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AdjMat.py
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AdjMat.py
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import torch
L = 12
def obc(x, y, L):
if x < 0 or x > L - 1:
x = None
if y < 0 or y > L - 1:
y = None
return x, y
def l2c(x, y, L):
x, y = obc(x, y, L)
if (x is None) or (y is None):
return None
else:
return x + y * L
def c2l(n, L):
assert n < L ** 2
return n % L, n // L
def Adj(L):
H = torch.zeros((L ** 2, L ** 2), dtype=torch.complex64)
for x in range(L):
for y in range(L):
n = l2c(x, y, L)
nx = l2c(x + 1, y, L)
ny = l2c(x, y + 1, L)
n1 = l2c(x + 1, y + 1, L)
n2 = l2c(x + 1, y - 1, L)
if nx is not None:
H[nx, n] = H[nx, n] + 1.
H[n, nx] = H[n, nx] + 1.
if ny is not None:
H[ny, n] = H[ny, n] + 1.
H[n, ny] = H[n, ny] + 1.
if n1 is not None:
H[n1, n] = H[n1, n] + 1.
H[n, n1] = H[n, n1] + 1.
if n2 is not None:
H[n2, n] = H[n2, n] + 1.
H[n, n2] = H[n, n2] + 1.
return H
if __name__ == "__main__":
adj = Adj(L) * 1e-2 + torch.diag_embed(torch.cat((torch.rand(8), torch.zeros(8))))
# save
torch.save(adj, 'datasets/AdjMat.pt')