def test_ConstantFunction(self):
M, N, K = 3,4,5
ig = ImageGeometry(M, N, K)
tmp = ig.allocate('random_int')
constant = 10
f = ConstantFunction(constant)
# check call
res1 = f(tmp)
self.assertAlmostEqual(res1, constant)
# check gradient with and without out
res1 = f.gradient(tmp)
out = ig.allocate()
self.assertNumpyArrayAlmostEqual(res1.as_array(), out)
out1 = ig.allocate()
f.gradient(tmp, out=out1)
self.assertNumpyArrayAlmostEqual(res1.as_array(), out1)
# check convex conjugate
res1 = f.convex_conjugate(tmp)
res2 = tmp.maximum(0).sum()
self.assertNumpyArrayAlmostEqual(res1.as_array(), res2.as_array())
# check proximal
tau = 0.4
res1 = f.proximal(tmp, tau)
self.assertNumpyArrayAlmostEqual(res1.as_array(), tmp.as_array())
def test_ConstantFunction(self):
k = ConstantFunction(constant=1)
ig = ImageGeometry(1,2,3)
x = ig.allocate(2)
grad = k.gradient(x)
out = ig.allocate(-1)
k.gradient(x, out=out)
#out.fill(-3)
self.assertNumpyArrayEqual(numpy.zeros(x.shape), grad.as_array())
self.assertNumpyArrayEqual(out.as_array(), grad.as_array())