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, 115 insertions, 0 deletions

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+%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent
+%
+%	Description
+%	The problem consists of one input variable X and one target variable
+%	T with data generated by sampling X at equal intervals and then
+%	generating target data by computing SIN(2*PI*X) and adding Gaussian
+%	noise. A 2-layer network with linear outputs is trained by minimizing
+%	a  sum-of-squares error function using on-line gradient descent.
+%
+%	See also
+%	DEMMLP1, OLGD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+
+% Generate the matrix of inputs x and targets t.
+
+ndata = 20;			% Number of data points.
+noise = 0.2;			% Standard deviation of noise distribution.
+x = [0:1/(ndata - 1):1]';
+randn('state', 42);
+rand('state', 42);
+t = sin(2*pi*x) + noise*randn(ndata, 1);
+
+clc
+disp('This demonstration illustrates the use of the on-line gradient')
+disp('descent algorithm to train a Multi-Layer Perceptron network for')
+disp('regression problems.  It is intended to illustrate the drawbacks')
+disp('of this algorithm compared to more powerful non-linear optimisation')
+disp('algorithms, such as conjugate gradients.')
+disp(' ')
+disp('First we generate the data from a noisy sine function and construct')
+disp('the network.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+% Set up network parameters.
+nin = 1;			% Number of inputs.
+nhidden = 3;			% Number of hidden units.
+nout = 1;			% Number of outputs.
+alpha = 0.01;			% Coefficient of weight-decay prior. 
+
+% Create and initialize network weight vector.
+net = mlp(nin, nhidden, nout, 'linear');
+% Initialise weights reasonably close to 0
+net = mlpinit(net, 10);
+
+% Set up vector of options for the optimiser.
+options = foptions;
+options(1) = 1;			% This provides display of error values.
+options(14) = 20;		% Number of training cycles. 
+options(18) = 0.1;		% Learning rate
+%options(17) = 0.4;		% Momentum
+options(17) = 0.4;		% Momentum
+options(5) = 1; 		% Do randomise pattern order
+clc
+disp('Then we set the options for the training algorithm.')
+disp(['In the first phase of training, which lasts for ',...
+    num2str(options(14)), ' cycles,'])
+disp(['the learning rate is ', num2str(options(18)), ...
+    ' and the momentum is ', num2str(options(17)), '.'])
+disp('The error values are displayed at the end of each pass through the')
+disp('entire pattern set.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+% Train using online gradient descent
+[net, options] = olgd(net, options, x, t);
+
+% Now allow learning rate to decay and remove momentum
+options(2) = 0;
+options(3) = 0;
+options(17) = 0.4;	% Turn off momentum
+options(5) = 1;		% Randomise pattern order
+options(6) = 1;		% Set learning rate decay on
+options(14) = 200;
+options(18) = 0.1;	% Initial learning rate
+
+disp(['In the second phase of training, which lasts for up to ',...
+    num2str(options(14)), ' cycles,'])
+disp(['the learning rate starts at ', num2str(options(18)), ...
+    ', decaying at 1/t and the momentum is ', num2str(options(17)), '.'])
+disp(' ')
+disp('Press any key to continue.')
+pause
+[net, options] = olgd(net, options, x, t);
+
+clc
+disp('Now we plot the data, underlying function, and network outputs')
+disp('on a single graph to compare the results.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+% Plot the data, the original function, and the trained network function.
+plotvals = [0:0.01:1]';
+y = mlpfwd(net, plotvals);
+fh1 = figure;
+plot(x, t, 'ob')
+hold on
+axis([0 1 -1.5 1.5])
+fplot('sin(2*pi*x)', [0 1], '--g')
+plot(plotvals, y, '-r')
+legend('data', 'function', 'network');
+hold off
+
+disp('Note the very poor fit to the data: this should be compared with')
+disp('the results obtained in demmlp1.')
+disp(' ')
+disp('Press any key to exit.')
+pause
+close(fh1);
+clear all;
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