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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. Example: entropy.empirical(freqs, unit="log2") [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.
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.
3 Σεπ 2019 · The kelly_back_dec and kelly_lay_dec functions allow for a quick calculation of the Kelly criterion given the true probability, the quoted price and a commision percentage.
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.
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.
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.
