semvec/pypi-packages huggingface dataset:
pygat 202411.1
GAT: Generative Attack Toolbox
Requires Python: >=3.10

<div align="center">

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  <source media="(prefers-color-scheme: light)" srcset="https://terrytengli.com/_fig/gat_light.jpeg">
  <img alt="GAT logo" src="https://terrytengli.com/_fig/gat_dark.jpeg" width="50%" height="50%">
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GAT: Generative Attack Toolbox.

<h3>

[Homepage](https://terrytengli.com/GAT/) | [Documentation](https://terrytengli.com/GAT/)

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</div>

---

## Get Start

1. Install from pypi.org.

```
pip install pygat
```

2. Install from source code.

```
git clone https://github.com/l1teng/GAT.git
cd GAT; pip install -e .
```

## Support Attacks

- [ ] [CDA-attack]()
- [ ] [CDA-attack]()
- [x] [AIM-attack](examples/aim_attack.py)
infonce 0.3
Pytorch-based InfoNCE loss for self-supervised learning
Requires Python: >=3.6

# InfoNCE Loss - PyTorch Implementation

The **InfoNCE loss** (Information Noise-Contrastive Estimation) is commonly used in contrastive learning to maximize the similarity between positive pairs while minimizing it between negative pairs. This repository provides a PyTorch implementation supporting both unsupervised and supervised modes.

## Classes

- **InfoNCE**: Positive pairs are generated by using two independent augmentations of the same batch.
- **SupervisedInfoNCE**: Positive pairs are defined using class labels, ensuring that negative pairs are not from the same class.

## Installation

To install the package , run:

```bash
pip install infonce
```

## Usage

### Unsupervised InfoNCE

```python
from infonce import InfoNCELoss

# Example usage
loss_fn = InfoNCELoss(temperature=0.07)
# augmentation1 and augmentation2 are two different augmentations of the same batch
features1 = model(augmentations1) 
features2 = model(augmentations2) 
features = torch.stack([features1, features2], dim=0)
loss = loss_fn(features)
```

### Supervised InfoNCE

```python
from infonce import SupervisedInfoNCELoss

# Example usage
loss_fn = SupervisedInfoNCELoss(temperature=0.07)
loss = loss_fn(features, labels)
```

## Contributing

Contributions are welcome! Please open an issue or submit a pull request.

## License

This project is licensed under the MIT License.

## Acknowledgements

This implementation is inspired by the original InfoNCE loss paper and various open-source implementations.
numtools 3.1.0
numeric-gmbH toolbox
Requires Python: <3.12,>=3.10

========
numtools
========

numeric-gmbH toolbox

Packages
--------

* ``csyslib``: basic coordinates transformations
* ``intzip``: convert sequence of integers into human-friendly string (and vice-versa)
* ``serializer``: mix in handling serialization to msgpack and json.
* ``vgextended``: extension of ``vg`` built on top of ``vg`` and ``numpy``
my-datetime-project 0.1.2
A simple Python project to print current date and time

# My Time Package

A simple package to get the current time.

## Installation

```
pip install my_datetime_project
```

## usage
```
from my_datetime_project import print_current_time
print(print_current_time())
# or
import my_datetime_project 
print(my_datetime_project.pct())

```