def test_approx_negative_error():
"""Check exception raised if matrix is negative"""
A = np.ones([6, 6])
A[0, 0] = -1
with pytest.raises(ValueError,
match="Input matrix must not have negative entries"):
hafnian(A, approx=True)
def test_complex_wrapper(self):
"""Check hafnian(A)=haf_complex(A) for a random
real matrix.
"""
A = np.complex128(np.random.random([6, 6]))
A += 1j * np.random.random([6, 6])
A += A.T
haf = hafnian(A)
expected = haf_complex(A)
assert np.allclose(haf, expected)
haf = hafnian(A, loop=True)
expected = haf_complex(A, loop=True)
assert np.allclose(haf, expected)
def test_valid_output(self, random_matrix, n, fill):
"""Tests that sparse loop hafnian matches full implementation"""
A = random_matrix(n, fill_factor=fill)
assert np.allclose(
hafnian_sparse(A, loop=True),
hafnian_sparse(A, D=set(range(len(A))), loop=True),
)
assert np.allclose(
hafnian_sparse(A, loop=False),
hafnian_sparse(A, D=set(range(len(A))), loop=False),
)
assert np.allclose(hafnian(A, loop=True), hafnian_sparse(A, loop=True))
assert np.allclose(hafnian(A, loop=False), hafnian_sparse(A,
loop=False))
def FockDensityMatrix(cov, mu, m, n, tol=1e-10):
if np.max(cov - cov.T) > tol:
raise ValueError("Covariance matrix must be symmetric.")
else:
cov = (cov + cov.T) / 2
N = np.int(cov.shape[0] / 2)
cov_Q = RtoQmat(cov)
cov_Q_inv = np.linalg.inv(cov_Q)
cov_Q_det = np.linalg.det(cov_Q)
mu_Q = RtoTvec(mu)
T1 = np.exp(-0.5 * np.dot(np.dot(np.conj(mu_Q), cov_Q_inv), mu_Q))
T2 = np.sqrt(cov_Q_det * np.prod(special.factorial(m)) *
np.prod(special.factorial(n)))
T = T1 / T2
X = np.block([[np.zeros([N, N]), np.eye(N)], [np.eye(N),
np.zeros([N, N])]])
A = np.dot(X, (np.eye(2 * N) - cov_Q_inv))
A = (A + A.T) / 2 # cancel the numeric error of symmetray
A_rp = np.repeat(A, np.hstack([n, m]), axis=0)
A_rp = np.repeat(A_rp, np.hstack([n, m]), axis=1)
gamma = np.dot(np.conj(mu_Q), cov_Q_inv)
gamma_rp = np.repeat(gamma, np.hstack([n, m]))
np.fill_diagonal(A_rp, gamma_rp)
prob = T * hafnian(A_rp, loop=True)
return prob
def test_4x4_zero_diag(self, random_matrix):
"""Check 4x4 loop hafnian with zero diagonals"""
A = random_matrix(4)
A = A - np.diag(np.diag(A))
haf = hafnian(A, loop=True)
expected = A[0, 1] * A[2, 3] + A[0, 2] * A[1, 3] + A[0, 3] * A[1, 2]
assert np.allclose(haf, expected)
def test_diag(self, n):
"""Check loophafnian of diagonal matrix is product of diagonals"""
v = np.random.rand(n)
A = np.diag(v)
haf = hafnian(A, loop=True)
expected = np.prod(v)
assert np.allclose(haf, expected)
def test_rank_r(r, n):
"""Test rank-r matrices"""
G = np.random.rand(n, r) + 1j * np.random.rand(n, r)
A = G @ G.T
haf = low_rank_hafnian(G)
expected = hafnian(A)
assert np.allclose(haf, expected)
def test_int_wrapper_loop(self):
"""Check hafnian(A, loop=True)=haf_real(A, loop=True) for a random
integer matrix.
"""
A = np.int64(np.ones([6, 6]))
haf = hafnian(A, loop=True)
expected = haf_real(np.float64(A), loop=True)
assert np.allclose(haf, expected)
def test_rank_one(n):
"""Test the hafnian of rank one matrices so that it is within
10% of the exact value"""
x = np.random.rand(n)
A = np.outer(x, x)
exact = factorial2(n - 1) * np.prod(x)
approx = hafnian(A, approx=True, num_samples=10000)
assert np.allclose(approx, exact, rtol=2e-1, atol=0)
def test_int_wrapper(self):
"""Check hafnian(A)=haf_int(A) for a random
integer matrix.
"""
A = np.int64(np.ones([6, 6]))
haf = hafnian(A)
expected = haf_int(np.int64(A))
assert np.allclose(haf, expected)
def test_block_ones(self, n, dtype, recursive):
"""Check hafnian([[0, I_n], [I_n, 0]])=n!"""
O = np.zeros([n, n])
B = np.ones([n, n])
A = np.vstack([np.hstack([O, B]), np.hstack([B, O])])
A = dtype(A)
haf = hafnian(A, recursive=recursive)
expected = float(fac(n))
assert np.allclose(haf, expected)
def test_4x4(self, random_matrix):
"""Check 4x4 loop hafnian"""
A = random_matrix(4)
haf = hafnian(A, loop=True)
expected = (A[0, 1] * A[2, 3] + A[0, 2] * A[1, 3] + A[0, 3] * A[1, 2] +
A[0, 0] * A[1, 1] * A[2, 3] + A[0, 1] * A[2, 2] * A[3, 3] +
A[0, 2] * A[1, 1] * A[3, 3] + A[0, 0] * A[2, 2] * A[1, 3] +
A[0, 0] * A[3, 3] * A[1, 2] + A[0, 3] * A[1, 1] * A[2, 2] +
A[0, 0] * A[1, 1] * A[2, 2] * A[3, 3])
assert np.allclose(haf, expected)
def test_real_wrapper(self):
"""Check hafnian(A)=haf_real(A) for a random
real matrix.
"""
A = np.random.random([6, 6])
A += A.T
haf = hafnian(A)
expected = haf_real(A)
assert np.allclose(haf, expected)
haf = hafnian(A, loop=True)
expected = haf_real(A, loop=True)
assert np.allclose(haf, expected)
A = np.random.random([6, 6])
A += A.T
A = np.array(A, dtype=np.complex128)
haf = hafnian(A)
expected = haf_real(np.float64(A.real))
assert np.allclose(haf, expected)
def test_slow_hafnian_four(self):
"""
Tests slow hafnian function against four-by-four graphs
"""
for i in range(1): # how many tests to perform
graph = gnp_random_graph(10, 0.5)
nx.draw(graph)
plt.show() # displays graph (cumbersome for large tests)
adj = to_numpy_array(graph) # create adjacency matrix
walrus = thewalrus.hafnian(adj) # calculate hafnian with Xanadu library
haf = hafnian.slow_hafnian(adj) # calculate my hafnian
self.assertEqual(walrus, haf) # compare!
def test_4x4(self, random_matrix, recursive):
"""Check 4x4 hafnian"""
A = random_matrix(4)
haf = hafnian(A, recursive=recursive)
expected = A[0, 1] * A[2, 3] + A[0, 2] * A[1, 3] + A[0, 3] * A[1, 2]
assert np.allclose(haf, expected)
def test_nan(self):
"""Check exception for non-finite matrix"""
A = np.array([[2, 1], [1, np.nan]])
with pytest.raises(ValueError):
hafnian(A)
def test_empty_matrix(self):
"""Check empty matrix returns 1"""
A = np.ndarray((0, 0))
res = hafnian(A)
assert res == 1
def test_odd_dim(self):
"""Check hafnian for matrix with odd dimensions"""
A = np.ones([3, 3])
assert hafnian(A) == 0
def test_non_symmetric_exception(self):
"""Check exception for non-symmetric matrix"""
A = np.ones([4, 4])
A[0, 1] = 0.0
with pytest.raises(ValueError):
hafnian(A)
def test_array_exception(self):
"""Check exception for non-matrix argument"""
with pytest.raises(TypeError):
hafnian(1)
def test_square_exception(self):
"""Check exception for non-square argument"""
A = np.zeros([2, 3])
with pytest.raises(ValueError):
hafnian(A)
def test_3x3(self, dtype, recursive):
"""Check 3x3 hafnian"""
A = dtype(np.ones([3, 3]))
haf = hafnian(A, recursive=recursive)
assert haf == 0.0
def test_identity(self, n, dtype, recursive):
"""Check hafnian(I)=0"""
A = dtype(np.identity(n))
haf = hafnian(A, recursive=recursive)
assert np.allclose(haf, 0)
def test_ones(self, n, dtype, recursive):
"""Check hafnian(J_2n)=(2n)!/(n!2^n)"""
A = dtype(np.ones([2 * n, 2 * n]))
haf = hafnian(A, recursive=recursive)
expected = fac(2 * n) / (fac(n) * (2**n))
assert np.allclose(haf, expected)
def test_2x2(self, random_matrix):
"""Check 2x2 loop hafnian"""
A = random_matrix(2)
haf = hafnian(A, loop=True)
assert np.allclose(haf, A[0, 1] + A[0, 0] * A[1, 1])
def test_3x3(self, dtype):
"""Check 3x3 loop hafnian"""
A = dtype(np.ones([3, 3]))
haf = hafnian(A, loop=True)
assert haf == 4.0
def test_approx_complex_error():
"""Check exception raised if matrix is complex"""
A = 1j * np.ones([6, 6])
with pytest.raises(ValueError, match="Input matrix must be real"):
hafnian(A, approx=True)
def test_identity(self, n, dtype):
"""Check loop hafnian(I)=1"""
A = dtype(np.identity(n))
haf = hafnian(A, loop=True)
assert np.allclose(haf, 1)
def test_ones_approx(n):
"""Check hafnian_approx(J_2n)=(2n)!/(n!2^n)"""
A = np.float64(np.ones([2 * n, 2 * n]))
haf = hafnian(A, approx=True, num_samples=10000)
expected = fac(2 * n) / (fac(n) * (2**n))
assert np.abs(haf - expected) / expected < 0.2
def test_ones(self, n, dtype):
"""Check loop hafnian(J_n)=T(n)"""
A = dtype(np.ones([n, n]))
haf = hafnian(A, loop=True)
expected = T[n]
assert np.allclose(haf, expected)
