semvec/pypi-packages huggingface dataset:
DeltaPI-FantasticFour 1.0.0
DeltaPI Package for CS207
Requires Python: >=3.6

# DeltaPI
# Group #14
This repository contains CS207, fall 2019, final project.
## Group Members
* Daniel Cox
* Anna Davydova
* Stephen Moon
* Valentina Toll Villagra
### Badges:
[![Build Status](https://travis-ci.com/IACS-CS-207-FantasticFour/cs207-FinalProject.svg?branch=master)](https://travis-ci.com/IACS-CS-207-FantasticFour/cs207-FinalProject)
[![codecov](https://codecov.io/gh/IACS-CS-207-FantasticFour/cs207-FinalProject/branch/master/graph/badge.svg)](https://codecov.io/gh/IACS-CS-207-FantasticFour/cs207-FinalProject)

This project aims to deliver an Automatic Differentiation Software DeltaPi that will calculate derivatives efficiently and accurately, making it useful across a wide spectrum of applications. In this package, we will execute the forward mode of Automatic Differentiation. We will also provide an extension package HedgeDeltaPi that will calculate delta for European Call and Put options, allowing for efficient delta hedging.

Installable at:
<https://pypi.org/project/DeltaPI-FantasticFour/>
ODENet 0.0.5
A small example package

# ODENet model for combustion 

ODENetMoel example
```
from ODENet import ODENetModel
dim_input = x_train.shape[1]
dim_label = y_train.shape[1]


model = ODENetModel(
    dim_input=dim_input,
    dim_label=dim_label,
)
model.summary()

loss_type = "mse"
model.compile(loss=loss_type, optimizer="adam", metrics=["accuracy"])
model.fit(x_train,y_train)
```
ADPYNE-207 0.1.4
Automatic Differentiation Package for Python
Requires Python: >=3.6

[![Build Status](https://travis-ci.org/PYNE-AD/cs207-FinalProject.svg?branch=master)](https://travis-ci.org/PYNE-AD/cs207-FinalProject)

[![Coverage Status](https://codecov.io/gh/PYNE-AD/cs207-FinalProject/branch/master/graph/badge.svg)](https://codecov.io/gh/PYNE-AD/cs207-FinalProject)

# cs207-FinalProject

## Group No. 12

- 
  Paulina Toro Isaza

- Yaowei Li

- Nikhil Vanderklaauw

- Emma Li

## Installation

### Requirements

1. Ensure you can run Python from the command line. 
   - You can check this by running: `python --version`
2. Ensure you can run  pip form the command line. 
   - You can check this by running: `pip --version`

### Install the package

1. Using virtual environment

   - Create an environment with the command: `conda create --name env_name python`
   - Activating the environment: `conda activate env_name`
   - You can get out of the environment (deactivate it) by typing: `conda deactivate`

2. Install the package from PyPi and dependencies

   - Type  `pip install ADPYNE-207`
TitleRank 0.1.0
A small example package
Requires Python: >=3.6

# TitleRank

## 基于纯标题的新闻排序法
该算法包主要包含三个模块：
* 基于Word-weight的新闻排序法
* 基于Weight-PageRank的新闻排序法
* 基于相似文章的新闻排序法