Example of LSH function:Set S of d integers.h(S) = list of integers at k random indexes (r)S = {12, 9, 13, 98 , 2, 4, 43, 21, 53, 99}k=3, r={3, 5, 9}h(s) = 98499Similar sets have high probability of hashing to 98499....
To overcome this problem we can modify the parameters to the termination condition by either considering a larger threshold value for the termination condition (larger τ) or comparing the resulting clusters with a clustering obtained earlier than 40 hash functions before, for example, 50 hash ...
Look at examples/example_fuzzy_hash.pySimHashfrom pyLSHash import SimHash, hamming sim_hash = SimHash() sh1 = sim_hash.get_hash(sentence1) sh2 = sim_hash.get_hash(sentence2) corr = 1 - hamming(sh1, sh2) / sim_hash.len_hash print(sh1) print(sh2) print('corr = {}'.format(...
finch dist ./example.fastq.sk ./refseq_sketches_21_1000.sk --max-dist 0.2 Here, we also set a maximum distance of 0.2 in order to filter out less closely related genomes (a distance of 0 would be an identical genome). Setting a maximum ensures that the only relevant results are return...
Example of LSH function: Set S of d integers. h(S) = list of integers at k random indexes (r) S = {12, 9, 13, 98 , 2, 4, 43, 21, 53, 99} k=3, r={3, 5, 9} h(s) = 98499 Similar sets have high probability of hashing to 98499. Use multiple different LSH functions...