36 lines
1.3 KiB
Python
36 lines
1.3 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import faiss
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import numpy as np
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d = 64 # dimension
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nb = 100000 # database size
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nq = 10000 # nb of queries
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np.random.seed(1234) # make reproducible
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xb = np.random.random((nb, d)).astype('float32') # 64-dim *nb queries
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xb[:, 0] += np.arange(nb) / 1000.
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xq = np.random.random((nq, d)).astype('float32')
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xq[:, 0] += np.arange(nq) / 1000.
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m = 8 # 8 specifies that the number of sub-vector is 8
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k = 4 # number of dimension in etracted vector
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n_bit = 4 # 4 specifies that each sub-vector is encoded as 4 bits
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bbs = 32 # build block size ( bbs % 32 == 0 ) for PQ
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index = faiss.IndexPQFastScan(d, m, n_bit, faiss.METRIC_L2, bbs)
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# construct FastScan Index
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assert not index.is_trained
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index.train(xb) # Train vectors data index within mockup database
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assert index.is_trained
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index.add(xb)
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D, I = index.search(xb[:5], k) # sanity check
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print(I)
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print(D)
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index.nprobe = 10 # make comparable with experiment above
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D, I = index.search(xq, k) # search
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print(I[-5:]) # neighbors of the 5 last queries
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