hopfield

torchhd.memory.hopfield(query: Tensor, memory: Tensor, kappa: int | None = None) → Tensor[source]

Parameters:

query (Tensor) – The query vector for the memory lookup.
memory (Tensor) – The items of memory for the memory lookup.

Shapes:

Query: \((*, d)\)
Memory: \((n, d)\)
Result: \((*, d)\)

Examples::

>>> items = torchhd.random(6, 512)
>>> read = memory.hopfield(items, items).sign()
>>> torchhd.cosine_similarity(read, items)
tensor([[ 1.0000,  0.0156, -0.0039, -0.0742,  0.0000, -0.0195],
        [ 0.0156,  1.0000, -0.0352, -0.0586,  0.0000, -0.0039],
        [-0.0039, -0.0352,  1.0000,  0.0156,  0.0820, -0.0234],
        [-0.0742, -0.0586,  0.0156,  1.0000, -0.0039,  0.0000],
        [ 0.0000,  0.0000,  0.0820, -0.0039,  1.0000,  0.0195],
        [-0.0195, -0.0039, -0.0234,  0.0000,  0.0195,  1.0000]])