Fix incorrect variable usage in EMG example (#136)

* EMG example fix

* Torch 2.0.0 now implements prod for bfloat16 on CPU

Author: mikeheddes

examples/emg_hand_gestures.py

@@ -39,7 +39,7 @@ def __init__(self, out_features, timestamps, channels):
     def forward(self, input: torch.Tensor) -> torch.Tensor:
         signal = self.signals(input)
         samples = torchhd.bind(signal, self.channels.weight.unsqueeze(0))
-        samples = torchhd.bind(signal, self.timestamps.weight.unsqueeze(1))
+        samples = torchhd.bind(samples, self.timestamps.weight.unsqueeze(1))
 
         samples = torchhd.multiset(samples)
         sample_hv = torchhd.ngrams(samples, n=N_GRAM_SIZE)

torchhd/functional.py

@@ -1221,7 +1221,7 @@ def ngrams(input: VSATensor, n: int = 3) -> VSATensor:
         \bigoplus_{i=0}^{m - n} \bigotimes_{j = 0}^{n - 1} \Pi^{n - j - 1}(V_{i + j})
 
     .. note::
-        For :math:`n=1` use :func:`~torchhd.functional.multiset` instead and for :math:`n=m` use :func:`~torchhd.functional.bind_sequence` instead.
+        For :math:`n=1` use :func:`~torchhd.multiset` instead and for :math:`n=m` use :func:`~torchhd.bind_sequence` instead.
 
     Args:
         input (VSATensor): The value hypervectors.

torchhd/tests/test_encodings.py

@@ -140,7 +140,7 @@ def test_value(self, vsa, dtype):
     def test_dtype(self, dtype):
         hv = torch.zeros(23, 1000, dtype=dtype).as_subclass(MAPTensor)
 
-        if dtype in {torch.float16, torch.bfloat16}:
+        if dtype in {torch.float16}:
             # torch.product is not implemented on CPU for these dtypes
             with pytest.raises(RuntimeError):
                 functional.multibind(hv)
@@ -287,7 +287,7 @@ def test_value(self):
     def test_dtype(self, dtype):
         hv = torch.zeros(23, 1000, dtype=dtype).as_subclass(MAPTensor)
 
-        if dtype in {torch.float16, torch.bfloat16}:
+        if dtype in {torch.float16}:
             # torch.product is not implemented on CPU for these dtypes
             with pytest.raises(RuntimeError):
                 functional.multibind(hv)