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from torch.nn.functional import cosine_similarity
def cor_distance(x, y, eps=1e-12):
# Analogous to L1 distance, but in terms of Pearson's correlation
return (1.0 - cosine_similarity(x, y, eps=eps)).sqrt().mean()
def cor_square_error(x, y, eps=1e-12):
# Analogous to MSE, but in terms of Pearson's correlation
return (1.0 - cosine_similarity(x, y, eps=eps)).mean()
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