semvec/pypi-packages huggingface dataset:
Afine 0.0.2
Afine is a gradient based PyTorch optimizer.
Requires Python: >=3.8
Keywords: affine, Afine, PyTorch, optimizer, AI, neural, NN

# Afine Optimizer
An optimizer that is superior to Adam. Based on @PyTorch

The training is more stable and reaches a more **elegant** optimal solution faster.

![image](rendering.png)
![image](renderingZoom.png)

## 思路说明
把每组参数看作高维向量. 优化步骤被分割为3个过程: 旋转、缩放、平移. 使用三个学习率分别控制三个过程的调整量. 

1.旋转
- 计算梯度对参数向量方向的影响.
- 根据旋转的稳定程度调整旋转的作用强度. 
- 使用近似旋转改变参数向量方向而不改变模长.

2.缩放
- 计算梯度对参数向量模长的影响.
- 根据模长改变的稳定程度调整缩放的作用强度.
- 权重衰减作用于这一步: 使用ArcSinh函数对向量放大带来阻力而促进向量缩小.
- 应用缩放变换.

3.平移
- 旋转和缩放对零向量无作用, 为使零初始化和模长较小的向量也能被训练, 需加入平移.
- 此处的平移带有惯性,且平移量与梯度稳定程度有关.

## Demo程序
demo程序在test.py文件中,直接运行即可得到此页面中Affine与RAdam的对比图.(依赖visdom)
demo中构建了一个神经网络用简陋的方式对一个包含动态随机参数的公式生成的周期性曲线进行预测.

## Install
```bash
pip install afine
```

## Use
```python
from Afine import Afine
...
optimizer=Affine(moduel.parameters(),lr_rotate = lr_rotate,lr_scale = lr_scale,lr_base = lr_base,weight_decay=weight_decay)
```

## HomePage
<https://github.com/PsycheHalo/Afine/>
Topsis-Ayush-102203119 1.0.2
A Python package for implementing TOPSIS

---

# **Topsis-Ayush-102203119**

A Python package for implementing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. This method is used for multi-criteria decision-making.

---

## **Installation**

Install the package using `pip`:

```bash
pip install Topsis-Ayush-102203119
```

---

## **Usage**

### **Command-Line Interface**

After installation, you can use the `topsis` command to calculate rankings for your dataset.

#### **Command Syntax**
```bash
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

- `<InputDataFile>`: Path to the input CSV file.
- `<Weights>`: Comma-separated list of weights for each criterion (e.g., `1,1,1,2`).
- `<Impacts>`: Comma-separated list of impacts for each criterion (`+` for positive, `-` for negative).
- `<ResultFileName>`: Path to save the output CSV file.

---

### **Input File Format**
- The input file must be a CSV with at least 3 columns.
- The **first column** should contain the names of the objects (e.g., M1, M2, ...).
- The remaining columns should contain **numeric criteria values**.

#### Example Input (`data.csv`):
```csv
Object,Criteria1,Criteria2,Criteria3,Criteria4
M1,250,16,12,5
M2,200,16,8,3
M3,300,32,16,4
M4,275,16,8,4
M5,225,32,16,2
```

---

### **Example Usage**
1. Prepare the input CSV file (e.g., `data.csv`).
2. Run the following command:

```bash
topsis data.csv 1,1,1,2 +,+,-,+ result.csv
```

3. The result will be saved in the specified output file (e.g., `result.csv`).

#### Example Output (`result.csv`):
```csv
Object,Criteria1,Criteria2,Criteria3,Criteria4,Topsis Score,Rank
M1,250,16,12,5,0.75,2
M2,200,16,8,3,0.45,4
M3,300,32,16,4,0.85,1
M4,275,16,8,4,0.55,3
M5,225,32,16,2,0.30,5
```

---

## **Requirements**

This package requires the following Python libraries:
- `numpy`
- `pandas`

These dependencies are automatically installed with the package.

---

## **License**

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

---
cs1440-arena 0.3.5
A Go game arena for CS410 bot competitions
Requires Python: >=3.8

# CS410 Arena

A Go game arena for running bot competitions in CS410.

## Running the Arena

Given that the submission files are in the `submissions` director (presumably from the autograder script),  
here's what you need to do to run the arena:  
Note: Some of this is still in development, so the instructions may change as I refine this in the future.  

1. CD into submissions/shared and build the base Dockerfile as cs410base by running:  

    ```bash
    docker build -f BaseDockerfile -t cs410base .
    ```

2. Update the corresponding config in go_config to match what settings you want to run.

3. CD back out to the root of the directory and run the following command:  

    ```bash
    docker network create tournament
    docker compose --env-file configs/go_config.env up
    ```

4. The arena should now be running. You can observe the logs in logs and the results in meta after it's done. This will take up a lot of image space depending on the number of submissions so make sure to clean up docker images after you're done.