semvec/pypi-packages huggingface dataset:
vacuum-cleaner 0.3
Deep Vacuum Cleaner
License: MIT
Keywords: radio astronomy deep learning cleaning science

# Deep Vacuum Cleaner

Radio telescope deconvolution based using a Conditional Generative Adversarial Deep Network.

Based on [pix2pix-tensorflow](https://github.com/affinelayer/pix2pix-tensorflow)

Whch is based on [pix2pix](https://phillipi.github.io/pix2pix/) by Isola et al.

[Article about this implemention](https://affinelayer.com/pix2pix/)


## preparations

You probably want to download a pretrained model.

download:

http://repo.kernsuite.info/vacuum/model.tar.xz

And extract to `share/vacuum/model`.


## Setup

```
$ pip install vacuum-cleaner

```

or if you want to try the GPU accelerated version:

```
$ pip install "vacuum-cleaner[gpu]"

```
But the tensorflow-gpu package is not the most portable package available.

## Usage

```
$ vacuum-clean dirty.fits psf.fits
```       



## Training

Have a look at `vacuum-train --help` or at the source. Intended to be trained with
[spiel](https://github.com/gijzelaerr/spiel/) as training data generator.
l3wtransformer 0.3.0
A word hashing method based on vectors of letter n-grams. Currently transforms text into sequences of numbers.
License: MIT
Keywords: machine-learning natural-language-processing preprocessing

l3wtransformer
==============

> A word hashing method to reduce the dimensionality of the bag-of-words term vectors. It is based on letter n-gram. Given a word (e.g. good), it first adds word starting and ending marks to the word (e.g. #good#). Then, breaks the word into letter n-grams (e.g. letter trigrams: #go, goo, ood, od#). Finally, the word is represented using a vector of letter n-grams.

[Huang et al.2013, Learning Deep Structured Semantic Models for Web Search using Clickthrough Data]

---

This implementation supports the transformation from **text into sequences of numbers**, with the numbers indicating the descending word frequency.

For example:

*Lorem ipsum dolor sit amet, consectetuer adipiscing elit...* is transformed into *23, 1, 80, 86, 47, 50001, 21, 59, 83, 93, 14, 50003, 4, 7*

Also, after each word flags indicating lower case, upper case, mixed case or initial capitalization are added.

### To do

There will be an implementation supporting the transformation from **text into bag-of-word vectors**.

Install
-------

```
pip install l3wtransformer
```

Usage
-----

```
from l3wtransformer import L3wTransformer

l3wt = L3wTransformer()

l3wt.fit_on_texts(['First example.', 'And one more!'])
l3wt.texts_to_sequences(['One example', '2nd exa.'])

# [[5, 18, 17, 50001, 2, 10, 24, 6, 15, 20, 50003], [16, 50003, 2, 10, 50003]]
```