semvec/pypi-packages huggingface dataset:
apportionpy 0.3.1
A Python package for apportionment.
Keywords: python,apportionment,apportion,hamilton,webster,adam,jefferson,Huntington,method,equal,proportions

A package that allows users to apportion seats to states based on population figures.

This apportionment package calculates results for the following methods:
 - Hamilton's method
 - Jefferson's method
 - Adam's method
 - Webster's method
 - Huntington Hill's method
 - Method of equal proportions

Hamilton's method calculations:
 - fair shares
 - initial fair shares
 - initial quotas
 - final quotas
 - initial divisor
 - modified divisor

Jefferson's method calculations:
 - fair shares
 - initial fair shares
 - initial quotas
 - final quotas
 - initial divisor
 - modified divisor
 - divisor history

Adam's method calculations:
 - fair shares
 - initial fair shares
 - initial quotas
 - final quotas
 - initial divisor
 - modified divisor
 - divisor history

Webster's method calculations:
 - fair shares
 - initial fair shares
 - initial quotas
 - final quotas
 - initial divisor
 - modified divisor
 - divisor history

Huntington Hill's method calculations:
 - fair shares
 - initial fair shares
 - initial quotas
 - final quotas
 - initial geometric means
 - final geometric means
 - initial divisor
 - modified divisor

Method of equal proportions calculations:
 - fair shares

Experimental features:
 - estimate the lowest possible divisor possible
 - estimate the highest possible divisor possible
one-Neuron-pypi-LijiAlex 0.0.3
Perceptron Implementation
Requires Python: >=3.7

# oneNeuron_pypi
one-Neuron-pypi-LijiAlex

# Description
A package to implement the functionality of a Perceptron

## Package description
* oneNeuron.perceptron [A Perceptron class]
* utils.all_utils.createModel [Creates a perceptron model]
* utils.all_utils.prepare_data [Prepares a pandas data frame from numpy array data]
* utils.all_utils.save_model [Saves the perceptron model created]
* utils.all_utils.save_plot [Saves the plot of the input vs output]

## Installation
pip install one-Neuron-pypi-LijiAlex

## How to import the Package

``` python
from oneNeuron.perceptron import perceptron
from utils.all_utils import createModel
```

## Sample code to create an AND model

``` python
from utils.all_utils import createModel

def main(data, eta, epoch, file_name, plot_name):
    createModel(data, eta, epoch, file_name, plot_name)    

if __name__ == '__main__': ##entry point
    AND = {
        "x1": [0,0,1,1],
        "x2": [0,1,0,1],
        "y": [0, 0, 0, 1]
    } #data

    ETA = 0.3 # learning rate between 0 and 1 (assume)
    EPOCHS = 10
    
    try:
        main(data=AND, eta=ETA, epoch=EPOCHS, file_name="and.model", plot_name="and.png")
    except Exception as e:
        raise e # raise exception in terminal
```
### Tips to create other models
* Copy the sample code for AND.
* Replace data with desired data.
* Give appropriate value for data,file_name and plot_name while calling main.

### Sample Project using the package
https://github.com/LijiAlex/oneNeuron

### What works
AND, OR, NOT, NOT, NAND

### What doesn't work
XOR, XNOR
VetstoriaNER 0.8
Species detection package for Vetstoria
Requires Python: >=3.6.9

# Species Recognition Model

Species detection package for Vetstoria

## Installation

Install the VetstoriaNER using the python package manager (pip)
    ```
    pip install VetstoriaNER
    ```

## How to work on with VetstoriaNER

1. Import to a script file

    ```
    from VetstoriaNER import EntityRecognizer
    ```

2. passing of parameters

    ```
    ner = EntityRecognizer('<model name or path>')
    recognizer = ner.recognizeSpecies('<phrase or the sentence>')
    ```   

#### Actual code will look like :
```
from VetstoriaNER import EntityRecognizer

ner = EntityRecognizer('recognize-model')
recognizer = ner.recognizeSpecies('i have a cat') 

print(recognizer)
```

## LICENSE

```
MIT License

Copyright (c) 2020 VetstoriaNER

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
```