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The Symbol Frequency Table Calculator The Symbol Frequency Table Calculator is a tool that takes in a text message and produces a table of the frequencies of each symbol (i.e., character) that appears in the message. The calculator can be customized to ignore spaces and/or case, allowing for a more accurate analysis of the message's symbol frequencies. |
Shannon Entropy This online calculator computes Shannon entropy for a given event probability table and for a given message. |
Huffman coding This online calculator generates Huffman coding based on a set of symbols and their probabilities. A brief description of Huffman coding is below the calculator. |
Wordpress plugin With Planetcalc plugin for Wordpress you can easily insert any planetcalc calculator in your wordpress-based site or blog pages. |
Hartley Information Calculator The Hartley Information Calculator determines the amount of information contained in a message with length n using Hartley's formula. |
Text compression using Huffman coding This online calculator compresses entered text using Huffman coding. It also displays built Huffman codes for the reference. |
Shannon Coding Calculator Generate Shannon coding for a set of symbols based on their probabilities. |
Shannon-Fano coding calculator This online calculator generates Shannon-Fano coding based on a set of symbols and their probabilities |
Conditional entropy This online calculator calculates entropy of Y random variable conditioned on X random variable and X random variable conditioned on Y random variable given a joint distribution table (X, Y) ~ p |
Specific Conditional Entropy This online calculator calculates entropy of Y random variable conditioned on specific value of X random variable and X random variable conditioned on specific value of Y random variable given a joint distribution table (X, Y) ~ p |
Joint Entropy This online calculator calculates joint entropy of two discrete random variables given a joint distribution table (X, Y) ~ p |
Decision tree builder This online calculator builds a decision tree from a training set using the Information Gain metric |