def test_perform(self):
x = tensor.matrix()
y = tensor.scalar()
z = tensor.iscalar()
f = function([x, y, z], fill_diagonal_offset(x, y, z))
for test_offset in (-5, -4, -1, 0, 1, 4, 5):
for shp in [(8, 8), (5, 8), (8, 5), (5, 5)]:
a = numpy.random.rand(*shp).astype(config.floatX)
val = numpy.cast[config.floatX](numpy.random.rand())
out = f(a, val, test_offset)
# We can't use numpy.fill_diagonal as it is bugged.
assert numpy.allclose(numpy.diag(out, test_offset), val)
if test_offset >= 0:
assert (out == val).sum() == min(min(a.shape), a.shape[1] - test_offset)
else:
assert (out == val).sum() == min(min(a.shape), a.shape[0] + test_offset)
def test_perform(self):
x = tensor.matrix()
y = tensor.scalar()
z = tensor.iscalar()
f = function([x, y, z], fill_diagonal_offset(x, y, z))
for test_offset in (-5, -4, -1, 0, 1, 4, 5):
for shp in [(8, 8), (5, 8), (8, 5), (5, 5)]:
a = numpy.random.rand(*shp).astype(config.floatX)
val = numpy.cast[config.floatX](numpy.random.rand())
out = f(a, val, test_offset)
# We can't use numpy.fill_diagonal as it is bugged.
assert numpy.allclose(numpy.diag(out, test_offset), val)
if test_offset >= 0:
assert (out == val).sum() == min(min(a.shape),
a.shape[1] - test_offset)
else:
assert (out == val).sum() == min(min(a.shape),
a.shape[0] + test_offset)
def fill_diagonal_with_fix_offset(a, val):
return fill_diagonal_offset(a, val, test_offset)
def fill_diagonal_with_fix_offset( a, val):
return fill_diagonal_offset( a, val, test_offset)
