semvec/pypi-packages huggingface dataset:
conjugate-bayes 0.0.1
Conjugate Bayesian linear regression and distribution models in Python..

### Conjugate Bayesian Models

Last update: June 2019.

---

Lightweight Python library implementing a few conjugate Bayesian models. For details on the derivations see [1].

```
pip3 install conjugate-bayes
```

We support the following:

#### To fit distribution models

- Beta-Bernoulli
- Gamma-Poisson
- Normal-Inverse-Gamma

#### To fit regression models

- Linear regression with Normal Inverse-Gamma prior
- Linear regression with Zellner's *g*-prior

#### Future work

- Dirichlet-Multinomial
- Normal-Inverse-Wishart

#### Usage

Below we show an example fitting a simple Bayesian linear regression with unknown beta and unknown variance.

```python
model = NIGLinearRegression(mu=np.zeros(2), v=100*np.eye(2), a=0.5, b=0.5)
model.fit(x_tr, y_tr)

sigma2 = model.get_marginal_sigma2()
beta = model.get_conditional_beta(sigma2=sigma2.mean())
```

The above example results in the following prediction intervals.

![ex_model](examples/ex.png)

For further details the `examples/` folder.

#### References

[1] P. D. Hoff, A First Course in Bayesian Statistical Methods (New York: Springer-Verlag, 2009).

#### License

This library is available under the MIT License.
http-api-sdk 2.3.0
扩展你的QQ/微信机器人用途，提供跨框架平台的PHP/Java/Python/NodeJS等编程语言SDK。https://github.com/ksust/HTTP--API

HTTP-API-SDK

============

**包含PHP/Java/Python/NodeJS四种编程语言SDK。**

`Github：https://github.com/ksust/HTTP--API <https://github.com/ksust/HTTP--API>`__

安装：pip install http-api-sdk

引入
::

    from httpapi.HTPSDK import *

    sdk = HTTPSDK.httpGet(request.body)

发布命令
::

    python setup.py sdist bdist_wheel --universal

    twine upload dist/*

Demo：
::

	#!/usr/bin/env python
	# -*-coding:utf-8-*-
	"""
	Demo Push
	HTTP-API Python SDK(Python2、python3)
	 * Created by PyCharm.
	 * User: yugao
	 * version 2.2.2
	 * Note: HTTPSDK for Python(适用于版本2.2.2插件):用于解析插件消息、构造返回数据，以及HTTP推送（发起HTTP请求）
	 * Contact: 开发者邮箱 admin@ksust.com
	"""

	from httpapi.HTTPSDK import *

	if sys.version_info.major == 2:
		reload(sys)  # python2请配置相应编码
		sys.setdefaultencoding('utf8')
		sys.setdefaultencoding('gb18030')

	push = HTTPSDK.httpPush("http://127.0.0.1:8080")
	print(push.getGroupList())
	print(push.sendPrivdteMsg('QQ', '你好'))

	forward = HTTPSDK.msgForwardPush('QQ', '授权码')
	print(forward.getGroupList())
	print(forward.getLoginQQ())
	print(forward.sendPrivdteMsg('QQ', '你好'))
	print(forward.getQQRobotInfo())
brom 1.0.1b9
Модуль, обеспечивающий взаимодействие с расширением «Бром» для 1С:Предприятие.
Requires Python: >=3.6

<h1>Бром-клиент для Python</h1>

Brom – это библиотека для Python, которая обеспечивает взаимодействие с расширением «Бром» для 1С:Предприятие.

Библиотека позволяет:

<ul>
<li>получать и редактировать данные объектов 1С (ссылочные объекты, константы, параметры сеанса);</li>
<li>получать выборки из коллекций с учетом сложных условий отбора и сортировки;</li>
<li>исполнять произвольные запросы на языке 1С, в том числе пакетные запросы и запросы с разметкой для построителя;</li>
<li>вызывать процедуры и функции 1С с возможностью передачи параметров и получения результата в естественном виде (без необходимости преобразования типов);</li>
<li>исполнять произвольные фрагменты кода на зыке 1С с возможностью передачи параметра и возврата результата вычисления;</li>
<li>получать данные о метаданных конфигурации с возможностью их частичной (постраничной) загрузки;</li>
<li>передавать в обоих направлениях данные как примитивных данных, так и сложные структурированные данные (ссылки, массивы, структуры, соответствия, таблицы значений, деревья, системные перечисления и многое другое).</li>
</ul>
bbcu.rvcu 1.0.1
Find RNA mutations and classify them by information form UMI (Unique Molecule Identifier)

**Edit a file, create a new file, and clone from Bitbucket in under 2 minutes**

When you're done, you can delete the content in this README and update the file with details for others getting started with your repository.

*We recommend that you open this README in another tab as you perform the tasks below. You can [watch our video](https://youtu.be/0ocf7u76WSo) for a full demo of all the steps in this tutorial. Open the video in a new tab to avoid leaving Bitbucket.*

---

## Edit a file

You’ll start by editing this README file to learn how to edit a file in Bitbucket.

1. Click **Source** on the left side.
2. Click the README.md link from the list of files.
3. Click the **Edit** button.
4. Delete the following text: *Delete this line to make a change to the README from Bitbucket.*
5. After making your change, click **Commit** and then **Commit** again in the dialog. The commit page will open and you’ll see the change you just made.
6. Go back to the **Source** page.

---

## Create a file

Next, you’ll add a new file to this repository.

1. Click the **New file** button at the top of the **Source** page.
2. Give the file a filename of **contributors.txt**.
3. Enter your name in the empty file space.
4. Click **Commit** and then **Commit** again in the dialog.
5. Go back to the **Source** page.

Before you move on, go ahead and explore the repository. You've already seen the **Source** page, but check out the **Commits**, **Branches**, and **Settings** pages.

---

## Clone a repository

Use these steps to clone from SourceTree, our client for using the repository command-line free. Cloning allows you to work on your files locally. If you don't yet have SourceTree, [download and install first](https://www.sourcetreeapp.com/). If you prefer to clone from the command line, see [Clone a repository](https://confluence.atlassian.com/x/4whODQ).

1. You’ll see the clone button under the **Source** heading. Click that button.
2. Now click **Check out in SourceTree**. You may need to create a SourceTree account or log in.
3. When you see the **Clone New** dialog in SourceTree, update the destination path and name if you’d like to and then click **Clone**.
4. Open the directory you just created to see your repository’s files.

Now that you're more familiar with your Bitbucket repository, go ahead and add a new file locally. You can [push your change back to Bitbucket with SourceTree](https://confluence.atlassian.com/x/iqyBMg), or you can [add, commit,](https://confluence.atlassian.com/x/8QhODQ) and [push from the command line](https://confluence.atlassian.com/x/NQ0zDQ).
imglyb-examples 0.1.1.dev0
Examples for imglyb.
Requires Python: >=3

# imglyb-examples

This is a repository of examples for [imglyb](https://github.com/imglib/imglyb).

## Dependencies
All dependencies will be installed automatically when installing `imglyb-examples` through conda.
 - [imglyb](https://github.com/hanslovsky/imglyb)
 - [numpy](http://www.numpy.org)
 - [scikit-image](https://github.com/scikit-image/scikit-image)

[imglyb](https://github.com/hanslovsky/imglyb) is available on conda or you can install it manually following the build instructions. All other depdendencies are available through your package manager, `pip`, or various anaconda channels.

## Install

### Manual
First, make sure that all dependencies are installed, then run
```bash
python setup.py install
```

### Conda
```bash
conda install -c hanslovsky imglyb-examples
```
If `imglyb` was not previously installed, re-source the current conda environment after installing to set the environment properly.

## Run
These examples are available.
```bash
# bash
# Basic visualization and application of Python and ImgLib2 filters
python -m imglyb_examples.butterfly
# Python implemented overlays for BigDataViewer
python -m imglyb_examples.bdv_hello_world
# Python implementation of a painting tool for BigDataViewer
python -m imglyb_examples.bdv_painter
# Embed BigDataViewer in Qt application (only Linux, and Qt and python-xlib dependencies)
python -m imglyb_examples.qt_awt
# Basic visualization of series of 2D NumPy arrays
python -m imglyb_examples.views_stack
```
I recommend starting your own code from these examples and extend as you need. On Linux and Windows you can run these commands like this, as well:
```bash
imglyb-examples.butterfly
imglyb-examples.bdv-hello-world
imglyb-examples.bdv-painter
mglyb-examples.qt-awt
imglyb-examples.views-stack
```
bigram-spam-classifier 0.0.6
A bigram approach for classifying Spam and Ham messages

**# bigram-spam-classifier <br />**
**# A bigram approach for classifying Spam and Ham messages**<br />

**# install with pip <br />**
pip install bigram-spam-classifier<br />

**# import in your python file <br />**
from bigram_spam_classifier import spamclassifier<br />

**# create an object of the classifier and pass your message as the parameter <br />**
classifier = spamclassifier.classifier("Customer service annoncement. You have a New Years delivery waiting for you. Please call 07046744435 now to arrange delivery")<br />

**# classify the message<br />**

cls = classifier.classify()<br />
print(cls)<br />

**# If returns 1 message is a Spam, if returns 0 message is a Ham <br />**

**# find the unigrams and bigrams in the message <br />**
unigrams = classifier.inputUnigrams<br />
print(unigrams)<br />
bigrams = classifier.inputBigrams<br />
print(bigrams)<br />

**# find the bigram probabilities of Spam and Ham <br />**
spam_probability = classifier.bigramPSpam<br />
print(spam_probability)<br />
ham_probability = classifier.bigramPHam<br />
print(ham_probability)<br />