Project description

Random Projection Hashing

Provides a PyTorch module for random projection hashing with GPU acceleration. Similar tensors (cosine similarity) will be assigned the same hash with high probability.

Installation

$ pip install rph

Usage

See example.py for an example on how to use rph on DNA strings. Please note that rph can be used with any arbitrary tensors. In summary, this package provides a module RandomProjectionHashModule(input_dim, hash_bits) where input_dim is the size of the input tensors and hash_bits is the number of bits to use for the final hash value. For example, choosing 4 bits will result in all tensors being split into 16 buckets only.

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This version

1.0.0

Feb 20, 2020

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Source Distribution

rph-1.0.0.tar.gz (2.0 kB view hashes)

Uploaded Feb 20, 2020 Source

Built Distribution

rph-1.0.0-py3-none-any.whl (3.2 kB view hashes)

Uploaded Feb 20, 2020 Python 3

Hashes for rph-1.0.0.tar.gz

Hashes for rph-1.0.0.tar.gz
Algorithm	Hash digest
SHA256	`fefb73a979bcaee910fb6c63cb649d187bcabd425b47fce3578031012a4be1fe`
MD5	`dc3156d0595e19c9c333a2772b67241b`
BLAKE2b-256	`9b673c941b21c7db3be5de8babebcf5e714806d949f7065e6fbf23ae9efab399`

Hashes for rph-1.0.0-py3-none-any.whl

Hashes for rph-1.0.0-py3-none-any.whl
Algorithm	Hash digest
SHA256	`8f8da4ff560f4a54c3c9d19a2c2554ad7e9b662c305195ad55fc5b93ce8e3a99`
MD5	`32184be27bdd68ead8f391f00101f8e1`
BLAKE2b-256	`3a3c347871765ae49b6851194adf0013fc63c506f852dc9124a0e8e73702559f`