# import numpy as np
# a = [1,2,3,4,5,6,7,8,9,10]
# print(a[0::2])
# print(a[1::2])
#
# src = np.array([
#             [30.2946, 51.6963],
#             [65.5318, 51.5014],
#             [48.0252, 71.7366],
#             [33.5493, 92.3655],
#             [62.7299, 92.2041]], dtype=np.float32)
# src[:, 0] += 8.0
# #print(src[:, 1])
# input_blob = np.zeros((2, 3, 2, 2), dtype=np.uint8)
# #print(input_blob[0])

import numpy as np
d = 512                           # dimension
nb = 23800                   # database size
nq = 1                      # nb of queries
np.random.seed(1234)             # make reproducible
xb = np.random.random((nb, d)).astype('float32')
#print(xb)
#print(np.arange(nb))
#print(xb[:, 0])

# xb[:, 0] += np.arange(nb) / 1000.
# #print(xb)
#xq = np.random.random((nq, d)).astype('float32')
#xq[:, 0] += np.arange(nq) / 1000.
import faiss   # make faiss available
nlist = 100
#quantizer = faiss.IndexFlatL2(d)  # the other index
#index = faiss.IndexIVFFlat(quantizer, d, nlist, faiss.METRIC_L2)
#index.train(xb)
index = faiss.IndexFlatL2(d)   # build the index
print(index.is_trained)
#print(xq.shape)
index.add(xb)                  # add vectors to the index
print(index.ntotal)
#index.nprobe=30
k = 1                          # we want to see 4 nearest neighbors
import time
for i in range(10):
    xq = np.random.random((nq, d)).astype('float32')
    start_time = time.time()
    D, I = index.search(xq, k)     # actual search
    end_time = time.time()
    print("faiss cost %fs"%(end_time - start_time))
    print(D,I)