Add FastScan refinement tutorial for python

tutorial/python/8-PQFastScanRefine.py

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+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import faiss
+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')    # 64-dim *nb queries
+xb[:, 0] += np.arange(nb) / 1000.
+xq = np.random.random((nq, d)).astype('float32')
+xq[:, 0] += np.arange(nq) / 1000.
+
+m = 8  # 8 specifies that the number of sub-vector is 8
+k = 4  # number of dimension in etracted vector
+n_bit = 4  # 4 specifies that each sub-vector is encoded as 4 bits
+bbs = 32  # build block size ( bbs % 32 == 0 ) for PQ
+
+index = faiss.IndexPQFastScan(d, m, n_bit, faiss.METRIC_L2)
+index_refine = faiss.IndexRefineFlat(index)
+# construct FastScan and run index refinement
+
+assert not index_refine.is_trained
+index_refine.train(xb)  # Train vectors data index within mockup database
+assert index_refine.is_trained
+
+index_refine.add(xb)
+params = faiss.IndexRefineSearchParameters(k_factor=3)
+D, I = index_refine.search(xq[:5], 10, params=params)
+print(I)
+print(D)
+index.nprobe = 10  # make comparable with experiment above
+D, I = index.search(xq[:5], k)  # search
+print(I[-5:])