KLdistance¶
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KLDis
(P, Q)[source]¶ Calculates the Kullback-Leibler Distance of two discrete probability distributions. P and Q are automatically normalised to have the sum of one on rows have the length of one at each.
Usage
dist = KLDis(P, Q)Inputs
- P = n x nbins
- Q = 1 x nbins or n x nbins(one to one)
Output
- dist = n x 1
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KLdistance
(data1, data2, num_iter, parameters)[source]¶ This function calculates the Kullback-Leibler Distance (KLD) between two distributions then runs a certain number of iterations where the labels are randomised and the KLD is calculated. Then, the distribution of the Disergences is plotted. Usage
dist = KLdistance(data1, data2, num_iter, parameters);Inputs
- data1 – n x m matrix where each column reprensents the values of a certain parameter for one of the populations (e.g. controls)
- data2 – same as data1 for the population to compare
- num_iter – number of randomisations
- parameters – a cell array of strings containing the name of each parameter
Output
- dist – an array of KL Distances at each iteration