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## Abstract
Currently we have 2 different approaches for assets search. Levenshtein distance and search by a substring.
The problem is that:
1. Right now we are doing levenshtein in python (not…
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```
What steps will reproduce the problem?
1. use std::vector for uxn::patl::levenshtein_distance
2. vector1: add vector1.resize(9,2). lookup: vector2: 1. vector2.resize(9,2)
and vector2.resize(32, 4…
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```
What steps will reproduce the problem?
1. use std::vector for uxn::patl::levenshtein_distance
2. vector1: add vector1.resize(9,2). lookup: vector2: 1. vector2.resize(9,2)
and vector2.resize(32, 4…
-
```
What steps will reproduce the problem?
1. use std::vector for uxn::patl::levenshtein_distance
2. vector1: add vector1.resize(9,2). lookup: vector2: 1. vector2.resize(9,2)
and vector2.resize(32, 4…
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## Use case
Approximative search leveraging Levenshtein distance between words
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I am trying to be able to run this anonymized script using cuDF or cuDF.pandas with GPU acceleration.
currently runs for over 5 hours for 2Million rows of data.
```python
import pandas as pd
from rap…
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```
What steps will reproduce the problem?
1. use std::vector for uxn::patl::levenshtein_distance
2. vector1: add vector1.resize(9,2). lookup: vector2: 1. vector2.resize(9,2)
and vector2.resize(32, 4…
-
```
What steps will reproduce the problem?
1. use std::vector for uxn::patl::levenshtein_distance
2. vector1: add vector1.resize(9,2). lookup: vector2: 1. vector2.resize(9,2)
and vector2.resize(32, 4…
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This issue tracks missing components in the rust port:
- [ ] Levenshtein
- [x] basic distances
- [x] cached distances
- [ ] simd implementation
- [ ] edit operations
- [x] Damera…
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```
What steps will reproduce the problem?
1. use std::vector for uxn::patl::levenshtein_distance
2. vector1: add vector1.resize(9,2). lookup: vector2: 1. vector2.resize(9,2)
and vector2.resize(32, 4…