Subtracting mean embeddings

[bwang482]

Are you sure this line is correct?
X_train = X_train - np.mean(X_train)

np.mean(X_train) gives a single value. Shouldn't it be np.mean(X_train, 0) ???

[GuilhermeZaniniMoreira]

I am gettings this error:
ValueError: operands could not be broadcast together with shapes (237191,) (300,)
There are 237.191 words with embeddins space equals to 300. How did you solve that?

[iR00i]

Shouldn't it be np.mean(X_train, 0) ???

If you go back to the original paper that proposed the "Post-Processing Algorithm" (All-but-the-Top: Simple and Effective Postprocessing for Word Representations), the authors outline computing the mean to be the following:

So i imagine the resulting mean should be a scaler computed from the entire matrix.