semvec/pypi-packages huggingface dataset:
acoustics 0.2.6
Acoustics module for Python.
Requires Python: >=3.6

# python-acoustics

[![Build Status](https://travis-ci.org/python-acoustics/python-acoustics.svg?branch=master)](https://travis-ci.org/python-acoustics/python-acoustics)

The `python-acoustics` module is a Python module with useful tools for acousticians.

## Installation

The latest release can be found on PyPI and installed with `pip install acoustics`.
Otherwise, you can clone this repository and install with `pip install` or `pip install -e` when you want an editable install.


## Examples

Several examples can be found in the `examples` folder.

## Tests

The test suite can be run with

`pytest`

## Documentation

Documentation can be found [online](http://python-acoustics.github.io/python-acoustics/).

## License

`python-acoustics` is distributed under the BSD 3-clause license. See `LICENSE` for more information.

## Contributing

Contributors are always welcome.
datapro-learning 1.0
learning common data structure

# DataPro algorithm

The task is learning the common structure of data. The DataPro algorithm is the algorithm that finds statistically significant patterns in a set of token sequences.
This repository is Implemented from the paper "Learning the Common Structure of Data" by Kristina Lerman and Steven Minton.

## installation

```
pip install datapro-learning
```

## Get started

How to use model with this lib:

```python
from datapro import DataPro
import pandas as pd

# Read data file
df = pd.read_excel("street_road.xlsx")
df = df.dropna()

# Choose a column.
data_sample = df["หน่วยรับผิดชอบ"]

# Create datapro object.
s = DataPro(alpha=0.05, k_percentage=10)

# Train with data
s.fit(data_sample)

# show result
print(s.evaluate_score())
```

# Reference

https://www.aaai.org/Papers/AAAI/2000/AAAI00-093.pdf