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信息论中各种熵的matlab实现，其中包括自信息量，互信息量，条件熵，联合熵，冗余度等等的计算。-various information theory entropy Matlab achieved, which include the volume of information, mutual information, the conditional entropy, Joint entropy, and so on the redundancy terms.
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计算条件熵，可以用来计算在某种条件下的条件熵的数值-we can use it to calculate the conditional entropy
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Conditional Entropy of a function
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离散二维随机变换熵的计算：
（1）利用random函数和归一化方法构造一个二维离散随机变量（X，Y）；
（2）分别计算X与Y的熵、联合熵、条件熵：H（X）、H（Y）、H（X,Y）H（X|Y）、I（X|Y）；
-Discrete two-dimensional random transform entropy calculation: (1) the use of random functions and normalization method to construct a two-
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This package provides a Maximum Entropy Modeling toolkit written in C++ with Python binding. It includes:
Conditional Maximum Entropy Model
L-BFGS Parameter Estimation
GIS Parameter Estimation
Gaussian Prior Smoothing
C++ API
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熵,联合熵,条件熵,平均互信息量的通用计算程序-Entropy, joint entropy, conditional entropy, average mutual information of the general-purpose computer program
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ID3决策树算法的JAVA实现.详细的实现过程：熵值条件熵，节点选择等。-ID3 decision tree algorithm JAVA implementation. Detailed implementation process: entropy conditional entropy, the node selection.
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信息论中的熵、条件熵、联合熵在C语言中编程实现。-the entropy H(X)，the conditional entropy H(X|Y)，the joint entropy H(X,Y)
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全部信息熵的计算，包括信息熵，条件信息熵以及信息增益。附带Andrew的信息增益教程-All information entropy calculations, including information entropy, conditional entropy and information gain. Andrew incidental information gain Tutorials
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进行文章里的字母实验，统计了文章中的26个英文字母的熵，联合熵和条件熵。利用matlab的统计工具进行了联合熵和条件熵的简单计算，得出26个字母在《圣经》中的概率分布直方图。
关键词：熵；信息量；统计理论;联合概率；条件概率(Carry on the letter experiment in the article, count the entropy of the 26 English letters in the article, combine entropy and condition
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conditional entropy, entropy, joint entropy, mutual information, relative entropy basic features are implemented in matlab approach
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熵值计算的matlab 程序包括样本熵，近似熵，条件熵等(Matlab program for entropy calculation Including sample entropy ,approximate entropy,conditional entropy and so on.)
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此代码为计算离散随机变量的熵、联合熵、条件熵、互信息的 Matlab 程序(This code is a matlab program for calculating entropy, joint entropy, conditional entropy and mutual information of discrete random variables)
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