[github-action] formatting fixes

torchhd/functional.py

@@ -178,9 +178,7 @@ def random_hv(
     if dtype in {torch.complex64, torch.complex128}:
         dtype = torch.float if dtype == torch.complex64 else torch.double
 
-        angle = torch.empty(
-            num_embeddings, embedding_dim, dtype=dtype, device=device
-        )
+        angle = torch.empty(num_embeddings, embedding_dim, dtype=dtype, device=device)
         angle.uniform_(-math.pi, math.pi)
         magnitude = torch.ones(
             num_embeddings, embedding_dim, dtype=dtype, device=device

torchhd/tests/basis_hv/test_circular_hv.py

@@ -48,14 +48,15 @@ def test_value(self, dtype):
                 (hv == True) | (hv == False)
             ).item(), "values are either 1 or 0"
         elif dtype in torch_complex_dtypes:
-            magnitudes= hv.abs()
-            assert torch.allclose(magnitudes, torch.tensor(1.0, dtype=magnitudes.dtype)), "magnitude must be 1"
+            magnitudes = hv.abs()
+            assert torch.allclose(
+                magnitudes, torch.tensor(1.0, dtype=magnitudes.dtype)
+            ), "magnitude must be 1"
         else:
             assert torch.all(
                 (hv == -1) | (hv == 1)
             ).item(), "values are either -1 or +1"
 
-
         hv = functional.circular_hv(8, 1000000, generator=generator, dtype=dtype)
         if dtype in torch_complex_dtypes:
             sims = functional.cosine_similarity(hv[0], hv)

torchhd/tests/basis_hv/test_level_hv.py

@@ -48,8 +48,10 @@ def test_value(self, dtype):
                 (hv == True) | (hv == False)
             ).item(), "values are either 1 or 0"
         elif dtype in torch_complex_dtypes:
-            magnitudes= hv.abs()
-            assert torch.allclose(magnitudes, torch.tensor(1.0, dtype=magnitudes.dtype)), "magnitude must be 1"
+            magnitudes = hv.abs()
+            assert torch.allclose(
+                magnitudes, torch.tensor(1.0, dtype=magnitudes.dtype)
+            ), "magnitude must be 1"
         else:
             assert torch.all(
                 (hv == -1) | (hv == 1)

torchhd/tests/basis_hv/test_random_hv.py

@@ -47,8 +47,10 @@ def test_value(self, dtype):
         if dtype == torch.bool:
             assert torch.all((hv == False) | (hv == True)).item()
         elif dtype in torch_complex_dtypes:
-            magnitudes= hv.abs()
-            assert torch.allclose(magnitudes, torch.tensor(1.0, dtype=magnitudes.dtype)), "magnitude must be 1"
+            magnitudes = hv.abs()
+            assert torch.allclose(
+                magnitudes, torch.tensor(1.0, dtype=magnitudes.dtype)
+            ), "magnitude must be 1"
         else:
             assert torch.all((hv == -1) | (hv == 1)).item()
 

torchhd/tests/test_encodings.py

@@ -122,7 +122,7 @@ def test_value(self, dtype):
     @pytest.mark.parametrize("dtype", torch_dtypes)
     def test_dtype(self, dtype):
         hv = torch.zeros(23, 1000, dtype=dtype)
-        
+
         if dtype == torch.uint8:
             with pytest.raises(ValueError):
                 functional.multibind(hv)

torchhd/utils.py

@@ -35,7 +35,7 @@ def plot_pair_similarity(memory: Tensor, ax=None, **kwargs):
         See https://matplotlib.org/stable/users/installing/index.html for more information."
         )
 
-    similarity  = functional.cosine_similarity(memory, memory).tolist()
+    similarity = functional.cosine_similarity(memory, memory).tolist()
 
     if ax is None:
         ax = plt.gca()