entropy: Calculates decision weights using the entrophy method

In avilesd/productConfig: Tools to analyze behavioral patterns from Product Configurators

Description

This function is used as the second step in weight.entropy for calculating a decision weight for each attr in the decision matrix. The methodology of the entropy method [2] for determining the weights out of a decision matrix is given by references [1] and [3]. See Details.

Usage

entropy(normalizedMatrix)

Arguments

normalizedMatrix

a numeric matrix. If indeed normalized it should only contain values between 0 and 1.

Details

The sum of the output of this functions should always equal 1.

Contrasting with differenceToIdeal small differences between value attributes are rewarded a lower value and thus a relative lower weight.

Note: the normalizing function used here normalize.sum has one limitation, if within a same attribute there are negative and positive values, the function will likely produce a NaN.

Value

a decision weight (numeric vector with a sum of 1)

References

[1]Hwang, C. L., & Yoon, K. (2012). Multiple attribute decision making: methods and applications a state-of-the-art survey (Vol. 186). Springer Science & Business Media.

[2]Shannon, C. E. (2001). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5(1), 35.

[3]Lotfi, F. H., and Fallahnejad, R. (2010). Imprecise Shannons entropy and multi attribute decision making. Entropy, 12(1), 53-62.

Examples

#Runnable
entropy(matrix(c(1.0, 0.85, 0.42, 0, 0.5, 0, 1, 0.7), 4, 2))
weights <- entropy(matrix(c(1.0, 0.85, 0.42, 0, 0.5, 0, 1, 0.7), 2, 4))
sum(entropy) should return 1

avilesd/productConfig documentation built on May 11, 2019, 4:08 p.m.