How to understand the recall@K in your code?

[ahugj]

@mscottml
First of all thank you for sharing the code, this is really great work.

I ran the experiment and got good results, but I can't understand the implementation of computational recall @ K in your code. Can you explain it to me? The two bold lines are shown below.

`def recall_k(self, k=1):
m = len(self.sim_mat)
match_counter = 0
for i in range(m):
pos_sim = self.sim_mat[i][self.gallery_labels == self.query_labels[i]]
neg_sim = self.sim_mat[i][self.gallery_labels != self.query_labels[i]]
thresh = np.sort(pos_sim)[-2] if self.is_equal_query else np.max(pos_sim)

        ****if np.sum(neg_sim > thresh) < k:   #  The  lines that I can not understand.
            match_counter += 1**** 

 return float(match_counter) / m`

Thank you!

[ahugj]

It suddenly dawned on me that the similarity here refers to the similarity between the inner line and the outlier. If the outlier is large, the prediction will be wrong