mirror of https://github.com/jlizier/jidt
50 lines
1.5 KiB
Matlab
Executable File
50 lines
1.5 KiB
Matlab
Executable File
% function conditionalentropyempirical(xn,yn)
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%
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% Computes the conditional Shannon entropy over all samples xn of a random
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% variable X, given 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 - conditional Shannon entropy of X given 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 = conditionalentropyempirical(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,Y) - H(X):
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% 1. joint entropy:
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H_XY = jointentropyempirical([xn, yn]);
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% 2. marginal entropy of Y: (calling 'joint' in case yn is multivariate)
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H_Y = jointentropyempirical(yn);
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result = H_XY - H_Y;
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end
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