# In AI/ML, using the Perceptron model, would it ever make sense to have both negative weights and data?

I understand the math but I want to make sure I understand the mapping back to real world scenarios. Thinking about it logically, I cannot think of a real world scenario where you would have a scenario where you would want both the weight of the data and the data itself to potentially be negative. Is there such a scenario I'm not thinking of?

Ex: $$y = sign(b + \sum_{i=1}^{d} w_ix_i)$$ where both $$w_i$$ and $$x_i$$ could be negative?

My reasoning is that I cannot think of a real world mapping where a double negative resulting in a positive makes sense.

Example: $$x$$ is temperature , $$y$$ is 1 if cold, -1 otherwise. $$y = \texttt{sign}(-1 \cdot -3) = 1.$$