Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

making code in the readme more clear #291

Merged
merged 1 commit into from
Oct 21, 2024
Merged

making code in the readme more clear #291

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
16 changes: 8 additions & 8 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -50,19 +50,19 @@ from topomodelx.nn.simplicial.san import SAN
from topomodelx.utils.sparse import from_sparse

# Step 1: Load the Karate Club dataset
dataset = tnx.datasets.karate_club(complex_type="simplicial")
karate_club_complex = tnx.datasets.karate_club(complex_type="simplicial")

# Step 2: Prepare Laplacians and node/edge features
laplacian_down = from_sparse(dataset.down_laplacian_matrix(rank=1))
laplacian_up = from_sparse(dataset.up_laplacian_matrix(rank=1))
incidence_0_1 = from_sparse(dataset.incidence_matrix(rank=1))
laplacian_down = from_sparse(karate_club_complex.down_laplacian_matrix(rank=1))
laplacian_up = from_sparse(karate_club_complex.up_laplacian_matrix(rank=1))
incidence_0_1 = from_sparse(karate_club_complex.incidence_matrix(rank=1))

x_0 = torch.tensor(np.stack(list(dataset.get_simplex_attributes("node_feat").values())))
x_1 = torch.tensor(np.stack(list(dataset.get_simplex_attributes("edge_feat").values())))
x_0 = torch.tensor(np.stack(list(karate_club_complex.get_simplex_attributes("node_feat").values())))
x_1 = torch.tensor(np.stack(list(karate_club_complex.get_simplex_attributes("edge_feat").values())))
x = x_1 + torch.sparse.mm(incidence_0_1.T, x_0)

# Step 3: Define the network
class Network(torch.nn.Module):
class TNN(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels):
super().__init__()
self.base_model = SAN(in_channels, hidden_channels, n_layers=2)
Expand All @@ -73,7 +73,7 @@ class Network(torch.nn.Module):
return torch.sigmoid(self.linear(x))

# Step 4: Initialize the network and perform a forward pass
model = Network(in_channels=x.shape[-1], hidden_channels=16, out_channels=2)
model = TNN(in_channels=x.shape[-1], hidden_channels=16, out_channels=2)
y_hat_edge = model(x, laplacian_up=laplacian_up, laplacian_down=laplacian_down)
```

Expand Down