BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 103 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/demglm2.m b/sourcecodes/bnt-master/netlab3.3/demglm2.m
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+%DEMGLM2 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 three
+%	Gaussian clusters, and a generalized linear model with softmax output
+%	is trained using iterative reweighted least squares. A plot of the
+%	data together with regions shaded by the classification given by the
+%	network is generated.
+%
+%	See also
+%	DEMGLM1, GLM, GLMTRAIN
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+
+% Generate data from three classes in 2d
+input_dim = 2;
+
+% Fix seeds for reproducible results
+randn('state', 42);
+rand('state', 42);
+
+ndata = 100;
+% Generate mixture of three Gaussians in two dimensional space
+mix = gmm(2, 3, 'spherical');
+mix.priors = [0.4 0.3 0.3];            % Cluster priors
+mix.centres = [2, 2; 0.0, 0.0; 1, -1];  % Cluster centres
+mix.covars = [0.5 1.0 0.6];
+
+[data, label] = gmmsamp(mix, ndata);
+id = eye(3);
+targets = id(label,:);
+
+% Plot the result
+
+clc
+disp('This demonstration illustrates the use of a generalized linear model')
+disp('to classify data from three 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')
+plot(data(label==3, 1), data(label==3, 2), 'go')
+title('Data')
+
+clc
+disp('Now we fit a model consisting of a softmax function of')
+disp('a linear combination of the input variables.')
+disp(' ')
+disp('The model is trained using the IRLS algorithm for up to 10 iterations')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+net = glm(input_dim, size(targets, 2), 'softmax');
+options = foptions;
+options(1) = 1;
+options(14) = 10;
+net = glmtrain(net, options, data, targets);
+
+disp(' ')
+disp('We now plot the decision regions given by this model.')
+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);
+[foo , class] = max(Z');
+class = class';
+colors = ['b.'; 'r.'; 'g.'];
+for i = 1:3
+  thisX = X(class == i);
+  thisY = Y(class == i);
+  h = plot(thisX, thisY, colors(i,:));
+  set(h, 'MarkerSize', 8);
+end
+title('Plot of Decision regions')
+
+hold off
+
+clc
+disp('Note that the boundaries of decision regions are straight lines.')
+disp(' ')
+disp('Press any key to end.')
+pause
+close(fh1);
+clear all; 
+