.fetch_seed())
+    ev = np.zeros((10, 10, 10))
     for dx in xrange(10):
         ev[dx, :, dx] = 2.
 
@@ -64,21 +64,21 @@ def test002_jacobian_matrix():
     Jx = tensor.jacobian(y, x)
     f = theano.function([x], Jx)
     vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
-    assert numpy.allclose(f(vx), ev)
+    assert np.allclose(f(vx), ev)
 
     # test when the jacobian is called with a tuple as wrt
     Jx = tensor.jacobian(y, (x,))
     assert isinstance(Jx, tuple)
     f = theano.function([x], Jx[0])
     vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
-    assert numpy.allclose(f(vx), ev)
+    assert np.allclose(f(vx), ev)
 
     # test when the jacobian is called with a list as wrt
     Jx = tensor.jacobian(y, [x])
     assert isinstance(Jx, list)
     f = theano.function([x], Jx[0])
     vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
-    assert numpy.allclose(f(vx), ev)
+    assert np.allclose(f(vx), ev)
 
     # test when the jacobian is called with a list of two elements
     z = tensor.matrix()
@@ -88,51 +88,51 @@ def test002_jacobian_matrix():
     vx = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
     vz = rng.uniform(size=(10, 10)).astype(theano.config.floatX)
     vJs = f(vx, vz)
-    evx = numpy.zeros((10, 10, 10))
-    evz = numpy.zeros((10, 10, 10))
+    evx = np.zeros((10, 10, 10))
+    evz = np.zeros((10, 10, 10))
     for dx in xrange(10):
         evx[dx, dx, :] = vx[dx, :]
         evz[dx, dx, :] = vz[dx, :]
-    assert numpy.allclose(vJs[0], evz)
-    assert numpy.allclose(vJs[1], evx)
+    assert np.allclose(vJs[0], evz)
+    assert np.allclose(vJs[1], evx)
 
 
 def test003_jacobian_scalar():
     x = tensor.scalar()
     y = x * 2
-    rng = numpy.random.RandomState(seed=utt.fetch_seed())
+    rng = np.random.RandomState(seed=utt.fetch_seed())
 
     # test when the jacobian is called with a tensor as wrt
     Jx = tensor.jacobian(y, x)
     f = theano.function([x], Jx)
-    vx = numpy.cast[theano.config.floatX](rng.uniform())
-    assert numpy.allclose(f(vx), 2)
+    vx = np.cast[theano.config.floatX](rng.uniform())
+    assert np.allclose(f(vx), 2)
 
     # test when the jacobian is called with a tuple as wrt
     Jx = tensor.jacobian(y, (x,))
     assert isinstance(Jx, tuple)
     f = theano.function([x], Jx[0])
-    vx = numpy.cast[theano.config.floatX](rng.uniform())
-    assert numpy.allclose(f(vx), 2)
+    vx = np.cast[theano.config.floatX](rng.uniform())
+    assert np.allclose(f(vx), 2)
 
     # test when the jacobian is called with a list as wrt
     Jx = tensor.jacobian(y, [x])
     assert isinstance(Jx, list)
     f = theano.function([x], Jx[0])
-    vx = numpy.cast[theano.config.floatX](rng.uniform())
-    assert numpy.allclose(f(vx), 2)
+    vx = np.cast[theano.config.floatX](rng.uniform())
+    assert np.allclose(f(vx), 2)
 
     # test when the jacobian is called with a list of two elements
     z = tensor.scalar()
     y = x * z
     Jx = tensor.jacobian(y, [x, z])
     f = theano.function([x, z], Jx)
-    vx = numpy.cast[theano.config.floatX](rng.uniform())
-    vz = numpy.cast[theano.config.floatX](rng.uniform())
+    vx = np.cast[theano.config.floatX](rng.uniform())
+    vz = np.cast[theano.config.floatX](rng.uniform())
     vJx = f(vx, vz)
 
-    assert numpy.allclose(vJx[0], vz)
-    assert numpy.allclose(vJx[1], vx)
+    assert np.allclose(vJx[0], vz)
+    assert np.allclose(vJx[1], vx)
 
 
 def test004_hessian():
@@ -140,8 +140,8 @@ def test004_hessian():
     y = tensor.sum(x ** 2)
     Hx = tensor.hessian(y, x)
     f = theano.function([x], Hx)
-    vx = numpy.arange(10).astype(theano.config.floatX)
-    assert numpy.allclose(f(vx), numpy.eye(10) * 2)
+    vx = np.arange(10).astype(theano.config.floatX)
+    assert np.allclose(f(vx), np.eye(10) * 2)
 
 
 def test_jacobian_disconnected_inputs():
@@ -152,12 +152,12 @@ def test_jacobian_disconnected_inputs():
     v2 = tensor.vector()
     jacobian_v = theano.gradient.jacobian(1 + v1, v2, disconnected_inputs='ignore')
     func_v = theano.function([v1, v2], jacobian_v)
-    val = numpy.arange(4.0).astype(theano.config.floatX)
-    assert numpy.allclose(func_v(val, val), numpy.zeros((4, 4)))
+    val = np.arange(4.0).astype(theano.config.floatX)
+    assert np.allclose(func_v(val, val), np.zeros((4, 4)))
 
     s1 = tensor.scalar()
     s2 = tensor.scalar()
     jacobian_s = theano.gradient.jacobian(1 + s1, s2, disconnected_inputs='ignore')
     func_s = theano.function([s2], jacobian_s)
-    val = numpy.array(1.0).astype(theano.config.floatX)
-    assert numpy.allclose(func_s(val), numpy.zeros(1))
+    val = np.array(1.0).astype(theano.config.floatX)
+    assert np.allclose(func_s(val), np.zeros(1))