Daniel@0: function [data, ndata1, ndata2, targets]=gen_data(ndata, seed)
Daniel@0: % Generate data from three classes in 2d
Daniel@0: % Setting 'seed' for reproducible results
Daniel@0: % OUTPUT
Daniel@0: % data : data set
Daniel@0: % ndata1, ndata2: separator
Daniel@0: 
Daniel@0: if nargin<1,
Daniel@0:    error('Missing data size');
Daniel@0: end
Daniel@0: 
Daniel@0: input_dim = 2;
Daniel@0: num_classes = 3;
Daniel@0: 
Daniel@0: if nargin==2,
Daniel@0:    % Fix seeds for reproducible results
Daniel@0:    randn('state', seed);
Daniel@0:    rand('state', seed);
Daniel@0: end
Daniel@0: 
Daniel@0: % Generate mixture of three Gaussians in two dimensional space
Daniel@0: data = randn(ndata, input_dim);
Daniel@0: targets = zeros(ndata, 3);
Daniel@0: 
Daniel@0: % Priors for the clusters
Daniel@0: prior(1) = 0.4;
Daniel@0: prior(2) = 0.3;
Daniel@0: prior(3) = 0.3;
Daniel@0: 
Daniel@0: % Cluster centres
Daniel@0: c = [2.0, 2.0; 0.0, 0.0; 1, -1];
Daniel@0: 
Daniel@0: ndata1 = round(prior(1)*ndata);
Daniel@0: ndata2 = round((prior(1) + prior(2))*ndata);
Daniel@0: % Put first cluster at (2, 2)
Daniel@0: data(1:ndata1, 1) = data(1:ndata1, 1) * 0.5 + c(1,1);
Daniel@0: data(1:ndata1, 2) = data(1:ndata1, 2) * 0.5 + c(1,2);
Daniel@0: targets(1:ndata1, 1) = 1;
Daniel@0: 
Daniel@0: % Leave second cluster at (0,0)
Daniel@0: data((ndata1 + 1):ndata2, :) = data((ndata1 + 1):ndata2, :);
Daniel@0: targets((ndata1+1):ndata2, 2) = 1;
Daniel@0: 
Daniel@0: data((ndata2+1):ndata, 1) = data((ndata2+1):ndata,1) *0.6 + c(3, 1);
Daniel@0: data((ndata2+1):ndata, 2) = data((ndata2+1):ndata,2) *0.6 + c(3, 2);
Daniel@0: targets((ndata2+1):ndata, 3) = 1;
Daniel@0: 
Daniel@0: if 0
Daniel@0:   ndata = 1;
Daniel@0:   data = x;
Daniel@0:   targets = [1 0 0];
Daniel@0: end