semvec/pypi-packages huggingface dataset:
pyigen 1.0.1.2
A package to generate a pyinstaller setup.py file!

# pyigen
v1.0.1.1 created by Armaan Aggarwal on May 15, 2019

This is a little package that can generate a pyinstaller setup.py script to 
turn your program into an .exe or .app file!

To know how to use this module, import pyigen and then call: 'pyigen.manual()'

View this package at GitHub at https://github.com/armaan115/pyigen
skipthoughts 0.0.1
Skipthoughts pretrained models for Pytorch
Keywords: pytorch pretrained models skipthoughts deep learning

# Skip-Thoughts.torch for Pytorcb

*Skip-Thoughts.torch* is a lightweight porting of [skip-thought pretrained models from Theano](https://github.com/ryankiros/skip-thoughts) to Pytorch.

## Installation

1. [python3 with anaconda](https://www.continuum.io/downloads)
2. [pytorch with/out CUDA](http://pytorch.org)

### Install from pip

3. `pip install skipthoughts`

### Install from repo

3. `git clone https://github.com/Cadene/skip-thoughts.torch.git`
4. `cd skip-thoughts.torch/pytorch`
5. `python setup.py install`


### Available pretrained models

#### UniSkip

It uses the `nn.GRU` layer from torch with the cudnn backend. It is the fastest implementation, but the dropout is sampled after each time-step in the cudnn implementation... (equals bad regularization)

#### DropUniSkip

It uses the `nn.GRUCell` layer from torch with the cudnn backend. It is slightly slower than UniSkip, however the dropout is sampled once for all time-steps in a sequence (good regularization).

#### BayesianUniSkip

It uses a custom GRU layer with a torch backend. It is at least two times slower than UniSkip, however the dropout is sampled once for all time-steps for each Linear (best regularization).

#### BiSkip

Equivalent to UniSkip, but with a bi-sequential GRU.

### Quick example

```python
import torch
from torch.autograd import Variable
import sys
sys.path.append('skip-thoughts.torch/pytorch')
from skipthoughts import UniSkip

dir_st = 'data/skip-thoughts'
vocab = ['robots', 'are', 'very', 'cool', '<eos>', 'BiDiBu']
uniskip = UniSkip(dir_st, vocab)

input = Variable(torch.LongTensor([
    [1,2,3,4,0], # robots are very cool 0
    [6,2,3,4,5]  # bidibu are very cool <eos>
])) # <eos> token is optional
print(input.size()) # batch_size x seq_len

output_seq2vec = uniskip(input, lengths=[4,5])
print(output_seq2vec.size()) # batch_size x 2400

output_seq2seq = uniskip(input)
print(output_seq2seq.size()) # batch_size x seq_len x 2400
```
table-generator 0.0.3
Generates code for html table from csv files

# Table Generator

Python package for generating html code for tables from csv files.

## Installation

### Install Using git

*If you wish to run this file from the command line I suggest you use the scripts from the repo at <https://github.com/lol-cubes/table-generator>*

```bash
git clone https://github.com/lol-cubes/table_generator.git
```

### Installl Using pip

```pip
pip install table_generator
```

## Usage:

### generate_html

Args:

`file` (str) - path to file containing csv table

`complete_file` (bool) -  whether or not to add html elements to make code function as a separate file

`header` (bool) - whether or not to turn first row of csv values into `<th>` tags

`pretty` (bool) - whether or not to prettify the output code

`attrs` (dict) - html attributes mapped to corresponding values

Example usage:
```python
from table_generator import generate_html

html_table = generate_html('data.csv', False, True, True, True, 
                           {"class": "table", "id": "data-table"})
```