def test_vec_to_sym_matrix():
# Check error if unsuitable size
vec = np.ones(31)
assert_raises_regex(ValueError, 'Vector of unsuitable shape',
vec_to_sym_matrix, vec)
# Check error if given diagonal shape incompatible with vec
vec = np.ones(3)
diagonal = np.zeros(4)
assert_raises_regex(ValueError, 'incompatible with vector',
vec_to_sym_matrix, vec, diagonal)
# Check output value is correct
vec = np.ones(6, )
sym = np.array([[sqrt(2), 1., 1.], [1., sqrt(2), 1.],
[1., 1., sqrt(2)]])
assert_array_almost_equal(vec_to_sym_matrix(vec), sym)
# Check output value is correct with seperate diagonal
vec = np.ones(3, )
diagonal = np.ones(3)
assert_array_almost_equal(vec_to_sym_matrix(vec, diagonal=diagonal), sym)
# Check vec_to_sym_matrix is the inverse function of sym_matrix_to_vec
# when diagonal is included
assert_array_almost_equal(vec_to_sym_matrix(sym_matrix_to_vec(sym)), sym)
# when diagonal is discarded
vec = sym_matrix_to_vec(sym, discard_diagonal=True)
diagonal = np.diagonal(sym) / sqrt(2)
assert_array_almost_equal(vec_to_sym_matrix(vec, diagonal=diagonal), sym)
def test_sym_matrix_to_vec():
sym = np.ones((3, 3))
sqrt2 = 1. / sqrt(2.)
vec = np.array([sqrt2, 1., sqrt2, 1., 1., sqrt2])
assert_array_almost_equal(sym_matrix_to_vec(sym), vec)
vec = np.array([1., 1., 1.])
assert_array_almost_equal(sym_matrix_to_vec(sym, discard_diagonal=True),
vec)
# Check sym_matrix_to_vec is the inverse function of vec_to_sym_matrix
n = 5
p = n * (n + 1) // 2
rand_gen = np.random.RandomState(0)
# when diagonal is included
vec = rand_gen.rand(p)
sym = vec_to_sym_matrix(vec)
assert_array_almost_equal(sym_matrix_to_vec(sym), vec)
# when diagonal given separately
diagonal = rand_gen.rand(n + 1)
sym = vec_to_sym_matrix(vec, diagonal=diagonal)
assert_array_almost_equal(sym_matrix_to_vec(sym, discard_diagonal=True),
vec)
# multiple matrices case when diagonal is included
vecs = np.asarray([vec, 2. * vec, 0.5 * vec])
syms = vec_to_sym_matrix(vecs)
assert_array_almost_equal(sym_matrix_to_vec(syms), vecs)
# multiple matrices case when diagonal is given seperately
diagonals = np.asarray([diagonal, 3. * diagonal, -diagonal])
syms = vec_to_sym_matrix(vecs, diagonal=diagonals)
assert_array_almost_equal(sym_matrix_to_vec(syms, discard_diagonal=True),
vecs)
def test_sym_matrix_to_vec():
sym = np.ones((3, 3))
sqrt2 = 1. / sqrt(2.)
vec = np.array([sqrt2, 1., sqrt2, 1., 1., sqrt2])
assert_array_almost_equal(sym_matrix_to_vec(sym), vec)
vec = np.array([1., 1., 1.])
assert_array_almost_equal(sym_matrix_to_vec(sym, discard_diagonal=True),
vec)
# Check sym_matrix_to_vec is the inverse function of vec_to_sym_matrix
n = 5
p = n * (n + 1) // 2
rand_gen = np.random.RandomState(0)
# when diagonal is included
vec = rand_gen.rand(p)
sym = vec_to_sym_matrix(vec)
assert_array_almost_equal(sym_matrix_to_vec(sym), vec)
# when diagonal given separately
diagonal = rand_gen.rand(n + 1)
sym = vec_to_sym_matrix(vec, diagonal=diagonal)
assert_array_almost_equal(sym_matrix_to_vec(sym, discard_diagonal=True),
vec)
# multiple matrices case when diagonal is included
vecs = np.asarray([vec, 2. * vec, 0.5 * vec])
syms = vec_to_sym_matrix(vecs)
assert_array_almost_equal(sym_matrix_to_vec(syms), vecs)
# multiple matrices case when diagonal is given seperately
diagonals = np.asarray([diagonal, 3. * diagonal, -diagonal])
syms = vec_to_sym_matrix(vecs, diagonal=diagonals)
assert_array_almost_equal(sym_matrix_to_vec(syms, discard_diagonal=True),
vecs)
def test_vec_to_sym_matrix():
# Check error if unsuitable size
vec = np.ones(31)
assert_raises_regex(ValueError, 'Vector of unsuitable shape',
vec_to_sym_matrix, vec)
# Check error if given diagonal shape incompatible with vec
vec = np.ones(3)
diagonal = np.zeros(4)
assert_raises_regex(ValueError, 'incompatible with vector',
vec_to_sym_matrix, vec, diagonal)
# Check output value is correct
vec = np.ones(6, )
sym = np.array([[sqrt(2), 1., 1.], [1., sqrt(2), 1.], [1., 1., sqrt(2)]])
assert_array_almost_equal(vec_to_sym_matrix(vec), sym)
# Check output value is correct with seperate diagonal
vec = np.ones(3, )
diagonal = np.ones(3)
assert_array_almost_equal(vec_to_sym_matrix(vec, diagonal=diagonal), sym)
# Check vec_to_sym_matrix is the inverse function of sym_matrix_to_vec
# when diagonal is included
assert_array_almost_equal(vec_to_sym_matrix(sym_matrix_to_vec(sym)), sym)
# when diagonal is discarded
vec = sym_matrix_to_vec(sym, discard_diagonal=True)
diagonal = np.diagonal(sym) / sqrt(2)
assert_array_almost_equal(vec_to_sym_matrix(vec, diagonal=diagonal), sym)