loc2lang_withpi.py 文件源码

def get_local_words(preds, vocab, NEs=[], k=50):
    #normalize the probabilites of each vocab
    normalized_preds = normalize(preds, norm='l1', axis=0)
    entropies = stats.entropy(normalized_preds)
    sorted_indices = np.argsort(entropies)
    sorted_local_words = np.array(vocab)[sorted_indices].tolist()
    filtered_local_words = []
    NEset = set(NEs)
    for word in sorted_local_words:
        if word in NEset: continue
        filtered_local_words.append(word)
    return filtered_local_words[0:k]