semvec/pypi-packages huggingface dataset:
mubelnet 1.0.0
Deep Bayesian unsupervised decoder networks. Use poisson or multinomial belief networks to cluster count data.
Requires Python: >=3.10

# Deep Bayesian unsupervised clustering
![multinomial belief network](./figures/multinomial_belief_net.svg "multinomial belief net")
This repository contains deep Bayesian unsupervised clustering models. In particular,
the multinomial belief network [1] and Zhou–Cong–Chen's Poisson gamma belief network [2].

## Installation
You can pip install this package by running:
```bash
pip3 install mubelnet
```

## Quick start guide

Let's create a two-layer multinomial belief network (like in the figure) with one and two hidden units, respectively. The kernel function, that advances the Markov chain by a single (Gibbs) step looks as follows:
```python
import haiku as hk
import jax
from mubelnet.nets import MultinomialBelief
from mubelnet.mcmc import sample_markov_chain

# Set up training data.
X_train = ...
n_features = X_train.shape[1]

@hk.transform_with_state
def kernel():
    """Advance Markov chain of belief net by one step."""
    n_hidden_units = (1, 2)
    model = MultinomialBelief(n_hidden_units, n_features)
    model(X_train)  # Do one Gibbs sampling step.

params, trace = sample_markov_chain(
    jax.random.key(42),
    kernel=kernel,
    n_samples=100,
    n_chains=2,
    n_burnin_steps=100,
)
```

## Documentation
Reference docs can be found on: https://hylkedonker.gitlab.io/mubelnet.

## Example handwritten digits
A more complete example, that shows how to train a network on the UCI ML hand-written digits datasets, see the [digits jupyter notebook](examples/digits.ipynb).

## Meta-mutational signatures
You can browse the meta-mutational signatures (based on COSMIC v3.3)  ]and their tri-nucleotide spectra in the [meta-signature overview](examples/mutational-signatures/README.md).

### Download
The weights of the meta-signatures and the hyperparameters are available in comma-separated format:
- [meta_signatures.csv](examples/mutational-signatures/meta_signatures.csv)
- [hyperparam_c.csv](examples/mutational-signatures/hyperparam_c.csv)
- [hyperparam_r.csv](examples/mutational-signatures/hyperparam_r.csv)


## References
[1] Donker _et al_. "[Multinomial belief networks for healthcare data](https://arxiv.org/abs/2311.16909)", Proceedings of Machine Learning Research 25:1–22, 2024 (2024).

[2]: Zhou, Cong, Chen. "[Augmentable gamma belief networks.](https://www.jmlr.org/papers/volume17/15-633/15-633.pdf)", J. Mach. Learn. Res. 17.1, 5656-5699 (2016).

## License
The code open sourced under the MIT license (see [LICENSE.txt](LICENSE.txt)).
audiogen-agc 0.1.1
Audiogen Codec
Requires Python: >=3.9

# Audiogen Codec (agc)

We are announcing the open source release of Audiogen Codec (agc) 🎉. A low compression 48khz stereo neural audio codec for general audio, optimizing for audio fidelity 🎵.

It comes in two flavors:

- agc-continuous 🔄
KL regularized, 32 channels, 100hz.
- agc-discrete 🔢
24 stages of residual vector quantization, 50hz.

AGC (Audiogen Codec) is a convolutional autoencoder based on the DAC architecture, which holds SOTA 🏆. We found that training with EMA and adding a perceptual loss term with CLAP features improved performance. These codecs, being low compression, outperform Meta's EnCodec and DAC on general audio as validated from internal blind ELO games 🎲.

We trained (relatively) very low compression codecs in the pursuit of solving a core issue regarding general music and audio generation, low acoustic quality and audible artifacts, which hinder industry use for these models 🚫🎶. Our hope is to encourage researchers to build hierarchical generative audio models that can efficiently use high sequence length representations without sacrificing semantic abilities 🧠.


This codec will power Audiogen's upcoming models. Stay tuned! 🚀

![ELO Image](assets/elo.png)

# Installation
```sh
pip install audiogen-agc
```

# Usage
```python
from agc import AGC

agc = AGC.from_pretrained("Audiogen/agc-continuous") # or "agc-discrete"

audio = torch.randn(1, 2, 480000) # 48khz stereo

z = agc.encode(audio) # (1, 32, 6000) or (1, 24, 3000)

reconstructed_audio = agc.decode(z) # (1, 2, 480000)
```

# Misc

Example colab: https://colab.research.google.com/drive/1MXeBYMY-dZ3Yas-5rXzggMONIlDDQ5VG#scrollTo=9mtfSc-r4dkn (credit: Christoph from LAION)

# Examples

https://audiogen.notion.site/Audiogen-Codec-Examples-546fe64596f54e20be61deae1c674f20
currentweatherapi 0.0.1
get current temperature from provider
Requires Python: >=3.6

How to use:

from currenttemperature.api import OpenMeteo, OpenWeather

open_meteo_obj = OpenMeteo(latitude,longitude)
print(open_meteo_obj.get_weather_temp())

open_weather_obj = OpenWeather(latitude,longitude,token_id)
print(open_weather_obj.get_weather_temp())