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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.
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.
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.
entropy is an R package that provides tools for estimating entropy, mutual information, and related quantities. These are fundamental concepts in information theory and have applications in various fields including statistics, machine learning, and data analysis.
How is the entropy calculated by the `entropy` package in R? Ask Question. Asked 9 years, 3 months ago. Modified 7 years, 7 months ago. Viewed 5k times. 3. So, as per the docs, I'm calling the function like this. 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.
entropy.empirical estimates the Shannon entropy H of the random variable Y from the corresponding observed counts y by plug-in of the empirical frequencies. KL.empirical computes the empirical Kullback-Leibler (KL) divergence from counts y1 and y2.
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.
