function demhint(nin, nhidden, nout)%DEMHINT Demonstration of Hinton diagram for 2-layer feed-forward network.%%	Description%%	DEMHINT plots a Hinton diagram for a 2-layer feedforward network with%	5 inputs, 4 hidden units and 3 outputs. The weight vector is chosen%	from a Gaussian distribution as described under MLP.%%	DEMHINT(NIN, NHIDDEN, NOUT) allows the user to specify the number of%	inputs, hidden units and outputs.%%	See also%	HINTON, HINTMAT, MLP, MLPPAK, MLPUNPAK%%	Copyright (c) Ian T Nabney (1996-2001)if nargin < 1 nin = 5; endif nargin < 2 nhidden = 7; endif nargin < 3 nout = 3; end% Fix the seed for reproducible resultsrandn('state', 42);clcdisp('This demonstration illustrates the plotting of Hinton diagrams')disp('for Multi-Layer Perceptron networks.')disp(' ')disp('Press any key to continue.')pausenet = mlp(nin, nhidden, nout, 'linear');[h1, h2] = mlphint(net);clcdisp('The MLP has been created with')disp(['    ' int2str(nin) ' inputs'])disp(['    ' int2str(nhidden) ' hidden units'])disp(['    ' int2str(nout) ' outputs'])disp(' ')disp('One figure is produced for each layer of weights.')disp('For each layer the fan-in weights are arranged in rows for each unit.')disp('The bias weight is separated from the rest by a red vertical line.')disp('The area of each box is proportional to the weight value: positive')disp('values are white, and negative are black.')disp(' ')disp('Press any key to exit.'); pause; delete(h1);delete(h2);