semvec/pypi-packages huggingface dataset:
genens 0.1.19
A genetic AutoML system for ensemble methods
Requires Python: >=3.6

# genens
genens is an AutoML system for pipeline optimization based on developmental genetic programming.

## Installation
Clone the repository.
```
git clone https://github.com/gabrielasuchopar/genens.git
pip install genens
```


-----
## Using genens
As for now, the GenensClassifier is ready to be used. It has an interface similar to other scikit-learn estimators. When `fit()` is called, the evolutionary optimization is run. After it finishes, `predict()` produces a prediction with the best of optimized pipelines. Alternatively, you can call `get_best_pipelines()` to get pipelines from the pareto front.

```
from genens import GenensClassifier
from sklearn.datasets import load_iris()

iris = load_iris()
train_X, test_X, train_y, test_y = train_test_split(iris.data, iris.target, test_size=0.25)

clf = GenensClassifier()
clf.fit(train_X, train_y)
... # process of evolution

pred = clf.predict(test_X)
```

## Tests
Directory ./genens/tests contains scripts for running dataset tests and produce data about evolution process along with pickle files of best optimized pipelines. 
Sample config files are included in ``./genens/tests/config``.

- Run genEns on a dataset specified in the config file.

``python ./genens/tests/run_datasets.py --out OUT_DIR config CONFIG``

- Runs genEns on the [OpenML-CC18 benchmarking suite](https://www.openml.org/s/99)

``python ./genens/tests/run_openml.py --out OUT_DIR --config CONFIG``

More tests are to be included in later releases.
singleton-firebase 0.0.2
A Singleton factory to firebase

# README #

This README would normally document whatever steps are necessary to get your application up and running.

### What is this repository for? ###

* Quick summary
* Version
* [Learn Markdown](https://bitbucket.org/tutorials/markdowndemo)

### How do I get set up? ###

* Summary of set up
* Configuration
* Dependencies
* Database configuration
* How to run tests
* Deployment instructions

### Contribution guidelines ###

* Writing tests
* Code review
* Other guidelines

### Who do I talk to? ###

* Repo owner or admin
* Other community or team contact
NormalDistribution 0.0.1
Normal (Gussian) Distributions
Requires Python: >=3.6

This package is created for statistical analysis and for machine learning 
least square linear rigression model cost function evaluation by Normal
(Gussian) distribution. It also can be use to calculate the mean and standard  
deviation of a data set.

It has two classes
class Distribution: This contains two methods
    method(class attributes)
    method data_loader(file): This takes in a file in text format and append
    the data to a data list for calculations.
    
class Normal: This contains six methods
    methods calculate_mean(), calculate_std(), distribution_plot(),
    normal_distribution(x), add_distribution(other), total_distribution()
    
    calculate_mean() returns the mean of the data set.
    
    calculate_std() returns the standard deviation of the data set.
    
    distribution_plot() returns the gussian plot of the data set.
    
    normal_distribution() returns the Gussian distribution value for a given
    point.
    
    to add two normal distributions together, add_distribution(other) where 
    other=Gussian returns the mean and standard deviation for the multiple 
    distribution
    
    total_distribution() returns the mean and the standard deviation for the 
    combined distribution
api5paisa 0.0.9
Unoffical Browser Based Python API of 5paisa
Keywords: 5paisa,5paisa api,python,sdk,trading,stock markets

# api5paisa

Unoffical Browser Based Python API of 5paisa made at [Unofficed](https://unofficed.com/).

Its created live today morning only. https://youtube.com/watch?v=2PXSfi0Rsho

Feel Free to Tweak it, Modify it and Add the rest of the missing functions to it at your pace.

## Installation

Use the package manager [pip](https://pypi.org/project/api5paisa/) to install api5paisa.

```bash
pip install api5paisa
```

## Usage

```python
from api5paisa import Api5Paisa

api = Api5Paisa()
api.login("email or clientId", "passowrd", "dob in the format ddMMyyyy")
#for easy access create ~/.5paisa.conf and user api.login() refer examples/5paisa.conf

#Get Margin
print(api.get_margin())

#Get Trade Book
print(api.get_trade_book())

#Firing Order
order_response = api.order('SBIN', 170.2, 1)
print(order_response)
print(api.get_order_book())

```

## Documentation
For Documentation and Sample Code, Refer - https://forum.unofficed.com/

## Contributing
For Discussion and Improving this Code, Join - https://www.unofficed.com/chat/

## License
[MIT](https://choosealicense.com/licenses/mit/)
cosmoplotian 0.0.1
cosmoplotian is for plotting
Requires Python: >=3.6,<4.0

Plotting of images related to cosmology
---------------------------------------

.. image:: http://img.shields.io/badge/powered%20by-AstroPy-orange.svg?style=flat
    :target: http://www.astropy.org
    :alt: Powered by Astropy Badge

.. image:: https://coveralls.io/repos/github/bthorne93/cosmoplotian/badge.svg?branch=master
    :target: https://coveralls.io/github/bthorne93/cosmoplotian?branch=master

.. image:: https://travis-ci.org/bthorne93/cosmoplotian.svg?branch=master
    :target: https://travis-ci.org/bthorne93/cosmoplotian

``cosmoplotian`` provides additional projections and colormaps for the
plotting of astronomical images.


License
-------

This project is Copyright (c) Ben Thorne and licensed under
the terms of the BSD 3-Clause license. This package is based upon
the `Astropy package template <https://github.com/astropy/package-template>`_
which is licensed under the BSD 3-clause license. See the licenses folder for
more information.
firebase-singleton 0.0.1
A Singleton factory to firebase

# README #

This README would normally document whatever steps are necessary to get your application up and running.

### What is this repository for? ###

* Quick summary
* Version
* [Learn Markdown](https://bitbucket.org/tutorials/markdowndemo)

### How do I get set up? ###

* Summary of set up
* Configuration
* Dependencies
* Database configuration
* How to run tests
* Deployment instructions

### Contribution guidelines ###

* Writing tests
* Code review
* Other guidelines

### Who do I talk to? ###

* Repo owner or admin
* Other community or team contact