semvec/pypi-packages huggingface dataset:
vyGeneric 0.0.2
Package for creating generic objects with transparent 'repr'
Requires Python: >=2.6

# vyGeneric Library

### vyGenericObjectRepr
`from vyGeneric import vyGenericObjectRepr`
* vyGenericObjectRepr(object) will return nice printable 'repr' for your objects
* If you inherit your class, say 'MyClass', from vyGeneric, then any object of 
  MyClass, say 'myObject', prints all its hidden info when you call 
  print(myObject)

### vyGeneric
`from vyGeneric import vyGeneric`
* A generic class which uses vyGenericObjectRepr for repr

### vyGenericDict
`from vyGeneric import vyGenericDict`
* A descendant of vyGeneric which can store items like a dictionary

### vyGenericObjectRepr
`from vyGeneric import vyGenericArray`
* A descendant of vyGeneric which can store items like a list
classic-FindS-safir72347 0.0.1
Find-S algorith finds the most specific hypothesis that fits all the positive examples.
Requires Python: >=3.0

Find-S Algorithm
===================


Find-S algorithm finds the most specific hypothesis that fits all the positive examples.

----------
Table of contents
-------------

[TOC]

<i class="icon-file"></i> Documentation
-------------

1.  Install the package
>  pip install classic_FindS

2. Import the library
>  from classic_FindS import FindS

3. Create an object for FindS class
> fs = FindS()

4. Fit your Training Set to the model
> fs.fit(X_train, y_train)

5. Predict your Test Set results
> y_pred = fs.predict(y_test)

----------



Example Code
-------------

1. Import the dataset and Preprocess
> import numpy as np
import pandas as pd
dataset = pd.read_csv('Covid-19_Data.csv')
result = {'Yes':1, 'No':0}
dataset['Covid_19'] = dataset['Covid_19'].map(result)
X = dataset.iloc[:, 0:5].values
y = dataset.iloc[:, -1].values

from sklearn.model_selection import KFold
kf = KFold(n_splits=10)
for train_index, test_index in kf.split(X,y):
	X_train, X_test = X[train_index], X[test_index]
	y_train, y_test = y[train_index], y[test_index]

2. Use the Find-S Library
>from classic_FindS import FindS
fs = FindS()            
S_hypothesis = fs.fit(X_train, y_train)
print("Specific Hypothesis : ", S_hypothesis)
y_pred = fs.predict(X_test) 


----------



Footnotes
-------------

You can find the code at my [Github](https://github.com/safir72347/ML-FindS-PyPi).



Connect with me on Social Media
-------------

[https://www.github.com/safir72347](www.github.com/safir72347)
[https://www.linkedin.com/in/safir72347/](https://www.linkedin.com/in/safir72347/)
[https://www.instagram.com/safir_12_10/](https://www.instagram.com/safir_12_10/)
redis-sentinel-kael-k 1.0.0
Redis connection fully managed by sentinel
Requires Python: >=3

# Python Redis Sentinel

Given a Redis + Sentinel service, this package allow you to connect simultaneously to both the master node
and to the slave nodes, transparently balancing the connection used to the user.

## Scope
The redis packages doesn't expose a tool to automatically choose between the master node and the slaves ones.
With this package, you get an object that automatically detect when to use the master one and when to connect to the slaves.

This is useful for the scenarios when you need to expose a Redis connection, you need to execute read and write operation,
but you want also take advantage of the read only nodes


## Mechanism
The automatic selection between master and slaves is detected by the Redis command executed:
into the class, there is an attribute (`REPLICAS_COMMANDS`) which contain the list of Redis' commands that will be executed on the slaves connection pool.
 
Those commands which are not listed in `REPLICAS_COMMANDS` are executed on the master node.
 
The main idea is to use the slave for the read operations (such as GET, KEYS ecc...) and to use the master just for the writes.

## Usage

Instantiate `RedisSentinel` passing a `redis.sentinel.Sentinel` instance, the service name,
and every connection parameter you would pass to the `redis.Redis` constructor (excepts obviously the host and the port, which are given by Sentinel)