comparison toolboxes/FullBNT-1.0.7/netlab3.3/demhmc3.m @ 0:e9a9cd732c1e tip

first hg version after svn

+%DEMHMC3 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling.
+%
+%	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. The model is a 2-layer network with linear outputs, and the
+%	hybrid Monte Carlo algorithm (with persistence) is used to sample
+%	from the posterior distribution of the weights.  The graph shows the
+%	underlying function, 300 samples from the function given by the
+%	posterior distribution of the weights, and the average prediction
+%	(weighted by the posterior probabilities).
+%
+%	See also
+%	DEMHMC2, HMC, MLP, MLPERR, MLPGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+
+% Generate the matrix of inputs x and targets t.
+ndata = 20;                     % Number of data points.
+noise = 0.1;                    % Standard deviation of noise distribution.
+nin = 1;                        % Number of inputs.
+nout = 1;                       % Number of outputs.
+
+seed = 42;                    % Seed for random number generators.
+randn('state', seed);
+rand('state', seed);
+
+x = 0.25 + 0.1*randn(ndata, nin);
+t = sin(2*pi*x) + noise*randn(size(x));
+
+clc
+disp('This demonstration illustrates the use of the hybrid Monte Carlo')
+disp('algorithm to sample from the posterior weight distribution of a')
+disp('multi-layer perceptron.')
+disp(' ')
+disp('A regression problem is used, with the one-dimensional data drawn')
+disp('from a noisy sine function.  The x values are sampled from a normal')
+disp('distribution with mean 0.25 and variance 0.01.')
+disp(' ')
+disp('First we initialise the network.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+% Set up network parameters.
+nhidden = 5;			% Number of hidden units.
+alpha = 0.001;                  % Coefficient of weight-decay prior. 
+beta = 100.0;			% Coefficient of data error.
+
+% Create and initialize network model.
+
+% Initialise weights reasonably close to 0
+net = mlp(nin, nhidden, nout, 'linear', alpha, beta);
+net = mlpinit(net, 10);
+
+clc
+disp('Next we take 100 samples from the posterior distribution.  The first')
+disp('300 samples at the start of the chain are omitted.  As persistence')
+disp('is used, the momentum has a small random component added at each step.')
+disp('10 iterations are used at each step (compared with 100 in demhmc2).')
+disp('The step size is 0.005 (compared with 0.002).')
+disp('The new state is accepted if the threshold')
+disp('value is greater than a random number between 0 and 1.')
+disp(' ')
+disp('Negative step numbers indicate samples discarded from the start of the')
+disp('chain.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+% Set up vector of options for hybrid Monte Carlo.
+nsamples = 100;		% Number of retained samples.
+
+options = foptions;     % Default options vector.
+options(1) = 1;		% Switch on diagnostics.
+options(5) = 1;		% Use persistence
+options(7) = 10;	% Number of steps in trajectory.
+options(14) = nsamples;	% Number of Monte Carlo samples returned. 
+options(15) = 300;	% Number of samples omitted at start of chain.
+options(17) = 0.95;	% Alpha value in persistence
+options(18) = 0.005;	% Step size.
+
+w = mlppak(net);
+% Initialise HMC
+hmc('state', 42);
+[samples, energies] = hmc('neterr', w, options, 'netgrad', net, x, t);
+
+clc
+disp('The plot shows the underlying noise free function, the 100 samples')
+disp('produced from the MLP, and their average as a Monte Carlo estimate')
+disp('of the true posterior average.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+nplot = 300;
+plotvals = [0 : 1/(nplot - 1) : 1]';
+pred = zeros(size(plotvals));
+fh1 = figure;
+hold on
+for k = 1:nsamples
+  w2 = samples(k,:);
+  net2 = mlpunpak(net, w2);
+  y = mlpfwd(net2, plotvals);
+  % Sum predictions
+  pred = pred + y;
+  h4 = plot(plotvals, y, '-r', 'LineWidth', 1);
+end
+pred = pred./nsamples;
+% Plot data
+h1 = plot(x, t, 'ob', 'LineWidth', 2, 'MarkerFaceColor', 'blue');
+axis([0 1 -3 3])
+
+% Plot function
+[fx, fy] = fplot('sin(2*pi*x)', [0 1], '--g');
+h2 = plot(fx, fy, '--g', 'LineWidth', 2);
+set(gca, 'box', 'on');
+
+% Plot averaged prediction
+h3 = plot(plotvals, pred, '-c', 'LineWidth', 2);
+
+lstrings = char('Data', 'Function', 'Prediction', 'Samples');
+legend([h1 h2 h3 h4], lstrings, 3);
+hold off
+
+disp('Note how the predictions become much further from the true function')
+disp('away from the region of high data density.')
+disp(' ')
+disp('Press any key to exit.')
+pause
+close(fh1);
+clear all;