A fast Python implementation of locality sensitive hashing with cosine distance
Installation
LSHCos depends on the following libraries:
numpy
To install:
$ pip install lshcosQuickstart
To create band 2 row 6 hashes for input data of 8 dimensions:
import numpy as np
from lshcos import LSHCos
### usage one:
lshcos = LSHCos(2, 6, 8)
lshcos.add([1,2,3,4,5,6,7,8],'v_1')
lshcos.add([2,3,4,5,6,7,8,9],'v_2')
lshcos.add([10,12,99,1,5,31,2,3],'v_3')
lshcos.query([1,2,3,4,5,6,7,7],2)
### usage two:
lshcos = LSHCos(2, 6, 8)
xxx = [('v_'+str(i),np.random.randn(8)) for i in range(100)]
yyy = np.random.randn(8)
for name,v in xxx:
lshcos.add(v,name)
lshcos.query(yyy)Main Interface
To initialize a LSHCos instance:
LSHCos(band_num, row_num, v_dim)parameters:
- band_num:
The number of band.
- row_num:
The number of row for each band.
- v_dim:
The dimension of the input vector.
1 maintainer
Metadata
1 maintainer
Release files for lshcos 0.0.2
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