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TriesWithFrequencies-0.0.1
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
This package provides tries (prefix trees) with frequencies implementation based on dictionaries.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | TriesWithFrequencies-0.0.1 |
| نام | TriesWithFrequencies |
| نسخه کتابخانه | 0.0.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Anton Antonov |
| ایمیل نویسنده | antononcube@posteo.net |
| آدرس صفحه اصلی | https://github.com/antononcube/Python-packages/tree/main/TriesWithFrequencies |
| آدرس اینترنتی | https://pypi.org/project/TriesWithFrequencies/ |
| مجوز | - |
# Tries with frequencies
This Python package has functions for creation and manipulation of
[Tries (Prefix trees)](https://en.wikipedia.org/wiki/Trie)
with frequencies.
The package provides Machine Learning (ML) functionalities, not "just" a
[Trie]((https://en.wikipedia.org/wiki/Trie))
data structure.
This Python implementation closely follows the Mathematica implementation [AAp2].
--------
## Installation
### From GitHub:
```shell
pip install -e git+https://github.com/antononcube/Python-packages.git#egg=TriesWithFrequencies-antononcube\&subdirectory=TriesWithFrequencies
```
### From PyPI:
```shell
python3 -m pip install TriesWithFrequencies
```
------------------------------------------------------------------------
## Setup
```python
from TriesWithFrequencies import *
```
------------------------------------------------------------------------
## Creation examples
In this section we show a few ways to create tries with frequencies.
Consider a trie (prefix tree) created over a list of words:
```python
tr = trie_create_by_split( ["bar", "bark", "bars", "balm", "cert", "cell"] )
trie_form(tr)
```
TRIEROOT => 6.0
├─b => 4.0
│ └─a => 4.0
│ ├─r => 3.0
│ │ └─k => 1.0
│ │ └─s => 1.0
│ └─l => 1.0
│ └─m => 1.0
└─c => 2.0
└─e => 2.0
├─r => 1.0
│ └─t => 1.0
└─l => 1.0
└─l => 1.0
Here we convert the trie with frequencies above into a trie with
probabilities:
```python
ptr = trie_node_probabilities( tr )
trie_form(ptr)
```
TRIEROOT => 1.0
├─b => 0.6666666666666666
│ └─a => 1.0
│ ├─r => 0.75
│ │ ├─k => 0.3333333333333333
│ │ └─s => 0.3333333333333333
│ └─l => 0.25
│ └─m => 1.0
└─c => 0.3333333333333333
└─e => 1.0
├─r => 0.5
│ └─t => 1.0
└─l => 0.5
└─l => 1.0
------------------------------------------------------------------------
## Shrinking
Here we shrink the trie with probabilities above:
```python
trie_form(trie_shrink(ptr))
```
TRIEROOT => 1.0
└─ba => 1.0
└─r => 0.75
└─k => 0.3333333333333333
└─s => 0.3333333333333333
└─lm => 1.0
└─ce => 1.0
└─rt => 1.0
└─ll => 1.0
Here we shrink the frequencies trie using a separator:
```python
trie_form(trie_shrink(tr, sep="~"))
```
TRIEROOT => 6.0
└─b~a => 4.0
└─r => 3.0
└─k => 1.0
└─s => 1.0
└─l~m => 1.0
└─c~e => 2.0
└─r~t => 1.0
└─l~l => 1.0
------------------------------------------------------------------------
## Retrieval and sub-tries
Here we retrieve a sub-trie with a key:
```python
trie_form(trie_sub_trie(tr, list("bar")))
```
r => 3.0
└─k => 1.0
└─s => 1.0
------------------------------------------------------------------------
## Classification
Create a trie:
```python
words = [*(["bar"] * 6), *(["bark"] * 3), *(["bare"] * 2), *(["cam"] * 3), "came", *(["camelia"] * 4)]
tr = trie_create_by_split(words)
tr = trie_node_probabilities(tr)
```
Show node counts:
```python
trie_node_counts(tr)
```
{'total': 13, 'internal': 10, 'leaves': 3}
Show the trie form:
```python
trie_form(tr)
```
TRIEROOT => 1.0
├─b => 0.5789473684210527
│ └─a => 1.0
│ └─r => 1.0
│ ├─k => 0.2727272727272727
│ └─e => 0.18181818181818182
└─c => 0.42105263157894735
└─a => 1.0
└─m => 1.0
└─e => 0.625
└─l => 0.8
└─i => 1.0
└─a => 1.0
Classify with the letters of the word \"cam\":
```python
trie_classify(tr, list("cam"), prop="Probabilities")
```
{'a': 0.5, 'm': 0.375, 'e': 0.12499999999999997}
------
## References
### Articles
[AA1] Anton Antonov,
["Tries with frequencies for data mining"](https://mathematicaforprediction.wordpress.com/2013/12/06/tries-with-frequencies-for-data-mining/),
(2013),
[MathematicaForPrediction at WordPress](https://mathematicaforprediction.wordpress.com).
[AA2] Anton Antonov,
["Removal of sub-trees in tries"](https://mathematicaforprediction.wordpress.com/2014/10/12/removal-of-sub-trees-in-tries/),
(2013),
[MathematicaForPrediction at WordPress](https://mathematicaforprediction.wordpress.com).
[AA3] Anton Antonov,
["Tries with frequencies in Java"](https://mathematicaforprediction.wordpress.com/2017/01/31/tries-with-frequencies-in-java/)
(2017),
[MathematicaForPrediction at WordPress](https://mathematicaforprediction.wordpress.com).
[GitHub Markdown](https://github.com/antononcube/MathematicaForPrediction).
[RAC1] Tib,
["Day 10: My 10 commandments for Raku performances"](https://raku-advent.blog/2020/12/10/day-10-my-10-commandments-for-raku-performances/),
(2020),
[Raku Advent Calendar](https://raku-advent.blog).
[WK1] Wikipedia entry, [Trie](https://en.wikipedia.org/wiki/Trie).
### Packages
[AAp1] Anton Antonov,
[Tries with frequencies Mathematica Version 9.0 package](https://github.com/antononcube/MathematicaForPrediction/blob/master/TriesWithFrequenciesV9.m),
(2013),
[MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction).
[AAp2] Anton Antonov,
[Tries with frequencies Mathematica package](https://github.com/antononcube/MathematicaForPrediction/blob/master/TriesWithFrequencies.m),
(2013-2018),
[MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction).
[AAp3] Anton Antonov,
[Tries with frequencies in Java](https://github.com/antononcube/MathematicaForPrediction/tree/master/Java/TriesWithFrequencies),
(2017),
[MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction).
[AAp4] Anton Antonov,
[Java tries with frequencies Mathematica package](https://github.com/antononcube/MathematicaForPrediction/blob/master/JavaTriesWithFrequencies.m),
(2017),
[MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction).
[AAp5] Anton Antonov,
[Java tries with frequencies Mathematica unit tests](https://github.com/antononcube/MathematicaForPrediction/blob/master/UnitTests/JavaTriesWithFrequencies-Unit-Tests.wlt),
(2017),
[MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction).
[AAp6] Anton Antonov,
[ML::TriesWithFrequencies Raku package](https://github.com/antononcube/Raku-ML-TriesWithFrequencies),
(2021),
[GitHub/antononcube](https://github.com/antononcube).
### Videos
[AAv1] Anton Antonov,
["Prefix Trees with Frequencies for Data Analysis and Machine Learning"](https://www.youtube.com/watch?v=MdVp7t8xQbQ),
(2017),
Wolfram Technology Conference 2017,
[Wolfram channel at YouTube](https://www.youtube.com/channel/UCJekgf6k62CQHdENWf2NgAQ).
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl TriesWithFrequencies-0.0.1:
pip install TriesWithFrequencies-0.0.1.whl
نصب پکیج tar.gz TriesWithFrequencies-0.0.1:
pip install TriesWithFrequencies-0.0.1.tar.gz