| Name | hyper-jax JSON |
| Version |
0.0.1
JSON |
| download |
| home_page | |
| Summary | Hyperdimensional computing with Jax |
| upload_time | 2023-10-23 17:15:02 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.10 |
| license | MIT License Copyright (c) 2023 mmarklar Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
hyperdimensional computing
jax
vector symbolic architecture
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
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|
# hyper-jax
[Hyperdimensional computing](https://en.wikipedia.org/wiki/Hyperdimensional_computing) with Jax. This library provides a very minimal implementation of MAP (Multiply, add, permute) operations over bipolar vectors originally proposed [here](https://www.researchgate.net/publication/215992330_Multiplicative_Binding_Representation_Operators_Analogy).
# Install
Coming soon...
# How to use
Generate 2 random vectors with dimension of 10 000
```
from generator import random_vectors
dimensions = 10000
count = 2
key = random.PRNGKey(0)
vectors = random_vectors(key, dimensions, count)
```
Bundle two hypervectors
```
from operation import bundle, unbundle
hypervector = bundle(vectors[0], vectors[1])
```
Unbundle two hypervectors
```
# original_vector == vectors[0]
original_vector = unbundle(hypervector, vectors[1])
```
Bind two hypervectors
```
from operation import bind, unbind
bound_vector = bind(vectors[0], vectors[1])
```
Unbind the hypervector
```
# original_vector == vectors[1]
unbound_vector = unbind(bound_vector, vectors[0])
```
Raw data
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"description": "# hyper-jax\n[Hyperdimensional computing](https://en.wikipedia.org/wiki/Hyperdimensional_computing) with Jax. This library provides a very minimal implementation of MAP (Multiply, add, permute) operations over bipolar vectors originally proposed [here](https://www.researchgate.net/publication/215992330_Multiplicative_Binding_Representation_Operators_Analogy).\n\n# Install\n\nComing soon...\n\n# How to use\nGenerate 2 random vectors with dimension of 10 000\n```\nfrom generator import random_vectors\n\ndimensions = 10000\ncount = 2\n\nkey = random.PRNGKey(0)\nvectors = random_vectors(key, dimensions, count)\n```\n\nBundle two hypervectors\n```\nfrom operation import bundle, unbundle\n\nhypervector = bundle(vectors[0], vectors[1])\n```\nUnbundle two hypervectors\n```\n# original_vector == vectors[0]\noriginal_vector = unbundle(hypervector, vectors[1])\n```\nBind two hypervectors\n```\nfrom operation import bind, unbind\n\nbound_vector = bind(vectors[0], vectors[1])\n```\nUnbind the hypervector\n```\n# original_vector == vectors[1]\nunbound_vector = unbind(bound_vector, vectors[0])\n```\n",
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