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, 95 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/demglm1.m b/sourcecodes/bnt-master/netlab3.3/demglm1.m
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+%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;
+