Entropy

Entropy, joint entropy and conditional entropy function for n variables in matlab

The following Matlab project contains the source code and Matlab examples used for entropy, joint entropy and conditional entropy function for n variables. for entropy H = entropy(S) this command will evaluate the entropy of S, S should be row matrix H = entropy([X;Y;Z]) this command will find the joint entropy for the 3 variables H = entropy([X,Y],[Z,W]) this will find H(X,Y/Z,W).

Calculates the sample entropy, in bits, of discrete variables. in matlab

The following Matlab project contains the source code and Matlab examples used for calculates the sample entropy, in bits, of discrete variables. . Entropy: Returns entropy (in bits) of each column of 'X'   by Will Dwinnell     H = Entropy(X)     H = row vector of calculated entropies (in bits)   X = data to be analyzed     Note 1: Each distinct value in X is considered a unique value.

Conditional entropy in matlab

The following Matlab project contains the source code and Matlab examples used for conditional entropy. ConditionalEntropy: Calculates conditional entropy (in bits) of Y, given X   H = ConditionalEntropy(Y,X)     H = calculated entropy of Y, given X (in bits)   Y = dependent variable (column vector)   X = independent variable(s)      Note 1: Each distinct value is considered a unique symbol.

Joint entropy in matlab

The following Matlab project contains the source code and Matlab examples used for joint entropy.   JointEntropy: Returns joint entropy (in bits) of each column of 'X'   Note: Each distinct value is considered a unique symbol.     H = JointEntropy(X)     H = calculated joint entropy (in bits)   X = data to be analyzed

Information theory toolbox in matlab

The following Matlab project contains the source code and Matlab examples used for information theory toolbox. This toolbox contains functions for discrete random variables to compute following quantities: 1)Entropy 2)Joint entropy 3)Conditional entropy 4)Relative entropy (KL divergence) 5)Mutual information 6)Normalized mutual information 7)Normalized variation information This toolbox is a tweaked and bundled version of my previous submissions.

Sample entropy in matlab

The following Matlab project contains the source code and Matlab examples used for sample entropy. SampEn is a measure of complexity that can be easily applied to any type of time series data, including physiological data such as heart rate variability and EEG data.

Permutation entropy in matlab

The following Matlab project contains the source code and Matlab examples used for permutation entropy. % Calculate the permutation entropy % Input: y: time series; % m: order of permuation entropy % t: delay time of permuation entropy, % Output: % pe: permuation entropy % hist: the histogram for the order distribution %Ref: G Ouyang, J Li, X Liu, X Li, Dynamic Characteristics of Absence EEG Recordings with Multiscale Permutation % % Entropy Analysis, Epilepsy Research, doi: 10.

Ecological information based approach in matlab

The following Matlab project contains the source code and Matlab examples used for ecological information based approach. This is a simple code for calculating the number of nodes, Total System Throughput (TST), Average Mutual Information (AMI), conditional entropy, effective connectivity, and effective number of roles of information flow based network represented in the form of a matrix.

Pages

Subscribe to RSS - Entropy