import numpy as np
import torch
def assign_by_euclidian_at_k(X, T, k):
"""
X : [nb_samples x nb_features], e.g. 100 x 64 (embeddings)
k : for each sample, assign target labels of k nearest points
"""
distances = torch.cdist(X, X)
# get nearest points
indices = distances.topk(k + 1, largest=False)[1][:, 1: k + 1]
return np.array([[T[i] for i in ii] for ii in indices])
def calc_recall_at_k(T, Y, k):
"""
T : [nb_samples] (target labels)
Y : [nb_samples x k] (k predicted labels/neighbours)
"""
s = sum([1 for t, y in zip(T, Y) if t in y[:k]])
return s / (1. * len(T))
