Αποτελέσματα Αναζήτησης
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.
1 Δεκ 2016 · In multiple linear regression, R-squared is the squared correlation between response vector and fitted values. Try model <- lm(trees); cor(trees[[1]], model$fitted.values) ^ 2 . Compare this with summary(model)$r.squared
7 Φεβ 2016 · NaN. 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.
Description. 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. Usage. Entropy(x, y = NULL, base = 2, ...) MutInf(x, y, base = 2, ...) Value.
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.
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.
