def test_grad_nonnegative_axis_3d(self):
data = self.rng.random((2, 3, 4)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 0), [data])
data = self.rng.random((2, 3, 4)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 1), [data])
data = self.rng.random((2, 3, 4)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 2), [data])
def test_grad_negative_axis_3d(self):
data = np.random.rand(2, 3, 4).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -1), [data])
data = np.random.rand(2, 3, 4).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -2), [data])
data = np.random.rand(2, 3, 4).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -3), [data])
def test_grad_negative_axis_4d(self):
data = self.rng.random((2, 3, 4, 2)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -1), [data])
data = self.rng.random((2, 3, 4, 2)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -2), [data])
data = self.rng.random((2, 3, 4, 2)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -3), [data])
data = self.rng.random((2, 3, 4, 2)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -4), [data])
def test_grad_none_axis(self):
data = self.rng.random((10)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, None), [data])
utt.verify_grad(lambda x: sort(x, 0), [data])
data = self.rng.random((2, 3)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, None), [data])
data = self.rng.random((2, 3, 4)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, None), [data])
def test_grad_nonnegative_axis_4d(self):
data = np.random.rand(2, 3, 4, 2).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 0), [data])
data = np.random.rand(2, 3, 4, 2).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 1), [data])
data = np.random.rand(2, 3, 4, 2).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 2), [data])
data = np.random.rand(2, 3, 4, 2).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 3), [data])
def test_sort(self):
x = matrix()
self._compile_and_check(
[x],
[sort(x)],
[np.random.randn(10, 40).astype(aesara.config.floatX)],
SortOp,
)
self._compile_and_check(
[x],
[sort(x, axis=None)],
[np.random.randn(10, 40).astype(aesara.config.floatX)],
SortOp,
)
def test_None(self):
a = dmatrix()
l = sort(a, None)
f = aesara.function([a], l)
gv = f(self.m_val)
gt = np.sort(self.m_val, None)
utt.assert_allclose(gv, gt)
def test3(self):
a = dvector()
w2 = sort(a)
f = aesara.function([a], w2)
gv = f(self.v_val)
gt = np.sort(self.v_val)
utt.assert_allclose(gv, gt)
def test4(self):
a = dmatrix()
axis = scalar()
l = sort(a, axis, "mergesort")
f = aesara.function([a, axis], l)
for axis_val in 0, 1:
gv = f(self.m_val, axis_val)
gt = np.sort(self.m_val, axis_val)
utt.assert_allclose(gv, gt)
def test2(self):
a = dmatrix()
axis = scalar()
w = sort(a, axis)
f = aesara.function([a, axis], w)
for axis_val in 0, 1:
gv = f(self.m_val, axis_val)
gt = np.sort(self.m_val, axis_val)
utt.assert_allclose(gv, gt)
def test_sort(self):
x = matrix()
self._compile_and_check(
[x],
[sort(x)],
[
self.rng.standard_normal(size=(10, 40)).astype(
aesara.config.floatX)
],
SortOp,
)
self._compile_and_check(
[x],
[sort(x, axis=None)],
[
self.rng.standard_normal(size=(10, 40)).astype(
aesara.config.floatX)
],
SortOp,
)
def test1(self):
a = dmatrix()
w = sort(a)
f = aesara.function([a], w)
utt.assert_allclose(f(self.m_val), np.sort(self.m_val))
