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import torch
import scipy.sparse as sp
import scipy.sparse.linalg as spl
import numpy as np
def normalize(L, half_interval = False):
assert(sp.isspmatrix(L))
M = L.shape[0]
assert(M == L.shape[1])
topeig = spl.eigsh(L, k=1, which="LM", return_eigenvectors = False)[0]
#print("Topeig = %f" %(topeig))
ret = L.copy()
if half_interval:
ret *= 1.0/topeig
else:
ret *= 2.0/topeig
ret.setdiag(ret.diagonal(0) - np.ones(M), 0)
return ret
def assemble(K, L, x):
(B, C_in, M) = x.shape
assert(L.shape[0] == M)
assert(L.shape[0] == L.shape[1])
assert(K > 0)
X = []
for b in range(0, B):
X123 = []
for c_in in range(0, C_in):
X23 = []
X23.append(x[b, c_in, :].unsqueeze(1)) # Constant, k = 0 term.
if K > 1:
X23.append(L.mm(X23[0]))
for k in range(2, K):
X23.append(2*(L.mm(X23[k-1])) - X23[k-2])
X23 = torch.cat(X23, 1)
assert(X23.shape == (M, K))
X123.append(X23.unsqueeze(0))
X123 = torch.cat(X123, 0)
assert(X123.shape == (C_in, M, K))
X.append(X123.unsqueeze(0))
X = torch.cat(X, 0)
assert(X.shape == (B, C_in, M, K))
return X
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