def test_constant_equals():
n = 10
constant = 1.5
w_const = NumpyFnConstWeighting(constant)
w_const2 = NumpyFnConstWeighting(constant)
w_other_const = NumpyFnConstWeighting(constant + 1)
w_other_exp = NumpyFnConstWeighting(constant, exponent=1)
const_sparse_mat = scipy.sparse.dia_matrix(([constant] * n, [0]),
shape=(n, n))
const_dense_mat = constant * np.eye(n)
w_matrix_sp = NumpyFnMatrixWeighting(const_sparse_mat)
w_matrix_de = NumpyFnMatrixWeighting(const_dense_mat)
assert w_const == w_const
assert w_const == w_const2
assert w_const2 == w_const
# Equivalent but not equal -> False
assert w_const != w_matrix_sp
assert w_const != w_matrix_de
# Different
assert w_const != w_other_const
assert w_const != w_other_exp
def test_constant_init(exponent):
constant = 1.5
# Just test if the code runs
NumpyFnConstWeighting(constant, exponent=exponent)
with pytest.raises(ValueError):
NumpyFnConstWeighting(0)
with pytest.raises(ValueError):
NumpyFnConstWeighting(-1)
with pytest.raises(ValueError):
NumpyFnConstWeighting(float('inf'))
def test_constant_dist(fn, exponent):
[xarr, yarr], [x, y] = noise_elements(fn, 2)
constant = 1.5
if exponent == float('inf'):
factor = constant
else:
factor = constant**(1 / exponent)
true_dist = factor * np.linalg.norm(xarr - yarr, ord=exponent)
w_const = NumpyFnConstWeighting(constant, exponent=exponent)
assert almost_equal(w_const.dist(x, y), true_dist)
# With free function
w_const_dist = npy_weighted_dist(constant, exponent=exponent)
assert almost_equal(w_const_dist(x, y), true_dist)
def test_constant_norm(fn, exponent):
xarr, x = noise_elements(fn)
constant = 1.5
if exponent == float('inf'):
factor = constant
else:
factor = constant**(1 / exponent)
true_norm = factor * np.linalg.norm(xarr, ord=exponent)
w_const = NumpyFnConstWeighting(constant, exponent=exponent)
assert almost_equal(w_const.norm(x), true_norm)
# With free function
w_const_norm = npy_weighted_norm(constant, exponent=exponent)
assert almost_equal(w_const_norm(x), true_norm)
def test_const_dist_using_inner(fn):
[xarr, yarr], [x, y] = noise_elements(fn, 2)
constant = 1.5
w = NumpyFnConstWeighting(constant)
true_dist = np.sqrt(constant) * np.linalg.norm(xarr - yarr)
# Using 3 places (single precision default) since the result is always
# double even if the underlying computation was only single precision
assert almost_equal(w.dist(x, y), true_dist, places=3)
# Only possible for exponent=2
with pytest.raises(ValueError):
NumpyFnConstWeighting(constant, exponent=1, dist_using_inner=True)
# With free function
w_dist = npy_weighted_dist(constant, use_inner=True)
assert almost_equal(w_dist(x, y), true_dist, places=3)
def test_init_weighting(exponent):
const = 1.5
weight_arr = _pos_array(odl.rn(3, float))
weight_mat = _dense_matrix(odl.rn(3, float))
spaces = [NumpyFn(3, complex, exponent=exponent, weighting=const),
NumpyFn(3, complex, exponent=exponent, weighting=weight_arr),
NumpyFn(3, complex, exponent=exponent, weighting=weight_mat)]
weightings = [NumpyFnConstWeighting(const, exponent=exponent),
NumpyFnArrayWeighting(weight_arr, exponent=exponent),
NumpyFnMatrixWeighting(weight_mat, exponent=exponent)]
for spc, weight in zip(spaces, weightings):
assert spc.weighting == weight
def test_constant_inner(fn):
[xarr, yarr], [x, y] = noise_elements(fn, 2)
constant = 1.5
true_result_const = constant * np.vdot(yarr, xarr)
w_const = NumpyFnConstWeighting(constant)
assert almost_equal(w_const.inner(x, y), true_result_const)
# Exponent != 2 -> no inner
w_const = NumpyFnConstWeighting(constant, exponent=1)
with pytest.raises(NotImplementedError):
w_const.inner(x, y)
def test_array_equiv():
rn = odl.rn(5)
weight_arr = _pos_array(rn)
weight_elem = rn.element(weight_arr)
diag_mat = weight_arr * np.eye(5)
different_vec = weight_arr - 1
w_vec = NumpyFnArrayWeighting(weight_arr)
w_elem = NumpyFnArrayWeighting(weight_elem)
w_diag_mat = NumpyFnMatrixWeighting(diag_mat)
w_different_vec = NumpyFnArrayWeighting(different_vec)
# Equal -> True
assert w_vec.equiv(w_vec)
assert w_vec.equiv(w_elem)
# Equivalent matrix -> True
assert w_vec.equiv(w_diag_mat)
# Different vector -> False
assert not w_vec.equiv(w_different_vec)
# Test shortcuts
const_vec = np.ones(5) * 1.5
w_vec = NumpyFnArrayWeighting(const_vec)
w_const = NumpyFnConstWeighting(1.5)
w_wrong_const = NumpyFnConstWeighting(1)
w_wrong_exp = NumpyFnConstWeighting(1.5, exponent=1)
assert w_vec.equiv(w_const)
assert not w_vec.equiv(w_wrong_const)
assert not w_vec.equiv(w_wrong_exp)
# Bogus input
assert not w_vec.equiv(True)
assert not w_vec.equiv(object)
assert not w_vec.equiv(None)
def test_constant_equiv():
n = 10
constant = 1.5
w_const = NumpyFnConstWeighting(constant)
w_const2 = NumpyFnConstWeighting(constant)
const_sparse_mat = scipy.sparse.dia_matrix(([constant] * n, [0]),
shape=(n, n))
const_dense_mat = constant * np.eye(n)
w_matrix_sp = NumpyFnMatrixWeighting(const_sparse_mat)
w_matrix_de = NumpyFnMatrixWeighting(const_dense_mat)
# Equal -> True
assert w_const.equiv(w_const)
assert w_const.equiv(w_const2)
# Equivalent matrix representation -> True
assert w_const.equiv(w_matrix_sp)
assert w_const.equiv(w_matrix_de)
w_different_const = NumpyFnConstWeighting(2.5)
assert not w_const.equiv(w_different_const)
# Bogus input
assert not w_const.equiv(True)
assert not w_const.equiv(object)
assert not w_const.equiv(None)
