1 files changed, 51 insertions, 0 deletions
diff --git a/scnn/chebyshev.py b/scnn/chebyshev.py
new file mode 100644
index 0000000..8b5e086
--- /dev/null
+++ b/scnn/chebyshev.py
@@ -0,0 +1,51 @@
+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