def test_transform(self, device, dtype):
M, N = 2, 32
K = 30
Bs = []
beta = []
bpg, bt = rand_bipartite(N // 2,
N - N // 2,
p=0.2,
dtype=dtype,
device=device,
return_partition=True)
Bs.append(bpg)
beta.append(bt)
mask = Bs[0].to_dense() != 0
for i in range(M - 1):
bpg, bt = rand_bipartite(
N // 2,
N - N // 2,
p=0.2,
dtype=dtype,
device=device,
return_partition=True,
)
dense = bpg.to_dense()
temp_mask = dense != 0
dense[mask] = 0
mask = temp_mask + mask
Bs.append(SparseTensor.from_dense(dense))
if i % 2 == 0:
beta.append(bt)
else:
beta.append(~bt)
beta = torch.stack(beta).T
qmf = QmfCore(bipartite_graphs=Bs, beta=beta, order=K)
x = torch.rand(N, dtype=dtype, device=device)
y = qmf.analyze(x)
assert y.shape == (2**M, N, 1)
z = qmf.synthesize(y)
assert z.shape == (2**M, N, 1)
assert (z.sum(0).squeeze() - x).abs().sum() != 0
z.squeeze_()
f_hat = z.sum(0)
dis = (f_hat - x).abs()
pf = snr(f_hat, x)
ppprint(dis, pf)
def test_dkl(self, p, dt):
N1 = 20
N2 = 10
delta = 0.1
Blil = rand_bipartite(N1, N2, p, dt).to_scipy("csr").tolil()
Sigma = compute_sigma(Blil, delta).tocsc()
print(dkl(Blil, Sigma, delta))
def test_bipartite_fix_th(self):
N1, N2 = 4, 6
N = N1 + N2
B = rand_udg(N, 0.6)
flag, vtx_color, Bb = is_bipartite_fix(B.to_dense(), fix_flag=False)
assert not flag # if fix_flag, always bipartite
B = rand_bipartite(N1, N2, 0.6)
flag, vtx_color, _ = is_bipartite_fix(B.to_dense(), fix_flag=False)
assert flag
def test_bipartite(self):
n_sample = 7
N1, N2 = 4, 6
N = N1 + N2
for i in range(n_sample):
B = rand_bipartite(N1, N2)
flag, vtx_color, _ = is_bipartite_fix(B.to_scipy("csr"),
fix_flag=False)
assert flag
NB = rand_udg(N, 0.8) # complete graph must be non-bipartite
flag, vtx_color, _ = is_bipartite_fix(NB.to_scipy("csr"),
fix_flag=False)
assert not flag
def test_one_level(self, dtype, device):
N1 = 6
N2 = 4
bptG, beta = rand_bipartite(N1,
N2,
0.5,
dtype=dtype,
return_partition=True)
basis = QmfOperator([bptG.to_scipy("csr")],
beta.view(-1, 1),
order=20,
device=device)
x = torch.ones(N1 + N2, 1, dtype=dtype, device=device)
y = basis.transform(x)
z = basis.inverse_transform(y)
print("\nsnr: ",
snr(z.permute(-1, -2), x.permute(-1, -2)).item(), "dB.")
print("dis: ", (z - x).abs().sum())
def test_one_level(self, dtype, device):
N1 = 60
N2 = 40
bptG, beta = rand_bipartite(N1,
N2,
0.2,
dtype=dtype,
return_partition=True)
basis = BiorthOperator([bptG.to_scipy("csr")],
beta.view(-1, 1),
k=2,
device=device)
x = torch.ones(N1 + N2, 1, dtype=dtype, device=device)
y = basis.transform(x)
z = basis.inverse_transform(y)
print("\nsnr: ",
snr(z.permute(-1, -2), x.permute(-1, -2)).item(), "dB.")
print("dis: ", (z - x).abs().sum())
self.display_density(basis.operator, basis.inv_operator)
def test_is_bipartite2(device):
ts_spm = rand_bipartite(4, 6, device=device)
assert is_bipartite(ts_spm)[0]
def test_rand_bipartite(self, device, dtype, density):
N1 = 6
N2 = 7
rand_bipartite(N1, N2, density, dtype, device)