semvec/pypi-packages huggingface dataset:
filesearch-0cch 0.0.1
Find files quickly on Windows (requires index)
Requires Python: >=3.8

# filesearch

Find files quickly on Windows (requires index)

Note: An administrator is required to run the program because it uses the USN Journal API.

``` python
import filesearch-0cch.filesearch as fs
obj = fs.filesearch()
obj.wait(1000) # Wait 1000 milliseconds and return true if indexing is complete
obj.search("kernel") # Search files
obj.result() # Get file list
```
vilha 0.0.2
ASGI server for amqp rpc protocol
Requires Python: <4.0,>=3.10

# Vilha

Rpc server inspired by nameko and aims to `extend` asgi protocol to allow use of asgi middlewares and starlette
with DI inspired by FastAPI

# Example 
```python
import asyncio
from vilha.server import Vilha
from vilha.starlette.applications import RpcRoute, Starlette
from vilha.di import Depends
import random

async def get_rnd():
    return random.randint(0,100)

async def test_method(rnd: Annotated[int, Depends(get_rnd)]):
    print("test_method")
    return rnd


st = Starlette(routes=[RpcRoute("test_method", test_method)])


async def main():
    await Vilha("test_service").run(st)


loop = asyncio.get_event_loop()
loop.create_task(main())

loop.run_forever()
```
xfilesearch 0.0.1
Find files quickly on Windows (requires index)
Requires Python: >=3.8

# filesearch

Find files quickly on Windows (requires index)

Note: An administrator is required to run the program because it uses the USN Journal API.

``` python
import filesearch
obj = filesearch.filesearch()
obj.wait(1000) # Wait 1000 milliseconds and return true if indexing is complete
obj.search("kernel") # Search files
obj.result() # Get file list
```
BGFS 1.1
A Biobjective Feature Selection neural network for High Dimensions, Low Sample data
Requires Python: >=3.8

Biobjective gradient descent for Feature Selection (BFS)
==========================================================

Code for BFS, a biobjective gradient descent feature selection method based on network sparsification. 

Installation
--------------
To install BFS from git, clone the project using the following command: 

::

    git clone https://forge.ibisc.univ-evry.fr/tissa/BFS/tree/master/BFS.git

To reproduce the conda environment with the required packages and appropriate versions,
use the following command: 

For Mac users: 
::

    conda env create -f BFS_mac.yml

For Linux users:
::

    conda env create -f BFS_linux.yml


To install BFS using pip:

:: 

    pip install BGFS

Usage
------
If downloaded using git, before using BFS, first go to the source files' directory using the following command:

::

    cd BFS/source


From there or when downloaded with pip, BFS can be loaded in a python script as any other module using the ``import`` command.
BFS can be used with the following commands:

::

    from BGFS.BFS import BFS

    model = BFS(n_attr, pref_vector1, pref_vector2, n_hidden, n_output)
    model.compile(init_optimizer, train_optimizer, init_learning_rate, train_learning_rate, metrics)
    model.fit(X_train, y_train, validation_data=(X_val, y_val))


Documentation
---------------
BFS documentation can be found on: https://bfss.readthedocs.io/

License
--------
BFS was created by Tina Issa. It is licensed under the terms
of the MIT license.