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# calculate shannon-entropy -sum(freqs * log2(freqs)) [1] 0.940286 As a side note, the function entropy.empirical is in the entropy package where you set the units to log2 allowing some more flexibility.
The entropy function allows to estimate entropy from observed counts by a variety of methods: method="ML":maximum likelihood, see entropy.empirical. method="MM":bias-corrected maximum likelihood, see entropy.MillerMadow. method="Jeffreys": entropy.Dirichlet with a=1/2. method="Laplace": entropy.Dirichlet with a=1.
7 Φεβ 2016 · when I calculate entropy for attribute B the result give me NaN that is due to zero (0) (log2 (0) is error ) . in such situation how can I fix this error or how can make H1 give me zero instead of NaN. ifelse(is.na(entropy), 0, entropy) should work. There is a package called 'entropy' in r if it works for you.
Implements various estimators of entropy for discrete random variables, including the shrinkage estimator by Hausser and Strimmer (2009), the maximum likelihood and the Millow-Madow estimator, various Bayesian estimators, and the Chao-Shen estimator.
v = c(0,4,3,6,7,3,2,3,4,5) entropy(discretize(v, numBins = 8, r = c(0,7))) and I get. [1] 1.834372. jolly good. Now, the question is: assuming that the following is the algorithm used to calculate the entropy – taken from Wikipedia.
This package implements various estimators of entropy for discrete random variables, including the shrinkage estimator by Hausser and Strimmer (2009), the maximum likelihood and the Millow-Madow estimator, various Bayesian estimators, and the Chao-Shen estimator.
We simply compute the entropy of the root node (Species) using 1.5849625, then subtract the sum of the bin entropies weighted by the proportion of data they represent exactly as per the IG formula shown above.
