def _run_minhash(data, seed, p):
hasher = pyhash.murmur3_32()
m = MinHash(num_perm=2**p)
for d in data:
m.digest(Hash(hasher(d, seed=seed)))
return m.count()
def _run_minhash(data, seed, p):
hasher = pyhash.murmur3_32()
m = MinHash(num_perm=2**p, hashobj=Hash)
for d in data:
m.update(hasher(d, seed=seed))
return m.count()
'estimating', 'the', 'similarity', 'between', 'documents']
m1, m2 = MinHash(), MinHash()
for d in data1:
m1.update(d.encode('utf8'))
for d in data2:
m2.update(d.encode('utf8'))
print("Estimated Jaccard for data1 and data2 is", m1.jaccard(m2))
s1 = set(data1)
s2 = set(data2)
actual_jaccard = float(len(s1.intersection(s2)))/float(len(s1.union(s2)))
print("Actual Jaccard for data1 and data2 is", actual_jaccard)
>>>
>>> m = MinHash(num_perm=256)
>>> m.count()
0.0
>>> from sparselsh import LSH
from scipy.sparse import csr_matrix
X = csr_matrix( [
[ 3, 0, 0, 0, 0, 0, -1],
[ 0, 1, 0, 0, 0, 0, 1],
[ 1, 1, 1, 1, 1, 1, 1] ])
# One class number for each input point
y = [ 0, 3, 10]
X_sim = csr_matrix( [ [ 1, 1, 1, 1, 1, 1, 0]])
lsh = LSH( 4,