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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.
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.
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.
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.
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.
Working through the function, we can see that we first split the feature (Sepal Length in this case) into 5 bins. We then produce a summary table including entropy using dplyr, and calculate the proportion of each records in each bin. Let’s look at the dd_data table below:
