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1 function n2 = mdndist2(mixparams, t)
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2 %MDNDIST2 Calculates squared distance between centres of Gaussian kernels and data
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3 %
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4 % Description
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5 % N2 = MDNDIST2(MIXPARAMS, T) takes takes the centres of the Gaussian
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6 % contained in MIXPARAMS and the target data matrix, T, and computes
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7 % the squared Euclidean distance between them. If T has M rows and N
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8 % columns, then the CENTRES field in the MIXPARAMS structure should
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9 % have M rows and N*MIXPARAMS.NCENTRES columns: the centres in each row
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10 % relate to the corresponding row in T. The result has M rows and
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11 % MIXPARAMS.NCENTRES columns. The I, Jth entry is the squared distance
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12 % from the Ith row of X to the Jth centre in the Ith row of
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13 % MIXPARAMS.CENTRES.
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14 %
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15 % See also
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16 % MDNFWD, MDNPROB
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17 %
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18
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19 % Copyright (c) Ian T Nabney (1996-2001)
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20 % David J Evans (1998)
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21
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22 % Check arguments for consistency
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23 errstring = consist(mixparams, 'mdnmixes');
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24 if ~isempty(errstring)
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25 error(errstring);
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26 end
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27
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28 ncentres = mixparams.ncentres;
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29 dim_target = mixparams.dim_target;
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30 ntarget = size(t, 1);
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31 if ntarget ~= size(mixparams.centres, 1)
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32 error('Number of targets does not match number of mixtures')
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33 end
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34 if size(t, 2) ~= mixparams.dim_target
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35 error('Target dimension does not match mixture dimension')
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36 end
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37
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38 % Build t that suits parameters, that is repeat t for each centre
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39 t = kron(ones(1, ncentres), t);
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40
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41 % Do subtraction and square
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42 diff2 = (t - mixparams.centres).^2;
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43
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44 % Reshape and sum each component
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45 diff2 = reshape(diff2', dim_target, (ntarget*ncentres))';
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46 n2 = sum(diff2, 2);
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47
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48 % Calculate the sum of distance, and reshape
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49 % so that we have a distance for each centre per target
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50 n2 = reshape(n2, ncentres, ntarget)';
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51
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