
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.

import numpy as np
d = 64                           # dimension
nb = 100000                      # database size
nq = 10000                       # nb of queries
np.random.seed(1234)             # make reproducible
xb = np.random.random((nb, d)).astype('float32')
xb[:, 0] += np.arange(nb) / 1000.
xq = np.random.random((nq, d)).astype('float32')
xq[:, 0] += np.arange(nq) / 1000.

import faiss                   # make faiss available
index = faiss.IndexFlatL2(d)   # build the index
print index.is_trained
index.add(xb)                  # add vectors to the index
print index.ntotal

k = 4                          # we want to see 4 nearest neighbors
D, I = index.search(xb[:5], k) # sanity check
print I
print D
D, I = index.search(xq, k)     # actual search
print I[:5]                    # neighbors of the 5 first queries
print I[-5:]                   # neighbors of the 5 last queries


