aggregationslib 0.0.253

Python implementation of Arithmetic, quasi arithmetic and other aggregating functions

Means

Means, Aggregation functions...

Example 1:

# example data
data = [0.2, 0.6, 0.7]
# configure function parameters
func1 = A_amn(p=0.5)
# use aggregation funciton
print(func1(data))

# Combine two aggregations - arithmetic mean and minimum
func2 = Combine2Aggregations(A_ar(), min)
# use combination of aggregation funciton
print(func2(data))

Example2:

To get information about aggregation function you can use __str__() or 'repr()' methods.

func1 = A_amn(p=0.5)
print(func1)
>>>A_amn(0.5)

func2 = Combine2Aggregations(A_ar(), A_md())
print(func2)
>>>A_armd

func3 = Combine2Aggregations(A_ar(), A_pw(r=3))
print(func3.__repr__()) # function parameters are printed in order: func1, func2
>>>A_arpw(r=3)

exponential(y, r=1) is given by equation

$$ A_6^{(r)}(x_1,...,x_n)= \frac{1}{r}\ln \Big(\frac{1}{n} \sum \limits_{k=1}^{n} e^{rx_k}\Big), where r \in \mathbb{R}, r \neq 0 $$

A_ar - Arithmetic mean

A_qd - Quadratic mean

A_gm - Geometric mean

A_hm - Harmonic mean

A_pw - Power mean

A_ex, A_ex2, A_ex3 - Exponential mean

A_lm - Lehmer mean

A_amn - Arithmetic minimum mean

A_amx - Arithmetic maximum mean

A_md - Median - ordered weighted aggregation

A_ol - Olimpic aggregation

A_oln - Olimpic aggregation

We can specify how many greatest and smallest records remove

Combine2Aggregations - Combine aggregation functions

Amn, Amx, Aar , Aex , Amd, Aow1, Aow1