recently I am learning NumPy's usage and I met a question which gives me 3 array:
- q: A numpy array of shape (1, K) (queries)
- k: A numpy array of shape (N, K) (keys)
- v: A numpy array of shape (N, 1) (values)
and ask me to do sum_i exp(-||q-k_i||^2) * v[i]
My code is:
(np.exp(np.sum((np.tile(q, (np.shape(k)[0], 1)) - k)**2, axis = -1)**0.5 * -1).T.dot(v))[0]
But the value is not correct.
For readability, I explain my previous one-line code as follows:
1.
If anyone knows where is my mistake, please help me, thank you. Stuck on this for a long time.
np.tile(q, (np.shape(k)[0], 1) // this makes a N line copy of q, for following operation.
(np.tile(q, (np.shape(k)[0], 1)) - k) // this is just let q - k, making an elementwise, same-size, q-k matrix
np.sum((np.tile(q, (np.shape(k)[0], 1)) - k)**2, axis = -1)**0.5 gives a row-wise L2-norm, so currently we have a (N,1) matrix, each row is the norm of the original q-k[i]
np.exp(np.sum((np.tile(q, (np.shape(k)[0], 1)) - k)**2, axis = -1)**0.5 * -1).T
Then we do exp of its -1 negative, and eventually, dot it with the v, to automatically get the sum.
I tried the np.exp(-np.linalg.norm((np.tile(q, (np.shape(k)[0], 1)) - k), axis=1, ord = 2)).T.dot(v)[0]
aka linalg.norm method, but it does not change...
question from:
https://stackoverflow.com/questions/66056124/where-is-my-mistake-in-this-complex-numpy-usage 
