https://github.com/nmslib/hnsw

https://github.com/nmslib/nmslib

pip install nmslib

这个会自动安装 pip install pybind11，结果报错：没有安装成功

装这个成功了：

pip install nmslib==1.7

  nmslib.cc(16): fatal error C1083: 无法打开包括文件: “pybind11/pybind11.h”: No such file or directory

  error: command 'C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\BIN\\x86_amd64\\cl.exe' failed with exit status 2

HNSW

使用demo：

Example Usage

import nmslib
import numpy

# create a random matrix to index
data = numpy.random.randn(10000, 100).astype(numpy.float32)

# initialize a new index, using a HNSW index on Cosine Similarity
index = nmslib.init(method='hnsw', space='cosinesimil')
index.addDataPointBatch(data)
index.createIndex({'post': 2}, print_progress=True)

# query for the nearest neighbours of the first datapoint
ids, distances = index.knnQuery(data[0], k=10)

# get all nearest neighbours for all the datapoint
# using a pool of 4 threads to compute
neighbours = index.knnQueryBatch(data, k=10, num_threads=4)

来源： https://nmslib.github.io/nmslib/quickstart.html

pybind demo下载：

https://github.com/5455945/cpp_demo

测试效果：1w条数据，查询不需要时间，构建需要30ms左右
import datetime
import numpy as np
import logging
# logging.basicConfig(level=logging.INFO)

import nmslib
for i in range(10):
# create a random matrix to index
    data = np.random.randn(10000, 128).astype(np.float32)
    time1=datetime.datetime.now()
    # initialize a new index, using a HNSW index on Cosine Similarity
    index = nmslib.init(method='hnsw', space='cosinesimil')
    index.addDataPointBatch(data)
    index.createIndex({'post': 2}, print_progress=False)
    print("time1", (datetime.datetime.now() - time1).microseconds)
    time1 = datetime.datetime.now()
    # query for the nearest neighbours of the first datapoint
    ids, distances = index.knnQuery(data[0], k=10)
    print("time2",(datetime.datetime.now()-time1).microseconds)
    print(ids,distances)
    # get all nearest neighbours for all the datapoint
    # using a pool of 4 threads to compute
    # neighbours = index.knnQueryBatch(data, k=10, num_threads=4)
    # print(neighbours)