forked from rafacarrascosa/countminsketch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
countminsketch.py
90 lines (75 loc) · 2.89 KB
/
countminsketch.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
# -*- coding: utf-8 -*-
import hashlib
import array
class CountMinSketch(object):
"""
A class for counting hashable items using the Count-min Sketch strategy.
It fulfills a similar purpose than `itertools.Counter`.
The Count-min Sketch is a randomized data structure that uses a constant
amount of memory and has constant insertion and lookup times at the cost
of an arbitrarily small overestimation of the counts.
It has two parameters:
- `m` the size of the hash tables, larger implies smaller overestimation
- `d` the amount of hash tables, larger implies lower probability of
overestimation.
An example usage:
from countminsketch import CountMinSketch
sketch = CountMinSketch(1000, 10) # m=1000, d=10
sketch.update("oh yeah")
sketch.update(tuple())
sketch.update(1, value=123)
print sketch["oh yeah"] # prints 1
print sketch[tuple()] # prints 1
print sketch[1] # prints 123
print sketch["non-existent"] # prints 0
Note that this class can be used to count *any* hashable type, so it's
possible to "count apples" and then "ask for oranges". Validation is up to
the user.
"""
def __init__(self, m, d):
""" `m` is the size of the hash tables, larger implies smaller
overestimation. `d` the amount of hash tables, larger implies lower
probability of overestimation.
"""
if not m or not d:
raise ValueError("Table size (m) and amount of hash functions (d)"
" must be non-zero")
self.m = m
self.d = d
self.n = 0
self.tables = []
for _ in xrange(d):
table = array.array("l", (0 for _ in xrange(m)))
self.tables.append(table)
def _hash(self, x):
md5 = hashlib.md5(str(hash(x)))
for i in xrange(self.d):
md5.update(str(i))
yield int(md5.hexdigest(), 16) % self.m
def add(self, x, value=1):
"""
Count element `x` as if had appeared `value` times.
By default `value=1` so:
sketch.add(x)
Effectively counts `x` as occurring once.
"""
self.n += value
for table, i in zip(self.tables, self._hash(x)):
table[i] += value
def query(self, x):
"""
Return an estimation of the amount of times `x` has ocurred.
The returned value always overestimates the real value.
"""
return min(table[i] for table, i in zip(self.tables, self._hash(x)))
def __getitem__(self, x):
"""
A convenience method to call `query`.
"""
return self.query(x)
def __len__(self):
"""
The amount of things counted. Takes into account that the `value`
argument of `add` might be different from 1.
"""
return self.n