wolffd@0: function [beta, p] = logist2Fit(y, x, addOne, w)
wolffd@0: % LOGIST2FIT 2 class logsitic classification
wolffd@0: % function beta = logist2Fit(y,x, addOne)
wolffd@0: %
wolffd@0: % y(i) = 0/1
wolffd@0: % x(:,i) = i'th input - we optionally append 1s to last dimension
wolffd@0: % w(i) = optional weight
wolffd@0: %
wolffd@0: % beta(j)- regression coefficient
wolffd@0: 
wolffd@0: if nargin < 3, addOne = 1; end
wolffd@0: if nargin < 4, w = 1; end
wolffd@0: 
wolffd@0: Ncases = size(x,2);
wolffd@0: if Ncases ~= length(y)
wolffd@0:   error(sprintf('size of data = %dx%d, size of labels=%d', size(x,1), size(x,2), length(y)))
wolffd@0: end
wolffd@0: if addOne
wolffd@0:   x = [x; ones(1,Ncases)];
wolffd@0: end
wolffd@0: [beta, p] = logist2(y(:), x', w(:));
wolffd@0: beta = beta(:);