semvec/pypi-packages huggingface dataset:
evolutionary-optimization-algorithm 3.0.5
A generic evolutionary algorithm for function optimisation.
Requires Python: >=3.8,<4.0

# Evolutionary-Optimization
A generic evolutionary algorithm for function optimization.

## Introduction
This package allows the user to optimise a function using an evolutionary algorithm.
An [evolutionary algorithm](https://en.wikipedia.org/wiki/Evolutionary_algorithm) 
uses the principles of evolution to find optimal solutions.

## Using the Package
### Getting Started 
To get started with this package install this package:

```bash
pip install evolutionary-optimization-algorithm
```

### Running Experiments
To run the code type the following in your terminal. The default experiment is a 
simple optimization of the $x^{2}$ using integers.
```bash
run_evolution
```
The parameters used for the run can be edited within the main.py file.

### Personalising Experiments
To personalise your experiment you can either use the prebuilt phenotypes and genotypes using our interface,
or you can build your own. 
To do so, you simply need to create a new phenotype / genotype class that 
inherits from the corresponding abstract class and implement the methods to suit your needs.
You can mimic the structure of the main script to run your own experiments, like so:
```python 
    genotype_class = Genotype.get_genotype(Genotypes.<your_phenotype>)
    phenotype_class = Phenotype.get_phenotype(Phenotypes.<your_genotype>)
    fitness_function_class = FitnessFunction.get_fitness_function(FitnessFunctions.<your_fitness_function>)
    fitness_function_instance = fitness_function_class()

    evolutionary_algorithm = Evolution(
        phenotype=phenotype_class(genotype_class()),
        number_of_individuals=<desired_number_of_individuals>,
        number_of_generations=<desired_number_of_generations>,
        fitness_function=fitness_function_instance,
        ratio_of_elite_individuals=<desired_elitism_ratio>
    )
```
You can also plot your fitness over time and the phenotype over time:
```python 
    evolutionary_algorithm.plot_fitness_score_over_time()
    phenotype_function_points_tuple = generate_points_for_function(
        phenotype=evolutionary_algorithm.population.best_individual,
        bottom_plotting_limit=-10,
        upper_plotting_limit=10,
        number_of_points=100,
    )
    evolutionary_algorithm.plot_phenotype_function_and_best_individuals(phenotype_function_points_tuple)
```
or plot a gif of the algorithm over time:
```python 
    evolutionary_algorithm.create_gif(phenotype_function_points_tuple)
```
WallStreetSocial 1.0.0.4
Is an open source piece of software that is designed to allow anyone to quickly get
Keywords: Database,WallStreetBets,Reddit

# Wall Street Social
Is an open source piece of software that is 
designed to allow anyone to quickly get familiar with the basics of textual analysis. It features a preconfigured database schema, preconfigured pipelines to fill the database with reddit comments, and a pretrained named entity recognition model that is used to pull out what stocks are being mentioned in a post.

## Getting Starting
This project model doesn't fit the pypi size specifications, to get this project working you will need the these steps.

```
pypi WallStreetBets
```

Download Model [here](https://github.com/JGolafshan/WallStreetSocial/blob/master/wsb_ner.zip)

unzip anywhere

```
from WallStreetSocial import helpers

# Uses model to find ticks in the data comment table
helpers.validate_model('C:Projects\\WallStreetSocial\\wsb_ner')

# Fetches Comments and Posts from a subreddit between two dates
helpers.run("WallStreetBets", start="2019-01-03", end="2019-02-04")


symbol = helpers.SummariseBase(symbol="AAPL")
symbol.display_stats()

```

## About the ML Model
The named entity recognition model in use is a compact spaCy model which has been trained on wallstreetbets specific data. The model has been trained in two parts: first, Gensim was used to create the word vectors. Word vectors are basically multidimensional representations of words in algebraic space that help the model determine things like word similarity. It helps give the model some form of contextual awareness of the words it is encountering. The model is then trained specifically on data from reddit. The data consists of thousands of comments that have labeled stocks and their respective positions in the text. For a visual representation of the word vectors, please see https://www.kaggle.com/johnhutton/visualization-of-wallstreetbets-word-vectors
artelebot 2.0
Arabic TeleBot
Keywords: telebot,artelebot


<h1 align="center">ttbot</h1>
<p align="center">It's a beautiful project that helps developers program.</p>

![](https://img.shields.io/badge/ttbot-orange?style=for-the-badge&logo=python.svg) 
<p align="center">
<a href="#"><img title="Made in Jo" src="https://img.shields.io/badge/MADE%20IN-Jo-red.svg?style=for-the-badge&logo=github"></a>

</p>
<p align="center">
<img alt="Nova-Tools' Github Stats" src="https://github-readme-stats.vercel.app/api?username=NovaTools4&show_icons=true&include_all_commits=true&hide_border=true" />

</p>
<p align="center">
<a href="#"><img title="telegram Num" src="https://img.shields.io/badge/telegram%20Num-The Jordan Ghost-red.svg?style=for-the-badge&logo=telegram"></a>
</p>
<p align="center">
<a href="https://github.com/NovaTools4/followers"><img title="Followers" src="https://img.shields.io/github/followers/NovaTools4?color=blue&style=flat-square"></a>
</p>

## Installation :
```
pip install tambotapi
```
### Logan Usage

``` python
from tambotapi import *

## Follow us on social media
[![Github](https://img.shields.io/badge/Github-NovaTools-orange?style=for-the-badge&logo=github)](https://github.com/NovaTools4/)
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