semvec/pypi-packages huggingface dataset:
text2digits 0.1.0
A small library to convert text numbers to digits in a string
Keywords: text2numbers words2numbers digits numbers

## Installation
```
pip3 install text2digits
```

## Usage
Python 3 only!
```
from text2digits import text2digits
t2d = text2digits.Text2Digits()
t2d.convert("twenty ten and twenty one")
> 2010 and 21
```

It can handle a variety of phrases. Spoken/Informal and formal language:

```
"A random string" -> 'A random string'
"I am thirty six years old with a child that is four. I would like to get him four cars!" -> 'I am 36 years old with a child that is 4. I would like to get him 4 cars!'
"I was born in twenty ten" -> 'I was born in 2010'
"I was born in nineteen sixty four" -> 'I was born in 1964'
"I am the fourth cousin" -> 'I am the 4 cousin'
"I am twenty nine" -> 'I am 29'
"it was twenty ten and was negative thirty seven degrees" -> 'it was 2010 and was negative 37 degrees'
"thirty twenty one" -> '3021'
"one thousand six hundred sixty six" -> '1666'
"one thousand and six hundred and sixty six" -> '1666'
"sixteen sixty six" -> '1666'
"eleven hundred twelve" -> '1112'
"Sixteen and seven" -> '16 and 7'
"twenty ten and twenty one" -> '2010 and 21'
"I was born in nineteen ninety two and am twenty six years old!" -> 'I was born in 1992 and am 26 years old!'
"three forty five" -> '345'
```

I find this useful if using Alexa/Lex to convert audio to text and have to convert the text to digits.

## Improvements/Issues
- Still need to add support for decimal numbers
- Need to add support for negative numbers

## Acknowledgements
I have heavily used code from the SO answers from here: https://stackoverflow.com/questions/493174/is-there-a-way-to-convert-number-words-to-integers
and improved upon them
py4k-means 0.0.1
K-Means Python Library
Requires Python: >=3.7

# py4k-means

K-Means Python Library

## Getting Started

This project is simply implementation of K-Means clustering algorithm in python programming language.

### Prerequisites

Numpy & Pandas


### Installing

The easiest way to install py4k-means is using pip

```
pip install py4k-means
```

### Usage
There is only 1 public method of k_means class. It is cluster. cluster method takes 3 argument namely x, k, and max_iter. x is the data its self, k is the number of cluster we are aiming to, while max_iter is maximum iteration. By default max_iter is 10. The return of this method is pandas DataFrame with 1 column addition, it is the label column obtained by K-Means algorithm
```
from py4k_means.cluster import k_means
import numpy as np
import matplotlib.pyplot as plt
x = [np.random.uniform(-5,5 ,2) for i in range(500)]
km = k_means()
y = km.cluster(x,4,20)
plt.scatter(y['x0'],y['x1'],c=y['label'],cmap='rainbow')
plt.show()
```
<a href="https://ibb.co/N61nXYN"><img src="https://i.ibb.co/pvWrgXK/Figure-1.png" alt="Figure-1" border="0"></a>
pdf-to-scan 0.0.3
A small script to make your pdfs seem like scanned
Requires Python: >=3.6

# PDFtoScan

PDFtoScan uses the idea implemented in [baicunko's](https://github.com/baicunko/scanyourpdf) website to make your PDFs look like they were scanned in a CLI-based app.

## Installation

```console
$ pip install pdf-to-scan
```

## Dependencies

The script requires `ghostscript` and `imagemagick` to run. 
These can be installed using,

### For Ubuntu
```console 
$ sudo apt-get install imagemagick
$ sudo apt-get install ghostscript
```

### For macOS
```console 
$ brew install imagemagick
$ brew install ghostscript
```

PDF support in ImageMagick might be disabled be default on your os/distro. To fix this, comment out this line in `/etc/ImageMagick-6/policy.xml`

`<policy domain="coder" rights="none" pattern="PDF" />`

Using `<!-- --->`
py4logistic-regression 0.0.4
Logistic Regression Python Library
Requires Python: >=3.7

# py4logistic-regression

Logistic Regression Python Library

## Getting Started

This project is simply implementation of logistic regression algorithm in python programming language.

### Prerequisites

Numpy


### Installing

The easiest way to install py4logistic-regression is using pip

```
pip install py4logistic-regression
```

### Usage
There is 2 public method of Logistic Regression class. It is learn and predict method, learn method takes 5 argument namely x_train, t_train, alpha, and epoch. It is the training data, it's label, learning rate, and number of iteration respectively. predict method takes 1 argument namely x_test. It is the data to be predicted
```
from py4logistic_regression.regression import logistic_regression
x_train = [[0,0],[0,1],[1,0],[1,1]]
t_train = [0,0,0,1]
classifier = logistic_regression()
classifier.learn(x_train,t_train,0.1,50)
x_test = [[0.02,0.25],[0.97,0.89]]
y = classifier.predict(x_test)
```
py4linear-regression 0.0.5
Linear Regression Python Library
Requires Python: >=3.7

# py4linear-regression

Linear Regression Python Library

## Getting Started

This project is simply implementation of linear regression algorithm in python programming language.

### Prerequisites

Numpy


### Installing

The easiest way to install py4linear-regression is using pip

```
pip install py4linear-regression
```

### Usage
There is 2 public method of Linear Regression class. It is learn and predict method, learn method takes 5 argument namely x_train, t_train, alpha, and epoch. It is the training data, it's label, learning rate, and number of iteration respectively. predict method takes 1 argument namely x_test. It is the data to be predicted
```
from py4linear_regression.regression import linear_regression
x_train = [[0,0],[0,1],[1,0],[1,1]]
t_train = [0,1,2,3]
classifier = linear_regression()
classifier.learn(x_train,t_train,0.01,250)
x_test = [[0.01,0.99],[0.99,0.01]]
y = classifier.predict(x_test)
```
wpkit 1.1.2.4
A small example package
Requires Python: >=3.0

# wpkit
### *StartLogging*
`0.2.2.4`:
```pydocstring
Add to wpkit.pan:
    class LocalFSHandle:
        A handle to operate files and dirs
Add wpkt.services:
    class LocalFSServer:
        Combine falsk app and LocalFSHandle as one
        Serves on specific port and dir to handle requests from frontend
    class DBServer:
        Combine flask app and BackupDB as one
        Serves as a cloud database
[Frontend]
Add to pan.js:
    class RemoteDB & class RemoteFS:
        Helper classes to communicate with backend LocalFSServers and DBServers
Add to win.js:
    class QMenubar:
        GUI component
Other:
    Improve helper functions of class QWindow
    Add support for deleting files@dirs to explorer.js
    Add support for saving file to editor.js
@date:2019-12-28
```

`0.2.1.6`:
```
Add to wpkit.piu: 
    class Table:
        A db that stored data as record object. 
    class FileStorageHelper(Piu):
        A Piu db binded to a Table db , that helps handle files. 
```

`0.2.1.2`:
```
Add to wpkit:
    cv: 
        class Helper}
Add to wpkit.piu: 
    class FileDict:
        A dict binded to a file with specific name. 
    class BackupDB:
        A db stores old values even when keys are updated. 
```