Skip to content

Fix AbstractFeature.degrade calling itself instead of degrade_function #792

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

Closed
Anai-Guo wants to merge 1 commit into from
Closed

Fix AbstractFeature.degrade calling itself instead of degrade_function #792

Show file tree
Hide file tree
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
2 changes: 1 addition & 1 deletion bindsnet/network/topology_features.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -291,7 +291,7 @@ def degrade(self) -> None:
be *subtracted* from the propagated spikes.
"""

return self.degrade(self.value)
return self.degrade_function(self.value)

def link(self, parent_feature) -> None:
# language=rst
Expand Down
9 changes: 9 additions & 0 deletions test/network/test_connections.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -341,6 +341,15 @@ def test_degradation_feature_output(self):
)
assert torch.allclose(conn.compute(s), s @ w - (0.5 * deg).sum(0), atol=1e-6)

def test_degradation_feature_degrade_applies_degrade_function(self):
# degrade() must hand the value to degrade_function. It used to call
# itself with an argument, which is a TypeError on the first call.
deg = torch.tensor([[0.2, 0.4], [0.6, 0.8], [0.1, 0.3]])
feature = tf.Degradation(
name="d", value=deg.clone(), degrade_function=lambda v: v * 0.5
)
assert torch.allclose(feature.degrade(), 0.5 * deg, atol=1e-6)

def test_probability_feature_deterministic_bounds(self):
# bernoulli(1) == 1 (always passes); bernoulli(0) == 0 (always blocked).
s = torch.tensor([[1.0, 0.0, 1.0], [1.0, 1.0, 1.0]])
Expand Down