semvec/pypi-packages huggingface dataset:
dmt-learn 0.0.81
dmt learn package
Requires Python: >=3.10

# DMT-HI: MOE-based Hyperbolic Interpretable Deep Manifold Transformation for Unspervised Dimensional Reduction

(Our Paper)[https://arxiv.org/abs/2410.19504]

The code includes the following modules:
* Datasets (Mnist, CIFAR-10, HCL, 20newsgroups)
* Training for DMT-HI
* Evaluation metrics 
* Visualisation
* Explainable Analyses

## Configurating python environment

We recommend using conda for configuration. You can refer to our `install-env.sh` to configure the environment.

```bash
conda create -n nml python=3.9
conda activate nml
bash install_env.sh
```

## Dataset

This project utilizes several datasets, including `20NG`, `HCL`, `MNIST`, and `CIFAR-10`. Please follow the instructions below to understand the dataset structure and usage.

### 1. 20NG Dataset
The `20NG` dataset is already included in this GitHub repository.

### 2. HCL Dataset
The `HCL` dataset must be manually downloaded from the following link: [Download HCL Dataset](https://gofile.me/7794C/rSolqImMJ). Once downloaded, please place the file `HCL60kafter-elis-all.h5ad` into the `data_path/` directory.

### 3. MNIST and CIFAR-10 Datasets
The `MNIST` and `CIFAR-10` datasets do not require manual download. These datasets will be automatically downloaded upon the first execution of the project.
Please ensure that you have a stable internet connection during the first run to automatically download these datasets.

## Run DMT-HI

You can run DMT-HI with a single line of code to get latent embedding.

### Minimun replication

Running minimal replication can be done with the following command:

```bash
python main.py fit -c=conf_new/nml4/mnist.yaml
```

## Analyses

After successfully running DMT-HI for the first time, you can use the built-in analyzer to further explore the results. Follow the steps below to configure and run the analyzer.

### Steps to Use the Analyzer

1. **Open `dash_main.py` File**:  
   Navigate to the file `dash_main.py` in the project directory.

2. **Modify the Model and Image Paths**:  
   In `dash_main.py`, update the following lines to match the model and image outputs generated by DMT-HI:
   - **Line 14**: Set the path to the saved model generated by DMT-HI.
   - **Line 17**: Set the path to the saved images generated during the model run.

3. **Run the Analyzer**:  
   After making the necessary changes, run the following command to start the analyzer:

   ```bash
   python dash_main.py
   ```

4. **Access the Analyzer**:  
   Once the script is running, it will return a local URL. Open the URL in your web browser to access the DMT-HI Analyzer.

Through this web-based interface, you can visualize latent embeddings.
viberandom 0.1.0
Random number generation library that uses Gemini to generate numbers with vibes
Requires Python: >=3.10

# VibeRandom 🎲

AI-powered random number generation using Gemini Flash API. Generate numbers that match your vibe!

## Installation

Install the package:
```bash
pip install viberandom
```

Set your Gemini API key as an environment variable:
```bash
export GEMINI_API_KEY=your_key_here
```

## Usage

```python
from viberandom import viberandom, viberandom_single

# Generate lucky numbers
lucky_nums = viberandom(1, 100, "lucky", count=5)
print(lucky_nums)  # e.g., [7, 77, 13, 888, 21]

# Generate a single chaotic number
chaos = viberandom_single(1, 1000, "chaotic")
print(chaos)  # e.g., 347

# Generate peaceful numbers
peaceful = viberandom(1, 50, "peaceful", count=3)
print(peaceful)  # e.g., [10, 20, 30]

# Generate energetic numbers
energy = viberandom(50, 100, "energetic", count=4)
print(energy)  # e.g., [89, 97, 83, 91]
```

## Vibe Ideas

Try these vibes for different number personalities:

- **lucky** - Numbers that feel fortunate (7, 77, 888, etc.)
- **chaotic** - Unpredictable, irregular numbers
- **peaceful** - Round, harmonious numbers
- **energetic** - High, dynamic numbers
- **mysterious** - Numbers with enigmatic qualities
- **cozy** - Warm, comfortable feeling numbers
- **dramatic** - Bold, striking numbers
- **vintage** - Numbers that feel classic or retro
- **cosmic** - Space-age, otherworldly numbers
- **spicy** - Numbers with kick and attitude

## Parameters

- `min_value` (int): Minimum value for random numbers (default: 1)
- `max_value` (int): Maximum value for random numbers (default: 100)
- `vibe` (str): The feeling/vibe for the numbers (default: "random")
- `count` (int): How many numbers to generate (default: 1)

## Fallback Behavior

If the AI fails to generate numbers (network issues, invalid API key, etc.), VibeRandom automatically falls back to regular random number generation to ensure your code never breaks.

## Testing

```bash
pytest tests/
```

## Dependencies

- google-generativeai
- pydantic
- typing-extensions

⚠️ **Requires Gemini API key. Experimental project - not for production use.**

## Fun Examples

```python
# Generate lottery numbers with "millionaire" vibes
lottery = viberandom(1, 69, "millionaire", count=6)

# Get a spooky number for Halloween
spooky = viberandom_single(13, 666, "spooky")

# Generate zen meditation numbers
zen = viberandom(1, 108, "zen", count=3)

# Create birthday party numbers
party = viberandom(1, 31, "celebration", count=1)
```

Have fun with AI-powered randomness! 🎉
Processamento-de-imagem-leonardo 0.0.2
Image processing package using skimage
Requires Python: >=3.8

# image_processing

description
The package image_processeing is used to:
    processing:
        - Histogram matching
        - Strutural similarity
        - Resize image
    Utils:
        - Read image
        - Save image
        - Plot image
        - Plot result
        - Plot histogram

## Instalation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

'''bash
pip install image_processing


## Author
Leonardo JosÃ©