Interesting (non-cryptographic) hashes implemented in pure Python. Included so far:
Each hash is implemented as its own type extended from the base class
Official repository and latest version: https://github.com/sean-public/python-hashes
To install the latest version, you can either do
easy_install python-hashes or
pip install python-hashes. You may need to use
sudo, depending on your environment.
Charikar similarity is most useful for creating 'fingerprints' of documents or metadata so you can quickly find duplicates or cluster items. It operates on lists of strings, treating each word as its own token (order does not matter, as in the bag-of-words model).
Here is a quick example session showing off similarity hashes:
>>> from hashes.simhash import simhash >>> hash1 = simhash('This is a test string one.') >>> hash2 = simhash('This is a test string TWO.') >>> hash1 <simhash.simhash object at 0x7f1f93070c90> >>> print hash1, hash2 10203485745788768176630988232 10749932022170787621889701832 >>> hash1.hex() '0x20f82026a01daffae45cfdc8L' >>> hash1.similarity(hash2) 0.875 # % of bits in common (calculated via hamming distance) >>> long(hash1) - long(hash2) -546446276382019445258713600L >>> hash1 < hash2 # Hashes of the same type can be compared True >>> a_list = [hash2, hash1, 4.2] >>> for item in a_list: print item 10749932022170787621889701832 10203485745788768176630988232 4.2 >>> a_list.sort() # Because comparisons work, so does sorting >>> for item in a_list: print item 4.2 10203485745788768176630988232 10749932022170787621889701832
It can be extended to any bitlength using the
>>> hash3 = simhash('this is yet another test', hashbits=8) >>> hash3.hex() '0x18' >>> hash4 = simhash('extremely long hash bitlength', hashbits=2048) >>> hash4.hex() '0xf00020585012016060260443bab0f7d76fde5549a6857ecL'
But be careful; it only makes sense to compare equal-length hashes!
>>> hash3.similarity(hash4) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "hashes/simhash.py", line 63, in similarity raise Exception('Hashes must be of equal size to find similarity') Exception: Hashes must be of equal size to find similarity
The Bloom filter is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set. False positives are possible, but false negatives are not. Elements can be added to the set but not removed.
Uses SHA-1 from Python's hashlib, but you can swap that out with any other 160-bit hash function. Also keep in mind that it starts off very sparse and become more dense (and false-positive-prone) as you add more elements.
Here is the basic use case:
>>> from hashes.bloom import bloomfilter >>> hash1 = bloomfilter('test') >>> hash1.hashbits, hash1.num_hashes # default values (see below) (28756, 7) >>> hash1.add('test string') >>> 'test string' in hash1 True >>> 'holy diver' in hash1 False >>> for word in 'these are some tokens to add to the filter'.split(): ... hash1.add(word) >>> 'these' in hash1 True
The hash length and number of internal hashes used for the digest are automatically
determined using your input values
false_positive_rate. The capacity
is the upper bound on the number of items you wish to add. A lower false-positive
rate will create a larger, but more accurate, filter.
>>> hash2 = bloomfilter(capacity=100, false_positive_rate=0.01) >>> hash2.hashbits, hash2.num_hashes (959, 7) >>> hash3 = bloomfilter(capacity=1000000, false_positive_rate=0.01) >>> hash3.hashbits, hash3.num_hashes (9585059, 7) >>> hash4 = bloomfilter(capacity=1000000, false_positive_rate=0.0001) >>> hash4.hashbits, hash4.num_hashes (19170117, 14)
The hash grows in size to accommodate the number of items you wish to add, but remains sparse until you are done adding the projected number of items:
>>> import zlib >>> len(hash4.hex()) 250899 >>> len(zlib.compress(hash4.hex())) 1068
Geohash is a latitude/longitude geocode system invented by Gustavo Niemeyer when writing the web service at geohash.org, and put into the public domain.
It is a hierarchical spatial data structure which subdivides space into buckets of grid shape. Geohashes offer properties like arbitrary precision and the possibility of gradually removing characters from the end of the code to reduce its size (and gradually lose precision). As a consequence of the gradual precision degradation, nearby places will often (but not always) present similar prefixes. On the other side, the longer a shared prefix is, the closer the two places are. For this implementation, the default precision is 12 (base32) characters long.
It's very easy to use:
>>> from hashes.geohash import geohash >>> here = geohash(33.0505, -1.024, precision=4) >>> there = geohash(34.5, -2.5, precision=4) >>> here.hash, there.hash ('evzs', 'eynk') >>> here.distance_in_miles(there) 131.24743425050551 >>> # The longer the hash, the more accurate it is >>> here.encode(33.0505, -1.024, precision=8) >>> here.hash 'evzk08wt' >>> here.decode() (33.050565719604492, -1.0236167907714844) >>> # Now try with 20 characters >>> here.encode(33.0505, -1.024, precision=20) >>> here.hash 'evzk08wm55drbqbww0j7' >>> here.decode() (33.050499999999936, -1.0239999999998339)
Most useful for filtering spam by creating signatures of documents to find near-duplicates. Charikar similarity hashes can be used on any datastream, whereas Nilsimsa is a digest ideal for documents (language doesn't matter) because it uses histograms of rolling trigraphs instead of the usual bag-of-words model where order doesn't matter.
The Nilsimsa hash does not output the same data as the reference implementation. Use at your own risk.