semvec/pypi-packages huggingface dataset:
primate 0.2.0
Primate AI: Tools for decision-making and NLP.
Requires Python: >=3.7

Primate AI: Tools for decision-making and NLP
=============================================

Primate AI is a Python package that bridges decision-making and natural language processing, inspired by intelligent behavior.

Features
--------

- **Decision-Making Models**:
  - Game-theoretic strategies (e.g., tit-for-tat).
  - Multi-agent simulations.
  - Reinforcement learning agents.

- **Natural Language Processing**:
  - Sentiment analysis.
  - Tokenization, text similarity, and vectorization.
  - Language evolution simulation.

- **Utility Tools**:
  - Logging and configuration management.
  - Performance profiling for PyTorch models.
  - Device helpers for GPU-accelerated computations.

Installation
------------

You can install the package via pip:

.. code-block:: bash

   pip install primate

Quick Start
-----------

Here are a few examples to get started:

### 1. Decision Agent

Simulate decision-making with game-theoretic strategies:

.. code-block:: python

   from primate.decision.agent import DecisionAgent

   # Create an agent with a strategy
   agent = DecisionAgent(strategy="tit_for_tat")
   decision = agent.decide(opponent_action="cooperate")
   print(f"Agent decided to: {decision}")

### 2. Sentiment Analysis

Analyze the sentiment of text data:

.. code-block:: python

   from primate.nlp.sentiment import SentimentAnalyzer

	# Initialize sentiment analyzer
	analyzer = SentimentAnalyzer()
	sentiment = analyzer.analyze("I love bananas!")
	print(f"Sentiment: {sentiment}")  # Output: "positive"

### 3. Tokenization with Transformers

Tokenize text using pre-trained transformer models:

.. code-block:: python

   from primate.nlp.tokenizer import Tokenizer

   # Initialize tokenizer
   tokenizer = Tokenizer(model_name="bert-base-uncased")
   tokenized = tokenizer.tokenize(["I love Python!", "NLP is fun."])
   print("Input IDs:", tokenized["input_ids"])
   print("Attention Masks:", tokenized["attention_mask"])

### 4. Model Profiling

Profile PyTorch models for parameter and operation details:

.. code-block:: python

   from primate.utils.helpers import PerformanceHelper
   import torch.nn as nn

   # Define a simple model
   class SimpleModel(nn.Module):
       def __init__(self):
           super(SimpleModel, self).__init__()
           self.linear = nn.Linear(10, 5)

       def forward(self, x):
           return self.linear(x)

   model = SimpleModel()
   profile = PerformanceHelper.profile_model(model, input_size=(1, 10))
   print(profile)

License
-------

This project is licensed under the MIT License.
cell-bin 1.5.6.dev0
A framework for generating single-cell gene expression data
Requires Python: ==3.8.*

<div align="center">
  <img src="docs/images/cellbin.png"><br/>
  <h4 align="center">
    A framework for generating single-cell gene expression data
  </h4>
</div>


[Release Notes](CHANGES.txt)

# Installation
Both on Windows or Linux (CentOS, Ubuntu), just
```shell
pip install -r requirements.txt
```

Pyvips linux installation
```shell
# linux environ install pyvips using conda
conda install --channel conda-forge pyvips
```

Pyvips windows install vips from [vips](https://github.com/libvips/libvips/releases)
```shell
pip install --user pyvips==2.2.1
```

```python
# add vips path when running python script
import os
vipsbin = r'c:\vips-dev-8.13\bin'
os.environ['PATH'] = vipsbin + ';' + os.environ['PATH']
```

# Develop mode
```shell
git clone https://gitlab.genomics.cn/biointelligence/implab/stero-rnd/cellbin/algorithms/cellbin.git
conda activate env # activate your env
python setup.py develop --no-deps  # develop mode, if you already have deps installed
```

# Usage
* API doc
* [How to get weights?](docs/weights.md)


## <div align="center">contribute</div>
We love your input! We want to make contributing to cellbin as easy and transparent as possible. Please see,
- When you need to change other people's modules, remember to note the change information in the change location. like,
``` a = 0  #  parameter value to zero [by jack]```

Thank you to all our contributors!


## <div align="center">Contact</div>

For cellbin bugs and feature requests please visit
autoAnSign 0.0.1.post1
安卓签到
Requires Python: >=3.6

# autotask_android

基于[airtest_mobileauto](airtest_mobileauto)的安卓自动化任务脚本

![GitHub forks](https://img.shields.io/github/forks/MobileAutoFlow/autoAnSign?color=60c5ba&style=for-the-badge)![GitHub stars](https://img.shields.io/github/stars/MobileAutoFlow/autoAnSign?color=ffd700&style=for-the-badge)


## 脚本开发环境说明
* 本脚本基于分辨率`960x540`, dpi`160`的安卓模拟器开发
* dpi或者分辨率不同很容易识别失败。
* 有的模拟器打开特定APP会闪退

安装

```
python -m pip install autoAnSign --upgrade
```

## 当前开发项目
### 基于url的签到
* `web_url`,直接打开特定url,实现签到, **适配任意的安卓设备**
* 将url存储到`web_url.txt`,下面是我常用的url
```
none
```

### 基于浏览器的签到
* *注: 本脚本于via-5.9.0测试通过, 需开启桌面模式、全屏、浏览器标识(windows/chrome),其他浏览器自行替换图片资源*
* 只能在境内的模拟器/容器内执行, 这些网站在不同地区显示的内容不同.
* `web_ablesci`, [科研通](https://www.ablesci.com/)每日签到
* `web_muchong`, [小木虫](https://muchong.com/bbs/)每日签到

### 基于app的签到
* `app_alicloud`, 阿里云盘每日签到(横屏版960x540)




## 运行
* 创建`tag.txt`则开启领取`tag.py`中定义的礼包

```
python run.py config.win.yaml
```

我的配置文件 `config.win.yaml`
```
mynode: 1
BlueStackdir: C:\Program Files\BlueStacks_nxt
BlueStack_Instance: {1: "Pie64_2"}
BlueStack_Windows: {1: "autotask"}
LINK_dict: {1: "Android:///127.0.0.1:5575"}
figdir: assets
logfile: {1: "result.1.txt"}
prefix: "autotask"
```


## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=MobileAutoFlow/autoAnSign&type=Date)](https://star-history.com/#MobileAutoFlow/autoAnSign&Date)