Metadata-Version: 2.1
Name: wowa
Version: 0.73
Summary: Calculate weighted OWA functions and extending bivariate means
Home-page: UNKNOWN
Author: Norbert Henseler
Author-email: nhenseler@web.de
License: UNKNOWN
Platform: UNKNOWN
Description-Content-Type: text/markdown
License-File: LICENSE

# wowa
This package calculates weighted OWA functions and extending bivariate means" Functions are:
- py_owa: callback for sorting in general 
- weightedf: symmetric base aggregator 

## Documentation
[User Manual](https://github.com/nhenseler/wowa/blob/main/docs/wowa-theory.pdf)

## Installation
To install type:
```python
$ pip install wowa
```
## Usage of py_owa( n, x, w)
```python
from wowa import py_owa
```
Callback function if sorting is needed in general 
### Parameters
#### Input parameters:
Input parameters:
n: size of arrays<br>
x[]: NumPy array of size n, float<br>   
w[]: NumPy array of size n, float<br>
#### Output parameters:
double y: sum of x[i] * w[i]  

## Usage of weightedf( x, p, w, cb, L)
```python
from wowa import weightedf 
```
Symmetric base aggregator. The weights must add to one and be non-negative.
### Parameters
#### Input parameters:
x[]: NumPy array of inputs, size n, float<br>   
p[]: NumPy array of weights of inputs x[], size n, float<br>   
w[]: NumPy array of weights for OWA, size n, float<br> 
cb: callback function<br>
L: number of binary tree levels. Run time = O[(n-1)L]  
#### Output parameters:
y = weightedf<br>

## Test
To unit test type:
```python
$ test/test.py
```

