function a = gmmactiv(mix, x)%GMMACTIV Computes the activations of a Gaussian mixture model.%%	Description%	This function computes the activations A (i.e. the  probability%	P(X|J) of the data conditioned on each component density)  for a%	Gaussian mixture model.   The data structure MIX defines the mixture%	model, while the matrix X contains the data vectors.  Each row of X%	represents a single vector.%%	See also%	GMM, GMMPOST, GMMPROB%%	Copyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997)% Check that inputs are consistenterrstring = consist(mix, 'gmm', x);if ~isempty(errstring)  error(errstring);endndata = size(x, 1);switch mix.covar_type  case 'spherical'    % Calculate squared norm matrix, of dimension (ndata, ncentres)    n2 = dist2(x, mix.centres);    % Calculate width factors    wi2 = ones(ndata, 1) * (2 .* mix.covars);    normal = (pi .* wi2) .^ (mix.nin/2);    % Now compute the activations    a = exp(-(n2./wi2))./ normal;  case 'diag'    a = zeros(ndata, mix.ncentres);    normal = (2*pi)^(mix.nin/2);    s = prod(sqrt(mix.covars), 2);    for i = 1:mix.ncentres      diffs = x - (ones(ndata, 1) * mix.centres(i, :));      a(:, i) = exp(-0.5*sum((diffs.*diffs)./(ones(ndata, 1) * ...	mix.covars(i,:)), 2)) ./ (normal*s(i));    end      case 'full'    a = zeros(ndata, mix.ncentres);    normal = (2*pi)^(mix.nin/2);    for i = 1:mix.ncentres      diffs = x - (ones(ndata, 1) * mix.centres(i,:));      % Use Cholesky decomposition of covariance matrix to speed computation      c = chol(mix.covars(:,:,i));      temp = diffs/c;      a(:,i) = exp(-0.5*sum(temp.*temp, 2))./(normal*prod(diag(c)));    endend