FlyHash
A novel hashing algorithm based on "A neural algorithm for a fundamental computing problem" by S. Dasgupta, C. F. Stevens, and S. Navlakha (2017)
FlyHash is a LSH algorithm that maps input data to a sparse hash embedding, where the dimension of the hash embedding is much larger than the input, and keeps the locality of the input data in the hash embedding.
FlyHash is designed to be cheap to compute, yet not ganranteeing memory efficiency. It is suitable for hashing small to medium sized data ($d$ ~ 10-1000) to a large hash embedding ($m$ ~ 100-10000).
Usage
Using a large hash_dim $m=100$ for a small input_dim $d=10$:
>>> import numpy as np
>>> from flyhash import FlyHash
>>> d = 10
>>> m = 100
>>> flyhash = FlyHash(d, m)
>>> data = np.random.randn(5, d)
>>> hashed_data = flyhash(data)
For detailed usage, please refer to the documentation.
Release files for FlyHash 1.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| FlyHash-1.1.1.tar.gz | 23.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| FlyHash-1.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 29.7 kB
Release files / FlyHash-1.1.1.tar.gz
| Download URL | FlyHash-1.1.1.tar.gz |
|---|---|
| Size | 23.4 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.10.6
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Release files / FlyHash-1.1.1-py3-none-any.whl
| Download URL | FlyHash-1.1.1-py3-none-any.whl |
|---|---|
| Size | 6.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.10.6
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