mirror of https://github.com/jlizier/jidt
51 lines
1.6 KiB
Matlab
Executable File
51 lines
1.6 KiB
Matlab
Executable File
% function mutualinformationempirical(xn,yn)
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%
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% Computes the mutual information over all samples xn of a random
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% variable X with samples yn of a random variable Y.
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%
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% Inputs:
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% - xn - matrix of samples of outcomes x. May be a 1D vector of samples, or
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% a 2D matrix, where each row is a vector sample for a multivariate X.
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% - yn - matrix of samples of outcomes x. May be a 1D vector of samples, or
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% a 2D matrix, where each row is a vector sample for a multivariate Y.
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% Must have the same number of rows as X.
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%
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% Outputs:
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% - result - mutual information of X with Y
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%
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% Copyright (C) 2017, Joseph T. Lizier
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% Distributed under GNU General Public License v3
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%
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function result = mutualinformationempirical(xn,yn)
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% Should we check any potential error conditions on the input?
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if (isvector(xn))
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% Convert it to column vector if not already:
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if (size(xn,1) == 1)
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% xn has only one row:
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xn = xn'; % Transpose it so it is only column
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end
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end
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if (isvector(yn))
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% Convert it to column vector if not already:
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if (size(yn,1) == 1)
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% yn has only one row:
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yn = yn'; % Transpose it so it is only column
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end
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end
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% Check that their number of rows are the same:
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assert(size(xn,1) == size(yn,1));
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% We need to compute H(X) + H(Y) - H(X,Y):
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% 1. joint entropy:
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H_XY = ???; % How to compute this empirically ...?
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% 2. marginal entropy of Y: (calling 'joint' in case yn is multivariate)
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H_Y = ???;
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% 3. marginal entropy of X: (calling 'joint' in case xn is multivariate)
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H_X = ???;
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result = H_X + H_Y - H_XY;
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end
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