wolffd@0: function [data, c, prior, sd] = dem2ddat(ndata)
wolffd@0: %DEM2DDAT Generates two dimensional data for demos.
wolffd@0: %
wolffd@0: %	Description
wolffd@0: %	The data is drawn from three spherical Gaussian distributions with
wolffd@0: %	priors 0.3, 0.5 and 0.2; centres (2, 3.5), (0, 0) and (0,2); and
wolffd@0: %	standard deviations 0.2, 0.5 and 1.0.  DATA = DEM2DDAT(NDATA)
wolffd@0: %	generates NDATA points.
wolffd@0: %
wolffd@0: %	[DATA, C] = DEM2DDAT(NDATA) also returns a matrix containing the
wolffd@0: %	centres of the Gaussian distributions.
wolffd@0: %
wolffd@0: %	See also
wolffd@0: %	DEMGMM1, DEMKMEAN, DEMKNN1
wolffd@0: %
wolffd@0: 
wolffd@0: %	Copyright (c) Ian T Nabney (1996-2001)
wolffd@0: 
wolffd@0: input_dim = 2;
wolffd@0: 
wolffd@0: % Fix seed for reproducible results
wolffd@0: randn('state', 42);
wolffd@0: 
wolffd@0: % Generate mixture of three Gaussians in two dimensional space
wolffd@0: data = randn(ndata, input_dim);
wolffd@0: 
wolffd@0: % Priors for the three clusters
wolffd@0: prior(1) = 0.3;
wolffd@0: prior(2) = 0.5;
wolffd@0: prior(3) = 0.2;
wolffd@0: 
wolffd@0: % Cluster centres
wolffd@0: c = [2.0, 3.5; 0.0, 0.0; 0.0, 2.0];
wolffd@0: 
wolffd@0: % Cluster standard deviations
wolffd@0: sd  = [0.2 0.5 1.0];
wolffd@0: 
wolffd@0: % Put first cluster at (2, 3.5)
wolffd@0: data(1:prior(1)*ndata, 1) = data(1:prior(1)*ndata, 1) * 0.2 + c(1,1);
wolffd@0: data(1:prior(1)*ndata, 2) = data(1:prior(1)*ndata, 2) * 0.2 + c(1,2);
wolffd@0: 
wolffd@0: % Leave second cluster at (0,0)
wolffd@0: data((prior(1)*ndata + 1):(prior(2)+prior(1))*ndata, :) = ...
wolffd@0: 	data((prior(1)*ndata + 1):(prior(2)+prior(1))*ndata, :) * 0.5;
wolffd@0: 
wolffd@0: % Put third cluster at (0,2)
wolffd@0: data((prior(1)+prior(2))*ndata +1:ndata, 2) = ...
wolffd@0: 	data((prior(1)+prior(2))*ndata+1:ndata, 2) + c(3, 2);