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def cotan(vectors0: TensorLike, vectors1: TensorLike):
if is_types([vectors0, vectors1], [Tensor, Parameter]):
return cotan_torch(vectors0, vectors1)
elif is_types([vectors0, vectors1], [ndarray]):
return cotan_ndarray(vectors0, vectors1)
else:
return cotan_ndarray(numpy.array(vectors0), numpy.array(vectors1))
def cotan_ndarray(vectors0: ndarray, vectors1: ndarray):
return numpy.sum(vectors0*vectors1) / numpy.sqrt(numpy.sum(numpy.square(numpy.cross(vectors0, vectors1)), axis=-1))
def cotan_torch(vectors0: Tensor, vectors1: Tensor):
return torch.sum(vectors0*vectors1) / torch.linalg.cross(vectors0, vectors1).square().sum(dim=-1).sqrt()
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