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info(freqs) [1] 0.940286. As a matter of improving your code, you can simplify this dramatically as you don't need a loop if you are provided a vector of class frequencies. For example: # calculate shannon-entropy. -sum(freqs * log2(freqs)) [1] 0.940286.
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.
Computes Shannon entropy and the mutual information of two variables. The entropy quantifies the expected value of the information contained in a vector. The mutual information is a quantity that measures the mutual dependence of the two random variables.
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.
Now, the question is: assuming that the following is the algorithm used to calculate the entropy – taken from Wikipedia $$ \mathrm{H}(X) = -\sum_{i} {\mathrm{P}(x_i) \log_b \mathrm{P}(x_i)} $$ my questions are the following.
Let’s create a function to compute entropy, and try it out. #compute Shannon entropy entropy <- function(target) { freq <- table(target)/length(target) # vectorize vec <- as.data.frame(freq)[,2] #drop 0 to avoid NaN resulting from log2 vec<-vec[vec>0] #compute entropy -sum(vec * log2(vec)) } entropy(setosa_subset$Species)
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. It also offers an R interface to the NSB estimator.
