Calculate weighted OWA functions and extending bivariate means
Project description
wowa
This package calculates weighted OWA functions and extending bivariate means" Functions are:
- py_WAM: WOWATree callback function if sorting is needed in general
- py_OWA: WOWATree callback function if no sorting is needed when used in the tree
- WOWATree: symmetric base aggregator
- WAn: processes the tree
- weightedOWAQuantifierBuild: calculates spline knots and coefficients for later use in weightedOWAQuantifier
- weightedOWAQuantifier: Calculates the value of the WOWA, with quantifier function obtained in weightedOWAQuantifierBuild
- ImplicitWOWA: Calculates implicit Weighted OWA function
- WAM: weighted arithmetic mean function
- OWA: ordered weighted averaging function
Documentation
Installation
To install type:
$ pip install wowa
Usage of py_OWA( n, x, w)
from wowa import py_OWA
WOWATree callback function if sorting is needed in general
Parameters
Input parameters:
n: size of arrays
x[]: NumPy array of size n, float
w[]: NumPy array of size n, float
Output parameters:
double y: aggregated sum
Usage of py_WAM( n, x, w)
from wowa import py_WAM
WOWATree callback function if no sorting is needed when used in the tree
Parameters
Input parameters:
n: size of arrays
x[]: NumPy array of size n, float
w[]: NumPy array of size n, float
Output parameters:
double y: aggregated sum
Usage of WOWATree( x, p, w, cb, L)
from wowa import WOWATree
Symmetric base aggregator. The weights must add to one and be non-negative.
Parameters
Input parameters:
x[]: NumPy array of inputs, size n, float
p[]: NumPy array of weights of inputs x[], size n, float
w[]: NumPy array of weights for OWA, size n, float
cb: Nallback function. Either pre-defined py_OWA() or py_WAM() or user defined of type float(ch*)(int, float[], float[], int)
L: Number of binary tree levels. Run time = O[(n-1)L]
Output parameters:
y: weightedf, double
Usage of WAn( x, w, L, F)
from wowa import WAn
Parameters
Input parameters:
x[]: NumPy array of inputs, size n, float
w[]: NumPy array of weights for OWA, size n, float
L: Number of binary tree levels
F: User defined callback function of type float(*F)( float, float)
Output parameters:
y: result of tree processing, double
Usage of weightedOWAQuantifierBuild( p, w)
from wowa import weightedOWAQuantifierBuild
Parameters
Input parameters:
p[]: NumPy array of weights of inputs x[], size n, float
w[]: NumPy array of weights for OWA, size n, float
Output parameters:
spline: the spline knots and coefficients for later use in weightedOWAQuantifier
T: the number of knots in the monotone spline
Usage of weightedOWAQuantifier( x, p, w, spline, T);
from wowa import weightedOWAQuantifier
Calculates the value of the WOWA, with quantifier function obtained in weightedOWAQuantifierBuild
Parameters
Input parameters:
x[]: NumPy array of inputs, size n, float
p[]: NumPy array of weights of inputs x[], size n, float
w[]: NumPy array of weights for OWA, size n, float
spline[]: keeps the spline knots and coefficients. Output from weightedOWAQuantifierBuild
T: the number of knots in the monotone spline
Output parameters:
y: double
Usage of ImplicitWOWA x, p, w)
from wowa import ImplicitWOWA
Calculates implicit Weighted OWA function
Parameters
Input parameters:
x[]: NumPy array of inputs, size n, float
p[]: NumPy array of weights of inputs x[], size n, float
w[]: NumPy array of weights for OWA, size n, float
Output parameters:
y: float
Usage of WAM( n, x, w)
from wowa import WAM
Weighted arithmetic mean function
Parameters
Input parameters:
n: size of arrays
x[]: NumPy array of size n, float
w[]: NumPy array of size n, float
Output parameters:
y: aggregated sum, float
Usage of OWA( n, x, w)
from wowa import OWA
Ordered weighted averaging function
Parameters
Input parameters:
n: size of arrays
x[]: NumPy array of size n, float
w[]: NumPy array of size n, float
Output parameters:
y: aggregated sum, float
Test
To unit test type:
$ test/test.py
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