semvec/pypi-packages huggingface dataset:
G2S 0.99.0
G2S Python interface
Requires Python: >=3.9

# G2S Package description

This package containe the interfaces for comunicating with the G2S server. G2S is a flexible MPS (Multiple Point Statistics) framework that provide QS (Quantile Sampling) and NDS (Narrow Distribution Selection).

For more infromation please visite [G2S website](https://gaia-unil.github.io/G2S/)
Levenshtein 0.27.3
Python extension for computing string edit distances and similarities.
Requires Python: >=3.10

# Levenshtein

<p>
  <a href="https://github.com/rapidfuzz/Levenshtein/actions">
    <img src="https://github.com/rapidfuzz/Levenshtein/workflows/Build/badge.svg"
         alt="Continuous Integration">
  </a>
  <a href="https://pypi.org/project/levenshtein/">
    <img src="https://img.shields.io/pypi/v/levenshtein"
         alt="PyPI package version">
  </a>
  <a href="https://www.python.org">
    <img src="https://img.shields.io/pypi/pyversions/levenshtein"
         alt="Python versions">
  </a>
  <a href="https://rapidfuzz.github.io/Levenshtein">
    <img src="https://img.shields.io/badge/-documentation-blue"
         alt="Documentation">
  </a>
  <a href="https://github.com/rapidfuzz/Levenshtein/blob/main/LICENSE">
    <img src="https://img.shields.io/github/license/rapidfuzz/Levenshtein"
         alt="GitHub license">
  </a>
</p>

## Introduction
The Levenshtein Python C extension module contains functions for fast
computation of:

* Levenshtein (edit) distance, and edit operations
* string similarity
* approximate median strings, and generally string averaging
* string sequence and set similarity

## Requirements
* Python 3.10 or later

## Installation
```bash
pip install levenshtein
```

## Documentation

The documentation for the current version can be found at [https://rapidfuzz.github.io/Levenshtein/](https://rapidfuzz.github.io/Levenshtein/)

## Support the project

If you are using Levenshtein for your work and feel like giving a bit of your own benefit back to support the project, consider sending us money through GitHub Sponsors or PayPal that we can use to buy us free time for the maintenance of this great library, to fix bugs in the software, review and integrate code contributions, to improve its features and documentation, or to just take a deep breath and have a cup of tea every once in a while. Thank you for your support.

Support the project through [GitHub Sponsors](https://github.com/sponsors/maxbachmann) or via [PayPal](https://www.paypal.com/donate/?hosted_button_id=VGWQBBD5CTWJU):

[![](https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif)](https://www.paypal.com/donate/?hosted_button_id=VGWQBBD5CTWJU).


## License

Levenshtein is free software; you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the Free
Software Foundation; either version 2 of the License, or (at your option)
any later version.

See the file [LICENSE](https://github.com/rapidfuzz/Levenshtein/blob/main/LICENSE) for the full text of GNU General Public License version 2.
squarem-jaxopt 0.1.4
SQUAREM accelerator for JAXopt solvers
Requires Python: >=3.11


[![PyPI version](https://img.shields.io/pypi/v/squarem-JAXopt.svg)](https://pypi.org/project/squarem-JAXopt/)
[![Documentation](https://img.shields.io/badge/docs-latest-blue.svg)](https://esbenscriver.github.io/squarem-JAXopt/)
[![CI](https://github.com/esbenscriver/squarem-JAXopt/actions/workflows/ci.yml/badge.svg)](https://github.com/esbenscriver/squarem-JAXopt/actions/workflows/ci.yml)
[![CD](https://github.com/esbenscriver/squarem-JAXopt/actions/workflows/cd.yml/badge.svg)](https://github.com/esbenscriver/squarem-JAXopt/actions/workflows/cd.yml)


# squarem-JAXopt
[JAX](https://github.com/jax-ml/jax) implementation of the SQUAREM accelerator for solving fixed-point equations, originally proposed by [Du and Varadhan (2020)](https://doi.org/10.18637/jss.v092.i07). 

The SQUAREM accelerator is implemented using [JAXopt](https://github.com/google/jaxopt), enabling efficient automatic differentiation of the fixed-point equations via the implicit function theorem (see [Blondel et al., 2022](https://arxiv.org/abs/2105.15183) for details).

## Installation

```bash
pip install squarem-jaxopt
```

## Usage

```python

import jax
import jax.numpy as jnp
from jax import random

from jaxopt import FixedPointIteration, AndersonAcceleration
from squarem_jaxopt import SquaremAcceleration

# Increase precision to 64 bit
jax.config.update("jax_enable_x64", True)

N = 100_000

a = random.uniform(random.PRNGKey(111), (N, 1))


def fun(x: jax.Array) -> jax.Array:
    y = (1 - a) + a * jnp.cos(x)
    return y


initial_guess = jnp.zeros_like(a)

fxp_none = FixedPointIteration(fixed_point_fun=fun, verbose=False)
result_none = fxp_none.run(initial_guess)

fxp_anderson = AndersonAcceleration(fixed_point_fun=fun, verbose=False)
result_anderson = fxp_anderson.run(initial_guess)

fxp_squarem = SquaremAcceleration(fixed_point_fun=fun, verbose=False)
result_squarem = fxp_squarem.run(initial_guess)

print("\n" + "="*60)
print("ALGORITHM COMPARISON TABLE")
print("="*60)
print(f"{'Algorithm':<25} {'Iterations':<12} {'Func Evals':<12} {'Error':<12}")
print("-"*60)
print(f"{'FixedPointIteration':<25} {result_none.state.iter_num:<12} {result_none.state.num_fun_eval:<12} {result_none.state.error:<12.2e}")
print(f"{'AndersonAcceleration':<25} {result_anderson.state.iter_num:<12} {result_anderson.state.num_fun_eval:<12} {result_anderson.state.error:<12.2e}")
print(f"{'SquaremAcceleration':<25} {result_squarem.state.iter_num:<12} {result_squarem.state.num_fun_eval:<12} {result_squarem.state.error:<12.2e}")
print("="*60)

```