comparison toolboxes/FullBNT-1.0.7/netlab3.3/demglm1.m @ 0:e9a9cd732c1e tip

first hg version after svn

+%DEMGLM1 Demonstrate simple classification using a generalized linear model.
+%
+%	Description
+%	 The problem consists of a two dimensional input matrix DATA and a
+%	vector of classifications T.  The data is  generated from two
+%	Gaussian clusters, and a generalized linear model with logistic
+%	output is trained using iterative reweighted least squares. A plot of
+%	the data together with the 0.1, 0.5 and 0.9 contour lines of the
+%	conditional probability is generated.
+%
+%	See also
+%	DEMGLM2, GLM, GLMTRAIN
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+
+% Generate data from two classes in 2d
+input_dim = 2;
+
+% Fix seeds for reproducible results
+randn('state', 42);
+rand('state', 42);
+
+ndata = 100;
+% Generate mixture of two Gaussians in two dimensional space
+mix = gmm(2, 2, 'spherical');
+mix.priors = [0.4 0.6];              % Cluster priors 
+mix.centres = [2.0, 2.0; 0.0, 0.0];  % Cluster centres
+mix.covars = [0.5, 1.0];
+
+[data, label] = gmmsamp(mix, ndata);
+targets = label - ones(ndata, 1);
+
+% Plot the result
+
+clc
+disp('This demonstration illustrates the use of a generalized linear model')
+disp('to classify data from two classes in a two-dimensional space. We')
+disp('begin by generating and plotting the data.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+fh1 = figure;
+plot(data(label==1,1), data(label==1,2), 'bo');
+hold on
+axis([-4 5 -4 5])
+set(gca, 'box', 'on')
+plot(data(label==2,1), data(label==2,2), 'rx')
+title('Data')
+
+clc
+disp('Now we fit a model consisting of a logistic sigmoid function of')
+disp('a linear combination of the input variables.')
+disp(' ')
+disp('The model is trained using the IRLS algorithm for 5 iterations')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+net = glm(input_dim, 1, 'logistic');
+options = foptions;
+options(1) = 1;
+options(14) = 5;
+net = glmtrain(net, options, data, targets);
+
+disp(' ')
+disp('We now plot some density contours given by this model.')
+disp('The contour labelled 0.5 is the decision boundary.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+x = -4.0:0.2:5.0;
+y = -4.0:0.2:5.0;
+[X, Y] = meshgrid(x,y);
+X = X(:);
+Y = Y(:);
+grid = [X Y];
+Z = glmfwd(net, grid);
+Z = reshape(Z, length(x), length(y));
+v = [0.1 0.5 0.9];
+[c, h] = contour(x, y, Z, v);
+title('Generalized Linear Model')
+set(h, 'linewidth', 3)
+clabel(c, h);
+
+clc
+disp('Note that the contours of constant density are straight lines.')
+disp(' ')
+disp('Press any key to end.')
+pause
+close(fh1);
+clear all;
+