From 7ef7516343db3678391939399f065565a6a4dcc5 Mon Sep 17 00:00:00 2001
From: Gard Spreemann <gspreemann@gmail.com>
Date: Tue, 15 Jan 2019 19:51:12 +0100
Subject: Typo fixes.

---
 scnn/scnn.py | 31 +++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)

diff --git a/scnn/scnn.py b/scnn/scnn.py
index 2eae3f3..f81ca19 100644
--- a/scnn/scnn.py
+++ b/scnn/scnn.py
@@ -40,3 +40,34 @@ class SimplicialConvolution(nn.Module):
         assert(y.shape == (B, self.C_out, M))
 
         return y
+
+# This class does not yet implement the
+# Laplacian-power-pre/post-composed with the coboundary. It can be
+# simulated by just adding more layers anyway, so keeping it simple
+# for now.
+#
+# Note: You can use this for a adjoints of coboundaries too. Just feed
+# a transposed D.
+class Coboundary(nn.Module):
+    def __init__(self, D, C_in, C_out):
+        super().__init__()
+
+        assert(len(D.shape) == 2)
+        assert(C_in > 0)
+        assert(C_out > 0)
+
+        self.C_in = C_in
+        self.C_out = C_out
+        self.D = D # Don't modify.
+
+        self.theta = nn.parameter.Parameter(torch.randn((self.C_out, self.C_in)))
+
+    def forward(self, x):
+        (B, C_in, M) = x.shape
+        assert(self.D.shape[1] == M)
+        assert(C_in == self.C_in)
+
+        y = torch.einsum("oi,nm,bim->bon", (self.theta, self.D, x))
+        assert(y.shape == (B, self.C_out, N))
+
+        return y
-- 
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