虚拟环境中直接:pip install annoy,然后大概可以按着官方文档体验一下最简单的case了: In [1]: import random In [2]: from annoy import AnnoyIndex...# f是向量维度 In [3]: f = 20 In [4]: t = AnnoyIndex(f) In [5]: for i in xrange(100): ...: v = [random.gauss...print(t.get_nns_by_item(0, 10)) [0, 45, 16, 17, 61, 24, 48, 20, 29, 84] # 此处测试从硬盘盘索引加载 In [10]: u = AnnoyIndex...json.dump(word_index, fp) ...: # 开始基于腾讯词向量构建Annoy索引,腾讯词向量大概是882万条 In [23]: from annoy import AnnoyIndex...# 腾讯词向量的维度是200 In [24]: tc_index = AnnoyIndex(200) In [25]: i = 0 In [26]: for key in tc_wv_model.vocab.keys
if not os.path.exists(fn_annoy) or not os.path.exists(fn_lmdb): i = 0 a = annoy.AnnoyIndex...会不开心的哦~ VEC_LENGTH = 50 FN_ANNOY = 'glove.6B.50d.txt.annoy' FN_LMDB = 'glove.6B.50d.txt.lmdb' a = annoy.AnnoyIndex
mmap@@YAPAXPAXIHHH_J@Z),该符号在函数 "public: virtual bool __thiscall AnnoyIndex<int,float,struct Euclidean...$AnnoyIndex@HMUEuclidean@@UKiss64Random@@@@UAE_NPBD@Z) 中被引用 LargeVis C:\Users\ndscbigdata4\Documents
100): global id2word, word2id # 自定义的读取word2vec的函数 items_vec = load_gensim() # 向量维度为200 a = AnnoyIndex...= pkl.load(open(id2word_path, "rb")) word2id = pkl.load(open(word2id_path, "rb")) annoy_index = AnnoyIndex
c.execute("SELECT COUNT(*) FROM embeddings") total_vectors = c.fetchone()[0] annoy_index = AnnoyIndex
2.4 完整的 Python API AnnoyIndex(f, metric)返回可读写的新索引,用于存储 f 维度向量。
您可以创建ANNOYIndex以便快速检索,如下所示: def create_annoy(target_features): t = AnnoyIndex(layer_dimension)...可以查询 ANNOY,如下所示: annoy_index = AnnoyIndex(10) annoy_index.load(os.path.join(work_dir, 'annoy.ann')) matches
领取专属 10元无门槛券
手把手带您无忧上云